a research operation

Research Operations: Definition, Procedure, and Tools

11 min read

With the vast number of people required to conduct research efficiently, orchestrating this symphony of manpower and resources can be incredibly challenging.

This guide will walk you through what research operations are, what role research operations managers play, which product analytics to track, and how you can get started with the process!

Research operations (also known as ResearchOps) consist of organizing people, resources, and processes to streamline research and maximize impact.
Research operations managers are in charge of strategic planning, resource allocation, software acquisition, participant management, employee onboarding, creating SOPs, and fostering collaboration.
The six stages of the research process are setting goals, building a team, establishing processes, recruiting participants, selecting tools, and analyzing results.
A few research methods you can use include usability testing, user interviews, in-app surveys, focus groups, card sorting, and tree testing — we’ll dive deeper into each one below!
The best tools for research operations are Userpilot for gathering insights, Optimal Workshop for conducting research, and Figma for testing prototypes.
Ready to gather more actionable insights from your users? Book a Userpilot demo today !

What is research operations?

Research operations — or ReserchOps — is the process of organizing people, resources, and processes to maximize the impact of research conducted along with the value it provides for an organization.

What does a research operations manager do?

Research operations managers have a fairly broad scope of work that includes responsibilities like:

Strategic planning. This includes setting goals, charting procedures, and figuring out which OKRs or KPIs will be used to track progress.
Resource allocation. A research operations manager is in charge of allocating available resources — such as cash, equipment, and personnel — in the most effective way possible to optimize the output of the department.
Software acquisition. When it comes to selecting and procuring the necessary tools or software for the research process, research operations managers will often handle the vetting process — or at least have the final say.
Participant management. The research operations manager is also responsible for recruiting study participants, collecting data, and managing the incentives offered to attract additional participants for upcoming studies.
Employee onboarding/training. This could include hands-on training as well as the distribution of training material.
Standard operating procedures. Research operations managers are in charge of creating guides, templates, internal wikis, and standard operation procedures (SOPs) that support both new and existing team members as they conduct research.
Team collaboration and communication. The research operations manager is responsible for fostering collaboration within their own team and, where appropriate, facilitating the communication necessary for cross-team collaboration.

How to get started with the research process?

While research processes are often complex endeavors, breaking things down into the six core steps makes it a lot easier to comprehend and chart what needs to get done. The six steps of the research process are:

Define objectives/goals

Build a research team

Establish standards/processes
Recruit participants
Select tools
Analyze results

Let’s take a closer look at each of these steps in the sections below!

Define research objectives and goals

Before anything else, you’ll want to set user research objectives and goals to ensure that the rest of the steps are completed with this core mission in mind. Clearly articulate the purpose, scope, and (desired) outcome of the research being conducted to ensure clarity across your entire team.

You can use goal-setting frameworks like SMART goals to streamline this process:

goal-setting framework for research operations

Speaking of your team, it’s time to decide:

How many people you’ll need
What each person does
Which communication channel everyone will use

Once you know this, it’s time to loop everyone else in and ensure that every team member understands their role — as well as the contributions expected of them. Failure to clearly assign responsibilities could lead to scope overlap or underutilized personnel.

Establish standards and processes to support researchers

Next, it’s time to lay out the standards and processes that will support researchers as they conduct tests, experiments, or other aspects of the study. For instance, this could include the process for screening participants and what criteria are used.

Tip: You’ll want to avoid excessive participant filtering to avoid selection bias (and other types of bias ) from tainting the results of your research.

You’ll also need processes for how to keep track of participants, which data to collect, and where to store that data. If you’re conducting UX research, the knowledge management features you need may already be baked into the software you’re using.

If not, you’ll need to build a new research repository from scratch to store all collected data.

Recruit research participants

Participant recruitment will be the next hurdle that you’ll need to overcome. User researchers who are trying to identify behavioral patterns in how people use a particular product or platform will likely use their own user base as the participant pool.

Of course, third-party services like Qualtrics are available for those who don’t have a large enough user base or don’t want to involve existing customers in their research practices.

Research teams can even post in a ResearchOps community to see if anyone is interested or has a reliable source of participants.

Cold email outreach is usually the last resort since it yields the lowest response rates.

Select tools to gather and manage research data

The research tools used could include survey platforms, analytics software, and product growth platforms — with a combination of the three research tools being the most common.

It’s also possible to get a full-suite product growth platform like Userpilot that includes all three in its native feature set.

For instance, Userpilot has path-tracking capabilities that help you analyze user navigation routes:

Analyze and iterate results for better research practice

Once participant data has been gathered by the software you selected, the final step of the process is to analyze the results from these research sessions. This is your opportunity to see if the outcome matches expectations, which hypotheses were proven/disproven, and what insights were uncovered.

You should also take this opportunity to improve the research process. This could include refining the methods used, addressing any challenges that were encountered while managing research participants, and re-training the research ops team.

A few questions to ask yourself when identifying areas to improve upon include:

How easy was it to recruit participants?
Was the data we collected helpful?
Which insights are actionable?

Of course, the questions you ask (and the answers you get) will be different depending on the type of research being done.

If you’ve just completed a UX research process , then UX researchers may look at which features, bugs, and elements need to be added, removed, or tweaked.

7 user research methods for research ops

Conducting quality user research is easier said than done, but thankfully, there are a few methods that have stood the test of time. The sections below will walk you through the tried-and-tested research methods that a research ops team can use to gather useful data.

Qualitative research vs quantitative research

The distinction between qualitative and quantitative research is an important one to make. Qualitative research includes written survey responses, feedback from focus groups, and one-on-one customer interviews.

Quantitative research includes scalar ratings, aggregate scores like NPS , product metrics, revenue growth, and other forms of quantifiable (often numeric) data collection. When applying quality user research, a combination of both qualitative and quantitative data is usually required.

In the example of NPS scores, the qualitative responses written by users are paramount to interpreting their scalar ratings and validating the final Net Promoter Score for the business or product. Here’s an example of how Userpilot uses issue tagging to identify patterns in qualitative NPS survey responses:

Userpilot NPS response tagging dashboard

Usability testing

Usability testing is a UX research method used to identify problems and areas for improvement while determining the overall “ease of use” for a product, platform, or service. There are a few different methods for conducting usability testing:

Guerilla testing. Going to a public location and asking for feedback from strangers.
Remote testing. Remote testing offers fast and cheap results (at the cost of validated accuracy).
Lab testing. Lab testing helps you gather in-depth, reliable feedback from a small group of people.
Five-second tests. Expose your product to a participant for five seconds to see what they noticed.
First-click testing. First-click testing measures how easy it is for users to find their happy path .
Card sorting. Print features/elements/content on cards then ask participants to categorize them.
Session replays. Watching session replays will show you the journey taken and interactions made.
Eye-tracking. Heatmaps can be used to show you which elements users look at the most.

Here’s a look at how Userpilot’s built-in feature heatmap can show you which features users interact with the most:

User interviews

One-on-one user interviews are the most in-depth form of research, but they’re also the most time-consuming approach a research project can take. Because of how much time (and sometimes money) these interviews take, it’s essential that you choose the right customers for these interviews.

The questions you need to ask when trying to select subjects for user interviews will usually come in the form of who, what, why, how, and/or where. You can see examples for each of these question types in the graphic below to help you choose the right participants to interview.

Surveys are a more scalable and cost-effective user research method that can reach more people in less time (especially with the help of automation software). For example, you can use in-app surveys to capture insights as the customer is actively using the product.

Here’s a look at Userpilot’s no-code survey builder:

Userpilot in-app survey builder dashboard

Once you’ve decided how you’re going to deliver the surveys to your users, you’ll need to figure out which research questions to ask them. Good survey questions must be clear, concise, and appropriate — while observing all relevant research ethics.

Conversely, any vague, biased, or inappropriate survey questions should be avoided at all costs.

Focus groups

If you struggle to access research data through quantitative product metrics, then seeking out qualitative feedback in the real world may be your next best option. The main drawback of focus groups is how cost-prohibitive they are for early-stage companies — especially bootstrapped startups.

The average payout for focus groups is $75 to $150 per participant, according to data from Drive Research — but this may vary depending on country/region and session length. Still, most sessions will run you upwards of $5,000, and two sessions are usually the standard to get a representative sample.

Companies with a loyal customer base could consider recruiting existing customers into focus groups and offering to pay them with store/subscription credit instead of cash. This can offset some of the upfront costs of running focus groups.

Card sorting

As alluded to earlier, card sorting is a testing format where you print concepts — usually features, elements, or content — onto physical cards, then ask your participants to group and categorize them based on their own preferences.

It’s okay to provide additional information about individual concepts when asked, but researchers should refrain from giving out any answers (or volunteering information) that would bias the participants towards/against sorting the cards in a certain way.

Here’s an example of how to provide clear instructions without influencing the participant’s card sorting:

Tree testing

Finally, we have tree testing which is a research method used by UX designers to evaluate how intuitive the website structure or product navigation is. These types of tests remove most content, graphics, and other clutter while only leaving navigational links.

This creates a wireframe-esque interface that serves as the sandbox environment for the test.

Researchers then ask participants to find specific items (such as features or pages) based on the visible structure and terminology alone. If participants struggle to navigate the website or product with the structure and links alone, then the UX research process indicates the need for tweaks to be made.

Tree testing is a type of UX architecture testing used to evaluate a proposed site structure by asking users to find items based on the website’s organization and terminology. This online test only displays the navigation links and removes any additional clutter.

Here’s an example of how to give tree testing instructions without providing any hints to participants:

Best tools for research operations

To ensure your research projects run smoothly and data analysis can be performed effectively, it’s important to have a research toolkit comprised of the tools that will be most helpful. We’ll show you three tools that are a must-have in any research team’s tech stack :

Userpilot for gathering research insights

Userpilot is a product growth platform that lets you gather data and also helps you with applying research insights through in-app guidance . Here’s an overview of how Userpilot can help you with gathering research insights:

Event tracking. Userpilot tracks event data so you can track user behavior . You’ll be able to monitor various behavioral data types and see how users interact with your product during research experiments.
In-app surveys. Userpilot lets you build in-app surveys that collect feedback, sort responses, and analyze customer sentiment — all without writing a single line code. This helps you survey your customers from within the product itself.
A/B testing. A/B testing different variants of your product, feature, or interface will give you more UX data to work with. You’ll also be able to objectively identify the best versions that resonate with your user base most.
Reporting. Userpilot’s advanced analytics supports multiple report types. These include user path analysis , funnel analysis , and trend reports that show you how your product success metrics are trending over time.
Analytics. Userpilot also has other analytics dashboards built into the platform that show you other data like product usage, feature adoption, user engagement, and behavioral analytics. This helps you see the most important metrics at a glance.

Here’s a sneak peek at what Userpilot’s analytics dashboards look like:

Optimal Workshop for conducting research

Optimal Workshop is a UX research platform that helps you set up and launch your research study. It includes features for card sorting, tree testing, and other efforts aimed at conducting user research in motion.

Here’s a look at Optimal Workshop’s card-sorting interface:

Figma for testing prototypes

Figma is a collaborative design tool that you can use to test prototypes with your team and show unfinished versions to study participants to get their feedback throughout the process. You could also use their collaborative whiteboard, FigJam, to brainstorm with focus groups or research team members.

This is what FigJam’s intuitive and minimalist interface looks like:

As you can see, research operations can support researchers in delivering the most actionable research findings . Regularly sharing insights gathered from research studies will help inform the business strategy based on the valuable insights accumulated in your data storage platform.

If you follow best user research practices, execute the processes needed to streamline workflows, and use the tools recommended in this guide, then you’re bound to scale research. If you’d like to combine scaling research with applying those insights, then it’s time to get your free Userpilot demo today!

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World Leaders in Research-Based User Experience

ResearchOps 101

August 16, 2020 2020-08-16

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ResearchOps is a specialized area of DesignOps focused specifically on components concerning user-research practices.

ResearchOps (ReOps): The orchestration and optimization of people, processes, and craft in order to amplify the value and impact of research at scale.

Standardizing research methods and supporting documentation (e.g., scripts, templates, and consent forms) to save time and enable consistent application across teams
Recruiting and managing research participants across studies
Ensuring research ethics are understood and upheld by individual researchers across studies
Educating research-team partners and leadership about the value of user research
Managing user-research insights and making data accessible throughout the team and the organization
Socializing success stories and ensuring that the overall impact of user research is known

The exponential growth of the UX profession means that more companies are realizing the value of UX and that the demand for UX and user research is increasing. This is great news: the value of our work is known and deemed necessary much more so than it was in the recent past.

The practical task of scaling research practices to meet this increased demand, however, often falls to existing UX research staff, with little guidance or additional bandwidth. Senior user researchers or research managers must deal with the responsibility and challenge of developing processes to scale their practices to match demand — all while simultaneously continuing to plan and facilitate research sessions.

If a company does 10× the amount of user research it used to, the cost shouldn’t be 11× the old budget, as is all too likely if more projects lead to more bureaucracy, coordination, and other overhead costs. The new cost should be 9× due to economies of scale and reuse of prep work across studies. In fact, the ResearchOps cost goal should really be 8× or lower.

ResearchOps can provide relief, with dedicated roles (or at least focused efforts, if dedicated roles are not feasible) to create and compile intentional strategies and tools for managing the operational aspects of research, so that researchers can focus on conducting studies and applying research insights.

Many people equate ResearchOps with participant management (e.g., screening and scheduling participants for research studies), because this aspect is often an immediately obvious pain point for researchers and takes much time. While participant management is certainly an important component of ResearchOps, it is not the only aspect. The full landscape of operational elements necessary for creating and scaling a research practice is much broader.

As a former contract ResearchOps Specialist at Uber aptly explained to me during a series of interviews that I conducted with DesignOps and ResearchOps professionals: “The value ResearchOps can bring is not just calling and getting a participant but building a program and establishing consistent quality for communications and research methods.”

ResearchOps addresses a tapestry of interwoven operational aspects concerning user research, where every component both affects and is affected by the other elements.

The ResearchOps model described below was created by identifying key focus areas from our DesignOps and ResearchOps practitioner interviews. It outlines 6 common focus areas of ResearchOps:

Participants: Recruiting, screening, scheduling, and compensating participants
Governance: Processes and guidelines for consent, privacy, and information storage
Knowledge: Processes and platforms for collecting, synthesizing, and sharing research insights
Tools: Enabling efficiencies in research through consistent toolsets and platforms
Competency: Enabling, educating, and onboarding others to perform research activities
Advocacy: Defining, sharing, and socializing the value of user research throughout the organization

As the cyclical design of the model conveys, these are not isolated elements, but interrelated factors that drive the need for and influence each other.

Research Ops model with 6 areas: participants, governance, knowledge, tools, competency and advocacy

Participant Management

The first component of ResearchOps — but not the only one — is participant management. This area includes creating processes for finding, recruiting, screening, scheduling, and compensating research-study participants. It’s often low-hanging fruit, because it’s typically the most apparent and immediate need of overloaded research teams.

Common ResearchOps activities and efforts within participant management include:

Building a database or panel of potential study participants or researching and selecting external recruiting platforms
Screening and approving participants
Managing communication with participants
Building frameworks for fair and consistent incentive levels based on participant expertise and required time investment

Governance guidelines are a necessity for any study involving participants. For example, consent templates must be compliant with existing data-privacy regulations, such as GDPR, and written in plain, transparent language. Additionally, as participants’ personally identifiable information (PII) is collected, the organization must follow legal regulations and ethical standards concerning where that information is stored, how long it is stored, how it is protected, and how its storage is made transparent to the participant. (PII refers to any data that could be used to identify a person, such as a full name, date of birth, or email address.)

Common ResearchOps activities and efforts within governance include:

Researching and understanding the application of data-privacy regulations, such as GDPR, to the UX-research process
Establishing ethically sound processes and communications
Writing and standardizing compliant and transparent consent forms for various study types and formats of data collected
Managing the proper maintenance and disposal of PII and study artifacts, such as interview scripts or audio- and video-session recordings

Knowledge Management

As data begins to accumulate from studies, the need for knowledge management becomes increasingly apparent. This element of ResearchOps is focused on collecting and synthesizing data across research studies and ensuring that it is findable and accessible to others. Not only can effectively compiled and managed research insights help research teams share findings and avoid repetitious studies, but they can also serve to educate those outside the team.

Common ResearchOps activities and efforts within knowledge management include:

Developing standardized templates for data collection during studies
Building a shared database of research insights (sometimes called a research repository) where findings from studies across the organization can be stored
Developing regular meetings or other avenues for sharing and updating the organization about known user insights
Coordinating with other teams conducting research (e.g., marketing or business intelligence) in order to create a comprehensive source of insights

Most of the activities discussed so far require tools or platforms. For example: What platform will be used to recruit and screen participants? What applications will be used to manage participant PII? What programs will be used to house all of the resulting research findings? Furthermore, tools that facilitate the actual research, such as remote usability-testing platforms, analytics, or survey platforms, or video-editing and audio- transcription tools, must be considered. While autonomy in choice can be valuable, auditing the research toolset to create some level of consistency across the team expedites sharing and collaboration.

Common ResearchOps activities and efforts within tools include:

Researching and comparing appropriate platforms for recruiting and managing participant information
Selecting research tools for usability testing, surveys, remote interviews, or any other types of research
Managing access privileges and platform seats across individual user researchers and teams
Auditing the research toolkit to ensure that all platforms and applications in use are compliant with data-privacy regulations
While buildings and facilities are usually not thought of as “tools,” ResearchOps should also manage any usability labs as well as non-lab testing rooms, including contracts for outsourced locations.

As the demand for and amount of research conducted continues to scale, it becomes critical to also grow the organization’s research capabilities and skills. The competency component is concerned with enabling more people to understand and do research. This effort often involves providing resources and education both to (1) researchers, so that they can continue to develop their skills, and (2) nonresearchers, so that they can integrate basic research activities into their work when researchers are unavailable (and know when to call for help instead of rolling their own study).

Common ResearchOps activities and efforts within competency include:

Developing standardized and consistent professional-development opportunities for researchers who want to grow deeply or broadly in their expertise
Establishing mentorship programs to onboard new researchers and help them learn and develop new research skills
Creating a playbook or database of research methods to onboard new researchers or educate others outside of the team
Developing formalized training or curricula to train nonresearchers and expose them to user-centered approaches and activities, so that basic research can be incorporated into work when researchers cannot scale to demand

The final component, advocacy, is concerned with how the value of UX research is defined and communicated to the rest of the organization. Simply put, what is being done to ensure that the rest of the organization is aware of the value and impact of research? For example, does the team socialize success stories and demonstrate the impact of user research? To come full circle on the cyclical nature of the model, proper advocacy helps ensure fuel and resources for all the other focus areas and ensures the ResearchOps practice can continue to scale effectively.

Common ResearchOps activities and efforts within advocacy include:

Creating a UX research-team mission or statement of purpose that can be used to talk about the team’s purpose with other colleagues
Developing case studies that demonstrate the impact of properly applied research findings on company metrics and KPI’s
Developing a process for regularly sharing insights and success stories with the rest of the organization (e.g., lunch-and-learns, email newsletters, posters,)

The 6 components in this model are specialized areas that research practices must consider in order to create consistent, quality research efforts across teams; however, there are other elements that must be considered and intentionally designed that are critical to the health of any research team or practice.

One such area is documented career pathways. The documentation and use of career pathways in general is rare. (In our recent DesignOps research , only 11% of respondents reported having a documented, shared growth path — an abysmal percentage.) But, especially within relatively nascent domains, such as ResearchOps, where there is no decisive, publicly available legacy of successful team structures or models for roles and responsibilities, it’s equally both critical and challenging to create and document such pathways.

To make sure that you include additional elements that are not represented in this ResearchOps model, reference our DesignOps framework . It provides a comprehensive landscape of potential focus areas for operationalizing design in general; many of these areas equally apply to creating a healthy, focused ResearchOps practice. Team structure and role definitions, consistent hiring and onboarding practices, team communication and collaboration methods, and workflow balance and planning are just a few additional areas to consider.

As mentioned, ResearchOps is a whole of many parts that are best considered holistically, because every component both affects and is affected by the other factors. However, when establishing a ResearchOps practice, not all aspects can be addressed at once.

The first step to figuring out where to start is understanding where the biggest pain points are. Are researchers overwhelmed with the logistics of recruiting and scheduling participants? Maybe participant management is the best starting point for the team. Is research data scattered and inaccessible to new team members, causing duplicative research efforts and poor research memory? Perhaps knowledge management is where the team should focus.

Begin by identifying the current problems that necessitate ResearchOps. Perform internal research to understand where the biggest pain points currently exist for research teams and research-team partners. For example, you could send out a survey or have focus groups with researchers to collect information on whether current processes enable them to be effective and what gets in their way the most. Additionally, carry out internal stakeholder interviews to uncover the biggest pain points for partners within the research process. This knowledge will help you create a clear role for ResearchOps.

Just remember, when it comes to scaling research, balance your focus between the component that you chose to address and the overall tapestry of considerations. Evolve and expand your focus as needs shift to maintain a balanced practice.

The ResearchOps Community is a group of ResearchOps professionals and researchers who have conducted extensive research to understand the way the UX community thinks about and addresses ResearchOps challenges. They have compiled a collection of resources and thought leadership on the topic, available on the group’s website .

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Research Ops: What it is, why it’s so important, and how to get started

1. Research Ops: What it is? 2. Research Ops: Why it’s so important? 3. Research Ops: How to get started? 4. Final Words

1. Research Ops: What it is?

First of all, ResearchOps are relevant in different industries, from It and software to the space business . It is the shorter version of Research Operations. It is similar to DevOps, SalesOps, and DesignOps, which are used to operationalise specific departments of the company, as can be understood from their names. Even though related, the job roles similar to the Ops Research Assistant are still too new to have a descriptive research report as yet. However, the closest definition describes the term as anything that supports researchers by easing their shoulders off operational work. It can be anything from people, strategy to software. If it causes researchers to save time from data collection and processing for data analysis, it can be considered Research Ops.

1.1 Research Ops: Purpose in a company

Research Ops aims not to help a single scientist or analyst, but to make the department reach its optimum efficiency and effectiveness in a company. The goal is achieved in the following ways:

Automating tasks
Taking care of the logistics behind quantitative research
Creating a framework for both quantitative & qualitative research
Establishes a workflow and process
Initiating repeatable methods
Ensuring that the process and implementation matches the strategy

2. Research Ops: Why it’s so important?

With the increase in competition in the consumer world, customer centricity is gaining more and more importance. Businesses and entrepreneurs are increasingly moving towards niche marketing and customisation. The importance of a unique selling proposition has reached newer heights. But where does Research Ops fit in all this? With companies focusing on consumer insights, which is a changing phenomenon, quicker access to reports or the research paper outline has become almost mandatory. The emergence of this requirement sheds light on the pressure created on the Research & Development department and the need for operationalisation for faster processing.

To put it into simpler words, the following are the main requirements of a company’s R&D department that the Research Ops helps in meeting:

Usability : Research Ops makes it possible for different departments to access any research data needed by them from a central operating system or software. As a result, everything from a research proposal, research paper outline, status, and analysis to reports becomes accessible to anyone within the company. In addition, it makes usability very easy, thereby improving the quality of the product and changing the overall focus and vision of the company.

Productivity : Research Ops promotes developing a framework, templates like research proposal example, workflow, and repeatability methods to ensure saving maximum time on operational work. It is an umbrella with several components that manage to reduce the processing time to a large extent, hence optimising efficiency. Here you can find some valuable tips on how to scale UX research.

Cost-Efficiency : The same factors affecting the efficiency of the R&D team of a company also helps in cost-cutting. A proper strategy, well-researched, and experimented method makes Research Ops responsible for saving budget and managing many other factors controlling and affecting the smooth operation of the department.

Quick reports : The automatisation and workload division with Research Ops helps the team produce quick and almost dynamic reports matching the spontaneously changing consumers. It allows companies to evolve with incredible speed and enjoy a competitive edge over others in the same industry.

With the growth and scaling of companies, the operationalisation of departments becomes necessary. The reason being, increase in sales affects each department, impacting the workload exponentially. Therefore, efficiency, resource-saving, and smart work become the ultimate goal in the growth stage of startups. And why not?! The management of several departments becomes an unknown territory for companies going through a sudden transition from a one-room startup to a multi-departmental corporation. Hence, the emergence of systems like DesignOps, DevOps, SalesOps, and now ResearchOps, for easy management of several departments. It also signifies that these Operational Practices are responsible for more than one component in a department, and so is the case with Research Ops as well.

2.1 Research Ops: The eight pillars or components

A R&D department has eight areas of operation which need the attention of the team. These areas, from time to time, cause hindrances in smooth and fast report delivery. Research Ops makes the process and delivery of consumer insights reports seamless, irrespective of the research question, team requirements, and challenges, making them the eight pillars of the practice.

2.1.1 Budget management

Similar to any other department, R&D also has its own expenses, which need proper planning and management. For example, some standard heads in a budget include salaries or fees to recruiting participants, travel expenses for outdoor research, regular costs of licenses, online tools and software, etc. The Research Ops solves the budgeting challenges by bringing a proper structure where the outflow is tracked and accounted for. In addition, allocating the budget for new research paper topics by negotiating with the company’s finance team for approval also falls under the responsibility of the Research Ops.

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2.1.2 Tools and infrastructure

Analysing and evaluating user data, both quantitative and qualitatively, requires maintaining a lab with software and other IT tools. Since IT may not be the forte of researchers, the team may need to coordinate with the responsible departments for smooth functioning. Besides being a liaison between departments, Research Ops also takes care of the lab infrastructure over time.

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2.1.3 Data and knowledge management

With the growing size of data and topics, data management and reports become increasingly difficult. Hence, forming a data repository becomes a significant role of the Research Ops to make the data accessible to the entire company from a central system. The emergence of operative practice also introduces structure into the R&D department. One of the first activities of the practice is to create a strategy for data collection, analysis, archiving, and sharing. The standardisation of a framework process makes a systematic direction for the R&D team, helping them to increase efficiency in their delivery.

2.1.4 Documentation

Scaling in a team indicates an increase in people, which instantly reveals the need for a system. A more significant number of people not only requires management but they are easily lost and confused. Thus, it becomes the Research Ops’ responsibility to develop a framework for processing, analysis, and reporting of the people in the R&D team. The documentation of frameworks like how to write a research paper, guidelines, etc., makes it easy for any new member to learn the process and quickly fit into the role. An adequately documented framework ensures consistent, high-quality reports without compromising on efficiency.

2.1.5 Internal communication

The R&D department is one area that every department uses, which indicates the role of internal communication. Research Ops takes the responsibility of socialising within the organisation in emails, newsletters, blogs, etc., to share monthly reports, latest findings from the department.

2.1.6 Sample pooling

User insights always require sample subjects for participation; however, attracting these participants for data collection from the desired demography is a massive task. Therefore, Research Ops strategies incentives based on the demography and then builds a pool of subjects for participation and aid in the data collection.

2.1.7 People

Again, human resource management and recruitment are needed in every department. Research Ops monitors the team’s requirements in terms of hires, training, skill learning, mentorship, conducting off-site or on-site meetings, etc.

2.1.8 Governance

Today, the privacy and security of user data is a sensitive topic. The Governance section of Research Ops assures that the collection, processing, and storing of information is ethically abiding by the legislation. In this article, you can find all the information you need to know about UX research.

3. Research Ops: How to get started

The scope of Research Ops can seem overwhelming in the beginning. Despite its possibilities, one of the most common worries surrounding the practice comes from companies asking how to get started? Since it covers eight pillars and each of the eight areas has a department of its own in a company, it can seem like a massive task. However, the key to a successful Research Ops team is taking it slow. Similar to a business idea, one of the first steps to starting the operative function requires finding its necessity through communication within the team.

Leading companies like Microsoft, Spotify, and Deliveroo pointed out that their R&D team became good at multitasking with the growth of the company. While it sounded great to the ears, the reality was that the team was giving away time from the job that they were hired to do, like studying customer needs, developing strategy, etc. Some even realised the blurry lines between their job requirements, noting that there was no ending to the list of jobs, while they knew where they had to start. Another big challenge that Microsoft said was the absence of documentation could cost the R&D team making similar expensive mistakes again in the future. Finding the challenges to the respective team became the first step to developing Research Ops in the companies described above.

3.1 Steps to build Research Ops

Following the footsteps of the well-known organisations with successful Research Ops, mentioned below are the four easy steps on how to get started:

3.1.1 Communication

Follow the format of a feedback form to ask your team the challenges they face. If the unit cannot find any obstacles, take a detour and ask about their tasks during the day. Suppose they have any suggestions for improvement. If forming questions is a difficulty, then concentrate on how the four areas mentioned below affect the respective team in the questionnaire:

Environment: What is the process? How are the participants found? Any constraints in the data collection or processing.
Scope: Frequency, methods, how and when of the research process.
People: Strengths and weaknesses of the team.
Organisation: Questions regarding task division, structure, internal communication.

3.1.2 Audit and analysis

Similar to data processing, after the collection of responses, audit and analyse to find the significant challenges faced by the team. Create a spreadsheet with the report of the findings for the next step in developing Research Ops. However, before moving forward, classify the challenges into areas, for example:

Human Resource Management & Recruitment
Data Archiving & Knowledge Management
Tools & Infrastructure

3.1.3 Start small

Post classification, it becomes much easier for a company to start Research Ops. However, again, it is best to not concentrate on all challenges in one go. Instead, it is best to begin by focusing on one area, solving the challenges, and moving to the next.

3.1.4 Grow and evolve

Once the Research Ops gets started, similar to other company departments, let it proceed and evolve to find continual challenges the R&D team faces. Then, with regular communication and feedback from the team and auditing responses, the Research Ops can grow and get a comprehensive structure for itself.

4. Final Words

It is interesting how certain companies can’t have enough Research Ops, while others are yet to realise its need. The bandwidth of the practice is so large that in larger companies where research is an integral part of the company practices, they have already started crossing interdisciplinary ops for better integration. However, before jumping to conclusions and following the trend, it is necessary to lay out the challenges of the R&D and scopes of Research Ops. The absence of planning and rushing can lead to budget wasting, says Spotify Research Ops lead. She made the mistake of giving away tasks to Research Ops that the researchers couldn’t have handled, and thus emphasises planning before execution.

Maggy Mächler

Maggy is a Senior B2B Marketing Manager with a background in Business Psychology. She’s passionate about driving software products and exploring user behaviour. Maggy contributes to the UX Research community through various content formats, from long-form articles to social media takeaways. She organizes user research talks, always seeking new angles and providing valuable input.

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An essential guide to research operations

UserTesting

Introduction

Is your user research department wasting time on repetitive tasks?

Perhaps your approach to UX reporting is chaotic, creating inconsistencies in metrics and confusion amongst leadership teams.

These are the kinds of problems that the discipline of Research Operations (or ResearchOps) seeks to solve. It provides tools, templates, and resources to those involved in user experience, ensures research data is protected, and builds confidence in UX across a department or organization.

In this guide, we’ll explain:

The definition of ResearchOps

The benefits of ResearchOps

The challenges of researchops, how to implement a researchops program.

Ownership of ResearchOps

What is ResearchOps?

A Research Operations program manages the people and processes involved in an organization's research discipline. It runs alongside other operations teams like DesignOps and DevOps.

There are five key components to a ResearchOps program, including:

Workflows such as consent forms and participant recruitment to streamline UX activities
Budget control when spending money on research tools, survey compensation, and more
Knowledge management and sharing research data with the entire organization
Responsibilities for each team member or department
Building UX advocacy across the entire organization

The goal is to make user research activities easier and more consistent. Research Operations is an important aspect of a centralized approach to customer and user insight that helps teams make better decisions, faster.

Now we know what ResearchOps is, let’s take a look at the benefits of having a program in place.

Save time and improve productivity

You need to run a new UX test . But before you’re anywhere close to conducting the test or iterating designs, you’ve got a checklist of things to remember—like participant recruitment, scheduling interviews, and other project management items.

Sound familiar?

Jason Forney is a Senior UX Researcher at Okta, an organization with a 10:1 ratio of designers to researchers. Because of this, Jason says, “maximizing our resources and our ability to do as much as we can with our head [count] is very important.”

Having a ResearchOps program in place significantly cuts the time spent on administrative tasks. You have a repeatable, efficient process to guide you through each step—with some even being automated and taken off your plate altogether.

"The team at Deliveroo spend up to half of their time on set-up work like booking a venue, sourcing participants, scheduling it all in, setting up contracts and consent forms, scanning the forms after the session, etc — leaving them less time to do the actual research work (interviews, surveys, fieldwork observation, usability testing , etc) that they’re experts in. This is obviously not ideal, especially as the business scales and demand for research grows. Hence the need for a research ops team to support the researchers and help operationalize what they do." - Saskia Liebenberg, Deliveroo ResearchOps Leader

Make insights easily accessible to other teams

There’s no doubt that measuring UX performance is critical. Our UX 360 Report found 77% of user experience professionals want to track UX improvements over time. Another 69% want to see their data relative to competitors.

The only problem is that many UX teams have data that is inaccessible to other teams. And they're not the only ones. Many other teams like Sales, Product, and Marketing often collect valuable insights that are inaccessible to teams that could otherwise benefit from them.

Let’s put that into perspective and say your design team is thinking of removing breadcrumbs from a few pages inside your mobile app as part of a redesign. But before the team does so, one of the designers decides to check your central knowledge hub and discovers that the breadcrumbs are there for a very important reason.

If your ResearchOps program hadn't established that knowledge hub, you might have wasted some valuable time and resources.

"Rather than beginning by asking, What kind of study do we need to run? We are now equipped to ask, What do we already know?" - Aaron Fulmer, Microsoft

As we mentioned above, it’s not just research insights that should be stored in the hub. All user and and customer experience insights should be centralized and organized into a single location, helping teams to better consolidate and collaborate regardless of where that insight originated from.

Get stakeholder buy-in for UX programs

We’ve already touched on the fact that UX teams struggle to measure their UX performance. Some 81% of digital experience professionals say their executive teams value UX— yet just 59% can effectively measure it.

A ResearchOps program helps with measurement. You’ll have standardized processes that explains:

Which UX metrics should be reported
How those UX metrics impact the wider business goals
Historical data to benchmark new studies

All of that data results in consistent UX reporting—a valuable asset that’ll go a long way in getting executive buy-in for user research.

Control data governance and security

Most organizations don’t have one team managing UX. In fact, user researchers are nearly as likely to say that each business unit is responsible for UX as they are to identify any specific C-level exec responsible for it.

(Because of this, fewer than 1-in-5 say their senior design or UX leader reports to the C-Suite.)

Lack of ownership over UX could land you in hot water—especially when sensitive data is involved.

Governments’ demands for companies that manage sensitive information are growing. Regulations like GDPR and the California Consumer Act mean all research insights need to be protected and secured.

That’s much easier to do when you have a ResearchOps program that details responsibilities for data governance.

Manage UX budgets effectively

User research can be expensive. Chances are, you have the following UX activities coming out of your budget:

Software fees
Incentives for research participants
Contractors
Travel expenses

A ResearchOps program gives someone responsibility for that budget. It’s a research operations manager’s job to approve expenses or find alternatives that lower cost (and improve your UX ROI ).

"We’ve worked hard over the past few years to create a strategic role for research. At Shopify, we never do research for the sake of doing research; we do it for the sake of informing strategic decision-making. This means that we will always prioritize the exploratory/strategic/descriptive over the tactical/causal/operational." - Dalia El-Shimy, Shopify UX Research Lead

Despite the benefits of having a Research Operations program, just 61% of research executives currently have a strategy for one in place.

Here are some of the stumbling blocks to creating a ResearchOps program:

Buy-in from the team

Consistency is a major bonus of ResearchOps. But it only works if the entire research team follows your new guidelines and workflows.

The reality is: people are creatures of habit. It’s not uncommon for UX researchers (especially those with years of experience) to continue using their own workflows. Convincing them to change to yours can be tricky.

This is likely why large companies struggle to implement ResearchOps. Interestingly, one study found that all small organizations surveyed had a Research Ops strategy in place (compared to just 80% of the large organizations with multiple team members to manage).

Time to set-up

The journey to operationalize your UX program isn’t straightforward. You’ll need multiple team members’ input, a tool that’ll help you manage each stage of the process, and time to create repeatable workflows for every repetitive task.

Each of those issues are a struggle for organizations that are short on time already.

ResearchOps can easily fall to the back burner for companies without time to invest in a strategy. But even though it’s a considerable investment, a ResearchOps program will end up saving you hours in the long run.

Ready to take advantage of a ResearchOps program? Here’s a step-by-step guide to implementing your own.

1. Choose a ResearchOps manager

The first step for any ResearchOps is to choose a leader. This is the person who will have total control over UX budgets, workflows, and data. It’s their responsibility to make sure your new guidelines are used across the entire organization.

The ideal ResearchOps leader is someone senior who has multiple years of experience under their belt. They need to:

Understand processes for UX activities—from planning through to reporting
Know what a reasonable UX budget looks like (and how to manage it)
Have great relationships with leaders of other departments and their own UX teams to ensure consistency
Be able to explain the value of a ResearchOps program

2. Understand your UX team’s skills and responsibilities

Once you’ve chosen your ResearchOps leader, they’ll start their program by thinking of the skills and responsibilities for each research team member.

A key part of any successful ResearchOps program is competency. Each team member should have access to guides, tutorials, and templates that’ll help them do their job faster, more consistently, and efficiently. A solid understanding of each job role will help you create those resources.

Remember: UX spans multiple departments. Once you’ve listed the skills and responsibilities for your department, expand into support, UX designers, and product teams.

3. Create repeatable workflows for each UX activity

By this stage of the ResearchOps process, you’ll know the tasks each team member is working on. Your job now is to create repeatable, scalable workflows for their daily activities.

List every research method your UX team is using. Document the process for each, then figure out whether you can:

Automate repetitive admin tasks
Create templates to save time—such as consent forms or persona development
Give control over a certain element (like recruiting participants) to a designated team member

Lucy Walsh kickstarted Spotify’s ResearchOps program by identifying low-hanging fruit she could tackle within a three to six-month timeframe. Lucy says this “would remove some of the time-consuming operational tasks that fell to our Researchers.”

"When I joined the team, Researchers were still printing consent forms, which would then have to be scanned and uploaded to a contract management system. Researchers were also still handling recruiting, whether this was via a third-party partner, or reaching out to our own internal lists of users. In my first few months, I, therefore, put in place processes to remove these time-consuming elements, including implementing digital signatures for consent forms and a Trello board for recruitment assistance." - Lucy Walsh, Spotify User Researcher

4. Develop an insights hub

The final stage of your ResearchOps program is to develop a knowledge base. This will act as the home for all of your research and customer insights. Encourage everyone to upload their UX research data and customer feedback into the platform whenever they run new tests—regardless of how big (or small) the test was, or which department was responsible.

You can even create guidelines for:

Tagging certain themes in your data
Hashtagging the type of data it is (qualitative or quantitative)
Passing research insights to other teams

As we touched on earlier, having a central knowledge base prevents you from repeating UX activities (and draining budgets). When data is stored in one library, you’ll also have a streamlined way for researchers to share their data with people who can act on the insights.

The best part? The overwhelming majority (72%) of digital team members are experiencing a surge in the demand for UX.

If you’re experiencing the same and employing new team members to cope with demand, this part of a ResearchOps program helps with onboarding. New hires will have immediate access to the library of research studies you’ve done, so nothing gets repeated—just built upon.

Start your ResearchOps program today

As you can see, ResearchOps is designed to take control over the people and research processes you’re using in your UX strategy, providing you with a centralized way to store and share UX data.

It’ll help you save time, improve productivity and get the stakeholder buy-in that most UX teams are crying out for.

You’ll overcome the operational challenges your user research department experiences and, perhaps most importantly, make better decisions, faster.

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User research strategy, Part 1: planning and the Research Operations

Jonathan Richardson

researchops-community

A user research strategy has to be flexible, realistic and achievable to succeed. And the best way to do this is to focus on the key research questions, the outcomes, outputs and ways of measuring this.

As a freelance user researcher I recently completed a user research strategy for the release of multiple products working with the Llibertat UX agency for the University of Southampton digital team .

Taking this case study I’ve created some general principles and strategies for efficiency. This approach offered flexibility; interviews could combine multiple projects’ research questions and so make the review and iteration more efficient.

This article is split in 2 parts:

part 1 (this one) deals with the planning, which is a key part of research operations or ResearchOps
part 2 deals with how to gather research questions to make efficient use of researchers’ time

What are research operations?

This post will focus on ResearchOps. Broadly speaking this involves setting the frameworks, management and processes to enable user researchers to get on with the job of research.

You can find out more in this post , where I also borrowed this definition from:

Why do I need a research strategy?

To be efficient.

User research is vital but it takes resources — primarily in time, but there are other costs, from incentive payments to subscriptions. You cannot research everything or else you will never finish. Instead you have to pick an area and proceed.

This applies whether you are managing multiple user researchers or just yourself as a team of one.

User research is also expansionary — existing teams need current products reviewed and upcoming products tested; other teams realise what it can do and want in on the action.

Research strategies and ResearchOps

As stated in the ResearchOps post , there are 8 key areas ResearchOps cover ( NielsenNorman list it as 6 ) and all 8should ideally be included in a a user research strategy.

To summarise how my posts on user research strategy relate to this ResearchOps process:

environment : how the organisation treats user research, whether it is still fairly new or mature in carrying it out, and the thoughts of key leaders helps frame any strategy
scope : the how, when, process and methods of the research (is it a team, what cadence do you expect)
recruitment and admin: create a central of users you can draw from with enough information to enable you to not have to recruit for each project
data and knowledge management: make sure the teams know the process for reviewing previous knowledge and sharing and centralising any project findings (ideally in a repository)
people: this strategy assumed that the team were competent in user research and that all would play a part in carrying out research but this is not a given
organisational context: engagement with stakeholders, both those involved with individual projects and the main strategy, is a vital part of this strategy both to keep these stakeholders informed and to get their feedback
governance: create the processes and policies for consent and data storage and make sure you follow GDPR and other legal and ethical requirements or policies
tools and infrastructure: if you don’t already have the required tools (software, subscriptions) in place get them before you start — if your strategy uncovers that you may need more tools, get them

Creating a user research strategy is not something you go down the 8 pillars and tick off when complete. It is a range of steps, often backtracking or reviewing, as I bounced between scope, governance, tools and so on. As such my posts will follow the approximate timeline I experienced.

But the aim was consistent — to amplify the value and impact of research at scale. And to quote a researcher from the Nielsen Norman article:

“The value ResearchOps can bring is not just calling and getting a participant but building a programme and establishing consistent quality for communications and research methods.”

The strategy I worked on then was both a way to build a programme of consistent quality research that supports and aligns with other team members and their goals to produce an optimal user experience.

Why the team needed a user research strategy

The teams I worked with needed a user research strategy because:

multiple, distinct projects all had the same deadline
these projects were officially in Beta but had varying depths of research beneath them
developers and delivery managers had their own goals and while they wanted to be research led, they had deadlines and priorities the user research had to fit around
everyone wanted to know what was feasible in the time allotted

I’ve always said that user research requires a great deal of project management skills and good organisation .

User researchers needed a strategy so that a good framework was in place to allow the researchers to crack on with the jobs they needed to do without worrying about how to plan and resource it.

Assemble what’s gone before and where the team needs to go

Before you do anything make sure you know what’s passed and what the team needs (not just wants) to happen and when. Then get the team — including key stakeholders — to review and agree on the main tickets of work.

Start with deskwork

Before I did anything, I did deskwork and spoke to key people, such that I:

Looked in the Research Repository for past documents and spoke to others involved if they could find more.
Spoke to delivery leads and development leads to find out what their key deadlines were.
Asked what was absolutely needed for that date, what the minimal viable product (MVP) was, and what would be an absolute blocker to achieving this.
Spoke to stakeholders — the heads of content, design, and other subject matter experts (SMEs) to get their perspectives.

In a way this is much like typical project management. Similar to my Coda project management toolbox or the Lean UX canvas .

In doing so I took the attitude of:

I wasn’t afraid to have lots of questions and was clear on what I did not know
I was there to listen and ask follow up questions
I returned to the team once I digested the information and combined it with other sources to produce follow up questions
I always asked who else I should speak to

Centralise thoughts

Notes need to be managed and centralised to allow you record, spot gaps, and to play around. To do this I created an online whiteboard (Miro in my case) to centralise information and included:

summaries of key documents
notes from my conversations with delivery leads and SMEs
groupings of notes and then summarised them to get what they related to
the questions that arose from this summarisation and analysis

When I felt confident with the background it was time to move onto a standard template of things that each project had to answer.

Get a rough idea of all the projects

To me, the main things that must be answered at a high level for a project in order to proceed are:

what is its purpose and why does this project have to be done now
conversely, why could this project be delayed or be deprioritised ( the devil’s advocate )
who are the key users (and non-users) to engage, including stakeholders that must be kept happy
when would the research count as ‘Done’

As mentioned though you can also go through the Lean UX canvas to get a more thorough overview. Whichever process you use, you are effectively seeking to go through the Five Ws :

why this project needs to go ahead
when it needs to be done
who the key people are — both stakeholders and team working on the project
how we think it can be completed
where in the organisation the project sits and under whom
what the key things researchers and the project team need to know and what will it look like when done

As stated, you also need a definition of done for when you feel you know enough about each project. For me it was when I could answer:

the size of the project ( T-shirt sizes )
the number of rounds needed
key user groups to speak to
when the project would be done
how to prioritise each project relative to each other

Time to start on the research questions

Once those rough answers were there it was time to finally start thinking about the research questions, as you now have enough information to start thinking about:

what is the main question — Must answer
What are the secondary research questions — Must answer
What other questions can we answer — Should answer

For example:

The main question we must answer could be ‘Can the product go live without major blockers?’

If for the main question we need the answer to be able to say “yes” or “no”, the secondary questions can be broader:

what are the main blockers and why are they blockers?
can we demonstrate through measurements that the new design/service is better than what it’ll replace?
what changes must be made from the Alpha?
which changes must be made before go-live, and which can be made after?

Team and stakeholder validation part 1

You should be engaging SMEs/project leads and then higher stakeholders (such as product owners) along with the wider team as you go along. But when you’re ready to go you must be able to confirm with them:

does the user research focus on the most important research questions?
can they do anything practical with the answers to the research questions (eg make a go/no go decision, go to the board and say that the system is better)?
if we only focus on certain user groups will we get valid feedback in order to launch?
are they happy with the potential outcomes (eg potential design changes, potential recommendations to move or even cancel a project)?

Once validated, I also took the teams through the plan so that all knew what was coming.

Make the tickets

By now you should have enough information to be able to make a start on creating tickets from your research questions.

Do this in your favoured format, whether in Jira tickets, Trello or physical notes. You may need to break down the research questions.

Create templates

To save time during the sessions aim to create as many templates as possible. In some cases such as discussion guides you will want to use different questions.

However you can still create guides that do a lot of the heavy lifting (introductions, background questions) so that you don’t have to spend too long updating them.

Things you could prepare before you start include:

consent forms
discussion guides
how sessions will run
whiteboards, notebooks and other ways of capturing feedback

Ready to start your research questions

Part 2 will look at how best to plan your research questions and methods so as to make the most out of the time available.

In summary we’ve covered:

why a research strategy is needed
why it’s important to gather relevant information and documents of previous work
why you need to review and centralise your thoughts on the previous work
interrogate your notes with the journalistic Five Ws approach of who, where, what, why, when, who and how
why it’s important to build templates to make your team’s work efficient
start your research questions but first validate your work with your team, SMEs and stakeholders

Jonathan is on LinkedIn .

Written by Jonathan Richardson

User researcher and writer with an focus on the journalistic and anthropological approach

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Achieve Impactful UXR: The Essential ResearchOps Framework

Master the 8 pillars framework, created by leading ux researchers to streamline and scale your research practice..

User research (UXR) is the backbone of creating successful products. But as research efforts grow, managing logistics, participants, and data can become overwhelming. This is where ResearchOps comes in to lay the groundwork for user-centric product development. Whether you're new to ResearchOps or a seasoned pro, this guide offers insights into building collaborative, efficient, and secure research practices.

Defining ResearchOps

ResearchOps (or ReOps) is a specialized area of design operations focused on optimizing and empowering user research (UXR) efforts. It includes the processes, tools, and strategies that streamline research. By streamlining workflows, ResearchOps frees researchers to focus on what matters most – uncovering user needs.

In order to increase product adoption, customer satisfaction, and revenue growth. From managing tools and resources to streamlining participant recruitment, ResearchOps is the engine that drives impactful user research for business success.

Why is Research Ops important?

Effective ResearchOps fosters a collaborative environment where the user's voice is at the heart of every product decision.

Research Ops helps organizations make more informed decisions, improve the user experience of their products or services, and drive business growth by aligning research efforts with strategic objectives.

The aim is to optimize research efforts' efficiency, quality, and impact while enabling better collaboration and knowledge sharing among researchers, facilitating cross-functional teams to access and leverage research findings.

re+ops, the world's biggest Research Ops community, outlined several core reasons why the practice is essential :

Protecting participant privacy

Making research easier to do (i.e., democratizing research )

Operationalizing respect for the people who participate in our research

Magnifying the impact of our research (knowledge sharing)

The Research Ops Framework

The re+ops community created a framework to define Research Ops, its functions, and its responsibilities within an organization. The initiative used survey data and outcomes from 33 #WhatisResearchOps workshops worldwide to highlight twelve key Research Ops elements.

This framework can enhance your understanding of Research Ops and enable you to structure operational systems and procedures using global best practices.

Knowledge management

Internal communications

Asset management, budget management, research spaces, participant recruitment, event management, team building and care, capability & opportunity, guidelines and templates.

The DIKW Pyramid — image credit: Ontotext

Knowledge management is a strategic approach of capturing, organizing, and leveraging research insights and information within an organization. It involves creating processes, tools, and systems that enable researchers —and other team members— to effectively store, share, and access knowledge generated through research activities. Empowering collaboration, knowledge sharing, and reuse of research insights across teams and projects. Therefore organizations make informed decisions based on user-centric data.

Some core components of knowledge management include:

Content management systems

Documentation of research findings

Research roadmaps

Centralized data repositories

Sharing— research, tools, findings, etc.

Best practices

Universal research vocabulary

Data gardening—retention schedule & archiving

The internal communications element is directly linked to knowledge management and involves establishing effective channels and processes for communication within the research team and across the organization. It ensures smooth information flow, collaboration, and alignment among researchers, stakeholders, and other teams.

Effective internal communications enable Research Ops to enhance collaboration, reduce duplication of efforts, and promote a shared understanding of research goals and outcomes within the organization.

Some core components of internal communications include:

Reporting—quarterly, customer, etc.

Information architecture

Platforms for socializing research

Asset management includes organizing and maintaining a centralized repository of raw and processed data, including research-related assets, such as research reports, participant data, interview recordings, and design artifacts. UXRs must make this data accessible, findable, and useful for team members while adhering to security and permissions.

Some core components of asset management include:

Findable assets

Security & privacy

Centralized storage and distribution

Effective storage for RAW data—i.e., unedited A/V

Managing personal identifiable information

Permissions

Tools are the software and platforms that facilitate various research activities, such as participant recruitment , data collection and analysis, collaboration, and knowledge management.

Research Ops tools fall into five primary categories:

Participant recruitment platforms streamline finding , recruiting participants , scheduling , and paying for research studies, ensuring representative and diverse user samples.

Research management platforms help manage and track research projects, participant recruitment, scheduling, and logistics, streamlining the end-to-end research process.

Data collection and analysis tools enable researchers to collect and analyze qualitative and quantitative data, such as surveys, interviews, usability testing, and analytics, providing valuable insights for decision-making.

Collaboration and communication tools facilitate seamless collaboration among researchers, stakeholders, and team members, enabling efficient knowledge sharing, feedback exchange, and project management.

Knowledge management systems assist in organizing, storing, and retrieving research assets, such as reports, insights, and design artifacts, making them easily accessible and reusable across the organization.

Some core components of tooling include:

Help me find the right tools

Tool sets—hardware, software, A/V equipment

Management—permissions, licenses, users, staff onboarding

Procurement

Planning of tools

Ethnio— a user research CRM to solve common UXR pain points

Before Ethnio, many customers:

Managed an Amazon account to send individual incentives—a lot of wasted time and resources

Didn’t do any live/in-the-moment participant recruiting—enter Intercepts

Manually created each calendar event for their sessions, which Ethnio’s Scheduling automates for both Google Calendar and Outlook.

Managed a participant database in a tool like Airtable, which is fine, but doesn’t have the complexity of Ethnio Pool with rules and governance, tracking, and automations.

Budget management is the planning, allocating, and tracking financial resources for research activities. It ensures that research projects are executed within budgetary constraints while maximizing the impact and value of the research investments.

Effective Research Ops budget management must consider these five factors:

Budget planning: Research Ops teams collaborate with stakeholders to define the research goals, objectives, and scope to estimate the required resources and associated costs.

Cost estimation: Based on the research requirements, Research Ops teams assess the costs involved in participant recruitment, research tools, technology, travel expenses, incentives, and other research-related expenses.

Budget allocation: Once the budget is determined, Research Ops teams allocate funds to various research projects, ensuring an appropriate distribution of resources based on priorities and strategic objectives.

Expense tracking: Research Ops teams monitor and track expenses throughout research projects, ensuring that spending aligns with the budget. They also identify potential cost overruns or variances and take necessary measures to mitigate them.

Reporting and analysis: Research Ops teams provide regular budget reports to stakeholders, outlining the utilization of funds, highlighting any significant variances, and providing insights into the return on investment from research activities.

Some core components of budget management include:

Tracking operational spend

Synching with broader budgetary processes

Budget approvals

Budget allocations per project

Research spaces are the physical or virtual environments where research activities take place. UXRs must design spaces that foster high-quality research, support seamless collaboration, and enhance the research experience for researchers and participants.

Researchers must consider procedures and protocols for these five essential research space components:

Physical research spaces: are dedicated rooms or facilities equipped with appropriate tools and equipment for conducting in-person research sessions, such as usability tests, interviews, or focus groups. Research Ops teams ensure these spaces are well-designed, comfortable, and equipped with the necessary technology and recording capabilities.

Virtual research spaces: include online platforms, video conferencing tools, and collaboration software that enable researchers to conduct remote studies, engage with participants, and facilitate remote collaboration among research team members.

Lab management: UXRs must manage and maintain physical research spaces, including scheduling, resource allocation, and ensuring the spaces are appropriate for each research session, including coordinating with facilities management, IT/AV support, and other stakeholders to ensure a smooth research environment.

Participant management: coordinate with participants, providing them with necessary instructions, directions, and any required equipment or technology to ensure a seamless research session.

Accessibility and inclusivity: Research Ops teams consider accessibility and inclusivity when designing and managing research spaces. They ensure that spaces and tools are accessible to participants with different abilities and accommodate diverse user needs.

Research Ops teams ensure research studies have the right participants , leading to more accurate insights and informed decision-making. They streamline the recruitment process, optimize participant selection, and create a positive experience for participants, enhancing the overall research operations within an organization.

Here are some of the key UXR responsibilities of participant recruitment:

Participant sourcing: Research Ops teams collaborate with researchers and stakeholders to define the target audience for research studies. They employ various methods to source participants , such as intercepts , recruiting from existing user databases , utilizing recruitment agencies, or leveraging online platforms and communities.

Screening and qualification: Research Ops teams must screen participants to determine their suitability for specific research studies. Screening involves assessing participants' demographics, background, experience, or any specific criteria defined for the study.

Consent and incentives: the administrative tasks related to participant recruitment, such as obtaining informed consent from participants, managing confidentiality agreements, and facilitating the provision of incentives or compensation for their time and effort.

Scheduling and logistics: UXRs coordinate with participants, team members, and stakeholders to schedule research sessions , ensuring their availability aligns with the research timeline. They handle logistics, such as sharing session details, preparing participants for the research process, and addressing any questions or concerns they may have.

Participant database management: a participant database captures and stores relevant information about participants, their preferences, and their history of participation. This database helps streamline future recruitment efforts and enables efficient participant management for ongoing research initiatives.

Some core components of participant recruitment include:

Quality for the price

Manage both internal and outsourced recruitment

Engaging with external recruiters

Managing recruitment tools

Incentive management

Need to understand design and research

Managing a secure database of consent forms

Thanking respondents

Developing and maintaining a diverse customer panel

Governance refers to establishing and implementing frameworks, processes, and policies that ensure research activities are conducted effectively, ethically, and in alignment with organizational objectives.

Clear governance procedures are especially critical for research democratization because they provide non-researchers with the frameworks, guardrails, oversight, tools, and processes to conduct proper research.

Here is a high-level overview of Research Ops governance and what UXRs must consider for effective implementation:

Research standards and guidelines: establish and communicate research standards and guidelines to maintain consistency and quality across research projects, including research methodologies, ethical considerations, data privacy regulations, and reporting requirements.

Compliance and ethics: standards related to research, such as obtaining informed consent from participants, protecting participant privacy and data, and adhering to industry regulations and guidelines.

Documentation and reporting: Ensure findings, insights, and recommendations are adequately recorded and shared with relevant stakeholders. UXRs establish templates and guidelines for research documentation, making it easier for teams to capture and communicate their work effectively.

Stakeholder alignment: UXRs facilitate communication channels, such as regular research meetings or reporting mechanisms, to share research insights, validate findings, and gather feedback.

Process optimization: Identify bottlenecks, streamline workflows, and implement best practices to optimize the research operations and enable researchers to focus on generating valuable insights.

Risk management: Identifying potential biases, ensuring data security and privacy, and implementing safeguards to protect participants and intellectual property.

London based research team in a UXR workshop. Image credit: Dave Hora .

Researchers are responsible for event management, including planning, coordination, and execution of research-related events, such as user research sessions, workshops, conferences, or training programs. UXRs ensure that sessions and activities run smoothly, participants have a positive experience, and they meet event objectives.

Some core components of event management include:

Arranging distinguished speakers

Social events

Company events

Team meetings

According to re+ops findings, "Research Ops is as much about people and forging relationships and connections as it is about efficiency, support, and data." This framework element celebrates team members, helping to recruit and retain them longer— especially in remote organizations where team members are more likely to feel disconnected .

Some core components of team building and care include:

Celebrations

Pre-approved counseling service

Secondment opportunities

Onboarding new staff

Image credit: re+ops community .

Developing researcher career progression is essential for growing the research team's experience, skill sets, confidence, and opportunities. This personal and team growth enhances their professional development and contributes to the overall success and effectiveness of the research operations within the organization.

Some core components of capability & opportunity include:

Professional development opportunities

Research skills and maturity matrix

Mentoring opportunities

Training opportunities

Book or reading club

Peer review sessions

Sparring sessions

Image credit: Rodrigo Dalcin .

Guidelines and templates help maintain consistency, efficiency, and quality in research activities. They enable researchers—and non-researchers—to focus more on the research rather than reinventing processes or struggling with documentation. These guidelines lead to improved research outcomes and better-informed decision-making within the organization.

Some core components of guidelines and templates include:

Database of methods

Shared templates & methodology = shared understanding

Research briefing and reporting templates

Maintaining the content

Platform for sharing guidelines and templates

Clear guidelines and rules

How-to guides

Recruitment guidelines

Ethics guidelines

Customer contact guidelines

The Eight Pillars of Scaling Research Ops

The re+ops community has also devised the eight pillars of Research Ops . The community-driven initiative created a strategy for scaling research and ResearchOps. This framework can be used as a checklist to identify problem areas, bottlenecks, and inefficiencies and implement effective solutions.

User Experience Research and Design Leader at Meta, Emma Boulton, summarized the eight pillars of Research Ops in a Medium article.

Environment: Why does research happen, and who engages with what I do?

People silos

Value of research

Internally focused

Stakeholders

Scope: How and when does research happen? What methods?

Cadence

Sharing insights

Prioritization

Integrating insights

Research as a team sport—democratization

Recruitment and admin: How do I manage all the project and participant admin?

Panel management

Participant coordination

Data and knowledge management: What happens to the findings, data, and insights?

Research library

Data gardening

Document templates

People: Who is responsible for carrying out research?

Community of practice

Professional development

Mature career paths

Organizational context: What are the internal and external constraints?

Business constraints

Market forces

Org maturity

Governance: What are the legal and ethical considerations?

Risk assessments

Tools and infrastructure: What systems and tools do I need for my projects?

By implementing a well-defined ResearchOps strategy, organizations can transform user research from isolated activities to a strategic asset that drives innovation and business success.

Elevate Your ResearchOps with Ethnio

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Ethnio is your partner in building a robust ResearchOps practice. Let's take your research to the next level.

Elevate Your UXR with ResearchOps and EthnioWhen researchers manage every aspect themselves, maintaining top-tier research practices across all stages can be challenging.

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Introduction to Operations Research

First Online: 17 June 2022

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H. A. Eiselt 3 &
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In its first section, this introductory chapter first introduces operations research as a discipline. It defines its function and then traces its roots to its beginnings. The second section highlights some of the main elements of operations research and discusses a number of potential difficulties and pitfalls. Finally, the third section of this chapter suggests an eight-step procedure for the modeling process.

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Eiselt, H.A., Sandblom, CL. (2022). Introduction to Operations Research. In: Operations Research. Springer Texts in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-97162-5_1

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Harnessing research operations: How teams can maximize research efficiency

User Research

Oct 11, 2023

Harnessing research operations: How teams can maximize research efficiency

Discover how to build a successful ReOps program which maximizes research efficiency, streamlines workflows, and evangelizes user research.

Ella Webber

A typical user research process has many moving parts, from recruiting the right participants and handling legal paperwork to conducting interviews, managing data, processing payments, and much more.

It can rapidly devolve into chaos where you're constantly trying to find time to manage all these tasks.

How can product teams and researchers stay on track, boost efficiency, and avoid unnecessary delays in your process?

Two words: research operations.

Research operations, also known as ResearchOps or ReOps , has emerged as an essential function of UX teams to optimize the research process and make it more impactful.

In this article, we’ll explore the concept of ResearchOps and its core pillars. We’ll also give you a step-by-step framework to create your own ReOps program in-house, and maximize the efficiency of your research efforts.

What is ResearchOps?

ResearchOps is a set of people, workflows, and tools designed to streamline user research . Research and product teams use this setup to organize the user research process , boost productivity, and leverage insights to enhance the user experience.

The discipline focuses on creating standardized workflows to ensure consistency, efficiency, and scalability in user research.

The term first popped up in 2018 when Kate Towsey , Research Ops Expert, tweeted about her new ResearchOps community, created on Slack to discuss the operational aspects of UX research.

Since then, ResearchOps has become a strategic subset of DesignOps, helping research teams efficiently manage the behind-the-scenes operations of user and product research , and streamline all steps in the process.

User experience has fast become a key differentiator for SaaS companies. In fact, our Continuous Research Report showed that 83% of product professionals surveyed said research should be conducted at every stage of the product development cycle —highlighting the need for an established research operations program.

Why does ResearchOps matter?

A good research operations setup can significantly improve the efficiency of conducting research and help research teams cut down on administrative tasks . It enables you to focus your efforts on the actual research workflows and remain highly productive.

These processes set you up for repeatable success when it comes to conducting UX research and extracting meaningful research insights quickly.

What’s more, your ResearchOps setup can also eliminate inconsistencies affecting the quality of output. You can produce more reliable results with a well-defined operational plan and the right tools at your disposal.

Another key reason to invest in ResearchOps is to help your UX teams track research performance and get buy-in from key stakeholders . You can build a more transparent and systematic approach to research, bringing everyone onto the same page, guaranteeing strategic alignment, and democratizing findings.

What does ResearchOps involve?

If you want to build your own ResearchOps program, start by understanding the key aspects involved in its function and setup.

Participant management

The success of your UX research efforts is closely tied to the quality of the research participants you recruit. Once you've defined the objectives and scope of your research, the next main step in ResearchOps is recruiting research participants .

Start by outlining what your ideal participants look like. This will give you more clarity on which specific user segments to target for your research. Screening is another critical aspect of making your participant recruitment process airtight.

Whether you’re getting applications from interested participants or choosing candidates from a database, you have to properly screen all applicants to choose participants who meet the criteria for your study.

It is possible to save time and skip this step, using tools like the Maze Panel , which allows you to choose the perfect participants from a diverse pool of over 280 million testers. All these testers are pre-screened and you can set multiple filters to narrow your search and identify candidates that match your user personas .

Here’s the tricky part: despite your best efforts to recruit and screen candidates, managing participants and executing your workflows can quickly become overwhelming. This is where tools like Reach can help streamline this entire process, by letting you:

Hire participants and build a database of testers
Set up workflows to communicate with participants
Get granular insights into every tester and select participants strategically

Tools like Reach make it easy to qualify participants in your database and run campaigns seamlessly. These solutions are time savers when it comes to participant management, so consider investing in one as part of your research operations toolstack.

Supporting researchers

Any user research program is only as good as its researchers, and it’s the ResearchOps team that helps empower the user researchers in your team. You can support your UX team members by building a good research tech stack.

Give them access to all essential UX research tools for implementing the end-to-end research process, including tools for remote testing, heat mapping, analytics, and more.

The ResearchOps team can also support researchers by setting up cross-functional workflows between different teams like customer success, product, marketing, and more. For example, ReOps can liaise with customer success and sales to understand what customers (and potential customers) are saying and help prioritize research initiatives. This guarantees your research insights are a two-way conversation, and are integrated well into product development and UX design discussions.

Knowledge management

Knowledge management setup is another essential part of ResearchOps. You need a centralized research repository to store all your findings and documentation. This knowledge hub will make it easier for teams to access past studies and research reports whenever needed, supporting teams from sales to product.

A centralized hub can go a long way in cultivating a user-centric culture and research democratization , erasing knowledge silos in favor of offering access to insights for everyone, from sales to support and product.

In the same vein, it’s a key step in evangelizing user research and helping to scale research , by exposing stakeholders and the wider organization to the work you’re doing.

You can also use this knowledge hub to share easy access to standard operating procedures (SOPs). These SOPs can standardize repeat workflows in your research projects, like onboarding participants or creating new surveys, to boost efficiency and ensure every research activity follows the same process.

UX research methods should meet certain ethical standards to respect participants’ privacy and protect their data. The ResearchOps team is in charge of ensuring research ethics standards are met during the research process.

One key element of this is sharing consent forms with all participants to get written permission to use their insights in your development efforts. These consent forms should also inform participants of how you'll use their data and the intention of your research.

Another governance responsibility is implementing measures for protecting sensitive information, in compliance with data protection laws. Supplement this with a rigorous quality assurance process to improve the quality of your research methods and make them more reliable.

The 8 pillars of research operations

After Kate Towsey started the ResearchOps Community in 2018, the team launched a global project titled #WhatIsResearchOps . The goal was to drive informed conversations around the role of a UX researcher and its many operational demands. This project culminated in a framework defining eight key pillars of research operations.

Let’s break down these eight pillars for establishing a strong research operations program.

1. Recruitment and admin

Start by setting up the tools and workflows for the admin side of your research projects. Think participant recruitment, tester database, incentives, and similar tasks.

Besides recruiting candidates, this pillar also focuses on building relationships with these participants for long-term engagements.

This pillar essentially covers everything you need to find the right participants and seamlessly conduct your research. It’s all about making your research efficient and error-free from start to finish.

2. Data and knowledge management

From user interviews to usability testing , user research will bring you a large volume of research data. You have to set up a system to effectively collect, analyze, and store this data for current and future usage.

This pillar focuses on building a good knowledge management hub for all research documentation, reports, and learnings. This is crucial for giving easy access to all stakeholders. It also requires that you follow data privacy regulations, and ensures you don’t misuse information from research studies.

3. Governance

All research activities should follow certain rules to maintain ethical integrity while pursuing your organizational priorities. The governance pillar reinforces globally-accepted rules and guidelines, like GDPR, that your research needs to align with.

This pillar is all about creating a strong governance framework and is crucial for making your research credible and reliable for future uses. You should also review this framework periodically to reflect changes in industry standards.

4. Tools and infrastructure

Your ResearchOps program needs a virtual laboratory to function effectively. You need a solid infrastructure to conduct research sessions, collect and process data, and derive useful insights.

This pillar covers all the research tools and resources you need to build a well-functioning ResearchOps lab. You have to design workflows to automate necessary aspects of your research methods and focus your team’s efforts on high-reward tasks.

5. Organizational context

Good user research doesn’t operate in a vacuum. You need to align your research projects with broader organizational goals.

This pillar emphasizes the need to infuse more context about your company goals, team culture, and strategic priorities into your research methods. When you align research operational tasks with your company’s overall objectives, you can leverage research findings more holistically across your entire team.

People are a critical part of any research operations function. You need a dedicated ResearchOps team to run the show and achieve targets. You also have to invest in researchers’ training and skill-building to consistently improve performance.

This pillar highlights the importance of constantly developing your UX team’s capabilities. It also requires you to establish strong collaboration between researchers and other stakeholders, like product managers, sales leaders, etc.

7. Environment

The environment for your research studies can directly influence the outcomes. This environment refers to the context in which you’re conducting research, including details like participants’ location, behavioral traits, and motivations around your product.

This pillar focuses on choosing the right physical and contextual environment for your research methods. A suitable environment maximizes the validity of your results and enables you to collect insights that lead to a well-informed decision-making process.

The scope defines the breadth and depth of your UX research study. It lays down your project goals, methodologies, and expected outcomes.

This pillar requires chalking out the scope of your research projects to inform everyone of the steps and resources involved. It keeps UX research teams more focused on the project, in order to deliver relevant insights.

Implementing research operations: a ReOps template

Ready to build your research operations function from the ground up? Here’s a tried-and-tested template to help you hit the ground running quickly.

Establish an effective ResearchOps framework

A ResearchOps program looks different in different organizations. This mostly depends on the growth stage and size of a company. Start by creating a research operations framework that aligns with your company’s overall objectives.

For example, if you’re an early-stage startup, your ReOps program should focus on understanding target users, identifying pain points, and validating product-market fit. Your UX research strategy would involve methods like rapid user testing and a clear prioritization of research objectives.

On the other hand, a growth-stage enterprise will focus more on scaling its research operations and investing more in optimizing the infrastructure.

Establishing your ResearchOps framework typically involves three steps:

Assessing your current state of research and research maturity to identify gaps and opportunities
Defining achievable goals and designing a ResearchOps team structure
Mapping out a strategy based on current performance and expected results

You should aim to identify your research team’s challenges and pain points before setting up your operations strategy. This foundational step will set the tone for your future efforts to build a tailored ReOps program.

Build a team, then define roles and responsibilities

Once you’ve drawn out a strategy for your user research operations, the next step is to build a dedicated ResearchOps team within your UX team .

Revisit your strategy and assign different tasks/processes to specific roles, like research coordinator, ResearchOps manager, data analyst, etc. This will give you an idea of who’s already doing what, the type of positions you need to fill, and the number of people to hire for each role.

You can also define the skills and expertise required for each role to make the hiring process more effective. If you’re hiring for these roles internally, you can set up sessions to train employees for their responsibilities.

Engage the team and standardize workflows

Keeping your team engaged in different tasks is another critical step in the process. You can schedule regular syncs with all stakeholders from different teams—product, design, sales, and marketing—to collect their thoughts on research operations.

This will help in building better collaboration and setting up standardized workflows for all teams. You can convert these processes into SOPs for maximizing consistency in efforts and improving cross-functional performance.

Develop a ResearchOps knowledge hub

Documentation lies at the core of a good research operations setup. You need a knowledge management hub to store all relevant documents for your ResearchOps program. This will include:

Detailed SOPs and guidelines for your research methods
Quick-access links to relevant resources for your ReOps team
Ready-to-use templates for reporting and other documentation
Well-organized folders containing research findings and data from past studies
A collection of FAQs answering questions and clarifying doubts about your processes

You can make this knowledge hub a centralized repository of crucial information for your research team. Moderate permissions to keep a few documents confidential and share edit access to let selected people update the content regularly.

This hub can also store your progress tracking reports. These reports will assess the effectiveness of your ResearchOps program against various metrics. You can share all reports with the team and compare your progress.

Incorporate ResearchOps into daily practices

Make ResearchOps a part of routine workflows to see the true impact of your strategy. You can schedule regular check-ins to discuss progress and get project updates. This would be a good place to discuss blockers and find areas of improvement.

You should also recognize and reward team members for their accomplishments. For example, if a new researcher completes their first user interview , celebrate their success with the team to build momentum.

Remember: don’t leave research operations on the back burner while you continue doing everything the old way. Make it a part of your daily processes and be persistent and consistent to make it a habit.

Optimize and monitor research operations

ResearchOps doesn’t work on a set-and-forget approach. You have to constantly review your framework and find scope for improvement.

That’s why it’s important to schedule periodic reviews with the entire team. The goal of these meetings should be to identify inefficiencies and bottlenecks holding you back.

While these meetings can happen on a monthly or quarterly timeline, you can use a feedback channel to collect team members’ inputs more frequently. This feedback can help you tackle day-to-day challenges and optimize performance.

ReOps in your user research

Research operations is a transformative approach to level up your research workflows by systematically integrating people, technology, and processes. A good ResearchOps program can create a thriving ecosystem for user research within your organization and set you up for long-term success.

In an era where UX design can make or break a product, ReOps ensures that every research endeavor is rooted in clarity, purpose, and strategic vision.

So, bookmark this guide and get ready to streamline your research infrastructure with an airtight ReOps program. What are you waiting for?

Research efficiency starts here

Kickstart your ResearchOps program by streamlining workflows and minimizing a hectic toolstack. Try Maze, the all-in-one continuous product discovery platform.

Frequently asked questions about ResearchOps

What does a research operations manager do?

A research operations manager is responsible for building the infrastructure for user research. Their primary responsibility is finding tools, setting up workflows, and documenting SOPs to streamline research activities. They also oversee different aspects of research operations, like participant recruitment, compliance, logistics, etc.

What are the 8 pillars of research operations?

The eight pillars of research operations are:

Recruitment and admin
Data and knowledge management
Tools and infrastructure
Organizational context
Environment

What does ReOps mean?

ReOps, also known as research operations or ResearchOps , refers to the people, tools, and processes required for conducting research. It's a discipline within DesignOps designed to help UX researchers tackle operational challenges and focus on their research projects.

What is the role of user research operations?

User research operations focus on improving the backend operations of any research program. This involves managing user testing sessions, coordinating with participants, collecting and storing data, and making insightful reports.

What is ResearchOps?

The operationalization of disciplines is a trend that has happened in the last decade, driven primarily by big technology and software companies. These organizations invest billions across functions like software development, IT, design, sales, marketing, and more. As these teams grow within an organization, so does the need to define processes, systems, and strategies to ensure efficiency, consistency, and quality at scale.

Many organizations including Atlassian , Airbnb , Deliveroo , Microsoft , and Spotify have individuals who specialize in operationalizing research. As more organizations hire for specialized ResearchOps roles the discipline is continues to evolve, driven significantly by the efforts of the ResearchOps community and their work to further define the role and best practices. The community was founded by Kate Towsey , Research Operations Manager at Atlassian , and has since grown to over 3,000 members.

.css-1nrevy2{position:relative;display:inline-block;} What is ResearchOps?

In 2018, the ResearchOps community facilitated several workshops and surveys to help define the emerging practice of ResearchOps. I quite like the definition they arrived at:

ResearchOps is the people, mechanisms, and strategies that set user research in motion. It provides the roles, tools, and processes needed to support researchers in delivering and scaling the impact of the craft across an organization.

The output of their research was a map of the elements, attributes, and activities that are involved in the ResearchOps discipline ( download a PDF version here ).

A successful ResearchOps function should be a force multiplier for user research – it isn’t just about making individual researchers more effective, but about leveling up how an organization does research. ResearchOps achieves this in the following ways:

Eliminating inefficiencies with research activities and implementing operational strategies to ensure research is consistent, repeatable, reliable, and high quality.

Democratizing research within an organization so that cross-functional teams are empowered to participate, collaborate, engage with, and understand customers.

Socialize research insights so that they are accessible to an organization and that individuals make deliberate decisions informed by customer insights.

What is ResearchOps responsible for?

The challenges and pain points that a team is facing should guide how ResearchOps focuses and operates. Depending on the size of an organization and the maturity of the research team, ResearchOps may be responsible for a variety of areas, including:

Governance;

Budget management;

Knowledge management;

Participant recruitment;

Tooling, physical space, and asset management;

Guidelines, templates, and documentation;

Internal communications; and

Team and people management.

Research activities can be a risky endeavor for organizations, especially among increasing consideration for data security and privacy legislation. ResearchOps is responsible for ensuring that data is collected, stored, and processed in a way that protects participant privacy and is compliant with relevant legislation (including the GDPR and the California Consumer Privacy Act ). ResearchOps can also be responsible for outlining the ethics and guidelines that govern how research is conducted and policies for how research data is accessed and retained.

Budget management

Research carries a lot of operational expenses – there are fees or paid incentives for recruiting participants, ongoing costs of software licenses and tooling, travel expenses for field research, and more. ResearchOps is responsible for tracking operational spend, allocating budget and resources amongst research projects, and negotiating the necessary budget approvals within an organization.

Knowledge management

As the body of research produced by an organization grows, so does the need to have a thoughtful strategy for how research data and insights are captured, analyzed, standardized, archived, and shared. ResearchOps is responsible for driving this strategy, usually through overseeing and managing an organization’s research repository , which is one central place, or source of truth, where people in an organization can go to find the latest research insights and reports from the research team.

Participant recruitment

Almost all research requires the engagement of participants to act as research subjects. Depending on the nature of the research activity, sourcing participants can be an involved and time-consuming activity. ResearchOps is responsible for sourcing participants from an appropriate demographic and sample, building a pool of people willing to participate in research and facilitating scheduling and payment of incentives.

Tooling, physical space, and asset management

Research sessions—especially evaluative research methods like usability testing—often rely on a physical space or lab for conducting the research, available and functioning IT / AV equipment, and software like Lookback to record and analyze the research data. ResearchOps is responsible for liaising with other teams like IT and office experience to ensure that researchers have what they need to do their job.

Guidelines, templates, and processes documentation

Scaling a research team requires guidelines, templates, and processes to ensure that research is efficient, consistent, and high quality. ResearchOps is responsible for driving this documentation and might work on producing training for teams in areas like participant recruitment, ethics, customer contact guidelines, how-to guides, and more.

Internal communications

In large organizations with sizeable research teams, there’s often a need to communicate internally with peers and stakeholders what the research team is working on and to socialize the findings from research activities. ResearchOps is responsible for managing this communication and might take responsibility for internal email newsletters, blog posts, or monthly updates that are shared within an organization.

Team and people management

While a research operations role might not necessarily have direct reports, ResearchOps may be responsible for supporting research managers and helping to upskill the research team by identifying opportunities for mentorship and training, onboarding new hires, and facilitating team offsites and internal meetings.

When to start with ResearchOps?

As the research team grows and the organization does more research, so the need for ResearchOps will grow. When researchers are spending significant time on operational tasks like participant recruitment, scheduling, and budgets, instead of talking to customers, it might be time to consider a thoughtful strategy for operational work.

At Deliveroo , ResearchOps Lead Saskia Liebenberg describes the need to operationalize research after it became obvious that researchers were only spending a fraction of their time doing actual research:

The team at Deliveroo spend up to half of their time on set-up work like booking a venue, sourcing participants, scheduling it all in, setting up contracts and consent forms, scanning the forms after the session, …

Likewise, Microsoft identified similar logistical pains affecting their research team. Research Operations Manager Aaron Fulmer discusses this in his blog post Microsoft’s evolution of ResearchOps :

Our researchers had been doing a great job multitasking, but as our operations scaled up, they had less and less time. We decided that we needed researchers to focus more on customer needs, partners, and research design.

Instead of tackling every aspect of ResearchOps, most organizations start by solving for the biggest challenges that the research team is facing. Fulmer focused Microsoft’s ResearchOps efforts on addressing their largest pain point: participants weren’t on time, studies were starting late, and there were frequent technical issues with lab spaces.

At Spotify , ResearchOps Lead Lucy Walsh talks about how her role shifted from handling participant recruitment to kickstarting the entire operations function in her blog The Evolution of Research Operations at Spotify :

Because there is only one me, I realized that as important as it was to establish where my work started, it was also critical to have clear guardrails of where it stopped.

She explains that by starting out with a clearly defined scope for the research operations function, she was able to execute on the day-to-day tasks to support research activities while also starting to work on the highest-priority process improvements, like digitizing consent forms.

Walsh was careful not to bite off more than she could chew when starting off with ResearchOps, acknowledging that “although there were opportunity areas for expansion of ops support, this starting point was manageable with our existing resources.”

As organizations do more and more research, so will the need for specialized individuals who can support researchers to deliver and scale the impact of their work. ResearchOps is an emerging and maturing discipline, and the role is being pushed forward by the thousand-strong passionate community of ResearchOps practitioners. Do you like the sound of ResearchOps? Why not check out this article on the characteristics of a top ResearchOps practitioners to find out more!

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operations research (OR)

Sarah Lewis

Operations research (OR) is an analytical method of problem-solving and decision-making that is useful in the management of organizations. In operations research, problems are broken down into basic components and then solved in defined steps by mathematical analysis.

The process of operations research can be broadly broken down into the following steps:

Identifying a problem that needs to be solved.
Constructing a model around the problem that resembles the real world and variables.
Using the model to derive solutions to the problem.
Testing each solution on the model and analyzing its success.
Implementing the solution to the actual problem.

Disciplines that are similar to, or overlap with, operations research include statistical analysis , management science, game theory, optimization theory, artificial intelligence and network analysis. All of these techniques have the goal of solving complex problems and improving quantitative decisions.

The concept of operations research arose during World War II by military planners. After the war, the techniques used in their operations research were applied to addressing problems in business, the government and society.

Characteristics of operations research

There are three primary characteristics of all operations research efforts:

Optimization- The purpose of operations research is to achieve the best performance under the given circumstances. Optimization also involves comparing and narrowing down potential options.
Simulation- This involves building models or replications in order to try out and test solutions before applying them.
Probability and statistics- This includes using mathematical algorithms and data to uncover helpful insights and risks, make reliable predictions and test possible solutions.

Importance of operations research

The field of operations research provides a more powerful approach to decision making than ordinary software and data analytics tools. Employing operations research professionals can help companies achieve more complete datasets, consider all available options, predict all possible outcomes and estimate risk. Additionally, operations research can be tailored to specific business processes or use cases to determine which techniques are most appropriate to solve the problem.

Uses of operations research

Operations research can be applied to a variety of use cases, including:

Scheduling and time management.
Urban and agricultural planning.
Enterprise resource planning ( ERP ) and supply chain management ( SCM ).
Inventory management .
Network optimization and engineering.
Packet routing optimization.
Risk management .

Continue Reading About operations research (OR)

The big picture of Operations Research

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What is Operations Research? | NC State OR

What is Operations Research?

Last Updated: 07/08/2024 | All information is accurate and still up-to-date

The Simple Answer: Operations Research (OR) is a discipline of problem-solving and decision-making. It uses advanced analytical methods to help management run an effective organization. Problems are broken down, analyzed and solved in steps.

Identify a problem
Build a model around the real-world problem
Use the model and data to arrive at solutions
Test the solution and analyze its success
Implement the solution

The Technical Answer: Operations Research, also known as management sciences, uses scientific methods to study systems that require human decision-making. Consequently, OR helps make the most effective systems design and operation decisions. Moreover, OR’s strength and versatility come from its diagnostic power through observation and modeling and its prescriptive power through analysis and synthesis.

Additionally, OR is interdisciplinary, drawing on and contributing to techniques from many fields. These include mathematics, engineering, economics and physical sciences. Furthermore, OR practitioners have solved various real-world problems. These range from optimizing telecommunications networks to planning armed forces deployment during wartime. Many new applications, therefore, come from current energy production and distribution issues.

The CEO of the Future is an Engineer

Studies show three times as many S&P 500 CEOs hold degrees in engineering rather than business administration. This trend includes operations research practitioners among the next generation of engineers and scientists. They are tomorrow’s business leaders.

Operations Research Offers Workplace Freedom

Operations research practitioners have offices but also work in the settings they aim to improve. For example, when collecting data, they may observe restaurant staff or watch factory workers assembling parts. Additionally, when solving problems, they analyze data in an office. This combination of fieldwork and analysis creates a dynamic and flexible work environment.

The World Needs more Operations Research

As companies compete globally, the need for operations research practitioners grows. They are engineers trained to improve productivity and quality. Their common goal is saving companies money and increasing performance.

Operations Research is all about Options

Operations research practitioners work in almost any industry worldwide. They can work in and out of the office while interacting with people and processes they aim to improve. This flexibility gives them a career advantage over other types of engineers. Operations research practitioners don’t need to specialize, keeping their options open. Consequently, they remain immune to the ups and downs of any individual industry.

Careers in Operations Research

When considering a career in operations research, it’s logical to ask, Will I be able to get a job?” Answer: “YES”

Operation Research Continues to Grow

According to the Bureau of Labor , operations research jobs will grow over 32% between the years 2022-2032. This is faster than average for all occupations.

Companies Seek Efficiency

Every day, companies seek new ways to reduce costs and raise productivity. They rely on operations research practitioners to develop efficient processes and reduce costs, delays, and waste. This need drives job growth for these engineers, even in slow-growing or declining manufacturing industries.

Path to Management

Many operations research practitioners become managers because their work involves management tasks.

A Promising Future

It’s a great time to be an operations research practitioner. They solve problems, and there’s never a shortage of those!

What is Industrial Engineering | NC State University

Why Operations Research is awesome — An introduction

Mathematics is the language of the universe, and it is, by definition, logical. but doing mathematics isn't just logic – it is a highly creative process of utilizing the tools math gives us. in operations research, you get to be creative with the tools of mathematics to solve some really exciting problems.

Alex Elkjær Vasegaard

Towards Data Science

Operations Research (OR) is an applied math field where mathematical tools are not just used to investigate mathematics further but rather to model, analyze, and solve problems within the OR domain.

Motivation to Understand Operations Research

Decision making of the future will be as close to fully automated as possible (think "Tony Stark" level automation). One of the research fields that investigates and furthers this transition is OR. At its core, OR is an applied mathematics field that integrates advanced analytics methods in decision support/making.

As the problems and decision environments become increasingly complex, it is essential to advance research that emphasizes the human-technology interface to avoid misconceptions. A classic horror example of the future is if a decision-maker seeks to maximize customer happiness, and the (AI-)system translates that into putting everyone in a dopamine-infused coma similar to that in "The Matrix" (which we may or may not already be in..).

But there are also more tangible issues that we struggle with today. E.g. Routing for package delivery where the total distance of the route should be minimized while still maximizing the number of delivered packages. The two objectives will, in extreme cases, either not let the drivers deliver any packages or not let them have any free time, but there are also a lot of sub-optimal instances in between. And these issues are only the tip of the iceberg, Jack. So it is crucial that future decision-makers can integrate their preferences properly to avoid these situations — and Operations Research investigates precisely this!

I came upon OR as a graduate student in math and economics, where two extensions were possible to study; either OR or Financial Engineering. Compared to each other, the latter deals with decision-making in finance, trading, and risk/investment, while Operations Research does so more generally within industry and business. Although, some terminology is putting Financial engineering as a more specialized sub-category to the then broader field of OR.

What is Operations Research?

Generally, OR is concerned with obtaining extreme values of some real-world objective functions; maximum (profit, performance, utility, or yield), minimum (loss, risk, distance, or cost). It incorporates techniques from mathematical modelling, optimization, and statistical analysis while emphasizing the human-technology interface. However, one of the difficulties in answering this question is that there is a lot of overlap in scientific terminology — and sometimes terms become extremely popular, affecting the landscape of the terminology. E.g. the popularity of vague and broad terms such as AI and Big Data which both work great for marketing but does nothing for the discussion on the research. Therefore, I have tried illustrating it in terms of ORs-related fields, subfields, and the addressed problems in Fig. 3.

Operations research had its historical origin in the 17th century when game-theoretic approaches and expected values were being utilized to solve problems. The modern version of OR originated during the second world war when it became apparent that the military needed to solve some of the significant logistic and supply chain problems that come with being in war.

Back then, it was defined as "a scientific method of providing executive departments with a quantitative basis for decisions regarding the operations under their control" and was coined "operational analysis" (still is in Denmark) or "quantitative management".

A future in Operations Research?

An attractive feature of OR is the applicability of the knowledge, skills, and tools in various industries. Today, OR is applied in a more or less specialized version in most businesses and industries — everything from agriculture, energy trading, production, and sales to the space industry, asset pricing, military operations, and demand forecasting. The most notable usecases are probably:

Supply chain management
logistics and inventory management
Routing and pathfinding problems
Predictive maintenance
Scheduling and assignment problems
Evaluation problems (multi-criteria decision-making)
Systems engineering
Forecasting

The common denominator in terms of tools is the four following skills, allowing you to:

Utilize mathematical optimization methods, such as linear programming, dynamic programming, stochastic programming, etc.
Develop solution algorithms. Often solutions are required in near real-time. That is, the optimal solution is not necessary. One 'just' wants a good enough solution. For large problems with high complexity (for example, NP-Hard problems), solution algorithms such as expert-inspired heuristics or bio-inspired genetics algorithm, ant colony optimization, or even neural networks or decision-tree-inspired gradient boosting methods. It depends on the framework of the problem and whether it is a model-based or data-based solution approach.
Conduct extensive simulations to investigate the robustness and flexibility aspects of the derived solution approaches. Either by Monte Carlo simulation, sensitivity analysis, etc.
Conduct extensive analysis of the problems—e.g. to identify critical paths in a network. As an example to illustrate the importance of a proper analysis, in network analysis, more specifically in traffic networks, it has been observed that by removing roads, it is possible to increase the flow of traffic. It is coined Braess's paradox, and it has also been found to trick other systems, such as electricity grids, biology, and team sports strategy. So it is vital to analyze one's solutions properly.

I hope this was informal and let you know what Operations Research is — my family, friends, and colleagues from other research areas have often been asking me to clarify the topic, I hope this aided you as well.

Written by Alex Elkjær Vasegaard

Postdoctoral researcher (Operations Research) — Interested in math, space, philosophy, movies, humans and how they all combine to shape life!

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Introduction
Basic aspects

Systems orientation

The interdisciplinary team, methodology, problem formulation, model construction.

Deriving solutions from models
Testing the model and the solution
Implementing and controlling the solution
Decision analysis and support
New software tools for decision making
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Linear programming
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Essential characteristics

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Table Of Contents

Three essential characteristics of operations research are a systems orientation, the use of interdisciplinary teams, and the application of scientific method to the conditions under which the research is conducted.

The systems approach to problems recognizes that the behaviour of any part of a system has some effect on the behaviour of the system as a whole. Even if the individual components are performing well, however, the system as a whole is not necessarily performing equally well. For example, assembling the best of each type of automobile part, regardless of make, does not necessarily result in a good automobile or even one that will run, because the parts may not fit together. It is the interaction between parts, and not the actions of any single part, that determines how well a system performs.

Thus, operations research attempts to evaluate the effect of changes in any part of a system on the performance of the system as a whole and to search for causes of a problem that arises in one part of a system in other parts or in the interrelationships between parts. In industry, a production problem may be approached by a change in marketing policy. For example, if a factory fabricates a few profitable products in large quantities and many less profitable items in small quantities, long efficient production runs of high-volume, high-profit items may have to be interrupted for short runs of low-volume, low-profit items. An operations researcher might propose reducing the sales of the less profitable items and increasing those of the profitable items by placing salesmen on an incentive system that especially compensates them for selling particular items.

Scientific and technological disciplines have proliferated rapidly in the last 100 years. The proliferation, resulting from the enormous increase in scientific knowledge, has provided science with a filing system that permits a systematic classification of knowledge. This classification system is helpful in solving many problems by identifying the proper discipline to appeal to for a solution. Difficulties arise when more complex problems, such as those arising in large organized systems, are encountered. It is then necessary to find a means of bringing together diverse disciplinary points of view. Furthermore, since methods differ among disciplines, the use of interdisciplinary teams makes available a much larger arsenal of research techniques and tools than would otherwise be available. Hence, operations research may be characterized by rather unusual combinations of disciplines on research teams and by the use of varied research procedures.

Until the 20th century, laboratory experiments were the principal and almost the only method of conducting scientific research. But large systems such as are studied in operations research cannot be brought into laboratories. Furthermore, even if systems could be brought into the laboratory, what would be learned would not necessarily apply to their behaviour in their natural environment , as shown by early experience with radar. Experiments on systems and subsystems conducted in their natural environment (“operational experiments”) are possible as a result of the experimental methods developed by the British statistician R.A. Fisher in 1923–24. For practical or even ethical reasons, however, it is seldom possible to experiment on large organized systems as a whole in their natural environments . This results in an apparent dilemma: to gain understanding of complex systems experimentation seems to be necessary, but it cannot usually be carried out. This difficulty is solved by the use of models , representations of the system under study. Provided the model is good, experiments (called “simulations”) can be conducted on it, or other methods can be used to obtain useful results.

Phases of operations research

To formulate an operations research problem, a suitable measure of performance must be devised, various possible courses of action defined (that is, controlled variables and the constraints upon them), and relevant uncontrolled variables identified. To devise a measure of performance, objectives are identified and defined, and then quantified. If objectives cannot be quantified or expressed in rigorous (usually mathematical) terms, most operations research techniques cannot be applied. For example, a business manager may have the acquisitive objective of introducing a new product and making it profitable within one year. The identified objective is profit in one year, which is defined as receipts less costs, and would probably be quantified in terms of sales. In the real world, conditions may change with time. Thus, though a given objective is identified at the beginning of the period, change and reformulation are frequently necessary.

Detailed knowledge of how the system under study actually operates and of its environment is essential. Such knowledge is normally acquired through an analysis of the system , a four-step process that involves determining whose needs or desires the organization tries to satisfy; how these are communicated to the organization; how information on needs and desires penetrates the organization; and what action is taken, how it is controlled, and what the time and resource requirements of these actions are. This information can usually be represented graphically in a flowchart, which enables researchers to identify the variables that affect system performance.

Once the objectives, the decision makers, their courses of action, and the uncontrolled variables have been identified and defined, a measure of performance can be developed and selection can be made of a quantitative function of this measure to be used as a criterion for the best solution.

The type of decision criterion that is appropriate to a problem depends on the state of knowledge regarding possible outcomes. Certainty describes a situation in which each course of action is believed to result in one particular outcome. Risk is a situation in which, for each course of action, alternative outcomes are possible, the probabilities of which are known or can be estimated. Uncertainty describes a situation in which, for each course of action, probabilities cannot be assigned to the possible outcomes.

In risk situations, which are the most common in practice, the objective normally is to maximize expected (long-run average) net gain or gross gain for specified costs, or to minimize costs for specified benefits. A business, for example, seeks to maximize expected profits or minimize expected costs. Other objectives, not necessarily related, may be sought; for example, an economic planner may wish to maintain full employment without inflation; or different groups within an organization may have to compromise their differing objectives, as when an army and a navy, for example, must cooperate in matters of defense.

In approaching uncertain situations one may attempt either to maximize the minimum gain or minimize the maximum loss that results from a choice; this is the “minimax” approach. Alternatively, one may weigh the possible outcomes to reflect one’s optimism or pessimism and then apply the minimax principle. A third approach, “minimax regret,” attempts to minimize the maximum deviation from the outcome that would have been selected if a state of certainty had existed before the choice had been made.

Each identified variable should be defined in terms of the conditions under which, and research operations by which, questions concerning its value ought to be answered; this includes identifying the scale used in measuring the variable.

A model is a simplified representation of the real world and, as such, includes only those variables relevant to the problem at hand. A model of freely falling bodies, for example, does not refer to the colour, texture, or shape of the body involved. Furthermore, a model may not include all relevant variables because a small percentage of these may account for most of the phenomenon to be explained. Many of the simplifications used produce some error in predictions derived from the model, but these can often be kept small compared to the magnitude of the improvement in operations that can be extracted from them. Most operations research models are symbolic models because symbols represent properties of the system. The earliest models were physical representations such as model ships, airplanes, tow tanks, and wind tunnels. Physical models are usually fairly easy to construct, but only for relatively simple objects or systems, and are usually difficult to change.

The next step beyond the physical model is the graph , easier to construct and manipulate but more abstract. Since graphic representation of more than three variables is difficult, symbolic models came into use. There is no limit to the number of variables that can be included in a symbolic model, and such models are easier to construct and manipulate than physical models.

Symbolic models are completely abstract. When the symbols in a model are defined, the model is given content or meaning. This has important consequences. Symbolic models of systems of very different content often reveal similar structure. Hence, most systems and problems arising in them can be fruitfully classified in terms of relatively few structures. Furthermore, since methods of extracting solutions from models depend only on their structure, some methods can be used to solve a wide variety of problems from a contextual point of view. Finally, a system that has the same structure as another, however different the two may be in content, can be used as a model of the other. Such a model is called an analogue . By use of such models much of what is known about the first system can be applied to the second.

Despite the obvious advantages of symbolic models there are many cases in which physical models are still useful, as in testing physical structures and mechanisms; the same is true for graphic models. Physical and graphic models are frequently used in the preliminary phases of constructing symbolic models of systems.

Operations research models represent the causal relationship between the controlled and uncontrolled variables and system performance; they must therefore be explanatory, not merely descriptive. Only explanatory models can provide the requisite means to manipulate the system to produce desired changes in performance.

Operations research analysis is directed toward establishing cause -and-effect relations. Though experiments with actual operations of all or part of a system are often useful, these are not the only way to analyze cause and effect. There are four patterns of model construction, only two of which involve experimentation: inspection, use of analogues , operational analysis, and operational experiments. They are considered here in order of increasing complexity.

In some cases the system and its problem are relatively simple and can be grasped either by inspection or from discussion with persons familiar with it. In general, only low-level and repetitive operating problems, those in which human behaviour plays a minor role, can be so treated.

When the researcher finds it difficult to represent the structure of a system symbolically, it is sometimes possible to establish a similarity, if not an identity, with another system whose structure is better known and easier to manipulate. It may then be possible to use either the analogous system itself or a symbolic model of it as a model of the problem system. For example, an equation derived from the kinetic theory of gases has been used as a model of the movement of trains between two classification yards. Hydraulic analogues of economies and electronic analogues of automotive traffic have been constructed with which experimentation could be carried out to determine the effects of manipulation of controllable variables. Thus, analogues may be constructed as well as found in existing systems.

In some cases analysis of actual operations of a system may reveal its causal structure. Data on operations are analyzed to yield an explanatory hypothesis , which is tested by analysis of operating data. Such testing may lead to revision of the hypothesis. The cycle is continued until a satisfactory explanatory model is developed.

For example, an analysis of the cars stopping at urban automotive service stations located at intersections of two streets revealed that almost all came from four of the 16 possible routes through the intersection (four ways of entering times four ways of leaving). Examination of the percentage of cars in each route that stopped for service suggested that this percentage was related to the amount of time lost by stopping. Data were then collected on time lost by cars in each route. This revealed a close inverse relationship between the percentage stopping and time lost. But the relationship was not linear; that is, the increases in one were not proportional to increases in the other. It was then found that perceived lost time exceeded actual lost time, and the relationship between the percentage of cars stopping and perceived lost time was close and linear. The hypothesis was systematically tested and verified and a model constructed that related the number of cars stopping at service stations to the amount of traffic in each route through its intersection and to characteristics of the station that affect the time required to get service.

In situations where it is not possible to isolate the effects of individual variables by analysis of operating data, it may be necessary to resort to operational experiments to determine which variables are relevant and how they affect system performance.

Such is the case, for example, in attempts to quantify the effects of advertising (amount, timing, and media used) upon sales of a consumer product. Advertising by the producer is only one of many controlled and uncontrolled variables affecting sales. Hence, in many cases its effect can only be isolated and measured by controlled experiments in the field.

The same is true in determining how the size, shape, weight, and price of a food product affect its sales. In this case laboratory experiments on samples of consumers can be used in preliminary stages, but field experiments are eventually necessary. Experiments do not yield explanatory theories, however. They can only be used to test explanatory hypotheses formulated before designing the experiment and to suggest additional hypotheses to be tested.

It is sometimes necessary to modify an otherwise acceptable model because it is not possible or practical to find the numerical values of the variables that appear in it. For example, a model to be used in guiding the selection of research projects may contain such variables as “the probability of success of the project,” “expected cost of the project,” and its “expected yield.” But none of these may be calculable with any reliability.

Models not only assist in solving problems but also are useful in formulating them; that is, models can be used as guides to explore the structure of a problem and to reveal possible courses of action that might otherwise be missed. In many cases the course of action revealed by such application of a model is so obviously superior to previously considered possibilities that justification of its choice is hardly required.

In some cases the model of a problem may be either too complicated or too large to solve. It is frequently possible to divide the model into individually solvable parts and to take the output of one model as an input to another. Since the models are likely to be interdependent, several repetitions of this process may be necessary.

What is Operations Research (OR)? Definition, Concept, Characteristics, Tools, Advantages, Limitations, Applications and Uses

Post last modified: 20 July 2022
Reading time: 25 mins read
Post category: Operations Research

What is Operations Research (OR)?

Operations Research (OR) may be defined as the science that aims for the application of analytical and numerical techniques along with information technology to solve organisational problems. There are various definitions of OR in the literature.

Table of Content

1 What is Operations Research (OR)?
2 Operations Research Definition
3 Concept of Operations Research
4 History of Operations Research
5.1 OR as a decision-making approach
5.2 OR as a scientific approach
5.3 OR as a computer-based approach
6 Objectives of Operations Research
7.1 Linear Programming
7.2 Simulation
7.3 Statistics
7.4 Assignment
7.5 Queuing Theory
7.6 Game Theory
7.7 Non-linear Programming
7.8 Dynamic Programming
7.9 Goal Programming
7.10 Network Scheduling
8.1 Increased productivity
8.2 Optimised outcomes
8.3 Better coordination
8.4 Lower failure risk
8.5 Improved control on the system
9.1 High costs
9.2 Dependence on technology
9.3 Reliance on experts
9.4 Unquantifiable factors
10.1 Resource distribution in projects
10.2 Project scheduling, monitoring and control
10.3 Production and facilities planning
10.4 Marketing
10.5 Personnel management
10.6 Supply chain management

Operations Research Definition

Some of the well-known operations research definitions are as:

Moarse and Kimbal (1946) defined OR as a scientific method of providing the executive department a quantitative basis for decision-making regarding the operations under their control.

According to Churchman, Ackoff and Arnoff (1957), OR is the application of scientific methods, techniques and tools to operational problems so as to provide those in control of the system an optimum solution to the problem.

McGraw-Hill Science & Technology Encyclopaedia states that OR is the application of scientific methods and techniques to decision-making problems.

Britannica Concise Encyclopaedia defines OR as the application of scientific methods to the management and administration of military, government, commercial, and industrial processes.

Decision-making problems arise when there are two or more alternative courses of action, each resulting in a different outcome. The goal of OR is to help select the alternative that will maximise the use of available resources and lead to the best possible outcome.

In this article, we introduce the topic of operation research that will allow students to gain an insight into the basic concepts of operations research. This will give them a better understanding of the upcoming chapters.

Concept of Operations Research

Decision-making is not a simple task in today’s socio-economic environment. Complex problems such as transportation, queuing, etc., are routinely presented and dealt with at the operational level. Moreover, higher attention is now being paid to a wide range of tactical and strategic problems.

Decision makers cannot afford to take decisions by simply taking their personal experiences or intuitions into account. Decisions made in the absence of suitable information can have seri- ous consequences. Being able to apply quantitative methods to deci- sion-making is, therefore, vital to decision-makers.

OR is a field of applied mathematics that makes use of analytical tools and mathematical models to solve problems and aid the management in decision-making. OR is an approach that allows decision makers to compare all possible courses of action, understand the likely outcomes and test the sensitivity of the solution to modifications or errors.

OR helps in making informed decisions, allocating optimal resource and improving the performance of systems. According to Ackoff (1965), the development (rather than the history) of OR as a science consists of the development of its methods, concepts, and techniques.

OR is neither a method nor a technique; it is or is becoming a science and as such is defined by a combination of the phenomena it studies.

History of Operations Research

The beginning of OR as a formal discipline can be traced back to 1937 when A.P. Rowe, Superintendent of the Bawdsey Research Station in the British Royal Air Force, sought British scientists to assist military leaders in the use of the recently developed radar system to detect enemy aircraft.

A few years later, the British Army and the Royal Navy also incorporated OR, again for assistance with the radar system. All three of Britain’s military services had set up formal OR teams by 1942. Similar developments took place in other countries (of which the most significant are those of the United States, in terms of further development of the discipline).

Once World War II ended, several British operations researchers relocated to government and industry. By the 1950s, the United States government and industry also incorporated OR programmes. In India, it was in 1949 when an Operation Research unit was set up at Regional Research Laboratory, Hyderabad, that OR came into being.

An OR team was also established at Defense Science Laboratory to resolve inventory, purchase and planning issues. The 1950s saw continual growth in the application of OR methods to non-defence activities in India. In 1953, the Indian Statistical Institute, Calcutta established an OR unit for national planning and survey-related issues. OR also became useful in the Indian Railways to resolve ticketing issues, train scheduling problems and so on.

Since then, OR, as a formal discipline, has expanded continuously in the last 70 years and is widely recognised as a central approach to decision-making in the management of different domains of an organisation.

With accessibility to faster and flexible computing facilities, OR has expanded further and is widely used in industry, finance, logistics, transportation, public health and government. One needsCharacteristics of Operations Research to bear in mind that OR is still a fairly new scientific discipline, despite its rapid evolution. This means that its methodology, tools and techniques, and applications still continue to grow rapidly.

Characteristics of Operations Research

OR aims to find the best possible solution for any problem. Its main goal is to help managers obtain a quantitative basis for decision-making. This results in increased efficiency, more control and better coordination in the organisation when fulfilling the required objectives.

OR is an interdisciplinary field involving mathematics and science. OR uses statistics, algorithms and mathematical modelling to provide the best possible solutions for complex problems. OR basically involves optimising the maxima or minima functions.

For example, a business problem could be the maximisation of profit, performance or yield or it could be related to minimising risk and loss. OR has various characteristics based on the different objectives for which it is used.

The characteristics of operations research (OR) are explained as follows:

OR as a decision-making approach

All organisations are faced with situations where they need to select the best available alterna- tive to solve a problem. OR techniques help managers in obtaining optimal solutions for their problems.

Additionally, OR techniques are also used by managers to understand the problems at hand in a better manner and make effective decisions. It is important to note that OR techniques help in improving the quality of decisions.

OR helps in finding bad answers to problems having worse answers. It means that for many problems, OR may not be able to give perfect replies but can help in improving the quality of decisions.

OR as a scientific approach

OR uses multiple scientific models along with tools and techniques to resolve complex problems while eliminating individual biasness. The scientific method involves observing and defining a problem, formulating and testing the hypothesis and analysing the results of the test. The results of the test determine whether the hypothesis should be accepted or rejected.

OR as an interdisciplinary approach: Since OR focuses on complex organisational problems, it includes expertise from different disciplines such as mathematics, economics, science and engineering. Having different experts ensures that the problem is analysed from different perspectives and alternative strategies are evolved for the selected problem.

Some of the complex problems that can be solved using OR include deciding or choosing optimal dividend policies, investment portfolio management, auditing, balance sheet and cash flow analysis, selection of product mix, marketing and export planning, advertising, media planning and packaging, procurement and exploration, optimal buying decisions, transportation planning, facilities planning, location and site selection, production cost and methods, assembly line, blending, purchasing and inventory control, etc.

OR as a systems approach: In OR, important interactions and their influence on the organisation as a whole are considered for decision-making. OR looks at problems from the perspective of the organisation:

To determine the potential for enhancing the performance of the system as a whole
To measure the impact of alterations in variables on the whole system
To find reasons for the malfunctioning of the system as a whole

OR as a computer-based approach

OR solves business problems using mathematical models, manipulating large amount of data and performing computations on these large data sets. It is almost impossible to do such computations and manipulations manually. Therefore, most OR-based problems are solved using computers.

Objectives of Operations Research

Operations research in an organisation is responsible for managing and operating as efficiently as possible within the given resources and constraints. In case of complex problems as listed in the previous section, normal analysis does not work and in such cases, OR approach helps an organisation in reaching a viable solution.

OR is basically a problem-solving and decision-making tool used by organisations for enhancing their productivity and performance. Apart from this, certain other objectives of OR are as follows:

Solving operational questions
Solving queries related to resources’ operations such as human resource scheduling, machine and material scheduling, utilisation of funds, etc.
Making informed decisions
Improving the current systems
Predicting all possible alternative outcomes
Evaluating risks associated with each alternative

OR is needed for the following reasons:

If the problem is a recurring one, it may make sense to create a model to make decision-making faster and better. OR provides a readymade model or process in such cases to help create a suitable model.
OR provides an analytical, logical and quantitative basis to represent the problem
OR models help in making sound decisions and decreases the risk of flawed decisions

Tools of Operations Research

OR is widely used in industries, businesses, governments, military establishments and agriculture. Most importantly, OR techniques are used by organisations. All the business decision areas, such as planning production and facilities, scheduling projects, minimising procurement costs, and selecting a product mix, which require optimisation of an objective, fall under the domain of operation research. OR uses a variety of tools to solve different business problems.

The most commonly used tools of OR are discussed below:

Linear Programming

Organisations use the Linear Programming (LP) technique to determine the optimal solutions that may be defined as either most profitable or least cost solutions. Businesses use LP techniques to assign jobs to machines, select product mix, select advertising media, select an investment portfolio, etc.

Simulation is another important OR tool wherein an expert con- structs a model that replicates a real business scenario. Simulation is extremely useful in cases where actual market testing is risky or impossible due to various reasons such as high expenditure.

It has widely been used in a variety of probabilistic marketing situations. For example, finding the Net Present Value (NPV) distribution of the market introduction of a product.

Statistics allows an organisation to evaluate the risks present in all the domains of the business. It enables an organisation to predict future trends and thus makes informed business decisions. The OR team compares different trade-offs and chooses the best alternative.

For example, statistics is used in solving various real-life problems such as deterministic optimisation. Some of the problems where statistics serve as the primary vehicle for OR include decision theory, optimal strategies for search engine marketing, credit scoring, queuing theory, stochastic programming and inventory management.

The assignment method deals with the issue of how to allocate a fixed number of facilities to different tasks in the most optimal manner. The aim is to minimise the cost/time of completing a number of tasks by a number of agents (person or equipment). For example, assigning method can be used to assign specific workers to specific tasks.

Queuing Theory

If a problem involves queuing, the Queuing or Waiting Line theory is used. Using this tool, the expected number of people waiting in line, expected waiting time, expected idle time for the server and so on can be calculated. Queuing theory can be used to solve problems related to traffic congestion, repair and maintenance of broken machines, air traffic scheduling and control, scheduling bank counters, etc

Game Theory

Game theory is useful in decision-making in cases where there are one or more opponents (or players) with conflicting interests. Just as in a game, where the success of one person is influenced by the choices made by the opponent, in the game theory, the actions of all the players influence the outcomes.

For example, game theory is used for selecting war strategies and military decisions, bidding at auctions, negotiations, product pricing, stock market decisions, etc.

Non-linear Programming

Non-linear problems are similar to linear problems except that they have at least one non-linear function or constraint. Non-linear models become useful in cases where the objective function of some of the constraints is not linear in nature.

For instance, a non-linear programming is used for making optimal decisions in the production process, optimising fractionated protocols in cancer radiotherapy, training recur- rent neural networks in time series prediction problems, etc.

Dynamic Programming

Dynamic programming models deal with problems in which decisions need to be made over multiple stages in a sequence and the current decisions affect both present and future stages.

For example, dynamic programming is used by Google Maps to find the shortest path between a source and a destination. It is also used in networking to sequentially transfer data from one sender to various receivers.

Goal Programming

Goal programming tools allow organisations to handle multiple and incompatible objectives. These models are quite similar to linear programming models with the difference being that goal programming can have multiple objectives whereas linear programs have only one.

For example, goal programming can be applied to corporate budgeting, financial planning, working capital management, financing decisions, commercial bank management, accounting control, etc.

Network Scheduling

Network scheduling methods are useful in planning, scheduling and monitoring projects of large scales common in construction industry, information technology, etc.

For example, network scheduling is used for assembly line scheduling, inventory planning and control, launching new advertisement campaigns, installing new equipment, controlling projects, etc.

Advantages of Operations Research

The field of OR contains robust tools that can be applied in a variety of fields such as transportation, warehouse, production management, assignment of jobs, etc. The application of OR tools and techniques helps in making the best decisions with the available data.

There are many advantages of OR, as shown in Figure:

Increased productivity

OR helps in increasing the productivity of organisations to a huge extent. The use of OR for effective control of operations allows the managers to take informed decisions. Effective and precise decision-making leads to improvement in the productivity of an organisation.

OR tools also help increase the efficiency of various routine tasks in an organisation such as inventory control, workforce-related, business expansion, technology upgrades, installation etc. All these ultimately contribute towards productivity improvement.

Optimised outcomes

Management is responsible for making various important decisions about the organisation. OR tools can be used by the management to find out various alternative solutions to a problem and selecting the best solution. Selection is based on the profits accrued and costs incurred.

Better coordination

OR can be used to synchronise the objectives of different departments which results in achieving the goals of all departments. Managers belonging to different departments become aware of the common objectives of the organisation, which ensures that different departments coordinate towards achievement of the said goals.

For example, OR helps in coordinating the goals of the marketing department with the production department schedule.

Lower failure risk

Using OR tools and techniques, managers can find all the alternative solutions and risks associated with a given problem. Prior information with respect to all the possible risks helps in reducing the risks of failure.

Improved control on the system

Managers can apply OR to take better control of the work since it provides comprehensive information about any given course of action. Since OR informs managers about the expected outcome, they can determine what standards of performance need to be expected from employees.

They can compare the actual performance of the employees with the standard performance and, therefore, control them in a better manner. It also enables managers to prioritise tasks in terms of their importance.

Limitations of Operations Research

There is no doubt with respect to the practical utility and usability of OR and its applications in real life. However, OR also suffers from several limitations as shown in Figure:

High cost is one of the biggest limitations of OR. It not only needs expensive technology to create mathematical equations but also experts to perform simulations. Therefore, while OR does provide effective solutions to a particular problem, it comes with a high cost attached.

Dependence on technology

OR is heavily reliant on technology. Computers are generally needed to model and analyse OR problems. Since technology is quite costly as well as subject to failure, its use is severely restricted.

Reliance on experts

OR requires a team of experts from different fields to perform the assessments. Hiring multiple experts can be costly. In addition, maintaining good communication and coordination among experts and making all experts work together is a critical task.

Unquantifiable factors

It is known that OR tools are based on mathematical models that include various information based on quantifiable factors. It means that the efficacy of a solution provided by OR tool depends on quantifiable factors.

However, there are certain important unquantifiable factors that cannot be included in the models. When this happens, solutions can often be inexact, inaccurate and therefore, inefficient.

Applications and Uses of Operations Research in Management

The list of OR applications is notable, given its considerable involvement in various managerial and decision-making processes at several organisational levels. It can be applied in a wide range of industries to help with complex problems in planning, policy-making, scheduling, forecasting, resource allocation, process analysis, etc.

It may be employed by virtually any industry to determine the best solution to any problem. Various human activities that need optimisation of resources can use OR.

The following are some areas where OR may be applied:

Resource distribution in projects

Various OR tools are used to determine which resources are to be allocated to which activities. For instance, OR can help in determining the allocation of ‘n’ number of jobs among two machines. Similarly, OR can also be applied to determine and allocate materials, workforce, time and budget to projects.

Project scheduling, monitoring and control

OR is applied to activities involving scheduling, inventory control, improvement of workflow, elimination of bottlenecks, business process re-engineering, capacity planning and general operational planning.

OR tools such as the Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT) are used for scheduling the different activities involved in a project. In addition, these tools are also used for continuous monitoring and control of the project.

Production and facilities planning

OR can be applied for activities involving site selection, factory size, facility planning, inventory forecasts, calculation of economic order quantities, computing reorder levels, maintenance policies, replacement policies, manpower planning, and assembly line scheduling, etc. All the important decisions and planning work related to facilities, manufacturing and maintenance can be completed using OR tools.

Application of OR can be done in budget allocation for advertising, choice of advertising media and product launch timing. For instance, how should a company allocate its budget for advertising a newly launched product on two TV channels, TV1 and TV2 within a given budget. A company may also use OR techniques to find out how many units of each product in a product mix should be produced to maximise demand.

Personnel management

OR also finds application in manpower planning, scheduling of training programs, wage administration, etc.

Finance and accounting: The application of OR in finance is concerned with effective capital planning, cash flow analysis, capital budgeting, credit policies, investment analysis and decisions, establishing costs for by-products and developing standard costs, portfolio management, risk management, etc.

Supply chain management

The application of OR in Supply Chain Management involves decision-making regarding the transportation of goods for the purpose of manufacturing and distribution. This further involves the selection of the shortest optimal routes so that the goods can be transported to maximum locations at minimum costs.

Business Ethics

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What is Ethics?
What is Business Ethics?
Values, Norms, Beliefs and Standards in Business Ethics
Indian Ethos in Management
Ethical Issues in Marketing
Ethical Issues in HRM
Ethical Issues in IT
Ethical Issues in Production and Operations Management
Ethical Issues in Finance and Accounting
What is Corporate Governance?
What is Ownership Concentration?
What is Ownership Composition?
Types of Companies in India
Internal Corporate Governance
External Corporate Governance
Corporate Governance in India
What is Enterprise Risk Management (ERM)?
What is Assessment of Risk?
What is Risk Register?
Risk Management Committee

Corporate social responsibility (CSR)

Theories of CSR
Arguments Against CSR
Business Case for CSR
Importance of CSR in India
Drivers of Corporate Social Responsibility
Developing a CSR Strategy
Implement CSR Commitments
CSR Marketplace
CSR at Workplace
Environmental CSR
CSR with Communities and in Supply Chain
Community Interventions
CSR Monitoring
CSR Reporting
Voluntary Codes in CSR
What is Corporate Ethics?

Lean Six Sigma

What is Six Sigma?
What is Lean Six Sigma?
Value and Waste in Lean Six Sigma
Six Sigma Team
MAIC Six Sigma
Six Sigma in Supply Chains
What is Binomial, Poisson, Normal Distribution?
What is Sigma Level?
What is DMAIC in Six Sigma?
What is DMADV in Six Sigma?
Six Sigma Project Charter
Project Decomposition in Six Sigma
Critical to Quality (CTQ) Six Sigma
Process Mapping Six Sigma
Flowchart and SIPOC
Gage Repeatability and Reproducibility
Statistical Diagram
Lean Techniques for Optimisation Flow
Failure Modes and Effects Analysis (FMEA)
What is Process Audits?
Six Sigma Implementation at Ford
IBM Uses Six Sigma to Drive Behaviour Change
Research Methodology
What is Research?
What is Hypothesis?
Sampling Method
Research Methods
Data Collection in Research
Methods of Collecting Data
Application of Business Research
Levels of Measurement
What is Sampling?
Hypothesis Testing
Research Report
What is Management?
Planning in Management
Decision Making in Management
What is Controlling?
What is Coordination?
What is Staffing?
Organization Structure
What is Departmentation?
Span of Control
What is Authority?
Centralization vs Decentralization
Organizing in Management
Schools of Management Thought
Classical Management Approach
Is Management an Art or Science?
Who is a Manager?

Operations Research

What is Operations Research?
Operation Research Models
Linear Programming Graphic Solution
Linear Programming Simplex Method
Linear Programming Artificial Variable Technique

Duality in Linear Programming

Transportation Problem Initial Basic Feasible Solution
Transportation Problem Finding Optimal Solution
Project Network Analysis with Critical Path Method

Project Network Analysis Methods

Project evaluation and review technique (pert), simulation in operation research, replacement models in operation research.

Operation Management

What is Strategy?
What is Operations Strategy?
Operations Competitive Dimensions
Operations Strategy Formulation Process
What is Strategic Fit?
Strategic Design Process
Focused Operations Strategy
Corporate Level Strategy
Expansion Strategies
Stability Strategies
Retrenchment Strategies
Competitive Advantage
Strategic Choice and Strategic Alternatives
What is Production Process?
What is Process Technology?
What is Process Improvement?
Strategic Capacity Management
Production and Logistics Strategy
Taxonomy of Supply Chain Strategies
Factors Considered in Supply Chain Planning
Operational and Strategic Issues in Global Logistics
Logistics Outsourcing Strategy
What is Supply Chain Mapping?
Supply Chain Process Restructuring
Points of Differentiation
Re-engineering Improvement in SCM
What is Supply Chain Drivers?
Supply Chain Operations Reference (SCOR) Model
Customer Service and Cost Trade Off
Internal and External Performance Measures
Linking Supply Chain and Business Performance
Netflix’s Niche Focused Strategy
Disney and Pixar Merger
Process Planning at Mcdonald’s

Service Operations Management

What is Service?
What is Service Operations Management?
What is Service Design?
Service Design Process
Service Delivery
What is Service Quality?
Gap Model of Service Quality
Juran Trilogy
Service Performance Measurement
Service Decoupling
IT Service Operation
Service Operations Management in Different Sector

Procurement Management

What is Procurement Management?
Procurement Negotiation
Types of Requisition
RFX in Procurement
What is Purchasing Cycle?
Vendor Managed Inventory
Internal Conflict During Purchasing Operation
Spend Analysis in Procurement
Sourcing in Procurement
Supplier Evaluation and Selection in Procurement
Blacklisting of Suppliers in Procurement
Total Cost of Ownership in Procurement
Incoterms in Procurement
Documents Used in International Procurement
Transportation and Logistics Strategy
What is Capital Equipment?
Procurement Process of Capital Equipment
Acquisition of Technology in Procurement
What is E-Procurement?
E-marketplace and Online Catalogues
Fixed Price and Cost Reimbursement Contracts
Contract Cancellation in Procurement
Ethics in Procurement
Legal Aspects of Procurement
Global Sourcing in Procurement
Intermediaries and Countertrade in Procurement

Strategic Management

What is Strategic Management?
What is Value Chain Analysis?
Mission Statement
Business Level Strategy
What is SWOT Analysis?
What is Competitive Advantage?
What is Vision?
What is Ansoff Matrix?
Prahalad and Gary Hammel
Strategic Management In Global Environment
Competitor Analysis Framework
Competitive Rivalry Analysis
Competitive Dynamics
What is Competitive Rivalry?
Five Competitive Forces That Shape Strategy
What is PESTLE Analysis?
Fragmentation and Consolidation Of Industries
What is Technology Life Cycle?
What is Diversification Strategy?
What is Corporate Restructuring Strategy?
Resources and Capabilities of Organization
Role of Leaders In Functional-Level Strategic Management
Functional Structure In Functional Level Strategy Formulation
Information And Control System
What is Strategy Gap Analysis?
Issues In Strategy Implementation
Matrix Organizational Structure
What is Strategic Management Process?

Supply Chain

What is Supply Chain Management?
Supply Chain Planning and Measuring Strategy Performance
What is Warehousing?
What is Packaging?
What is Inventory Management?
What is Material Handling?
What is Order Picking?
Receiving and Dispatch, Processes
What is Warehouse Design?
What is Warehousing Costs?

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Introduction to Operations Research

Post author By Hemant More
Post date March 10, 2019
5 Comments on Introduction to Operations Research

What is Operations Research?

Operations Research is also known as management science, decision science or industrial engineering. It helps in providing quantitative aid to the management in a decision-making process.
According to the Operational Research Society of UK, Operational Research is the application of methods of modern science on complex problems arising in the direction and management of large systems of men, machines, materials and money in the industry, business, government, and defence. Its distinctive approach is to develop a scientific model of the system, incorporating measurements of factors such as change and risk, with which to predict and compare the outcomes of alternative decisions, strategies or controls. The purpose is to help management determine its policy and actions scientifically.
According to Randy Robinson, Operations Research is the application of scientific methods to improve the effectiveness of operations, decisions, and management. By means such as analyzing data, creating mathematical models and proposing innovative approaches, Operations Research professionals develop scientifically based information that gives insight and guides decisionmaking. They also develop related software, systems, services and products.
According to T. L. Saaty Operations Research is a tool for improving the quality of answers. He says, “Operations Research is the art of giving bad answers to problems which otherwise have worse answers”.
According to P. M. Morse and G. E. Kimbal Operations Research is a quantitative approach and described it as “ a scientific method of providing executive departments with a quantitative basis for decisions regarding the operations under their control”.
According to Miller and Starr Operations Research is applied decision theory, which uses any scientific, mathematical or logical means to attempt to cope with the problems that confront the executive, when he tries to achieve thorough-going rationality in dealing with his decision problem”.

Characteristics of Operations Research:

It has a decision-making approach.

The main objective of Operations Research is to find the best or optimal solution to the problem under consideration. Operations Research is the scientific study of large systems with a view to identify problem areas and provide the managers with a quantitative basis for decisions which will enhance their effectiveness in achieving the specified objectives.
Thus Operations Research helps in identifying, understanding, analyzing an on-going problem to make a sound decision. This approach leads to better control and coordination within an organization.

It has a scientific approach

It uses various scientific methods, models and tools to solve complex organizational problems. By this approach, personal bias can be avoided.

It has an interdisciplinary team approach

A team effort is required to solve industrial problems which are complex in nature. An OR team consists of experts from science, mathematics and engineering i.e. they are from different disciplines. They are expert in their field. Each one of them evaluates the problem in their own perspective and provide alternative strategies to an on-going problem. From these strategies, optimum strategy is selected using OR tools.
In this way, each member of the team by utilizing his experience and expertise may be in a position to suggest an approach that otherwise may not be thought of.

It has a system approach

The nonfunctioning of any aspect of the operational system of an organization has a significant effect on the system as a whole.
The main aim of the system approach is to trace for each strategy (proposal) all significant and indirect effects on all sub-system on a system and to evaluate each action in terms of effects for the system as a whole. For a system approach, mathematical models are used. The models of OR need a lot of computation and therefore, the use of computers becomes necessary in OR. Using computers complex problems requiring a large number of calculations can be solved easily.

It is a continuing process

It cannot stop on the application of the model to one problem, because the application of the model to one problem may create new problems in other sectors and in the implementation of the decisions taken. Thus OR is a continuous process.

Stages of Development of Operations Research

Step i: observe the problem environment:.

The first step of OR study is the observation of the environment in which the problem exists. This step includes activities like conferences, site visit, research, observations etc. These activities give sufficient information and support to OR analyst to proceed and formulate the problem in a better way.

Step II: Analyze and define the problem:

In this step analyzation and definition of the problem is done. In addition to the problem definition, the objectives, uses and limitations of OR study of the problem are also defined. The end results of this step are a clear grasp of the need for a solution and understanding of its nature

Step III: Develop a model

The next step is to develop the model, which is a representation of same real or abstract situation. These models are basically mathematical models representing systems, process, or environment in form of equations, relationships or formulae. In this step, the interrelationships among variables are defined. The proposed model is tested in the field under different environmental constraints and modified in order to work.
A model may also be modified if the management is not satisfied with the answer that it gives or the answer is not as per the objectives.

Step IV: Select appropriate data input

To get a correct result from OR, the input data should be authenticated. Activities in this step include analyzing internal-external data and facts, collecting opinions and using computer data banks. Purpose of this step is to test the model and provide sufficient input data.

Step V: Provide a solution and test its reasonableness

In this step, the solution to the problems is obtained with the help of model and data input. Before implementing this solution, the solution itself is used for testing and finding limitations. If the solution is not reasonable and the model is not behaving properly, then updating and modification of the model is carried out. This step is repeated until the desired objective is achieved. Thus the model is fine-tuned.

Step VI: Implement the solution

This is the last phase of the OR study. In OR the decision-making is scientific but the implementation of decision involves many behavioural issues involving the workers and supervisors to avoid further conflicts. Therefore, before implementation, the implementation authority has to resolve the issues. A properly implemented solution obtained through OR techniques results in improved working conditions and wins management support.
Tags Decision-making aproach , Interdisciplinary , Operations research , Scientific approach , System approach

5 replies on “Introduction to Operations Research”

thanks a lost

Powerful introduction thank you very much

This is a very clear, understanding and self explanatory one . Thanks

Thank you . This information is useful in my studies. I am doing business degree and i am going utilize this information for the benefit of my country, continent and the globe at large.

useful lesson to be applied in my business course

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Research: How IT Can Solve Common Problems in DEI Initiatives

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Lessons from three organizations that successfully leveraged IT to drive structural change.

The authors’ research found that three persistent problems plague DEI initiatives: They do not connect to operational or strategic goals and objectives; they do not include the rank-and-file; and they are often implemented through periodic efforts like annual diversity training that aren’t integrated into day-to-day work processes. Organizations can overcome these problems by using IT in three ways.

Diversity, equity, and inclusion (DEI) programs are under attack. Confronted by high costs, mixed outcomes , unclear organizational benefits , and a political and regulatory backlash , organizations are rolling back their initiatives. Google and Meta, for example, recently reduced investment in their DEI programs and let go of DEI staff.

Monideepa Tarafdar is Charles J. Dockendorff Endowed Professor at the Isenberg School of Management at the University of Massachusetts Amherst.
Marta Stelmaszak is an assistant professor of information systems at the Isenberg School of Management at the University of Massachusetts Amherst.

Biden announces $150 million in research grants as part of his ‘moonshot’ push to fight cancer

President Joe Biden promotes his “moonshot” initiative aimed at reducing cancer deaths in New Orleans. The president announces $150 million in awards from the Advanced Research Projects Agency for Health supporting eight research teams around the country.

President Joe Biden and first lady Jill Biden listen during a demonstration of cancer research and detection techniques at Tulane University, Tuesday, Aug. 13, 2024, in New Orleans. (AP Photo/Mark Schiefelbein)

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President Joe Biden listens as Tulane University President Michael Fitts speaks during a demonstration of cancer research and detection techniques at Tulane University, Tuesday, Aug. 13, 2024, in New Orleans. (AP Photo/Mark Schiefelbein)

President Joe Biden greets former New Orleans Mayor Mitch Landrieu and his wife Cheryl Tuesday, Aug. 13, 2024, at Louis Armstrong International Airport in New Orleans. (AP Photo/Mark Schiefelbein)

President Joe Biden talks with reporters Tuesday, Aug. 13, 2024, at Louis Armstrong International Airport in New Orleans. (AP Photo/Mark Schiefelbein)

President Joe Biden speaks to reporters as he departs the White House for a trip to New Orleans, Tuesday, Aug. 13, 2024, in Washington. (AP Photo/Manuel Balce Ceneta)

FILE - President Joe Biden speaks on the cancer moonshot initiative at the John F. Kennedy Library and Museum, Sept. 12, 2022, in Boston. (AP Photo/Evan Vucci)

President Joe Biden speaks to reporters as he leaves the White House for a trip to New Orleans, La., Tuesday, Aug. 13, 2024, in Washington. (AP Photo/Manuel Balce Ceneta)

President Joe Biden and first lady Jill Biden board Air Force One as they arrive to depart, Tuesday, Aug. 13, 2024, at Joint Base Andrews, Md., en route to New Orleans. (AP Photo/Mark Schiefelbein)

President Joe Biden, escorted by Air Force Col. Angela Ochoa, Commander, 89th Airlift Wing, walks to Air Force One as he arrives to depart, Tuesday, Aug. 13, 2024, at Joint Base Andrews, Md., en route to New Orleans. (AP Photo/Mark Schiefelbein)

NEW ORLEANS (AP) — President Joe Biden is zeroing in on the policy goals closest to his heart now that he’s no longer seeking a second term , visiting New Orleans on Tuesday to promote his administration’s “moonshot” initiative aiming to dramatically reduce cancer deaths.

The president and first lady Jill Biden toured medical facilities that receive federal funding to investigate cancer treatments at Tulane University. Researchers used a piece of raw meat to demonstrate how they are working to improve scanning technology to quickly distinguish between healthy and cancerous cells during surgeries.

The Bidens then championed the announcement of $150 million in awards from the Advanced Research Projects Agency for Health. Those will support eight teams of researchers around the country working on ways to help surgeons more successfully remove tumors from people with cancer. It brings the total amount awarded by the agency to develop breakthrough treatments for cancers to $400 million.

Cancer surgery “takes the best surgeons and takes its toll on families,” Biden said. He said the demonstration of cutting-edge technology he witnessed would offer doctors a way to visualize tumors in real time, reducing the need for follow-on surgeries.

“We’re moving quickly because we know that all families touched by cancer are in a race against time,” Biden said.

The teams receiving awards include ones from Tulane, Dartmouth College, Johns Hopkins University, Rice University, the University of California, San Francisco, the University of Illinois Urbana-Champaign, the University of Washington and Cision Vision in Mountain View, California.

Before he leaves office in January, Biden hopes to move the U.S. closer to the goal he set in 2022 to cut U.S. cancer fatalities by 50% over the next 25 years, and to improve the lives of caregivers and those suffering from cancer.

“I’m a congenital optimist about what Americans can do,” Biden said. “There’s so much that we’re doing. It matters”

Experts say the objective is attainable — with adequate investments.

“We’re curing people of diseases that we previously thought were absolutely intractable and not survivable,” said Karen Knudsen, CEO of the American Cancer Society and the American Cancer Society Cancer Action Network.

Cancer is the second-highest killer of people in the U.S. after heart disease. This year alone, the American Cancer Society estimates that 2 million new cases will be diagnosed and 611,720 people will die of cancer diseases.

Still, “if all innovation ended today and we could just get people access to the innovations that we know about right now, we think we could reduce cancer mortality by another 20 to 30%,” Knudsen said.

The issue is personal enough for Biden that, in his recent Oval Office address about bowing out of the 2024 campaign, the president promised to keep fighting for “my cancer moonshot so we can end cancer as we know it.”

“Because we can do it,” Biden said then.

He said in that speech that the initiative would be a priority of his final months in office, along with working to strengthen the economy and defend abortion rights, protecting children from gun violence and making changes to the Supreme Court, which he called “extreme” in its current makeup during a recent event.

Both the president and first lady have had lesions removed from their skin in the past that were determined to be basal cell carcinoma, a common and easily treated form of cancer. In 2015, their eldest son, Beau, died of an aggressive brain cancer at age 46.

“It’s not just personal,” Biden said Tuesday. “It’s about what’s possible.”

The president’s public schedule has been much quieter since he left the race and endorsed Vice President Kamala Harris , making Tuesday’s trip stand out.

Advocates have praised Biden for keeping the spotlight on cancer, bringing stakeholders together and gathering commitments from private companies, nonprofit organizations and patient groups.

They say that the extra attention the administration has paid has put the nation on track to cut cancer death rates by at least half, preventing more than 4 million deaths from the disease, by 2047. It has done so by bolstering access to cancer treatments and reminding people of the importance of screening, which hit a setback during the coronavirus pandemic.

“President Biden’s passion and commitment to this effort has made monumental differences for the entire cancer community, including those who are suffering from cancer,” said Jon Retzlaff, the chief policy officer at the American Association for Cancer Research.

Looking ahead, Retzlaff said, “The No. 1 thing is for us to see robust, sustained and predictable annual funding support for the National Institutes of Health. And, if we see that through NIH and through the National Cancer Institute, the programs that have been created through the cancer moonshot will be allowed to continue.”

Initiatives under Biden include changes that make screening and cancer care more accessible to more people, said Knudsen, with the American Cancer Society.

For instance, Medicare has started to pay for follow-up colonoscopies if a stool-based test suggests cancer, she said, and Medicare will now pay for navigation services to guide patients through the maze of their cancer care.

“You’ve already paid for the cancer research. You’ve already paid for the innovation. Now let’s get it to people,” Knudsen said.

She also said she’d like to see the next administration pursue a ban on menthol-flavored cigarettes, which she said could save 654,000 lives over the next 40 years.

Scientists now understand that cancer is not a single disease, but hundreds of diseases that respond differently to different treatments. Some cancers have biomarkers that can be targeted by existing drugs that will slow a tumor’s growth. Many more targets await discovery.

“We hope that the next administration, whoever it may be, will continue to keep the focus and emphasis on our national commitment to end cancer as we know it,” said Dr. Crystal Denlinger, CEO of the National Comprehensive Cancer Network, a group of elite cancer centers.

Johnson reported from Washington state.

UNC Chapel Hill

We’ve Updated Our Mission, Vision and Values

August 19, 2024

By Rhea Hebert

For the past year, we’ve been celebrating an important milestone. In September 2023, CHER marked ten years since its founding.

We’ve seen a lot of change in that time. Our team has grown. We’ve worked with lots of different communities. We’ve deepened partnerships. We’ve started new partnerships.

Now, we’re preparing for our next ten years. As part of our preparation, CHER leadership and staff began reviewing updates to the center mission, vision and values. We started this work in October 2023 through a series of meetings and gatherings.

We gathered feedback across the center to reflect on the changes and where we want to go. Over 40 members across CHER were involved in this process.

The work to update the mission, vision and values sets the foundation for our next five years. CHER is developing specific goals, objectives and implementation plans for this five-year period. We will continue to co-create and share out as we progress.

We’re pleased to share updated mission, vision, value and inclusive research statements.

We look forward to continuing to work in community to help build healthy communities for all.

Our mission is to authentically partner with communities for innovative health equity research, practice and education.

Our vision is to be a transformative leader ensuring care systems advance health for all.

Strategic values

Our strategic values are the core principles that guide CHER’s decision-making and actions.

Social impact We amplify community voices in our work to create lasting change rooted in social justice.
Authentic collaboration We promote shared knowledge, visioning and decision-making achieved through respectful, intentional communication and contributions from all team members (internal and external).  
Trustworthiness We foster honesty, transparency and openness in interactions with others to build a supportive and trusting environment.
Compassion  & Respect We lead with kindness and cultural humility, listening to others with the intent to understand and value others’

Operational Values

Our operational values are core principles that guide CHER’s organizational culture and identity, team interactions and sense of purpose in day-to-day functions and activities.

Meaningful  work We find purpose in our work by prioritizing excellence in scientific research through innovation, education, equity and 
Health and Balance We maintain practices to manage workplace stress and foster joy and grace in our work to support the emotional, psychological and social wellbeing of our team.
Creativity We welcome ideas and perspectives that embrace bold approaches to tackle complex health challenges and inequities.
Intellectual growth We seek to challenge ourselves and grow from personal and professional development opportunities.

Inclusive research statement

Everyone, regardless of what they look like, where they live, how much they earn or who they love, deserves to live in a community that gives them the opportunity to make healthy choices. Healthy communities have people, families, jobs and spaces that keep them well.

We bring together the expertise of communities and researchers to understand what people need to thrive where they are. Our community-researcher teams collect data and learn about what’s working well and what’s getting in the way of health and thriving. Together we develop and promote solutions that create opportunities for healthy choices. This is how our work with communities advances health for everyone.

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Kestrel Supercomputer Ready To Energize Renewable Energy Research

A group of people stand in front of a building

After more than two years of hard work, the Kestrel supercomputer completed its full buildout to reach 44 petaflops of computing power focused on renewable energy and energy efficiency research.

Built by Hewlett Packard Enterprise, the high-performance computing system boasts more than five times the computing power of the National Renewable Energy Laboratory's (NREL's) previous supercomputer, Eagle, and will supercharge the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE).

With this summer's completion of the installation, Kestrel now adds 132 graphics processing unit (GPU) nodes—each hosting four NVIDIA H100 GPU processors—to the 2,314 existing central processing unit (CPU) nodes with two Intel Sapphire Rapids processors. The new GPU nodes are already in use by more than 60 projects, elevating the work at EERE to new heights by enabling emerging artificial intelligence and machine learning workflows.

"The new Kestrel GPU nodes are proving to be extremely powerful—our project has developed algorithms that showcase Kestrel's impressive GPU acceleration for materials and chemistry modeling," said NREL's Derek Vigil-Fowler, principal investigator on the Beyond-DFT Electrochemistry with Accelerated and Solvated Techniques (BEAST) project, funded by DOE's Office of Science. "We are leveraging the GPUs on Kestrel for high-fidelity simulation of electrocatalytic systems, with the aim of designing better catalysts for water electrolysis, fuel cells, and carbon dioxide reduction. The ability to simulate complex models of catalytic systems with high fidelity is invaluable to understanding factors that determine electrocatalytic performance, and Kestrel GPUs have demonstrated excellent efficiency and scaling for these simulations."

Text version for Kestrel Supercomputer: Why GPUs Matter

The work to install Kestrel in NREL's Energy Systems Integration Facility user facility data center kicked off with the arrival of the first phase of equipment—including 2,314 CPU nodes and a 95-petabyte parallel storage system—in March 2023. The CPU phase of Kestrel was installed last summer and was made available to early users, and then Kestrel opened for all projects for the start of the 2024 fiscal year. In November 2023, the CPU phase of Kestrel landed at #67 on the 62 nd edition of the TOP500 , an industry-standard list of the 500 most powerful computers in the world, showcasing 14.3 petaflops of performance from the CPU capability alone on Kestrel.

3-dimensional rendering of the Kestrel supercomputer

Excitement swelled as the remainder of the Kestrel system arrived in February 2024: GPU nodes featuring NVIDIA's latest H100 GPUs. Testing and validation of the new GPU nodes, and their integration into the full Kestrel system, was completed in May, when early users were invited to test their codes and help get the system ready for all users. Kestrel's GPU nodes are now released to all Kestrel users to accelerate research critical to the energy transition.

Text version for Kestrel Supercomputer: Taking Flight at NREL

"With Kestrel, researchers have access to advanced computing capabilities to do high-quality research at the pace and scale necessary to enable the energy transition," said NREL's Kristin Munch, laboratory program manager for advanced computing and Kestrel project manager. "We've been building to this moment for more than two years with a tremendous team working diligently to bring this impressive system online for EERE researchers."

Now that Kestrel is fully complete, researchers are already plugging in and using Kestrel's power to accelerate energy research, driving advancements in energy efficiency, sustainable transportation, renewable power, and energy systems integration. The FY 2023 Advanced Computing Annual Report details how high-performance computing is advancing clean energy research and scientific collaboration.

"We are super excited to have the full capabilities of Kestrel available to the research community," said NREL's Aaron Andersen, advanced computing operations group manager. "Kestrel's CPU nodes utilize 100% direct liquid cooling for all components. From an efficiency standpoint, Kestrel has more than two times the efficiency of our previous supercomputer, Eagle, providing 10.4 gigaflops per watt versus Eagle at 4.7 gigaflops per watt. Kestrel continues NREL's leadership in efficient computing."

Learn more about Kestrel and its capabilities.

IMAGES

Summer 2022 MATH 428: Principles of Operations Research
Operation Research Definition
What is Operations Research?. A Comprehensive Introduction to…
PPT
Why Operations Research is awesome
Phases of operations research Source: https://www.researchgate.net The

COMMENTS

Research Ops: What It Is and Why It's So Important
From Building a Research Practice . There are six common focus areas (core components) of the Research Ops framework:. Participant management includes recruiting, screening, scheduling, and distributing incentives to research participants. This is primarily what many people think of when they think of "research operations." Governance involves the safety, legality, and ethics of research.
Research Operations: Definition, Procedure, and Tools
Research operations (also known as ResearchOps) consist of organizing people, resources, and processes to streamline research and maximize impact. Research operations managers are in charge of strategic planning, resource allocation, software acquisition, participant management, employee onboarding, creating SOPs, and fostering collaboration.
ResearchOps 101
ResearchOps 101. Kate Kaplan. August 16, 2020. Summary: The practice of Research Operations (ResearchOps) focuses on processes and measures that support researchers in planning, conducting, and applying quality research at scale. ResearchOps is a specialized area of DesignOps focused specifically on components concerning user-research practices.
Research Ops: What it is, why it's so important, and ...
Productivity: Research Ops promotes developing a framework, templates like research proposal example, workflow, and repeatability methods to ensure saving maximum time on operational work. It is an umbrella with several components that manage to reduce the processing time to a large extent, hence optimising efficiency.
An Essential Guide to Research Operations
A Research Operations program manages the people and processes involved in an organization's research discipline. It runs alongside other operations teams like DesignOps and DevOps. There are five key components to a ResearchOps program, including: The goal is to make user research activities easier and more consistent.
Defining and Scaling User Research: Unveiling the Power of Operations
Research operations (aka 'ReOps' and 'Research Ops') is a crucial aspect of product development and design that has been growing in its popularity and is increasingly being adopted by ...
User research strategy, Part 1: planning and the Research Operations
What are research operations? This post will focus on ResearchOps. Broadly speaking this involves setting the frameworks, management and processes to enable user researchers to get on with the job ...
Achieve Impactful UXR: The Essential ResearchOps Framework
ResearchOps (or ReOps) is a specialized area of design operations focused on optimizing and empowering user research (UXR) efforts. It includes the processes, tools, and strategies that streamline research. By streamlining workflows, ResearchOps frees researchers to focus on what matters most - uncovering user needs.
Introduction to Operations Research
The subject matter, operations research or management science (even though there may be philosophical differences, we use the two terms interchangeably), has been defined by many researchers in the field. Definitions range from "a scientific approach to decision making" to "the use of quantitative tools for systems that originate from real life," "scientific decision making," and ...
Harnessing research operations: How teams can maximize research efficiency
A research operations manager is responsible for building the infrastructure for user research. Their primary responsibility is finding tools, setting up workflows, and documenting SOPs to streamline research activities. They also oversee different aspects of research operations, like participant recruitment, compliance, logistics, etc.
What is ResearchOps and when to start?
Research carries a lot of operational expenses - there are fees or paid incentives for recruiting participants, ongoing costs of software licenses and tooling, travel expenses for field research, and more. ResearchOps is responsible for tracking operational spend, allocating budget and resources amongst research projects, and negotiating the ...
What is Operations Research and Why is it Important?
By. Sarah Lewis. Operations research (OR) is an analytical method of problem-solving and decision-making that is useful in the management of organizations. In operations research, problems are broken down into basic components and then solved in defined steps by mathematical analysis. The process of operations research can be broadly broken ...
Operations research
Operations research. Operations research ( British English: operational research) (U.S. Air Force Specialty Code: Operations Analysis), often shortened to the initialism OR, is a discipline that deals with the development and application of analytical methods to improve decision-making. [ 1] The term management science is occasionally used as a ...
What is Operations Research?
The Simple Answer: Operations Research (OR) is a discipline of problem-solving and decision-making. It uses advanced analytical methods to help management run an effective organization. Problems are broken down, analyzed and solved in steps. The Technical Answer: Operations Research, also known as management sciences, uses scientific methods to ...
Why Operations Research is awesome
Operations research had its historical origin in the 17th century when game-theoretic approaches and expected values were being utilized to solve problems. The modern version of OR originated during the second world war when it became apparent that the military needed to solve some of the significant logistic and supply chain problems that come ...
Operations research
operations research, application of scientific methods to the management and administration of organized military, governmental, commercial, and industrial processes.. Basic aspects. Operations research attempts to provide those who manage organized systems with an objective and quantitative basis for decision; it is normally carried out by teams of scientists and engineers drawn from a ...
Operations research
Through controls the problem-solving system of which operations research is a part learns from its own experience and adapts more effectively to changing conditions. Operations research - Problem-Solving, Modeling, Analysis: Three essential characteristics of operations research are a systems orientation, the use of interdisciplinary teams, and ...
(PDF) Operational Research: Methods and Applications
69 Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Wrocław, Poland 70 Naveen Jindal School of 1 arXiv:2303.14217v2 [math.OC] 27 Aug 2023
What Is Operations Research? (Definition and Examples)
Operations research is a scientific discipline that involves using mathematics and analytical principles to aid problem-solving and decision-making for organizations. Learning more about operations research can help you use it to make more informed business decisions. In this article, we explain what operations research is, discuss its benefits ...
What Is Operations Research (OR)? Definition, Concept, Characteristics
Operations Research Definition. Some of the well-known operations research definitions are as: Moarse and Kimbal (1946) defined OR as a scientific method of providing the executive department a quantitative basis for decision-making regarding the operations under their control.. According to Churchman, Ackoff and Arnoff (1957), OR is the application of scientific methods, techniques and tools ...
Operations Research (3): Theory
Operations Research (OR) is a field in which people use mathematical and engineering methods to study optimization problems in Business and Management, Economics, Computer Science, Civil Engineering, Electrical Engineering, etc. The series of courses consists of three parts, we focus on deterministic optimization techniques, which is a major ...
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Operations research is the use of statistical analysis and mathematical optimization techniques to help organizations solve problems and improve decision-making. The ability to harness vast amounts of data on day-to-day operations has created opportunities to rigorously optimize processes for cost, quality control, inventory management, and ...
Operations Research: Concept, stages involved, scope of OR
Characteristics of Operations Research: It has a decision-making approach. The main objective of Operations Research is to find the best or optimal solution to the problem under consideration. Operations Research is the scientific study of large systems with a view to identify problem areas and provide the managers with a quantitative basis for ...
Can Operation Warp Speed Serve as a Model for Accelerating Innovations
Operation Warp Speed (OWS) was a U.S. government-led program to accelerate the development, production, and administration of COVID-19 vaccines. The program cut the typical ten-year timeline needed to develop a new vaccine down to ten months and began vaccinating vulnerable populations within a year after launch.
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The University of Missouri Research Reactor (MURR) has entered into a memorandum of agreement with the Institute of Nuclear Power Operations (INPO). This significant milestone underscores MURR's unwavering commitment to operational excellence, safety and continuous improvement in the field of nuclear research and operations.
Research: How IT Can Solve Common Problems in DEI Initiatives
The authors' research found that three persistent problems plague DEI initiatives: They do not connect to operational or strategic goals and objectives; they do not include the rank-and-file ...
Biden announces $150 million in research grants for his cancer
The Bidens then championed the announcement of $150 million in awards from the Advanced Research Projects Agency for Health. Those will support eight teams of researchers around the country working on ways to help surgeons more successfully remove tumors from people with cancer. It brings the total amount awarded by the agency to develop ...
We've Updated Our Mission, Vision and Values
Operational Values. Our operational values are core principles that guide CHER's organizational culture and identity, team interactions and sense of purpose in day-to-day functions and activities. Meaningful work We find purpose in our work by prioritizing excellence in scientific research through innovation, education, equity and ...
China-US tensions erode co-operation on science and tech
US-China co-operation has been introduced in some research areas of strong perceived mutual interest. In January, the White House's top science adviser said the two countries would work together ...
Kestrel Supercomputer Ready To Energize Renewable Energy Research
"We are super excited to have the full capabilities of Kestrel available to the research community," said NREL's Aaron Andersen, advanced computing operations group manager. "Kestrel's CPU nodes utilize 100% direct liquid cooling for all components. From an efficiency standpoint, Kestrel has more than two times the efficiency of our previous ...

Research Operations: Definition, Procedure, and Tools

What is research operations?

What does a research operations manager do?

How to get started with the research process?

Build a research team

Define research objectives and goals

Establish standards and processes to support researchers

Recruit research participants

Select tools to gather and manage research data

Analyze and iterate results for better research practice

7 user research methods for research ops

Qualitative research vs quantitative research

Usability testing

User interviews

Focus groups

Card sorting

Tree testing

Best tools for research operations

Userpilot for gathering research insights

Optimal Workshop for conducting research

Figma for testing prototypes

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3.1.2 Audit and analysis

3.1.3 Start small

3.1.4 Grow and evolve

4. Final Words

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