problem definition in operation research

What is Operations Research? | NC State OR

What is Operations Research?

Last Updated:  08/16/2022 and all information on this page is accurate and up-to-date

The simple answer: Operations Research (OR) is a discipline of problem-solving and decision-making that 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 methodology to study systems whose design or operation requires human decision-making. OR provides the means for making the most effective systems design and operation decisions. The strength and versatility of OR stem from its diagnostic power through observation and modeling and its prescriptive power through analysis and synthesis.

OR is interdisciplinary, drawing on and contributing to the techniques from many fields, including mathematics and mathematical sciences, engineering, economics and the physical sciences. OR practitioners have successfully solved a wide variety of real-world problems, varying from the optimal design of telecommunications networks in uncertain demand to the planning for an optimal deployment of armed forces during wartime. Many new applications originate from current societal energy production and distribution problems, environmental pollution control, health maintenance, and software production.

The CEO of the Future is an Engineer

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

Operations Research Offers Workplace Freedom

Operations research practitioners have offices and work in the settings they are trying to improve. When collecting data, they may observe the staff working in a restaurant or watch workers assembling parts in a factory. When solving problems, they are in an office analyzing the data they or others have collected.

The World Needs more Operations Research

As companies battle in the competitive world market, the need for operations research practitioners grows. Why? They are the engineers trained to be productivity and quality improvement specialists. They share the common goal of saving companies money and increasing performance.

Operations Research is all about Options

Operations research practitioners work in almost any industry, anywhere in the world. They can work in and out of the office while interacting with people and processes they want to improve. This flexibility gives them a career advantage over other types of engineering. Operations research practitioners have the luxury of not specializing. They can keep their options open. This makes them 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 employment will continue to grow by over 25 percent during the next decade. This is faster than the average for all occupations.

Companies look for new ways to reduce costs and raise productivity every day. They will turn to operation research practitioners to develop more efficient processes and reduce costs, delays and waste. This leads to job growth for these engineers, even in manufacturing industries with slow-growing or declining employment. Because their work is done in management, many operations research practitioners leave the occupation to become managers.

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

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Operational Research in Health-care Settings

Rajesh kunwar.

Department of Community Medicine, TS Misra Medical College, Department of Community Medicine, Prasad Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

V. K. Srivastava

Origin of the term operational research (OR), also known as operations research, can be traced back to World War II when a number of researches carried out during military operations helped British Forces produce better results with lesser expenditure of ammunition. The world soon realised the potential of this kind of research and many disciplines especially management sciences, started applying its principles to achieve better returns on their investments.

Following World War II in 1948, the World Health Organization (WHO) came into existence with research as one of its core functions. It emphasized the need of identifying health-related issues needing research and thereby generation, dissemination, and utilization of the newly acquired knowledge for health promotion.[ 1 ] In 1978, Alma Ata Declaration acknowledged that primary health care was well known globally but, at the same time, also noted that modalities of its implementation were likely be different in different countries depending on their socioeconomic conditions, availability of resources, development of technology, and motivation of the community. A number of issues were yet to be resolved and researched before primary health care was operationalized under local conditions.[ 2 ]

T HE D EFINITION

The kind of research that Alma Ata Declaration recommended for improvement of health-care delivery is essentially OR. Described as “the science of better,” it helps in identifying the alternative service delivery strategy which not only overcomes the problems that limit the program quality, efficiency, and effectiveness but also yields the best outcome.[ 3 ] In its report on “The Third Ten Years of the WHO,” WHO has highlighted the usefulness of OR in improvement of health-care delivery in terms of its efficiency, effectiveness, and wider coverage by testing alternative approaches even in countries with limited national resources.[ 4 ]

OR has been variously defined. Dictionary of Epidemiology defined it as a systematic study of the working of a system with the aim of improvement.[ 5 ] From a health program perspective, OR is defined as the search for strategies and interventions that enhance the quality and effectiveness of the program.[ 6 ] A global meeting held in Geneva in April 2008 to develop the framework of OR, defined the scope of OR in context to public health as “ Any research producing practically usable knowledge (evidence, finding, information, etc.) which can improve program implementation (e.g., effectiveness, efficiency, quality, access, scale up, sustainability) regardless of the type of research (design, methodology, approach), falls within the boundaries of OR .”[ 7 ]

OR, however, is different from clinical or epidemiological research. It addresses a specific problem within a specific program. It examines a system, for example, health-care delivery system, and experiments in the environment specific to the program with alternative strategies to find the most suitable one and has an objective of improvement in the system. On the other hand, clinical or epidemiological research studies individuals and groups of individuals in search of new knowledge. In addition, ethical issues, which form an integral part of all clinical and epidemiological research, have their role poorly defined in OR, more so if it is based on secondary data.

The keyword in all the definitions is improvement, which is to be brought about by means of research in the operation of an ongoing program. Its characteristics include:

• It focuses on a specific problem in an ongoing programme
• It involves research into the problem using principles of epidemiology
• It tests more than one possible solution and provides rational basis, in the absence of complete information, for the best alternative to improve program efficiency
• It requires close interaction between program managers and researchers
• It succeeds only if the research is conducted in the existing environment and study results are implemented in true letter and spirit.

T HE P ROCESS

In health-care settings, an ongoing health program often fails to achieve its expected objective and the program managers are faced with problems factors responsible for which are not apparent. This is the stage where process of OR is initiated. In a standard OR process, planning begins with organization of a research team, which should have a mix of people with different backgrounds such as epidemiology, biostatistics, health managements, etc., The program managers may not be able to carry out the research themselves because of their work responsibility and in all probability, their biased views. However, they need to have a working relationship with the research team to ensure smooth conduct of the research and ownership of the result by all parties.

According to Fisher et al ., OR is a continuous process of problem identification, selection of a suitable strategy/intervention, experimentation of the selected strategy/intervention, dissemination of the findings, and utilization of the information so derived.[ 8 ] However, it may not always be possible to follow a step by step approach in OR since it is carried out in the existing environment, and many of the activities may be taking place simultaneously. The process involves the following steps [ Figure 1 ].

Process of operational research

Identifying problems

Like any other research, it is essential to have a research question as to the first and foremost step for beginning the process of OR. Discussion with program managers and staff, review of project reports and local documentation, discussion with experts in the field and literature search gives an insight into why the problem is occurring and what are possible solutions; and help in the identification of the research question. OR methods are useful for the systematic identification of problems and the search for potential solutions. Structured approaches to identifying options, such as the strategic choice approach or systematic creativity approaches have great potential for use in low-resource settings.[ 9 ]

Choosing interventions

Choosing appropriate interventions is clearly a crucial step. Effectiveness, safety, cost, and equity should all be considered, and researchers should be familiar with standard textbook methods for assessing these. Finding the best combinations and delivery methods is a major research exercise in its own right. Modeling different intervention strategies before rollout is now ubiquitous in many industries but is less common in healthcare.[ 10 ] Modeling work has been done on ways to reduce maternal mortality and in cervical cancer screening in low-resource settings.[ 11 ]

An appropriate intervention design, depending on available time and resources, should have a written protocol spelling out details of steps to be taken during implementation. Only valid and reliable instruments – be it quantitative or qualitative study-should be used; and wherever possible, a pilot study be carried out to further refine the conduct of the intervention. The contribution that OR and management science can make to design and delivery is not restricted to high technology. Oral rehydration therapy is a “low-tech–low-cost–high-impact” innovation, in which OR was used to explore ways it could be administered using readily available ingredients by laypeople, with an escalation pathway to treatment by health-care professionals when necessary.[ 12 ]

Small-scale projects generally need considerable modifications to work on a larger scale. Classic OR techniques such as simulation modeling can be used in locating services, managing the supply chain, and developing the health-care workforce.

Integrating into health systems

After analysis of the result, the information gathered should be disseminated to stakeholders and decision-makers. The modalities of information utilization should have been predecided and included in the research proposal. Successes in global health programs often result from synergistic interactions between individual, community and national actors rather than from any single “magic bullet.” A greater focus is needed on how interventions should be used in a complex behavioral environment, to better capture the dynamics of social networks, and to understand how complex systems can adapt positively to change. This is a task where OR and management science tools can be useful, as demonstrated by systems analysis of programs for cervical cancer prevention[ 13 ] or agent simulation modeling of spread of HIV in villages.[ 14 ]

E VALUATION

One of the greatest challenges for global health is the measurement and evaluation of performance of projects and programs. The WHO defines evaluation as “ the systematic and objective assessment of an ongoing or completed initiative, its design, implementation, and results. The aim is to determine the relevance and fulfillment of objectives, efficiency, effectiveness, impact, and sustainability .”[ 15 ] It may or may not lead to improvement.

Accelerated Child Survival and Development (ACSD) program, an initiative of UNICEF, was implemented in eleven West African countries from 2001 to 2005 with an objective of reducing mortality among under-fives by at least 25% by the end of 2006. Retrospective evaluation of the program was carried out in Benin, Ghana, and Mali by comparing data of ACSD focus districts with those of remainder districts. It showed that the difference in coverage of preventive interventions in ACSD focus areas before and after program implementation was not significant in Benin and Mali. This probably resulted in failure of ACSD program to accelerate survival of under-fives in-focus areas of Benin and Mali as compared to comparison areas. The inputs obtained from the evaluation of the program if translated into policy or national program would have delivered the desired result of ACSD program implementation.[ 16 ] Evaluation, thus, is fundamental to good management and is an essential part of the process of developing effective public policy. It is a complex enterprise, requiring researchers to balance the rigors of their research strategies with the relevance of their work for managers and policymakers.[ 17 ]

Standard control trial approaches to evaluation are sometimes feasible and appropriate but often a more flexible systems-oriented approach is required, together with modeling to help assess the effectiveness of preventive interventions.[ 18 ] Decision tree modeling can give rapid insights into the operational effectiveness and cost-effectiveness of procedures[ 19 ] and programs.[ 20 ]

O PERATIONAL R ESEARCH IN H EALTH-CARE S ETTINGS : E XAMPLES

The relevance of OR in health-care settings cannot be overemphasized. It has been successfully used all over the world in various health programs such as family planning, HIV, tuberculosis (TB), and malaria control programs to name a few. Its role in causing improvement in various health programs and the development of policies has been acknowledged globally. Sustained OR efforts of several decades helped in developing the Global strategy for control of TB. India and Malawi provide the most successful example of OR in this field.[ 21 ] In India, it was demonstrated by OR that successful implementation of DOTS strategy throughout the country led to reduction in the prevalence of TB, reduction in fatality due to TB and release of hospital beds occupied by TB patients; and thereby a potential gain to the Indian economy.[ 22 ]

For the treatment of TB, about half of TB patients in India rely on the private sector. In spite of it being a notifiable disease, TB notification from private sector has been a challenge. In 2014, Delhi state, by adopting direct “one to one” sensitization of private practitioners by TB notification committee, was able to accelerate notification of TB cases from the private sector.[ 23 ]

In view of the growing burden of multidrug-resistance TB (MDR-TB), an OR was conducted in the setting of Revised National Tuberculosis Programme on patients with presumptive MDR-TB in North and Central Chennai, in 2014 to determine prediagnosis attrition and pre-treatment attrition, and factors associated with it. Prediagnosis and pretreatment attrition were found 11% and 38%, respectively. The study showed that patients with smear-negative TB were less likely to undergo drug susceptibility testing (DST) and more attention was required to be paid to this group for improving DST.[ 24 ]

One of the most successful examples of OR in India is the experimental study carried out in Gadchiroli district of Maharashtra from 1993 to 1998. In their path-breaking field trial, Bang et al . trained village level workers in neonatal care who subsequently made home visits at scheduled intervals and managed premature birth/low birthweight, birth asphyxia, hypothermia, neonatal sepsis, and breastfeeding problems. This led to a significant reduction in neonatal mortality rates in intervention villages.[ 25 ] Encouraged by the success of this field trial, Home-Based Newborn Care has been adopted by many districts in India to combat neonatal mortality.

In leprosy case detection campaign (LCDC), introduced under National Leprosy Eradication Programme of India in 2016, false-positive diagnosis is a major issue. A study carried out in four districts of Bihar found 30% false-positive cases during LCDC. Using “appreciative inquiry” as a tool, Wagh et al . were able to achieve a decline in false-positive diagnosis.[ 26 ]

OR has been successfully used in hospital settings too. In Latin America, unsafe abortions used to be one of the most common causes of high maternal mortality. Billings and Bensons reviewed ten completed OR projects conducted in public sector hospitals of seven Latin American countries. Their findings indicated that sharp curettage replaced by manual vacuum aspiration for conducting abortion reduced the requirement of resources for postabortion care, reduced cost, and length of hospital stay and reduced maternal mortality.[ 27 ]

C ONCLUSION

Following Alma Ata declaration and Millennium Development Goals, all countries of the world have instituted their own National Health Programmes in a bid to improve health of their countrymen. Although health programs are in place, Governments are committed, guidance from the WHO is available, support from NGOs have been garnered, still many countries have not been able to achieve their desired goals. Operational Research is now being used as a key instrument, especially in resource-poor countries, to tap the untapped information. Administrators are using it as a searchlight for discovering what is still in the dark. It is there to stay. It is high time that the scientific community working in health-care settings gets acquainted with the nuances of OR and uses it more often for improving the outcome of health programs and for making them more efficient and effective.

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Conflicts of interest.

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R EFERENCES

Understanding Phases and Processes of Operations Research (O.R.) Study

Table of Contents

In this blog, we will provide a comprehensive overview of the different stages involved in O.R. and the essential processes used in each phase. An O.R. study comprises several phases, each with its unique processes, methodologies, and tools. In this blog, we’ll examine each phase’s objectives and processes to understand how O.R. can be used to address complex problems.

Phases of Operations Research Study

An O.R. study typically comprises six phases:

• Problem definition and formulation
• Data collection
• Model construction and selection
• Model validation and verification
• Solution generation and analysis
• Implementation and monitoring

1. Problem Definition and Formulation

In this phase, the problem is identified and defined, and the objectives of the study are determined. The scope of the study, the constraints, and assumptions are also defined in this phase. The primary output of this phase is a clear statement of the problem, which helps guide subsequent phases.

2. Data Collection

In this phase, the necessary data for the study are collected. Data can be collected from various sources, including databases, surveys, and historical records. Data quality is crucial at this stage, and it is essential to ensure that the data collected is complete, accurate, and relevant.

3. Model Construction and Selection

In this phase, a mathematical or simulation model is developed to represent the problem. The model should be simple enough to be solved using available techniques, but complex enough to capture the essential characteristics of the problem. Different modeling techniques can be used, including optimization, queuing theory, and simulation.

4. Model Validation and Verification

In this phase, the model is tested and validated to ensure that it accurately represents the problem. The model’s assumptions are tested, and the output is compared to real-world data to verify its accuracy. If the model is found to be inaccurate, it is refined or reconstructed in the previous phase.

5. Solution Generation and Analysis

In this phase, the model is solved using appropriate techniques to obtain the optimal solution. The solution is analyzed to determine whether it meets the objectives and constraints of the problem. Sensitivity analysis is also performed to determine how changes in the input parameters affect the output.

6. Implementation and Monitoring

In this phase, the solution is implemented, and the outcomes are monitored to ensure that they meet the objectives. This phase also involves developing an implementation plan, providing support to the implementation team, and addressing any challenges that arise.

The six phases of O.R. study provide a structured approach to problem-solving, ensuring that the decision-making process is comprehensive and efficient. Each phase requires a specific set of processes, tools, and methodologies that are tailored to the particular problem. By understanding the phases and processes of O.R. study, you can appreciate the complexity and scope of O.R. as an effective problem-solving technique.

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Operations Research

1 Operations Research-An Overview

• History of O.R.
• Approach, Techniques and Tools
• Phases and Processes of O.R. Study
• Typical Applications of O.R
• Limitations of Operations Research
• Models in Operations Research
• O.R. in real world

2 Linear Programming: Formulation and Graphical Method

• General formulation of Linear Programming Problem
• Optimisation Models
• Basics of Graphic Method
• Important steps to draw graph
• Multiple, Unbounded Solution and Infeasible Problems
• Solving Linear Programming Graphically Using Computer
• Application of Linear Programming in Business and Industry

3 Linear Programming-Simplex Method

• Principle of Simplex Method
• Computational aspect of Simplex Method
• Simplex Method with several Decision Variables
• Two Phase and M-method
• Multiple Solution, Unbounded Solution and Infeasible Problem
• Sensitivity Analysis
• Dual Linear Programming Problem

4 Transportation Problem

• Basic Feasible Solution of a Transportation Problem
• Modified Distribution Method
• Stepping Stone Method
• Unbalanced Transportation Problem
• Degenerate Transportation Problem
• Transhipment Problem
• Maximisation in a Transportation Problem

5 Assignment Problem

• Solution of the Assignment Problem
• Unbalanced Assignment Problem
• Problem with some Infeasible Assignments
• Maximisation in an Assignment Problem
• Crew Assignment Problem

6 Application of Excel Solver to Solve LPP

• Building Excel model for solving LP: An Illustrative Example

7 Goal Programming

• Concepts of goal programming
• Goal programming model formulation
• Graphical method of goal programming
• The simplex method of goal programming
• Using Excel Solver to Solve Goal Programming Models
• Application areas of goal programming

8 Integer Programming

• Some Integer Programming Formulation Techniques
• Binary Representation of General Integer Variables
• Unimodularity
• Cutting Plane Method
• Branch and Bound Method
• Solver Solution

9 Dynamic Programming

• Dynamic Programming Methodology: An Example
• Definitions and Notations
• Dynamic Programming Applications

10 Non-Linear Programming

• Solution of a Non-linear Programming Problem
• Convex and Concave Functions
• Kuhn-Tucker Conditions for Constrained Optimisation
• Quadratic Programming
• Separable Programming
• NLP Models with Solver

11 Introduction to game theory and its Applications

• Important terms in Game Theory
• Saddle points
• Mixed strategies: Games without saddle points
• 2 x n games
• Exploiting an opponent’s mistakes

12 Monte Carlo Simulation

• Reasons for using simulation
• Monte Carlo simulation
• Limitations of simulation
• Steps in the simulation process
• Some practical applications of simulation
• Two typical examples of hand-computed simulation
• Computer simulation

13 Queueing Models

• Characteristics of a queueing model
• Notations and Symbols
• Statistical methods in queueing
• The M/M/I System
• The M/M/C System
• The M/Ek/I System
• Decision problems in queueing

Operations Research

• Living reference work entry
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• First Online: 01 January 2017
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• Ilan Vertinsky 2  

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Operations research (OR) is both a profession and an academic discipline. It involves the application of advanced analytical methods to improve executive and management decisions. This survey highlights the types of OR models and techniques in common use. It explores the roots of OR and its theoretical and professional evolution, and presents the current trends which shape its future.

This chapter was originally published in The New Palgrave Dictionary of Economics , 2nd edition, 2008. Edited by Steven N. Durlauf and Lawrence E. Blume

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Vertinsky, I. (2008). Operations Research. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95121-5_1370-2

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operations research

Definition of operations research

called also operational research

Examples of operations research in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'operations research.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

1943, in the meaning defined above

Dictionary Entries Near operations research

operations analysis

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“Operations research.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/operations%20research. Accessed 28 Apr. 2024.

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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

( Click on Topic to Read )

• 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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• Department of Industrial and Systems Engineering >
• PhD Program >
• PhD in Industrial Engineering >

Operations Research

Degree concentration for phd, industrial engineering.

Operations Research (OR) is the application of scientific and especially mathematical methods to the study and analysis of problems involving complex systems. 

PhD students interested in OR apply methods such as mathematical programming, stochastic modeling, and discrete-event simulation to the solution of problems in complex systems such as logistics, supply chain optimization, long-range planning, energy and environmental systems, urban and health systems, and manufacturing.

We have an active student body - in fact, UB hosts one of the founding chapters of Omega Rho, the National Operations Research Honor Society. Students are also active participants in the Institute for Operations Research and the Management Sciences (INFORMS).

Our faculty and students conduct OR research funded by such agencies as the National Science Foundation, the Office of Naval Research, the Air Force Office of Scientific Research, the Department of Homeland Security, the Department of Transportation and the National Institute of Justice, as well as national and local corporations and foundations such as United Airlines, Praxair, Lockheed Martin, Boeing, and the Fire Protection Research Foundation. We often work in teams with faculty and students with research interests in manufacturing, production systems and human factors to solve problems beyond the expertise of any single discipline.

Graduating students take positions in national and international corporations, academic institutions and research laboratories. 

Required Core Courses

ISE PhD students who concentrate in OR complete at a minimum:

• IE 572 Linear Programming
• IE 573 Discrete Optimization
• IE 575 Stochastic Methods
• IE 576 Applied Stochastic Processes
• IE 5xx/6xx Operations Research/IE Elective
• IE 555 Programming for Analytics (Must be completed within the first two years of the program) 

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How Pew Research Center will report on generations moving forward

Journalists, researchers and the public often look at society through the lens of generation, using terms like Millennial or Gen Z to describe groups of similarly aged people. This approach can help readers see themselves in the data and assess where we are and where we’re headed as a country.

Pew Research Center has been at the forefront of generational research over the years, telling the story of Millennials as they came of age politically and as they moved more firmly into adult life . In recent years, we’ve also been eager to learn about Gen Z as the leading edge of this generation moves into adulthood.

But generational research has become a crowded arena. The field has been flooded with content that’s often sold as research but is more like clickbait or marketing mythology. There’s also been a growing chorus of criticism about generational research and generational labels in particular.

Recently, as we were preparing to embark on a major research project related to Gen Z, we decided to take a step back and consider how we can study generations in a way that aligns with our values of accuracy, rigor and providing a foundation of facts that enriches the public dialogue.

A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations.

We set out on a yearlong process of assessing the landscape of generational research. We spoke with experts from outside Pew Research Center, including those who have been publicly critical of our generational analysis, to get their take on the pros and cons of this type of work. We invested in methodological testing to determine whether we could compare findings from our earlier telephone surveys to the online ones we’re conducting now. And we experimented with higher-level statistical analyses that would allow us to isolate the effect of generation.

What emerged from this process was a set of clear guidelines that will help frame our approach going forward. Many of these are principles we’ve always adhered to , but others will require us to change the way we’ve been doing things in recent years.

Here’s a short overview of how we’ll approach generational research in the future:

We’ll only do generational analysis when we have historical data that allows us to compare generations at similar stages of life. When comparing generations, it’s crucial to control for age. In other words, researchers need to look at each generation or age cohort at a similar point in the life cycle. (“Age cohort” is a fancy way of referring to a group of people who were born around the same time.)

When doing this kind of research, the question isn’t whether young adults today are different from middle-aged or older adults today. The question is whether young adults today are different from young adults at some specific point in the past.

To answer this question, it’s necessary to have data that’s been collected over a considerable amount of time – think decades. Standard surveys don’t allow for this type of analysis. We can look at differences across age groups, but we can’t compare age groups over time.

Another complication is that the surveys we conducted 20 or 30 years ago aren’t usually comparable enough to the surveys we’re doing today. Our earlier surveys were done over the phone, and we’ve since transitioned to our nationally representative online survey panel , the American Trends Panel . Our internal testing showed that on many topics, respondents answer questions differently depending on the way they’re being interviewed. So we can’t use most of our surveys from the late 1980s and early 2000s to compare Gen Z with Millennials and Gen Xers at a similar stage of life.

This means that most generational analysis we do will use datasets that have employed similar methodologies over a long period of time, such as surveys from the U.S. Census Bureau. A good example is our 2020 report on Millennial families , which used census data going back to the late 1960s. The report showed that Millennials are marrying and forming families at a much different pace than the generations that came before them.

Even when we have historical data, we will attempt to control for other factors beyond age in making generational comparisons. If we accept that there are real differences across generations, we’re basically saying that people who were born around the same time share certain attitudes or beliefs – and that their views have been influenced by external forces that uniquely shaped them during their formative years. Those forces may have been social changes, economic circumstances, technological advances or political movements.

When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

The tricky part is isolating those forces from events or circumstances that have affected all age groups, not just one generation. These are often called “period effects.” An example of a period effect is the Watergate scandal, which drove down trust in government among all age groups. Differences in trust across age groups in the wake of Watergate shouldn’t be attributed to the outsize impact that event had on one age group or another, because the change occurred across the board.

Changing demographics also may play a role in patterns that might at first seem like generational differences. We know that the United States has become more racially and ethnically diverse in recent decades, and that race and ethnicity are linked with certain key social and political views. When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

Controlling for these factors can involve complicated statistical analysis that helps determine whether the differences we see across age groups are indeed due to generation or not. This additional step adds rigor to the process. Unfortunately, it’s often absent from current discussions about Gen Z, Millennials and other generations.

When we can’t do generational analysis, we still see value in looking at differences by age and will do so where it makes sense. Age is one of the most common predictors of differences in attitudes and behaviors. And even if age gaps aren’t rooted in generational differences, they can still be illuminating. They help us understand how people across the age spectrum are responding to key trends, technological breakthroughs and historical events.

Each stage of life comes with a unique set of experiences. Young adults are often at the leading edge of changing attitudes on emerging social trends. Take views on same-sex marriage , for example, or attitudes about gender identity .

Many middle-aged adults, in turn, face the challenge of raising children while also providing care and support to their aging parents. And older adults have their own obstacles and opportunities. All of these stories – rooted in the life cycle, not in generations – are important and compelling, and we can tell them by analyzing our surveys at any given point in time.

When we do have the data to study groups of similarly aged people over time, we won’t always default to using the standard generational definitions and labels. While generational labels are simple and catchy, there are other ways to analyze age cohorts. For example, some observers have suggested grouping people by the decade in which they were born. This would create narrower cohorts in which the members may share more in common. People could also be grouped relative to their age during key historical events (such as the Great Recession or the COVID-19 pandemic) or technological innovations (like the invention of the iPhone).

By choosing not to use the standard generational labels when they’re not appropriate, we can avoid reinforcing harmful stereotypes or oversimplifying people’s complex lived experiences.

Existing generational definitions also may be too broad and arbitrary to capture differences that exist among narrower cohorts. A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations. The key is to pick a lens that’s most appropriate for the research question that’s being studied. If we’re looking at political views and how they’ve shifted over time, for example, we might group people together according to the first presidential election in which they were eligible to vote.

With these considerations in mind, our audiences should not expect to see a lot of new research coming out of Pew Research Center that uses the generational lens. We’ll only talk about generations when it adds value, advances important national debates and highlights meaningful societal trends.

• Age & Generations
• Demographic Research
• Generation X
• Generation Z
• Generations
• Greatest Generation
• Methodological Research
• Millennials
• Silent Generation

Kim Parker is director of social trends research at Pew Research Center

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