operations research skills and techniques

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Operations Research Analyst skills for your resume and career

Operations research analysts need a variety of technical skills to excel in their role. According to Pooyan Kazemian Ph.D. , Assistant Professor of Operations at Case Western Reserve University, "a working knowledge of mathematical modeling, optimization, statistical analysis, machine learning, and programming languages such as R and Python are fundamental technical skills needed for most OR Analyst positions." They must also be proficient in using tools like visualization, MATLAB, and SQL. Additionally, they need to be able to apply scientific methods and techniques to assess data collected during experiments.

On the other hand, soft skills are equally important for operations research analysts. Kazemian emphasizes the importance of "communication skills and teamwork" as they often work as part of a team to solve real-world problems. They need to be able to work effectively with other team members and communicate their findings clearly.

15 operations research analyst skills for your resume and career

1. operations research.

Operations research is the discipline of applying analytical methods and mathematical or statistical models to optimize decision-making in complex systems. Operations research analysts use this discipline to analyze problems and provide solutions. They apply statistical and operations research techniques to analyze the effects of various mechanisms on a system. They also use operations research methods to solve a variety of issues. As Amir Ali Ahmadi , Professor of Operations Research and Financial Engineering at Princeton University, advises, "When faced with an operations research problem, first formulate the simplest possible mathematical model that captures the essence of the problem. Bells and whistles can be added later, but intuition often comes from simple models."

• Provided economic risk and operations research analyses supporting decision-making on major new initiatives, strategic planning and investment and spending priorities.
• Provide operations research and systems simulation analysis on the operational performance and effectiveness utilizing government approved simulation models.

DoD, or the Department of Defense, is the executive department of the federal government responsible for the coordination and supervision of all agencies and functions concerning national security and the military. Operations research analysts use DoD in various ways, such as leading projects sponsored by the department, tracking federal budgets, analyzing funding allocations, and providing data sets to command elements. They also use DoD frameworks to support their work, like establishing price to win in compliance with DoD small business concerns or developing network architectures that include DoD Architectural Framework products.

• Project lead of DoD sponsored completed computer simulation projects.
• Tracked federal budget cycle and analyzed annual DOD POM, for impacts to programs of interest to US Strategic Command.

Python is a programming language used for a variety of tasks, including data analysis and statistical analysis. Operations research analysts use Python to develop systems, like online user profiling systems, and to analyze data. They also use it to perform in-depth text analysis and to verify instrument settings. As Dr. Jackie Gallagher, Associate Professor and Chair of the Department of Geography at the University of Mary Washington, states, "GIS skills are incredibly valuable. our undergraduate certificate, especially if it includes Python programming language, is very valuable."

• Developed an online user profiling system in Python
• Created Python scripts to analyze data collected from testing log files and to verify instrument settings.

4. Statistical Analysis

Statistical analysis is the process of analyzing data to extract useful information. Operations research analysts use statistical analysis to solve business problems. They use it to analyze information, develop practical solutions, and detect trends. They apply statistical analysis to operational assessments, testing and development of software systems, and cost research. They also use it to write test reports, provide training, and identify patterns in complex problems.

• Completed statistical analysis of the initial design verification process to further the development of an advanced energy surgical product.
• Traveled to various company locations to train users in newly developed programs for tracking financial consolidations and statistical analysis.

C++ is a programming language that allows developers to create applications, systems software, and games. Operations research analysts use C++ to develop software for various tasks, such as scheduling paper machines in real-time systems. They also use it to develop software for other purposes, such as in Visual Studio Net, alongside other languages like Visual Basic and C#.

• Developed software using Visual Studio Net in Visual Basic, C#, and C++.
• Developed and implemented real-time systems to schedule paper machines in C++.

6. Data Analysis

Data analysis is the process of collecting, organizing, and making sense of information. Operations Research Analysts use data analysis to develop predictive models, identify solutions to management problems, and study the effects of new technology. They also use it to improve operational processes, study financial performance, and create customer-specific reports. As John Lyden , Professor and Chair of Religious Studies at the University of Nebraska - Omaha, puts it, "Data analysis and interpretation will be highly valued" in this field.

• Design and implement the data analysis processors for all simulation-based studies in air & missile and chemical-biological defense analyses.
• Performed data analysis and developed predictive models based on historical data in support of program and management objectives.

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7. data collection.

Data collection is the process of gathering information or data from various sources. Operations research analysts use data collection to improve internal reporting, integrate case management systems, and manage databases. They also use it to analyze and report data, monitor test execution, and provide training and direction for large-scale data collection efforts.

• Worked cross-functionally to improve the quality of internal reporting by systematically integrating data collection into the case management system.
• Entered configuration file/instruction data into C4I operating systems and test platforms directing data reporting and data collection processes.

8. Visualization

Visualization is the process of creating images, diagrams, or animations to communicate information. Operations research analysts use visualization to show correlation intensities, create dashboards and reports, and deliver competitive intelligence reporting. They also apply visualization techniques to effectively communicate high-level metrics with various departments. As Dr. Glenn Whitehouse , Associate Dean and Associate Professor at Florida Gulf Coast University, puts it, "Learn data visualization applications like Tableau. Anyone can learn what the buttons do, but not everyone understands the principles of an effective presentation. Apply your skills of reflective thought to the use of your new workplace skills, and you'll have a leg up over a lot of your peers."

• Designed visualization components to show correlation intensities with Crystal Reports.
• Used different business intelligence and data visualization tools such as Spitfire and Excel to create dashboards and reports.

Matlab is a programming language used for numerical computation and data analysis. Operations research analysts use Matlab to create charts and data for clients, simulate industry-standard figures, calibrate models, and develop algorithms for various applications. For example, they might use Matlab to analyze video data or advance laboratory research protocols.

• Constructed Matlab scripts to output requested charts and data for client specific basis.
• Utilized enhanced firm-specific software in conjunction with MATLAB to simulate industry-standard figures.

10. Statistical Methods

Statistical methods are techniques used to analyze data. Operations research analysts use these methods to solve problems, identify areas for improvement, and develop solutions. They apply these methods to data analysis, predictive modeling, and policy documentation analysis. They also use statistical methods to present their findings to program managers.

• Develop and evaluate alternative solutions to problems using statistical methods/software and present to Program managers using power point.
• Apply scientific and statistical methods to analyze processes, identify improvement areas, and develop solutions.

Math is the study of numbers and shapes. Operations research analysts use math to model and test complex systems. They use advanced financial math to analyze annuity policies and complete necessary tests. They also apply basic math to prepare daily audits and weekly spreadsheets. As Professor Susan Barton of Brigham Young University Hawaii's Faculty of Math and Computing puts it, "With a bachelor's degree in Mathematics or Applied Mathematics, the starting salary and various types of career prospects are very good... each of these careers has a predicted growth rate that exceeds 22% through 2026."

• Utilized advanced financial math skills to mathematically model annuity policies for regression testing of annuity administration system.
• Experienced in the use of basic math in preparing team tool box daily audits and weekly spread sheets.

12. Emerging Technologies

Emerging technologies are new and developing innovations that have the potential to improve performance. Operations research analysts use these technologies to enhance capabilities, provide direction, and uncover trends. They also evaluate their application to improve warfighter capabilities against WMD. As Travis Grosser Ph.D. , Associate Professor of Management at the University of Connecticut School of Business, puts it, "Understanding emerging technologies like artificial intelligence, blockchain/Web3, and data analytics will be increasingly valuable."

• Evaluated the application of emerging technologies and concepts to enhance warfighter capabilities against WMD.
• Serve as subject matter expert to provide direction and oversight in issues, emerging technologies, and coordination for action items.

13. Mathematical Models

Mathematical models are tools used to understand and analyze complex systems. Operations research analysts use these models to make predictions and decisions. They apply them to various aspects of operations, like cost allocation, decision-making, and policy-making. They also use mathematical models to identify problems and develop solutions, such as optimizing schedules or predicting budget expenditures.

• Performed analysis for major phases of varied systems using mathematical models, instrumentation, modeling, and simulation.
• Applied quantitative analysis and Six Sigma practices to mathematical model the daily operations and formulated min-cost algorithms.

SQL, or Structured Query Language, is a programming language used to manage and manipulate data in relational database management systems. Operations research analysts use SQL to query databases, extract and prepare data for analysis, and create reports. They also use SQL to integrate data from different sources, clean data, and develop models. For example, they might write SQL code to extract information from a historical airline database for use in an optimization model, or use SQL to extract data for analysis in a locomotive assignment project.

• Developed SQL code to query the Bureau of Transportation Statistics historical airline database and prepare the data for the optimization model.
• Developed Extract Transform Load (ETL) procedures to integrate many data sources with SQL Server Integration Services (SSIS).

15. PowerPoint

PowerPoint is a software application used for creating slide show presentations. Operations research analysts use PowerPoint to prepare and present clear and concise updates and reports to high-level executives. They also use it to create training manuals and written reports for customer requirements.

• Prepared clear and concise PowerPoint project updates and presentations for high level executives.
• Documented the implementation of IGS (PowerPoint).

5 Operations Research Analyst Resume Examples

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What soft skills should all Operations Research Analysts possess?

Pooyan Kazemian Ph.D.

Assistant Professor of Operations , Case Western Reserve University

What hard/technical skills are most important for Operations Research Analysts?

What skills stand out on operations research analyst resumes, what operations research analyst skills would you recommend for someone trying to advance their career.

Amir Ali Ahmadi

Professor of Operations Research and Financial Engineering and Director of the Certificate Program in Optimization and Quantitative Decision Science , Princeton University

List of operations research analyst skills to add to your resume

The most important skills for an operations research analyst resume and required skills for an operations research analyst to have include:

• Operations Research
• Statistical Analysis
• Data Analysis
• Data Collection
• Visualization
• Statistical Methods
• Emerging Technologies
• Mathematical Models
• Linear Programming
• Scientific Methods
• Project Management
• Analytical Support
• Analyze Data
• Cost Analysis
• Analytical Methods
• Program Management
• Technical Reports
• Risk Analysis
• Systems Analysis
• Technical Expertise
• Software Development
• National Security
• Prototyping
• Process Improvement
• Cost Estimates
• Operational Test
• Simulation Models
• Test Methods
• Test Results

Updated June 25, 2024

Editorial Staff

The Zippia Research Team has spent countless hours reviewing resumes, job postings, and government data to determine what goes into getting a job in each phase of life. Professional writers and data scientists comprise the Zippia Research Team.

Operations Research Analyst Related Skills

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• Client Services Analyst Skills
• Co-Operations Internship Skills
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• Document Analyst Skills
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Operations Research Analyst Related Careers

• Business Analyst
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Operations Research Analyst Related Jobs

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Top 12 Operations Research Analyst Skills to Put on Your Resume

In the competitive field of operations research, having a robust set of skills on your resume can significantly enhance your job prospects. This article outlines the top 12 skills that operations research analysts should showcase to stand out in the job market and excel in their careers.

Operations Research Analyst Skills

Python is a high-level, versatile programming language widely used for mathematical and statistical analysis, including optimization, simulation, and data manipulation, making it an essential tool for operations research analysts.

Why It's Important

Python is crucial for an Operations Research Analyst because it offers powerful libraries (such as NumPy, Pandas, and SciPy) for data analysis, optimization, and simulation, enabling efficient problem-solving and decision-making processes.

How to Improve Python Skills

To improve your Python skills as an Operations Research Analyst, focus on mastering libraries and tools that are essential for data analysis, optimization, and simulation. Here's a concise guide:

Learn Key Libraries : Focus on NumPy for numerical computations, Pandas for data manipulation, Matplotlib and Seaborn for data visualization, and SciPy for scientific computing. Additionally, Pyomo or PuLP can be crucial for linear and integer programming.

Practice Problem-Solving : Engage with platforms like LeetCode or HackerRank to enhance your algorithmic thinking and problem-solving skills using Python.

Real-World Projects : Apply your knowledge on real-world datasets available on platforms like Kaggle . Participate in competitions or work on personal projects related to operations research.

Stay Updated and Network : Follow Python and Operations Research communities on platforms like Stack Overflow , Reddit , and LinkedIn to stay updated on the latest trends and connect with professionals.

Continuous Learning : Enroll in specialized online courses from platforms like Coursera , edX , or Udacity that offer courses tailored to Python for data science and operations research.

By focusing on these areas, you'll significantly enhance your Python skills and apply them effectively in Operations Research projects.

How to Display Python Skills on Your Resume

R is a programming language and software environment used for statistical analysis, data visualization, and modeling, widely utilized by operations research analysts for data-driven decision making and optimization.

R is important for an Operations Research Analyst because it provides a powerful and flexible statistical analysis toolkit for data manipulation, statistical modeling, and visualization, essential for optimizing complex systems and making data-driven decisions.

How to Improve R Skills

Improving your R skills, particularly for an Operations Research Analyst, involves a combination of enhancing your programming proficiency, statistical knowledge, and understanding of OR-specific packages. Here’s a concise guide:

Master the Basics : Ensure a strong foundation in R syntax, data structures (vectors, matrices, data frames), and basic operations. The R for Data Science book is an excellent starting point.

Learn Tidyverse : The Tidyverse collection of packages, including ggplot2 for data visualization, dplyr for data manipulation, and tidyr for data tidying, is crucial for efficient data analysis in R.

Statistical & Mathematical Modeling : Deepen your understanding of statistical methods and mathematical modeling relevant to operations research. The CRAN Task View on Optimization and Mathematical Programming is a valuable resource.

Simulation & Optimization : Learn to use R for simulation and optimization, core activities in operations research. Packages like simmer for discrete-event simulation and ompr for mathematical programming can be very useful.

Advanced Data Analysis : Enhance your skills in advanced data analysis techniques, including time series analysis, forecasting, and machine learning. The Forecasting: Principles and Practice book is an excellent resource for forecasting.

Practical Application : Apply your skills to real-world operations research problems. Kaggle offers datasets and competitions that can provide practical experience.

Continuous Learning : Operations research and R are both rapidly evolving fields. Stay updated by following blogs like R-bloggers and R Weekly , and participating in forums like Stack Overflow and RStudio Community .

Networking and Collaboration : Engage with the community through conferences, meetups, and online forums. The useR! conference is an annual event bringing together R users from various disciplines.

Contribute to Open Source : Consider contributing to R packages relevant to operations research or developing your own. This can improve your coding skills and understanding of the underlying methodologies.

Professional Development : Pursue advanced courses and certifications. Platforms like Coursera , edX , and DataCamp offer courses specifically designed for data analysis and operations research using R.

By following these steps and continuously practicing, you can significantly improve your R skills as an Operations Research Analyst.

How to Display R Skills on Your Resume

MATLAB is a high-level programming and numerical computing environment used for algorithm development, data analysis, visualization, and numerical computation, which can support Operations Research Analysts in modeling, simulation, and optimization tasks.

MATLAB is important for an Operations Research Analyst because it offers powerful computational capabilities, extensive libraries for optimization, simulation, and data analysis, enabling efficient formulation and solving of complex mathematical models to support decision-making processes.

How to Improve MATLAB Skills

To improve MATLAB skills for an Operations Research Analyst, follow these concise steps:

Master the Basics : Start with MATLAB Onramp for a quick, interactive introduction ( MATLAB Onramp ).

Learn Specific Toolboxes : Focus on toolboxes relevant to Operations Research, like Optimization Toolbox ( Optimization Toolbox ) and Statistics and Machine Learning Toolbox ( Statistics and Machine Learning Toolbox ).

Practice Real-World Problems : Apply your skills to solve problems on platforms like Project Euler ( Project Euler ) or Kaggle ( Kaggle ) to gain practical experience.

Collaborate and Share : Use MATLAB Central ( MATLAB Central ) to share your work, get feedback, and learn from the community.

Stay Updated : Keep up with the latest features and functions through MATLAB’s release notes ( Release Notes ) and blogs ( Blogs ).

Utilize Online Courses and Tutorials : Platforms like Coursera ( Coursera ) and edX ( edX ) offer courses specifically designed for MATLAB and its applications in Operations Research.

Practice Coding : Consistent practice is key. Challenge yourself with new problems and try to write efficient, clean code.

By following these steps and regularly challenging yourself with new problems, you can significantly improve your MATLAB skills in the context of Operations Research.

How to Display MATLAB Skills on Your Resume

SQL (Structured Query Language) is a standardized programming language used for managing and manipulating relational databases, enabling operations research analysts to efficiently query, update, and analyze data stored in a structured format.

SQL is crucial for an Operations Research Analyst as it enables the efficient retrieval, manipulation, and analysis of vast datasets, facilitating data-driven decision-making and optimization strategies.

How to Improve SQL Skills

Improving your SQL skills, especially as an Operations Research Analyst, involves enhancing your ability to write efficient queries, understand complex database structures, and utilize advanced SQL features for data analysis. Here’s how to do it:

Master the Basics : Ensure you have a strong foundation in SQL syntax, including SELECT statements, WHERE clauses, JOINs, GROUP BY, and ORDER BY. W3Schools SQL Tutorial is a great place to start.

Learn Advanced SQL Features : Familiarize yourself with window functions, common table expressions (CTEs), and recursive queries. The PostgreSQL documentation provides comprehensive insights into these advanced features.

Optimize Query Performance : Understand indexing, query execution plans, and how to analyze and optimize SQL queries for better performance. Use The Index, Luke offers excellent guidance on indexing and SQL performance.

Practice Regularly : Apply your skills on real-world datasets or through platforms like HackerRank and LeetCode that offer SQL challenges.

Stay Updated and Network : SQL standards and database technologies evolve, so follow blogs, forums, and participate in communities such as Stack Overflow and Reddit’s r/SQL .

Learn from Real-world Scenarios : Analyze case studies or tutorials that focus on operations research and SQL, which can provide context-specific insights beneficial for your role. Towards Data Science often features relevant case studies and examples.

By systematically enhancing your SQL skills through these steps, you can significantly improve your data manipulation, analysis capabilities, and overall effectiveness as an Operations Research Analyst.

How to Display SQL Skills on Your Resume

Tableau is a powerful data visualization tool used by Operations Research Analysts to analyze, visualize, and share insights from complex data sets, facilitating data-driven decision-making.

Tableau is important for an Operations Research Analyst because it enables efficient data visualization and analysis, facilitating the discovery of insights and trends that inform decision-making and strategy optimization in operational contexts.

How to Improve Tableau Skills

To enhance your Tableau skills as an Operations Research Analyst, focus on mastering advanced data visualization techniques, integrating complex data sources, and leveraging Tableau's analytics capabilities for deeper insights.

Master Advanced Visualization Techniques : Learn to create complex and insightful visualizations that go beyond basic charts and graphs. This includes mastering the use of parameters, dynamic filters, and calculated fields to make interactive dashboards. Tableau Training offers comprehensive guides and tutorials.

Integrate Complex Data Sources : Work on integrating disparate data sources into Tableau to create a unified view of your analysis. This might involve using Tableau Prep or connecting Tableau directly to databases or real-time data streams. Tableau Data Integration provides resources and best practices.

Leverage Tableau’s Advanced Analytics : Dive into Tableau’s advanced analytics features like forecasting, clustering, and statistical functions to extract deeper insights from your data. This can significantly enhance your operations research analyses. The Tableau Analytics blog section offers insights and examples.

Automation and Scripting : Automate repetitive tasks and integrate custom scripts using Tableau’s APIs for more efficient workflows. This can save time and allow for more complex analyses. The Tableau Developer Program provides resources to get started.

Participate in Community and Training : Engage with the Tableau Community and participate in Tableau Public to learn from others. Continuous learning through Tableau Public and Tableau User Groups can provide new ideas and techniques.

Certification and Continuous Learning : Consider obtaining a Tableau certification to validate your skills and stay updated with the latest features and best practices. Tableau Certification details the process and resources needed.

By focusing on these areas, you can significantly improve your Tableau skills, enabling you to deliver more value in your role as an Operations Research Analyst.

How to Display Tableau Skills on Your Resume

Excel is a spreadsheet software developed by Microsoft, used for data analysis, modeling, and visualization, essential for Operations Research Analysts to solve complex problems, optimize processes, and support decision-making.

Excel is crucial for an Operations Research Analyst because it provides powerful tools for data analysis, modeling, and visualization, enabling efficient problem-solving and decision-making processes.

How to Improve Excel Skills

To improve your Excel skills as an Operations Research Analyst, focus on mastering advanced functions, data analysis tools, and optimization techniques:

Advanced Excel Functions : Enhance your proficiency with complex formulas and functions such as INDEX-MATCH , INDIRECT , and array formulas. ExcelJet offers a comprehensive guide to advanced functions here .

PivotTables and Data Analysis : Develop expertise in using PivotTables for summarizing, analyzing, and presenting your data. Microsoft provides a step-by-step tutorial on PivotTables here .

Solver and Optimization : Familiarize yourself with Excel's Solver tool for solving optimization problems, crucial for operations research. Learn how to use Solver here.

VBA Programming : Gain skills in VBA to automate repetitive tasks and develop custom functions for unique analysis requirements. Chandoo.org provides an introduction to Excel VBA here .

Power Query for Data Transformation : Utilize Power Query to efficiently import, transform, and automate the preparation of data. Microsoft's guide to getting started with Power Query is available here.

Power BI for Visualization : Explore Power BI for advanced data visualization and reporting. Although it's a separate tool, it integrates well with Excel. Start learning Power BI here .

By focusing on these areas, you'll significantly enhance your Excel skills relevant to operations research, making you more efficient in data analysis, optimization, and decision-making processes.

How to Display Excel Skills on Your Resume

CPLEX is a high-performance mathematical optimization solver that enables Operations Research Analysts to solve linear programming, mixed-integer programming, and quadratic programming problems efficiently to optimize operational decisions and resource allocation.

CPLEX is important for an Operations Research Analyst because it provides a powerful and efficient tool for solving large-scale linear, integer, and mixed-integer optimization problems, enabling the analyst to find optimal solutions to complex decision-making issues efficiently.

How to Improve CPLEX Skills

Improving CPLEX performance involves several strategies focused on model formulation, parameter tuning, and computational environment optimization. Here's a concise guide:

Model Formulation :

• Simplify Model : Reduce the model size by removing non-essential variables and constraints. Simplification Techniques
• Use Strong Formulations : Implement tighter constraints and stronger formulations to reduce the feasible region. Formulation Techniques

Parameter Tuning :

• Automatic Tuning Tool : Utilize CPLEX's parameter tuning tool to find the best parameter settings for your specific model. Parameter Tuning
• Select Appropriate Parameters Manually : Experiment with parameters that control branching strategies, cut generation, and heuristics. Parameters Reference

Computational Environment :

• Parallelism : Configure CPLEX to use multiple cores effectively to speed up computations. Parallel Optimization
• Distributed Computing : For extremely large problems, consider using distributed algorithms available in CPLEX. Distributed MIP

Algorithmic Choices :

• Presolve and Probing : Activate presolve settings to reduce problem size and complexity before solving. Presolve Settings
• Select the Right Algorithm : Depending on your problem type (LP, MILP, QP, etc.), ensure you're using the most efficient algorithm. Algorithms in CPLEX

Benchmarking and Analysis :

• Performance Profiling : Use CPLEX's solution progress and performance analysis tools to identify bottlenecks. Solution Progress
• Comparative Benchmarks : Test your model on different settings and compare performance to identify optimal configurations. Benchmarking

Improving CPLEX's performance is an iterative process that involves understanding your model deeply, experimenting with various settings, and leveraging CPLEX's extensive suite of tools and options for optimization.

How to Display CPLEX Skills on Your Resume

Gurobi is a powerful mathematical optimization solver that supports a wide range of problem types, including linear programming (LP), mixed-integer linear programming (MILP), and more. It is designed to help Operations Research Analysts find optimal solutions to complex decision-making problems efficiently.

Gurobi is important for an Operations Research Analyst because it provides a powerful, high-performance optimizer for solving complex linear, integer, and quadratic programming problems, enabling efficient decision-making and strategic planning across various industries.

How to Improve Gurobi Skills

To improve your Gurobi performance for Operations Research applications, follow these concise strategies:

Model Simplification : Simplify your model by removing unnecessary constraints and variables. Focus on the most influential factors to reduce complexity. Gurobi Modeling Basics

Use of Warm Start : Initialize your model with a feasible solution if available, to help Gurobi find the optimal solution faster. Warm Start Guide

Parameter Tuning : Adjust Gurobi's parameters to optimize performance for your specific model. Use the Parameter Tuning Tool to automate this process. Parameter Tuning Tool

Parallel Computing : Leverage parallel computing capabilities by enabling multi-threading, if supported by your hardware, to reduce computation time. Parallel Computing

Cutting Planes and Heuristics : Enable or adjust settings for cutting planes and heuristics to improve solution times for integer programs. Cutting Planes

Presolve : Utilize Gurobi's presolve to reduce model size before the optimization starts, which can significantly improve performance. Presolve

Stay Updated : Always use the latest version of Gurobi, as each release often includes performance improvements and new features. Gurobi Downloads

Consult Gurobi Support and Documentation : For complex issues, consult Gurobi's comprehensive documentation and consider reaching out to their support team for advice tailored to your specific problem. Gurobi Support

By implementing these strategies, you can significantly improve the performance of Gurobi for your Operations Research models.

How to Display Gurobi Skills on Your Resume

SAS (Statistical Analysis System) is a software suite used for advanced analytics, multivariate analysis, business intelligence, data management, and predictive analytics, commonly utilized by Operations Research Analysts for data analysis and statistical modeling.

SAS (Statistical Analysis System) is crucial for Operations Research Analysts because it provides powerful tools for data analysis, statistical modeling, and predictive analytics, enabling them to extract actionable insights, optimize processes, and make data-driven decisions in complex operational environments.

How to Improve SAS Skills

Improving your SAS (Statistical Analysis System) skills as an Operations Research Analyst involves enhancing your ability to manipulate data, perform complex analyses, and interpret results to support decision-making processes. Here's a concise guide to help you get started:

Online Courses and Tutorials : Enroll in SAS-specific courses to strengthen your understanding of basic and advanced functionalities. Websites like Coursera and LinkedIn Learning offer comprehensive courses designed for various expertise levels.

Practice with Real Data : Apply your skills on real-world datasets available at repositories like the UCI Machine Learning Repository or Kaggle . This hands-on approach will help you understand practical challenges and solutions.

SAS Official Resources : Utilize SAS Support and SAS Communities for documentation, user forums, and insightful discussions. These platforms are valuable for troubleshooting and learning best practices from the global SAS user community.

Certification : Consider obtaining SAS certification to validate your skills and knowledge. The SAS Certification Program offers various credentials that can enhance your resume and professional standing.

Networking : Join SAS user groups or online forums, such as the SAS Community , to connect with other professionals. Sharing experiences and solutions can provide new perspectives and insights.

Stay Updated : The field of analytics is rapidly evolving. Keep abreast of the latest SAS features, techniques, and industry trends by reading related blogs, attending webinars, and participating in conferences.

By following these steps and consistently applying yourself, you will significantly improve your SAS skills, contributing to your effectiveness and efficiency as an Operations Research Analyst.

How to Display SAS Skills on Your Resume

SPSS (Statistical Package for the Social Sciences) is a powerful statistical software tool used for data analysis, manipulation, and visualization, particularly valuable for operations research analysts in making data-driven decisions and forecasting.

SPSS is important for an Operations Research Analyst because it provides sophisticated statistical analysis tools, enabling them to efficiently analyze large datasets, derive insights, and support decision-making processes with empirical evidence.

How to Improve SPSS Skills

To enhance your proficiency with SPSS as an Operations Research Analyst, focus on the following strategies:

Enhance Your Statistical Knowledge : Deepen your understanding of statistical methods relevant to operations research. Books like "Discovering Statistics Using IBM SPSS Statistics" by Andy Field offer a comprehensive guide. Andy Field's Guide

Master SPSS Syntax : Learn to write and manipulate SPSS syntax for more efficient data manipulation and analysis. The SPSS Syntax Reference Guide, available through the IBM Documentation , is an invaluable resource.

Utilize Online Tutorials and Courses : Platforms like Coursera and Udemy offer courses tailored to enhancing SPSS skills. Coursera's Data Analysis and Interpretation Specialization is a good starting point.

Join SPSS Forums and Communities : Engage with other operations research analysts and SPSS users in forums such as the IBM SPSS Community to exchange knowledge and solve common challenges.

Practice on Real Datasets : Apply your skills on real-world datasets available on repositories like the UCI Machine Learning Repository ( UCI Repository ) to gain practical experience.

Stay Updated : Keep abreast of new features and updates in SPSS by regularly visiting the IBM SPSS Software Updates page.

By focusing on these areas, you’ll significantly improve your SPSS skills, making you a more effective and efficient Operations Research Analyst.

How to Display SPSS Skills on Your Resume

Simul8 is a discrete event simulation software tool used by Operations Research Analysts to model, analyze, and optimize the operations of complex systems or processes, enabling data-driven decision-making by predicting system behavior under various scenarios.

Simul8 is important for an Operations Research Analyst as it provides a powerful simulation tool for modeling, analyzing, and optimizing complex systems and processes, enabling data-driven decision-making and efficiency improvements.

How to Improve Simul8 Skills

To improve Simul8 for Operations Research Analysts, consider the following concise strategies:

Enhance Your Understanding : Deepen your knowledge of Simul8's capabilities through official tutorials and webinars to leverage advanced features effectively.

Use Visual Logic : Master Visual Logic to customize simulations with scripting for complex scenarios, enabling more precise modeling and analysis.

Optimize Parameters : Apply experimentation features within Simul8 for parameter optimization, ensuring the most efficient process configurations.

Integrate Data Sources : Integrate external data sources for real-time input and validation. Learn how to connect databases here.

Leverage Plug-Ins : Explore and incorporate plug-ins to extend functionality or integrate with other software tools for a comprehensive analysis environment.

Community Engagement : Engage with the Simul8 community through forums for tips, tricks, and troubleshooting, enhancing your mastery of the tool.

Training and Certification : Consider official training and certification programs to improve proficiency, ensuring optimal use of Simul8 for complex operations research analysis.

By focusing on these areas, Operations Research Analysts can significantly enhance their proficiency and effectiveness in using Simul8 for simulation and analysis tasks.

How to Display Simul8 Skills on Your Resume

Arena is a discrete-event simulation software tool that allows Operations Research Analysts to model and analyze complex systems and processes in order to optimize performance and make informed decisions.

Arena is important for an Operations Research Analyst because it provides a powerful simulation tool that enables the modeling, analysis, and optimization of complex systems and processes, facilitating data-driven decision-making and operational efficiency improvements.

How to Improve Arena Skills

Improving Arena, a discrete event simulation software, involves the application of operations research (OR) techniques to enhance model accuracy, efficiency, and usefulness. Here's a concise guide:

Model Validation and Verification : Ensure your model accurately represents the real-world process. Use historical data for validation and perform rigorous testing for verification. Arena Validation and Verification

Optimization Integration : Incorporate optimization algorithms to find the best configuration of variables. Consider using Arena's OptQuest or linking external OR tools like MATLAB or Python for complex optimizations. OptQuest for Arena

Input Data Analysis : Use statistical analysis to understand and model input data correctly, applying tools like ExpertFit to enhance the accuracy of your input distributions. ExpertFit

Output Data Analysis : Analyze simulation outputs using statistical methods to ensure results are significant and applicable. Utilize Arena's output analyzers or external statistical software like R for in-depth analysis. Arena Output Analyzer

Scalability and Parallelization : For large-scale simulations, consider strategies to improve model scalability, such as simplifying the model where possible or using parallel processing techniques. Parallel Simulation

Sensitivity Analysis : Conduct sensitivity analyses to understand the impact of input variables on the output. This helps in identifying critical variables that affect the system performance. Sensitivity Analysis

Continuous Learning and Training : Stay updated with the latest Arena features and best practices in simulation modeling through training and workshops. Arena Training

By applying these operations research strategies, you can significantly improve the performance and utility of your Arena simulation models.

How to Display Arena Skills on Your Resume

Related Career Skills

• Operations Analyst
• Sales Operations Analyst
• Business Operations Analyst
• Research Analyst
• Market Research Analyst
• Marketing Research Analyst

CAREER PATHWAYS

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How to Become an Operations Research Analyst

By Ibrahim Okunade

Published: March 25, 2024

Intrigued by numbers, problem-solving, and optimizing processes to make impactful decisions?

If your answer to this question is yes, the role of an operations research analyst might perfectly suit you. This guide explores the data-driven world of operations research analysts, diving into their diverse skill sets, the industries they serve, and the potential career opportunities available.

Career Summary

Operations research analyst salary.

Variables like an analyst’s level of education, years of experience, geographic location, industry, and the size and reputation of the employing organization affect the salary of research analysts.

As per Glassdoor , the salary breakdown for operations research analysts is as follows:

• Entry Salary (US$75k)
• Median Salary (US$95k)
• Executive Salary (US$121k

Operations research analysts surely belong to the category of high-income earners , considering the fact that the national average income for US citizens is $61,900 .

Operations Research Analyst Job Description

An operations research analyst is responsible for using advanced analytical techniques to solve complex problems and optimize processes within various industries. Their primary task involves collecting and analyzing data, formulating mathematical models, and applying optimization methods to provide data-driven insights and recommendations.

By identifying inefficiencies and proposing improvements, operations research analysts play a crucial role in enhancing decision-making, streamlining operations, and maximizing resource utilization.

Operations Research Analyst Career Progression

• Entry-Level Operations Research Analyst: Assists senior analysts, handles basic research, and performs statistical analyses.
• Junior Operations Research Analyst: Takes on more responsibilities, working independently on smaller projects. They develop a deeper understanding of various optimization techniques and may contribute to designing and implementing analytical models.
• Senior Operations Research Analyst: Takes on more complex and strategic projects. They play a lead role in analyzing data, developing sophisticated mathematical models, and providing key insights to decision-makers.
• Lead or Principal Operations Research Analyst: Leads larger projects and oversees multiple initiatives. They collaborate closely with stakeholders from different departments to identify optimization opportunities and align solutions with organizational objectives. Lead analysts are key contributors to shaping the analytical direction of their organizations.
• Operations Research Manager or Director: Responsible for managing a team of analysts and overseeing the execution of projects. They also play a significant role in setting the overall analytical strategy and driving innovation within the organization.

• Opportunity to work in diverse industries.
• Continuous learning opportunities.
• The field offers highly competitive salaries.
• Multiple opportunities for career advancement.
• The field has a positive job outlook.
• Balancing multiple projects simultaneously can be demanding.
• Challenging communication with non-technical stakeholders.
• Continuous need to update skills due to rapidly evolving technology.
• Dealing with complex and ambiguous data.
• Occasional resistance to data-driven decision-making culture.

Useful Skills to Have as an Operations Research Analyst

• Mathematical Modeling
• Data and Statistical Analysis
• Optimization Techniques
• Decision Analysis
• Communication Skills
• Project Management

Popular Operations Research Analyst Specialties

• Supply Chain Optimization
• Revenue Management
• Healthcare Analytics
• Financial Modeling and Risk Analysis
• Decision Support Systems
• Market Research and Forecasting

How to become an Operations Research Analyst

Complete Your Education

The first step in your operations research analyst journey is to complete your education.

You can start by earning a bachelor’s degree in operations research or other relevant fields, such as data science, mathematics, or a related discipline. The specific coursework you take will depend on the program you are enrolled in. However, most programs will include courses in mathematics, statistics, computer science, and operations research.

Do I Need a Degree to Become an Operations Research Analyst?

Yes, you need a degree to become an operations research analyst . In most cases, a bachelor’s degree in operations research and other relevant fields is the barest minimum, as some job openings require applicants to possess graduate qualifications.

Some specialized roles may require a master’s degree or even a Ph.D. in operations research, data science, or business analytics for more.

How Long Does it Take to Get a Degree in Operations Research?

A student’s individual circumstances and the level of degree are some factors that impact the duration it takes to get a degree. The same holds true for operations research programs.

Here is a breakdown of the expected timeframe it takes to complete different types of operations research degrees:

• Bachelor’s Degree: A bachelor’s degree in operations research usually takes four years to complete . Students typically need to complete around 120 to 130 credit hours of coursework , which includes general education requirements, core Operations Research courses, and elective courses.
• Master’s Degree: A Master’s degree in Operations Research usually takes around two years to complete . The duration may vary based on whether the program is full-time or part-time. Master’s programs typically require 50 to 70 credit hours of coursework , including advanced operations research topics and potentially a thesis or capstone project.
• Ph.D. Degree: Pursuing a Ph.D. in Operations Research is a more research-intensive path and can take anywhere from four to six years or more to complete . The duration depends on factors such as the individual’s research progress and the complexity of the dissertation. Ph.D. programs typically involve coursework, comprehensive exams, and extensive research leading to the completion of a doctoral dissertation.

How Much Does it Cost to Get a Degree in Operations Research?

A student’s residency status (in-state vs. out-of-state), type of school (public vs. private), and degree type are some of the factors that determine the cost of getting your degree in operations research. Thus, the cost is not fixed.

According to College Tuition Compare , in-state students studying for undergraduate degrees could pay as low as $13,319 for their tuition and fee. The fee could be as high as $51,100 for out-of-state students. The tuition and fees for students pursuing graduate degrees in-state cost as low as $14,220. Out-of-state students could pay as much as $35,980 for their graduate degree in operations research.

It is equally important to factor in additional costs like the cost of living, textbooks, and other miscellaneous resources.

Can I Become an Operations Research Analyst Through Online Education?

Yes, you can become an operations research analyst through online education . Online education has evolved significantly, and many reputable universities now offer fully accredited online programs in fields like operations research, data science, mathematics, and related disciplines. These online programs provide a flexible and convenient way for individuals to acquire the necessary skills and knowledge required for a career as an operations research analyst.

What are Some Web Resources to Learn Skills to Become an Operations Research Analyst?

As a data-driven field, things evolve and change quickly in the field of operations research. This is why it is important to keep up with new developments through digital channels. Several web resources offer valuable courses, tutorials, and materials to learn the skills needed to improve as an operations research analyst. These resources cover topics such as optimization techniques, mathematical modeling, data analysis, and more.

Here are some reputable web resources to get you started:

• INFORMS (Institute for Operations Research and the Management Sciences) : I NFORMS offers various resources, including webinars, tutorials, and conference presentations, which can be valuable for learning about the latest advancements and applications in operations research.
• The Operational Research Society : The Operation Research Society is a community that supports professional operational researchers across industries and academia. The website helps operations research analyst broaden their knowledge and also helps them stay updated with current trends in the field.
• Analytics Vidhya : While not specifically focused on operations research, Analytics Vidhya offers a vast collection of tutorials, articles, and resources on data science, machine learning, and optimization techniques relevant to operations research analysts.
• O’Reilly Data Show Podcast : The O’Reilly Data Show Podcast explores the opportunities and techniques driving big data and data science. It is useful to both aspiring and experienced data professionals, providing valuable insights that inspire innovation and problem-solving. Through in-depth interviews with leading experts and researchers, the podcast offers diverse perspectives and approaches to tackling complex data challenges.

Complete Additional Training

Operations research analysts need to be proficient in quantitative analysis, mathematical modeling, statistical methods, and data analysis. Learn data analysis techniques and programming languages commonly used in the field, such as Python , R , or MATLAB . Proficiency in these tools allows you to work with large datasets, clean data, and perform statistical analysis.

You should also familiarize yourself with optimization methods like linear programming, integer programming, dynamic programming, and other algorithms used to optimize systems and processes.

Gain Practical Experience

With the array of skills learned so far, the next step is to try your hands on real-life projects. There are two major ways to do this. You can either seek internship positions or work on research projects related to operations research. You can do this during your academic years or while transitioning into the field professionally.

Research projects can be an excellent way to deepen your understanding of specific operations research methodologies and explore niche areas within the field. Collaborating with professors or industry mentors on research initiatives hones your analytical abilities and equips you with the experience of formulating research questions, conducting experiments, and interpreting results.

This practical experience exposes you to real-world problem-solving, allowing you to apply your analytical skills in practical scenarios and work with actual data.

What Are Internship Opportunities for an Operations Research Analyst?

Internships provide valuable hands-on experience, exposure to real-world problem-solving, and an opportunity to showcase your skills to potential employers. They can be a significant stepping stone to launch your career as an operations research analyst and pave the way for future job opportunities within your preferred industry or sector.

Internship opportunities for an operations research analyst can be found in various industries and organizations that require analytical problem-solving and optimization skills. This includes consulting, technology, government, finance, manufacturing, retail , transportation, and healthcare.

During these internships, you could be involved in various tasks, such as data analysis, strategic planning, financial modeling, production optimization, supply chain management, or patient care process enhancement.

When searching for internships , utilize job platforms, career websites, and your university’s resources. Networking, both in-person and online, can uncover valuable opportunities. If you’re interested in a specific organization, don’t hesitate to contact them directly. Before applying, tailor your resume to the role and create a compelling cover letter.

Remember, the goal of an internship is not just to get work experience, but to learn and grow in your chosen field. Look for opportunities that align with your career goals and interests.

What Skills Will I Learn as an Operations Research Analyst?

As an operations research analyst, you gain a versatile skill set to expertly analyze data, optimize processes, and provide valuable insights for informed decision-making. This role nurtures diverse competencies vital for addressing complex challenges and driving efficiency across different domains.

Here are some key skills you will learn and enhance in this role:

• Mathematical Modeling and Optimization Techniques: You will learn how to construct mathematical models to represent real-world problems, whether they involve optimizing resources, scheduling tasks, or allocating budgets. You will also learn various optimization methods, such as linear programming, integer programming, dynamic programming, and heuristic algorithms, to find the best solutions to complex problems.
• Data Analysis and Interpretation: Analyzing and interpreting data is a core aspect of the role. You will learn how to work with data, clean it, and extract valuable insights to support decision-making.
• Decision Analysis: Operations research analysts assess and evaluate potential decisions under uncertainty. You will learn how to apply decision theory and risk analysis to make informed choices.
• Computer Programming : Learning programming languages like Python, R, or MATLAB will allow you to implement and automate your analytical models and conduct data analysis efficiently. In addition, familiarity with specialized software and tools used in Operations Research, such as Gurobi , CPLEX , or Excel Solver , is crucial for effective analysis and optimization.
• Quantitative Problem-Solving: You will become adept at tackling complex problems and breaking them down into solvable components, applying quantitative and analytical methods to reach optimal solutions.
• Communication Skills: While your core skills help you to tackle complex problems, your communication skills will help you present the information clearly. Therefore, operations research analysts must be able to effectively communicate their findings and recommendations to both technical and non-technical stakeholders.
• Project Management: In some cases, operations research analysts work on projects from conception to implementation. You will gain project management skills to coordinate and execute analytical projects effectively.
• Critical Thinking: Critical thinking is an important skill for an operations research analyst. Developing strong critical thinking abilities allows you to approach problems from various angles and devise innovative solutions.

Balancing Work and Life as an Operations Research Analyst

The work-life balance of operations research analysts can differ based on various factors. They typically work in office settings, and some may have the option to work remotely, which could provide a better work-life balance. However, their work-life balance can fluctuate depending on project demands. During busy periods or tight deadlines, they might need to work extra hours to complete tasks, but they may experience more flexibility when projects are less intense.

The industry and sector they work in also influence their work-life balance. Some industries may have busier periods, while others may offer more predictable schedules.

The workload and company culture also plays a significant role. Organizations that prioritize employee well-being may offer more flexibility and benefits promoting work-life balance. The level of autonomy and time management skills can also affect how much control they have over their work-life balance.

Experience and career level matter too. Junior analysts may have more structured schedules and limited decision-making authority, while senior-level analysts with more experience may enjoy a bit more autonomy.

Overall, achieving a satisfactory work-life balance is possible for operations research analysts, provided they prioritize their well-being and work in organizations with a positive work culture.

Earn Additional Certifications (optional)

While not always mandatory, obtaining additional certifications can be beneficial for operations research analysts. These certifications can enhance their skills, demonstrate expertise in specific areas, and make them more competitive in the job market. The relevance and necessity of certifications depend on the industry, job requirements, and individual career goals.

Here are some certifications that operations research analysts may consider:

• Certified Analytics Professional (CAP) : Offered by the Institute for Operations Research and the Management Sciences (INFORMS), CAP certification validates expertise in analytics and demonstrates proficiency in data-driven decision-making.
• Certified Data Professional (CDP) : Offered by the Institute for Certification of Computing Professionals (ICCP), this certification validates expertise in data management and data governance.
• Six Sigma Certifications : Six Sigma is a quality improvement methodology that uses statistical methods to identify and eliminate defects in processes. It is a valuable tool for operations research analysts because it can help them to improve the efficiency and effectiveness of their organizations.

Before pursuing any certification, you should assess your career goals, the industry’s demand for specific certifications, and how the certification aligns with your skill set. Additionally, some employers may offer support or incentives for obtaining certifications, so it’s worth considering the potential benefits both for professional development and career advancement.

What’s the Career Outlook for Operations Research Analysts?

As per the U.S. Bureau of Labor Statistics , there’s a promising forecast for operations research analysts, with a projected job growth of 23% between 2021 and 2031. This expansion rate significantly surpasses the average for all other U.S. occupations. Moreover, it’s estimated that about 10,300 new opportunities for operations research analysts will emerge annually over this ten-year period.

This reflects a robust job market and ample opportunities for individuals seeking to enter or advance in the field of operations research. The increased reliance on data-driven decision-making and the need to optimize processes across various industries are driving the demand for operations research analysts.

As organizations strive to enhance efficiency and make well-informed choices, skilled analysts who can provide valuable insights through data analysis and optimization techniques are highly sought after.

With such positive job prospects and a diverse range of industries to choose from, aspiring operations research analysts can look forward to a rewarding and promising career path in the coming years.

What are the Job Opportunities for an Operations Research Analyst?

Operations research analysts have a wide range of job opportunities across various industries. Their expertise in analyzing data, optimizing processes, and providing valuable insights makes them valuable assets in different domains.

Here are some common job opportunities for operations research analysts:

• Supply Chain Analyst: Supply chain analysts work on optimizing supply chain operations, including inventory management, distribution, and logistics, to enhance efficiency and reduce costs.
• Financial Analyst : Operations research analysts in finance focus on portfolio optimization, risk management, and investment decision-making using mathematical modeling and statistical analysis.
• Healthcare Analyst: In the healthcare sector, analysts use operations research techniques to optimize patient flow, resource allocation, and healthcare delivery processes.
• Marketing Analyst: Marketing analysts leverage data analysis and optimization methods to improve marketing campaigns, customer segmentation, and pricing strategies.
• Transportation Analyst: Transportation analysts focus on optimizing transportation routes, scheduling, and logistics to enhance transportation efficiency and reduce expenses.
• Government Analyst: Operations research analysts in government agencies work on policy analysis, resource allocation, and decision-making to improve public services and operations.
• Energy Analyst: In the energy sector, analysts use operations research techniques to optimize energy distribution, resource planning, and demand forecasting.
• Quality Analyst: Quality analysts use operations research techniques to optimize quality control processes and improve product or service quality.
• Revenue Management Analyst : Revenue management analysts focus on optimizing pricing and revenue strategies for businesses in industries like airlines and hospitality.
• Risk Analyst: Risk analysts use operations research methods to assess and manage risks in various industries, including finance and insurance.
• Environmental Analyst: Environmental analysts apply operations research techniques to address environmental challenges and optimize sustainability efforts.

Their versatile skill set allows operations research analysts to contribute to diverse sectors and tackle complex challenges across industries. Their ability to make data-driven decisions and improve efficiency makes them valuable assets in today’s data-centric and highly competitive business landscape.

What Type of Organizations Hire Operations Research Analysts?

Operations research analysts are sought after by a wide range of organizations that value data-driven decision-making, process optimization, and problem-solving. They are crucial in improving efficiency, reducing costs, and enhancing decision-making in various industries. So, what type of organizations can you work in as an operations research analyst?

Here are some of them:

• Consulting Firms: Management and strategy consulting firms hire operations research analysts to provide data-driven insights and optimize processes for their clients across different industries.
• Technology Companies: Technology companies use operations research analysts to optimize algorithms, improve user experiences, and enhance various operations, such as supply chain management and resource allocation.
• Manufacturing and Industrial Companies: Manufacturing and industrial organizations employ operations research analysts to optimize production processes, inventory management, and distribution networks.
• Financial Institutions: Banks, investment firms, and insurance companies hire these professionals to improve risk management, portfolio optimization, fraud detection, and customer analytics.
• Healthcare Organizations: Hospitals, healthcare providers, and pharmaceutical companies utilize operations research analysts to optimize patient flow, resource allocation, and healthcare delivery.
• Government Agencies: Federal, state, and local government agencies employ operations research analysts for policy analysis, resource allocation, and process optimization in various public services.
• Transportation and Logistics Companies: Transportation companies, logistics providers, and airlines need the expertise of operations research analysts to optimize routes, schedules, and fleet management.
• Retail and E-commerce Companies: Retailers and e-commerce platforms also need the expertise of operations research analysts to optimize inventory management, pricing strategies, and supply chain operations.
• Energy and Utility Companies: Energy providers and utilities employ operations research analysts to optimize energy distribution, resource planning, and demand forecasting.
• Aerospace and Defense Companies: Aerospace and defense organizations utilize Operations research analysts to optimize complex projects, resource allocation, and logistics.

In addition to these organizations, operations research analysts also work in academia. They are typically suited to roles that require a holistic analysis of data to make decisions.

Should I become an Operations Research Analyst?

Whether or not you should become an operations research analyst is a personal decision. However, if you are considering this career path, you should peruse the information in this guide and assess a typical operations research analyst job description to understand the requirements of the job.

Operations research analysts use mathematical models and statistical analysis to solve complex problems in different industries. They work with data to identify inefficiencies and develop solutions that improve efficiency and effectiveness. The job of an operations research analyst can be challenging and demanding, but it can also be very rewarding. If you are interested in a career that combines analytical thinking, problem-solving, and creativity, then operations research may be a good fit for you.

Finally, explore the industries and organizations that hire operations research analysts. This will give you an idea of the diverse opportunities available and the potential for growth and career advancement.

Careers Related to Operations Research Analyst

• Business Analyst
• Data Analyst
• Financial Analyst
• Management Analyst
• Statistician

About the Author

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Ultimate Guide for Operations Research Analysts

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Many problems in the world can be solved by applying mathematical concepts. People with an innate knack for solving complex math problems and who are computer savvy are well-suited for a career as an operations research analyst.

Operations research analysts observe business practices and fine-tune every minute detail of a corporation to provide solutions on operating in the most efficient and cost-effective manner. Companies make significant investments to acquire data, but need specialists to transform the data into actionable steps. These analysts save businesses time and money.

What is an Operations Research Analyst?

Operations research analysts can be employed within a wide range of fields, including business, logistics, healthcare, and insurance companies. Military veterans might work for the Department of Defense. Common job titles include data or applied scientists, research engineers, and derivative dealers.

Some uses of operations research include scheduling tv programs to maximize viewers, product placement in grocery stores, pricing airline tickets, creating routes for delivery companies or truck drivers, and managing financial portfolios to reduce risk of loss. Tech giants such as Amazon, Apple, Facebook, and Google depend heavily upon operations research. These companies gather large quantities of information about their users and and their day-to-day logistics to offer more personalized and efficient services and products.

Operations research analysts must be adept at analyzing data utilizing a variety of mediums, such as analytical software programs, simulations, and predictive modeling. Data is then used to create algorithms to predict behavior patterns.

Job responsibilities that fall under the operations research umbrella include setting up price points to maximize profits, inventory management, and supply chain logistics. The operations research analyst typically works in an office setting, and commonly collaborates with the sales and engineering departments. Operations research analysts may also spend time in the field observing work processes, interviewing individuals involved in every step of a job process, as well as gain insight into solving problems from experts.

The median salary in 2019 for  operations research analysts  was $84,810. The top 10 percent earned an annual salary exceeding $140,790, and the bottom 10 percent earned wages less than $48,670. Operations research analysts working for the government sector were amongst the highest earners and also had the greatest job security. In contrast, those employed in the finance and insurance sector yielded lower median wages.

Beyond a higher than average salary, there are numerous other advantages of being an operations research analyst. U.S. News and World Report ranks operations research analysts 4th among Best Business Jobs, based on factors such as anticipated career growth, stress level, and the ability to maintain a work-life balance. Additionally, operations research analyst positions were ranked amongst the Best STEM Jobs and the Best 100 Jobs.

Many decisions are made in a single day at a large corporation. Making rash or emotion-driven decisions could be very costly to a company, but operations research analysts prevent these mistakes from happening. An operations research analysts will carefully evaluate the positive and negative implications of each possible choice while considering available resources, the cost of adding manpower, and purchasing additional materials.

In order to achieve these goals, mathematical equations are used to predict the desired outcome. For example, McDonald’s and other fast-food restaurants have updated their menus to offer healthier choices for consumers, such as smoothies and salads. The expanded menu selections required packaging for salads and special smoothie blenders.

Leading up to these changes, the operations research analysts might be tasked with researching the cost of these materials, holding focus groups to determine the likelihood consumers would purchase these items, conduct tastings, and price items according to similar competitors on the market. They would research how long the salads will stay fresh and might test them in a handful of sample stores throughout the nation to predict inventory needs. The test run can cut down on waste caused by overstocking and needing to throw away expired products.

McDonald’s also offers the option of ordering through an automated machine in the restaurant’s lobby. This cut the need for some employees, but the machines had to be purchased and have to undergo routine maintenance and occasionally need repairs. The operations research analyst would create mathematical formulas analyzing cost versus savings. It is important for operations research analysts to be confident and persuasive presenters, because they will present findings to clients or the corporation’s executive decision-makers.

Most positions are full-time roles, but some consulting projects are also available. Employees typically work traditional office hours from 9 AM-5 PM, but overtime may be needed when approaching deadlines or if a client requires a quick turnaround for a project. Travel may also be required to meet with clients or to ensure work processes are handled the same at multiple branches of a company. After gaining sufficient experience, professionals may open a consulting firm, leading to a more flexible schedule and greater control over which projects to accept.

What is the History of Operations Research Analysis?

During World War I, the term “ operations research ” was coined by the British military. With shortages during war times, it was critical to track and ration food and supplies, and strategically place medics and troops. The British assigned these roles to scientists. Shortly after, the private sector also recognized a need for operations research. Car manufacturers such as Ford and other corporations utilizing assembly lines began to see the value of OR.

Operations research was introduced as a field of academic study by universities in the 1940s. Advanced math courses began to emphasize practical applications to solve complex problems, and courses would emphasize the use of machinery. In fact, Harvard taught courses on the use of milling machines.

In the 1960s, courses shifted to service management and service operations, as banks and electrical companies began to employ operations research analysts. Classes in the 1980s and 1990s were concentrated on digital technology and prepared students for innovative new roles at Yahoo and Microsoft. The curriculum has evolved and expanded over the years in order to remain relevant as new industries have recognized the prominence of OR.

How do Students Prepare to Become an Operations Research Analyst?

With the proper education, your future as an operations research analyst could take you anywhere. Companies are viewing their budgets from a much more strategic viewpoint and seeking ways to effectively cut non-essential costs. Since operations research analysts are needed in virtually every industry, there will continue to be a demand for this occupation in the foreseeable future. In fact, according to the Bureau of Labor Statistics, between 2018 and 2028, approximately 28,100 jobs will be added. This 26% increase is much higher than most careers.

While there are some entry-level positions for individuals holding a bachelor’s degree, applicants with a master’s or Ph.D. will have the best job prospects. Degrees specifically in operations research are rare but can be found at a handful of elite universities, including Cornell, Columbia, CUNY, Princeton, UC Berkley, the U.S. Coast Guard Academy, and the Air Force Institute of Technology.

Due to the limited availability of OR programs, it is common for students to earn a degree in Analytics, Computer Science, Mathematics, Business, Management Science, or Engineering. Many high schools also offer a STEM or Business and Industry endorsement, which will lay the groundwork for succeeding in college.

It is also imperative to properly prepare for interviews. Candidates are frequently challenged with brain puzzles during interviews and either given time limits to solve them or points based on how quickly they complete the puzzles. Chess and other analytical games can help candidates sharpen their cognitive skills and increase memory capacity. Candidates should also polish their LinkedIn profiles, and stay connected to the tech community through Twitter and other social media platforms.

Undergraduate

Students need a solid grasp on advanced-level math courses and should expect to take Statistics, Calculus, and Linear Algebra. Computer science is also an important part of the curriculum, as much of the data analysis is computed via database software. Some of the other classes undergraduate students commonly take, include Management Science, Sustainable Operations, and Project Contracting and Procurement.

Since operations research analysts work in such diverse fields, it’s also important to be well-rounded by taking courses in accounting, business, political science, economics, communication, and engineering. Candidates with a bachelor’s degree are well prepared to assume positions such as industrial engineers, logisticians, management analysts, market research analysts, and software developers.

Relevant graduate degree options include an MBA or a specialized master’s degree in Management Science , Computer Science , or Applied Statistics. Many of these programs allow students to tailor their degree with a concentration in an industry-specific sector, such as finance, insurance, technology, or science. Applicants to graduate programs are usually required to submit GRE or GMAT scores and have completed undergraduate coursework in quantitative analysis methods.

Courses will be research-heavy, but students who enjoy learning new information and exploring why things work the way they do will thrive. Some of the core courses you might take as a graduate student include Probability Theory, Deterministic Model, Stochastic Model, and Simulation. Electives are also offered in Data Mining, Financial Risk Management, Game Theory, and Pricing Models. Other important skills to develop include cryptography, computer programming, blockchain design, and numerical modeling. Graduates of master’s level programs are eligible for advanced positions as economists, mathematicians, and statisticians.

Ph.D. programs most desirable to operations research analyst recruiters include Computer Science, Physics, and Mathematics. Doctoral graduates are highly sought out for top-tier management positions, and some enter the world of academia.

Doctoral students interested in academia should become a quantitative research intern to learn how to work as part of a research team. SIG Susquehanna, one of the world’s largest quantitative trading firms, offers a 10-week internship for Ph.D. students . Key features of this robust internship include hands-on, proprietary trading practice under the guidance of a mentor, finance courses, small group breakout sessions, and industry lectures.

Education Format

Right now, COVID has affected OR programs at colleges and made online courses a necessity. Coursework will be delivered via interactive apps, simulation tools, and online projects. Since operations research analytics is a very specific niche, there are few options for earning a degree in that major online. By broadening your degree options to those in computer science, math, and management science programs, students can find suitable choices.

Columbia University, rated by U.S. News and World Report among Best Value Schools, offers an OR program with an active alumni group. The University of Southern California offers a flexible M.S. in Operations Research Engineering, which allows students to participate in real-time lectures, or students have the option to watch prerecorded lessons at a time convenient for their schedule. Carnegie Mellon University features an online, hybrid MBA with a concentration in Operations Research through the Tepper School of Business. This MBA requires six on-campus weekend residencies per year, so it’s best for students who already live in Pennsylvania or a nearby state.

Organizational Involvement

INFORMS has been providing guidance to  operations research analysts  to save lives, money, and solve problems for 25 years. This prestigious organization offers networking and learning opportunities through an annual four-day seminar-style INFORMS meeting and the INFORMS Business Analytics Conference, both of which feature a career fair.

Throughout the year, there are also numerous webinars, with topics ranging from business analytics initiatives to home buying using data-driven decisions.

Students and practitioners are invited to join and contribute to this vibrant professional community. Students can work in small groups to tackle a real-world problem utilizing data software in the INFORMS OR and Analytics Student Team Competitions. These teams present their projects to a panel of professional judges. Students can also be mentored by seasoned professionals. Professionals can distinguish themselves by earning a premier CAP certification and can give back to the community by providing pro bono analytics services to non-profit organizations.

Another great organization to join is the  American Production and Inventory Control Society . College chapters can be found spanning the nation, but professionals are encouraged to get involved too.

Professionals can bolster their career path by becoming Certified in Production and Inventory Management, Supply Chain Professional, Supply Chain Operations Reference, or Logistics, Transportation, and Distribution. Further learning enrichment includes virtual and in-person workshops on Sales and Operations Planning, Inventory Control, and Principles of Material Requirements Planning. This non-profit organization also offers volunteer opportunities, monthly social events, and a job database.

Operations research analysts interested in working for the military should consider joining the  Military Operations Research Society . Membership in this organization quickly pays off, with discounts to the annual symposium, special meetings, and Monday tutorials. Students have the opportunity to be mentored and compete at the annual Education and Professional Development Colloquium. Graduate students can earn prizes for their research contributions. Job seekers may land their first post-graduation position by posting their resume to the dynamic database accessible to industry employers. Special events are available for Junior Analysts, who are under 40 years old and have less than ten years of experience. Students can easily stand out amongst their peers by earning a certificate in Critical Skills for Analytical Professionals, Cyber Wargaming, Excel Functions for Data Analysts, or U.S. National Security Risk Analysis. Prior military experience will be helpful for operations research analyst professionals pursuing national defense positions but is not a prerequisite for most jobs.

Graduates of a bachelor’s program can attain entry-level positions and work their way up the ladder. Still, there are also ample opportunities to gain hands-on experience prior to graduation. College students and new employees may initially participate in extensive job shadowing of more experienced analysts before being turned loose with their own responsibilities.

A college capstone course gives students practical experience, and internships allow students to establish a network of professional contacts. Indeed currently has internship positions for a Technology Business Analyst with Warner Media, Data Analyst with Cisco Systems, and a Program Analyst Trainee with the U.S. Department of Justice. Some internships are currently offered in a virtual format.

What are the Future Trends in Operations Research Analysis?

Operations Research has undergone many changes since the beginning of the pandemic, but one thing that’s certain is OR is a stable field that’s here to stay.

It is more important now than ever for businesses to increase revenue and decrease costs, two skills that operations research analysts have mastered. Product demand has swiftly changed. Nobody could have predicted that toilet paper would become such a hot commodity in 2020, but on the other hand, many products are taking up shelf space in warehouses. OR leaders can assess inventory and create models to predict how to adjust production. These adjustments can ensure businesses are prepared for orders, but also able to maintain healthy cash flow.

Customers have cut their spending significantly. Therefore, consumer behavior studies are an important component in understanding current buying habits and strengthening customer relationships. Operations research analysts can stay up-to-date on the latest industry trends by subscribing to Analytics Magazine and OR/MS Today.

In the future, businesses can expect to automate more functions, such as scheduling and reminders. Operations research analysts will contribute to creating effective workplace tools. Social distancing measures associated with COVID have slowed down hands-on training for new hires, but operations research analysts have made virtual training programs accessible to more employees.

Jobs on the horizon include those emphasizing globalization, sustainability, communication, and system designs. The biopharmaceutical industry has also seen a major increase during COVID, and operations research analysts have become one of the most in-demand jobs in the field.

Technology Impacts

Since technology rapidly changes, it is essential for operations research analysts to stay up-to-date on the latest research platforms, software tools, and analytical methods by attending professional development sessions. IBM , Microsoft , Oracle Corporation , and the Institute for the Certification of Computing Professionals offer professional certificates such as Microsoft Dynamics 365, Oracle Linux and Solaris, and IBM Certified Systems Administrator.

The use of personal technology devices, including cell phones, tablets, and laptops, have hastened the speed of life. Amazon has made next day delivery the norm. Don’t have time to make dinner tonight? Place an order from the Grubhub app, and voila; problem solved in less than an hour! Operations research analysts must continuously find ways to help companies fulfill consumers’ needs more efficiently or face the consequences of losing customers to competitors. Technology has also made it easier to transmit and analyze data, creating a niche for OR.

The COVID pandemic has caused many businesses to have to creatively rethink their approach in order to stay afloat. Operations research analysts have been primed to handle these challenges.

Government and hospital operations research analysts acquired PPE and ventilators during a time when these vital materials were in short supply, and have significantly contributed to analyzing data regarding positivity rates, disease transmission, and effective contact tracing methods. Operations research analysts have also assisted restaurants as they transitioned to primarily take-out or curbside, as state laws reduced dining room capacity. Remote work opportunities have also increased, as employers have seen relatively high productivity levels from employees working from home.

Operations research analysts can maximize their potential career outlooks by learning as much as they can about collaboration tools, such as Microsoft Teams, Slack, and G Suites. Meetings will also likely be held through Zoom, Skype, and Google Hangouts, so knowing how to navigate these systems is essential.

There’s never been a better time to prepare for a career as an operations research analyst. Students should consider pursing degrees and courses in business, operations, industrial engineering, and analytics. Also, expanding their knowledge with classes outside of their major could help them be more well-rounded and increase their marketability during a tough economy. And with the wide array of online degrees and courses available, students are just a click away from furthering their potential.

For recent graduates who are having trouble securing a job, they should continue to perform relevant research and attempt to have it published in professional journals. They can also consider using this time to earn a higher degree, and buff up their resume with certifications and online continuing education courses. Continue applying for jobs, because there will be a resurgence in positions as the economy steadily improves. Those with recent contributions to the OR field will be swept up first by hiring managers.

Related Resources

Online Business Analytics MBA Ranking Online Master’s in Analytics Management

Operations Research Analyst

Career introduction.

An Operations Research Analyst is responsible for analyzing complex data to help organizations make better decisions. They use mathematical models and statistical analysis to identify problems, develop solutions, and improve overall efficiency. This job requires strong analytical skills, attention to detail, and the ability to communicate complex information to non-technical stakeholders.

What does an Operations Research Analyst do?

An Operations Research Analyst is responsible for analyzing complex data sets and developing mathematical models to help organizations make better decisions. They work in a variety of industries, including healthcare, finance, and government, and are often tasked with finding ways to optimize processes, reduce costs, and improve efficiency.

Using statistical analysis, simulation, and other techniques, they help organizations identify patterns and trends, forecast future outcomes, and evaluate the potential impact of different scenarios. In addition to technical skills, Operations Research Analysts also need strong communication skills to present their findings to stakeholders and collaborate with colleagues.

This challenging and rewarding career offers opportunities for growth and advancement, as well as the chance to make a real difference in the world.

Physicality

Abstraction, a day in the life of an operations research analyst.

An Operations Research Analyst spends their day analyzing complex data sets and creating models to help organizations make better decisions. They work closely with other professionals in their field, such as engineers and computer scientists, as well as business executives and managers.

The job requires excellent interpersonal skills as they must communicate their findings and recommendations to stakeholders in a clear and concise manner. They may also collaborate with other departments to identify areas for improvement and implement solutions.

The social aspect of the job is crucial as they must build relationships with clients and team members to ensure that projects run smoothly. Overall, the Operations Research Analyst role is a dynamic and challenging career path that requires both technical expertise and strong social skills.

Is being an Operations Research Analyst hard?

Operations research analysts are professionals who use mathematical and analytical methods to help organizations make better decisions. They work in a variety of industries, including finance, healthcare, and government. While the job may seem challenging, it is also highly rewarding.

Operations research analysts must have strong critical thinking skills, be able to work well under pressure, and have a deep understanding of mathematics and statistics. However, with the right education and training, anyone can become an operations research analyst.

It is a career that offers great potential for growth and advancement, as well as the opportunity to make a real difference in the world. So, if you're looking for a challenging and fulfilling career, consider becoming an operations research analyst.

Operations Research Analyst vs similar Professions?

Operations research analysts use advanced mathematical and analytical methods to help organizations solve complex problems and make better decisions. While this career path shares some similarities with other professions such as data analysts and management consultants, it is unique in its focus on optimization.

Operations research analysts are specifically trained to identify the most efficient and effective ways of achieving organizational goals, and they use a wide range of tools and techniques to do so. This can involve everything from statistical modeling and simulation to linear programming and decision analysis.

Ultimately, the goal of an operations research analyst is to help their clients or employers make data-driven decisions that lead to better outcomes and improved performance.

Is becoming an Operations Research Analyst something for you?

The best way to find out is to take our personality-career test. It only takes 20 minutes and will help you decide whether to change careers or start a career.

1st Edition

Operations Research Methods, Techniques, and Advancements

• Taylor & Francis eBooks (Institutional Purchase) Opens in new tab or window

Description

This era of science and engineering has attracted researchers tasked with evaluating performance and optimization of problems in the field of operations research. The book covers mathematical analysis, methods and applications involving processes such as system performance, optimization, inventory theory, reliability theory, and queueing theory. Operations Research: Methods, Techniques, and Advancements explores recent and innovative methods and advancements associated with the mathematical theory of operations research. It offers a detailed overview of mathematical modelling for general industrial systems and emphasizes the latest ideas for the benefit of society and the research community. Intended for a broad range of readers, this book is useful to academicians, industrialists, researchers, students, academia and specialists from various disciplines and those working in the industry.

Table of Contents

Amit Kumar is working as an Assistant Professor in the Department of Mathematics, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India.. He has been taught several core courses in pure and applied mathematics at undergraduate and postgraduate levels. He has done his Bachelors and Master’s form Chaudhary Charan Singh University Meerut, India, in 2006 and 2009 respectively. In 2016 he completed his Doctorate in Applied mathematics form Graphic Era (Deemed to be University), Dehradun Uttarakhand, India, in the field of Reliability theory. He published several research papers/ Books/ book chapters in various esteemed international journals/Books including Taylor & Francis, Springer, Emerald, World Scientific, InderScience and many other national and international journals of repute and also presented his works at national and international conferences. He is a reviewer of many international journals including Elsevier, Springer, Emerald, John Wiley, Taylor & Francis etc. His fields of research are Operations Research, Reliability theory, Fuzzy Reliability, and System Engineering. Mangey Ram received the Ph.D. degree major in Mathematics and minor in Computer Science from G. B. Pant University of Agriculture and Technology, Pantnagar, India. He has been a Faculty Member for around twelve years and has taught several core courses in pure and applied mathematics at undergraduate, postgraduate, and doctorate levels. He is currently a Research Professor at Graphic Era (Deemed to be University), Dehradun, India. Before joining the Graphic Era, he was a Deputy Manager (Probationary Officer) with Syndicate Bank for a short period. He is Editor-in-Chief of International Journal of Mathematical, Engineering and Management Sciences and the Guest Editor & Member of the editorial board of various journals. He is a regular Reviewer for international journals, including IEEE, Elsevier, Springer, Emerald, John Wiley, Taylor & Francis and many other publishers. He has published 200 plus research publications in IEEE, Taylor & Francis, Springer, Elsevier, Emerald, World Scientific and many other national and international journals of repute and also presented his works at national and international conferences. His fields of research are reliability theory and applied mathematics. Dr. Ram is a Senior Member of the IEEE, life member of Operational Research Society of India, Society for Reliability Engineering, Quality and Operations Management in India, Indian Society of Industrial and Applied Mathematics, member of International Association of Engineers in Hong Kong, and Emerald Literati Network in the U.K. He has been a member of the organizing committee of a number of international and national conferences, seminars, and workshops. He has been conferred with "Young Scientist Award" by the Uttarakhand State Council for Science and Technology, Dehradun, in 2009. He has been awarded the "Best Faculty Award" in 2011; "Research Excellence Award" in 2015; and recently "Outstanding Researcher Award" in 2018 for his significant contribution in academics and research at Graphic Era Deemed to be University, Dehradun, India.

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Master’s in Operations Research

WHAT IS OPERATIONS RESEARCH?

Operations research (OR) is the discipline of applying advanced analytical methods—such as optimization, statistics, machine learning, and probability—to make better decisions that impact society and the world positively.

Phone:  617-253-3601 Email:   [email protected]

MIT’s master’s degree (SM) program in operations research (OR) teaches you important OR techniques—with an emphasis on the practical, real-world applications of OR—through a combination of challenging coursework and hands-on research. 

In addition to the writing competency requirements, our rigorous curriculum includes seven graduate-level courses in such areas as optimization, applied probability, and statistics as well as advanced topics in OR that complement your academic interests and career goals. You must complete 66 credit units, of which at least 42 must be in advanced subjects, to earn your degree.

What’s more, you’ll put classroom theory into practice by writing a thesis based on independent research you’ve conducted under the guidance of your faculty advisor. Prior to graduation, you’ll present your research to your fellow ORC members.

Upon completion of our two-year program, you’ll be ready to put your knowledge and skills to good use in a variety of fields, including business, education, and research. In fact, recent graduates of our program have been sought after for such positions as technical staff members in business or industry, government planners, and private consultants.

For more information about the Master’s program, please see our Master’s Degree Syllabus .

Dual SM in OR

Students who are currently enrolled in another master’s degree (SM) program at MIT may consider pursuing a dual SM in OR. In order to pursue a dual masters degree in the OR program you would need to submit an application to the Center for consideration for admission. Once accepted into the program, you must be currently enrolled in another SM program at MIT in order to pursue the dual SM. The dual SM program requires that you complete coursework for both programs. Courses must be distinct and you cannot double count courses. For more information, please contact the ORC at 617-253-3601 or orc_staff​ @mit.edu .

For more information about ORC course offerings, please see our  Course Offerings page.

How to become an operations research analyst

Is becoming an operations research analyst right for me.

The first step to choosing a career is to make sure you are actually willing to commit to pursuing the career. You don’t want to waste your time doing something you don’t want to do. If you’re new here, you should read about:

Still unsure if becoming an operations research analyst is the right career path? Take the free CareerExplorer career test to find out if this career is right for you. Perhaps you are well-suited to become an operations research analyst or another similar career!

Described by our users as being “shockingly accurate”, you might discover careers you haven’t thought of before.

How to become an Operations Research Analyst

To become an operations research analyst, you typically need a combination of education, skills, and experience. Here are the general steps you can take to pursue a career as an operations research analyst:

• Education: Obtain a bachelor's degree in a relevant field such as operations research , industrial engineering , mathematics , statistics , computer science , or a related quantitative discipline. A strong foundation in mathematics, statistics, and computer programming is crucial. Consider taking courses or specializing in areas such as optimization, mathematical modeling, data analysis, and computer simulation.
• Gain Relevant Skills: Develop proficiency in programming languages such as Python, R, or MATLAB, as well as statistical analysis tools and optimization software. Acquire knowledge of mathematical modeling techniques, optimization algorithms, and data analysis methods. Familiarize yourself with operations research concepts, decision theory, and simulation techniques.
• Gain Practical Experience: Seek internships or co-op opportunities to gain practical experience in applying operations research techniques to real-world problems. This experience will help you develop skills in data analysis, modeling, and problem-solving, while also providing exposure to industry practices and tools.
• Pursue Advanced Degree (Optional): While not always required, obtaining a master's degree or higher in operations research or a related field can enhance your competitiveness and open up more advanced career opportunities. A graduate degree often provides in-depth knowledge, research experience, and exposure to specialized areas within operations research.
• Build a Strong Portfolio: Engage in projects, research, or case studies that demonstrate your proficiency in operations research techniques. Develop a portfolio showcasing your problem-solving skills, data analysis capabilities, and the ability to apply operations research methodologies to practical scenarios.
• Networking and Professional Development: Attend industry conferences, seminars, and workshops to network with professionals in the field. Join professional organizations such as INFORMS (Institute for Operations Research and the Management Sciences) and participate in their events and activities. Networking can provide valuable connections and insights into job opportunities.
• Job Search and Application: Explore job opportunities in industries such as logistics, consulting firms, healthcare, finance, government agencies, and manufacturing. Utilize job search platforms, industry-specific websites, and professional networks to find openings for operations research analysts. Tailor your resume and cover letter to highlight your relevant skills and experience. Prepare for interviews by reviewing operations research concepts, discussing your problem-solving approach, and demonstrating your ability to work with data and models.

Certifications There are several certifications for operations research analysts that can enhance their credentials and demonstrate their expertise in relevant areas.

• Certified Analytics Professional (CAP): Offered by the Institute for Operations Research and the Management Sciences (INFORMS), CAP is a widely recognized certification in the field of analytics. It validates your knowledge and skills in various analytics domains, including data management, modeling, and deployment.
• Professional Researcher Certification (PRC): Offered by the Marketing Research Association (MRA), PRC is a certification for professionals involved in market research and analytics. While not specific to operations research, it covers important aspects of data analysis, research design, and statistical methods.
• SAS Certified Advanced Analytics Professional: This certification by SAS Institute validates your proficiency in using SAS software for advanced analytics, including predictive modeling, optimization, and data mining. SAS is widely used in the analytics industry, and this certification demonstrates your expertise in utilizing SAS tools for operations research tasks.
• IBM Data Science Professional Certificate: Offered by IBM through online learning platforms like Coursera, this certificate program provides training in data science, including topics such as data analysis, machine learning, and data visualization. It showcases your understanding of data-driven decision-making and analytical skills.
• Microsoft Certified: There are several Microsoft certifications that can be relevant for operations research analysts. For example, the Microsoft Certified: Azure AI Engineer Associate certification demonstrates expertise in designing and implementing AI solutions, which can be applicable in the operations research field.

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• Undergraduate
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MSc Operations Research & Analytics

• Graduate taught
• Department of Mathematics
• Application code G2U1
• Starting 2024
• Home full-time: Limited availability
• Overseas full-time: Limited availability
• Location: Houghton Street, London

The MSc Operations Research & Analytics provides you with the skills needed to apply mathematical methods to real-world analytics problems faced by companies and decision-makers in finance, consulting, technology, healthcare and other sectors.

With study in practice and theory, you will gain insight into analytics problems. On the practical side, you will learn how to model a range of real-world problems using optimisation, stochastic simulation, machine learning, and statistics, using specialist software taught in tutorial sessions. On the theoretical side, you will learn to recognise canonical underlying mathematical problems, and how to solve them with state-of-the-art methods. Courses are taught by faculty members with world-leading research profiles, who can provide insights that will give you a deeper understanding and a competitive edge.

In the first term, you will learn about the fundamentals of operations research. In the second term, you can choose from a range of courses in mathematics, statistics, finance, and management. Course topics include algorithms and computation, optimisation, game theory, and a range of topics in machine learning and AI.

You will undertake a final project where, working in a consultancy role and using the tools you have learned in the degree, you will tackle a real problem faced by a partner organisation. Past and present partners include Amazon, BT, British Airways, Emirates Airlines, FICO, Ford Motor Company, Just Eat, Legal and General, the National Audit Office, and Transport for London. As an alternative to the project, more theoretically minded students can write a dissertation supervised by a faculty member.

The programme is designed for students wishing to deepen and broaden their mathematics knowledge, and gain skills in high demand in the marketplace.

Programme details

For more information about tuition fees and entry requirements, see the fees and funding and assessing your application sections.

Entry requirements

Minimum entry requirements for msc operations research & analytics.

An upper second class honours (2:1) degree in a relevant discipline (or equivalent). Students should normally have taken university courses including calculus, linear algebra, and statistics. Appropriate work experience will also be considered.

Competition for places at the School is high. This means that even if you meet our minimum entry requirement, this does not guarantee you an offer of admission.

If you have studied or are studying outside of the UK then have a look at our  Information for International Students  to find out the entry requirements that apply to you.

Assessing your application

We welcome applications from all suitably qualified prospective students and want to recruit students with the very best academic merit, potential and motivation, irrespective of their background.

We carefully consider each application on an individual basis, taking into account all the information presented on your application form, including your:

- academic achievement (including predicted and achieved grades) - statement of academic purpose - two academic references - CV

See further information on supporting documents

You may also have to provide evidence of your English proficiency, although you do not need to provide this at the time of your application to LSE.   See our English language requirements .

When to apply

Applications for this programme are considered on a rolling basis, meaning the programme will close once it becomes full. There is no fixed deadline by which you need to apply, however, to be considered for any LSE funding opportunity, you must have submitted your application and all supporting documents by the funding deadline. See the fees and funding section for more details. 

Fees and funding

Every graduate student is charged a fee for their programme.

The fee covers registration and examination fees payable to the School, lectures, classes and individual supervision, lectures given at other colleges under intercollegiate arrangements and, under current arrangements, membership of the Students' Union. It does not cover living costs or travel or fieldwork.

Tuition fees 2024/25 MSc Operations Research & Analytics

Home students: £29,472 Overseas students: £29,472

The Table of Fees shows the latest tuition amounts for all programmes offered by the School.

For this programme, the tuition fee is the same for all students regardless of their fee status. However any financial support you are eligible for will depend on whether you are classified as a home or overseas student, otherwise known as your fee status. LSE assesses your fee status based on guidelines provided by the Department of Education.

Further information about fee status classification.

Fee reduction

Students who completed undergraduate study at LSE and are beginning taught graduate study at the School are eligible for a  fee reduction  of around 10 per cent of the fee.

Scholarships and other funding

The School recognises that the  cost of living in London  may be higher than in your home town or country, and we provide generous scholarships each year to home and overseas students.

This programme is eligible for needs-based awards from LSE, including the  Graduate Support Scheme ,  Master's Awards , and  Anniversary Scholarships . 

Selection for any funding opportunity is based on receipt of an offer for a place and submitting a Graduate Financial Support application, before the funding deadline. Funding deadline for needs-based awards from LSE:  25 April 2024 .

This programme is also eligible for   Economic and Social Research Council (ESRC) funding  when you apply as part of a 1+3 research programme. Selection for the ESRC funding is based on receipt of an application for a place – including all ancillary documents, before the funding deadline.

Funding deadline for the ESRC funding:  15 January 2024.

In addition to our needs-based awards, LSE also makes available scholarships for students from specific regions of the world and awards for students studying specific subject areas.  Find out more about financial support.

Government tuition fee loans and external funding

A postgraduate loan is available from the UK government for eligible students studying for a first master’s programme, to help with fees and living costs. Some other governments and organisations also offer tuition fee loan schemes.

Find out more about tuition fee loans

Further information

Fees and funding opportunities

Information for international students

LSE is an international community, with over 140 nationalities represented amongst its student body. We celebrate this diversity through everything we do.  

If you are applying to LSE from outside of the UK then take a look at our Information for International students . 

1) Take a note of the UK qualifications we require for your programme of interest (found in the ‘Entry requirements’ section of this page). 

2) Go to the International Students section of our website. 

3) Select your country. 

4) Select ‘Graduate entry requirements’ and scroll until you arrive at the information about your local/national qualification. Compare the stated UK entry requirements listed on this page with the local/national entry requirement listed on your country specific page.

Programme structure and courses

You will take three compulsory courses and will choose courses from a range of options within the Department and across other relevant departments, including Management and Statistics. 

(* denotes half unit)  

Fundamentals of Operations Research * Introduces a range of Operations Research techniques including linear programming, the simplex method and duality, Markov chains, queueing theory and birth and death processes, inventory models and dynamic programming.

Modelling in Operations Research * Provides hands-on training in the art of converting real-world problems to optimisation and simulation models, inputting the models into specialist software, solving the optimisation problem or exercising the simulation model, and deriving applicable conclusions about the original problem.

Data Analysis and Statistical Methods * Studies common techniques of statistical inference, together with theoretical justification. The techniques are then applied to linear and logistic regression and basic time series models. Statistical software R constitutes an integral part of the course and provides hands-on experience of data analysis. 

Either Project in Operations Research & Analytics A project in a host organisation taking a consultancy role. Or Dissertation in Operations Research & Analytics An independent research project of 10,000 words on an approved topic of your choice.

Courses to the value of one and a half units from a range of options.

For the most up-to-date list of optional courses please visit the relevant School Calendar page .

You must note, however, that while care has been taken to ensure that this information is up to date and correct, a change of circumstances since publication may cause the School to change, suspend or withdraw a course or programme of study, or change the fees that apply to it. The School will always notify the affected parties as early as practicably possible and propose any viable and relevant alternative options. Note that the School will neither be liable for information that after publication becomes inaccurate or irrelevant, nor for changing, suspending or withdrawing a course or programme of study due to events outside of its control, which includes but is not limited to a lack of demand for a course or programme of study, industrial action, fire, flood or other environmental or physical damage to premises.

You must also note that places are limited on some courses and/or subject to specific entry requirements. The School cannot therefore guarantee you a place. Please note that changes to programmes and courses can sometimes occur after you have accepted your offer of a place. These changes are normally made in light of developments in the discipline or path-breaking research, or on the basis of student feedback. Changes can take the form of altered course content, teaching formats or assessment modes. Any such changes are intended to enhance the student learning experience. You should visit the School’s  Calendar , or contact the relevant academic department, for information on the availability and/or content of courses and programmes of study. Certain substantive changes will be listed on the  updated graduate course and programme information page.

Teaching and assessment

Contact hours and independent study.

Teaching will combine traditional lectures with seminars. Several of the courses, including two of the three compulsory ones, will involve training in a programming language or use of specialised computational tools. These parts of those courses will have accompanying computer lab sessions in which students will actively develop their programming skills by applying them to a range of problems in OR. Most courses on the degree are quantitative, but one optional course may, depending on your choice, study OR-related methods or applications from a qualitative perspective. During the summer, you are required to do either a project in Operations Research & Analytics or a Dissertation in Operations Research & Analytics. The project involves work in a host organisation (in business, government, health, or a social non-profit organisation), in a consultancy role, typically turning a real problem faced by the organisation into a mathematical model whose solution provides tangible benefit. You will be marked on a project report. The Dissertation requires study of an area of research, or an application of advanced techniques, and a report of findings. 

Within your programme you will take a number of courses, including half unit courses and full unit courses, to a total of 4 units. In half unit courses, on average, you can expect 35 contact hours in total and for full unit courses, 40-60 contact hours in total. This includes sessions such as lectures, seminars or workshops. Hours vary from course to course and you can view indicative details in the  Calendar  within the Teaching section of each  course guide .

You are also expected to complete independent study outside of class time. This requires you to manage the majority of your study time yourself, reading, thinking, solving problems, doing software exercise, and undertaking research.

Teaching methods

LSE is internationally recognised for its teaching and research and therefore employs a rich variety of teaching staff with a range of experience and status. Courses may be taught by members of faculty, such as assistant, associate, and full professors. Many departments now also employ guest teachers and visiting members of staff, LSE teaching fellows, and graduate teaching assistants who are usually doctoral research students and in the majority of cases teach on undergraduate courses only. You can view indicative details for the teacher responsible for each course in the relevant  course guide .

All taught courses are required to include formative coursework which is unassessed. It is designed to help prepare you for summative assessment which counts towards the course mark and to the degree award. LSE uses a range of formative assessment, such as essays, problem sets, case studies, reports, quizzes, mock exams and many others. Summative assessment may be conducted during the course or by final examination at the end of the course. An indication of the formative coursework and summative assessment for each course can be found in the relevant  course guide .

Academic support

You will also be assigned an academic mentor who will be available for guidance and advice on academic or personal concerns.

There are many opportunities to extend your learning outside the classroom and complement your academic studies at LSE.  LSE LIFE  is the School’s centre for academic, personal and professional development. Some of the services on offer include: guidance and hands-on practice of the key skills you will need to do well at LSE: effective reading, academic writing and critical thinking; workshops related to how to adapt to new or difficult situations, including development of skills for leadership, study/work/life balance and preparing for the world of work; and advice and practice on working in study groups and on cross-cultural communication and teamwork.

LSE is committed to enabling all students to achieve their full potential and the School’s  Disability and Wellbeing Service  provides a free, confidential service to all LSE students and is a first point of contact for all disabled students.

Student support and resources

We’re here to help and support you throughout your time at LSE, whether you need help with your academic studies, support with your welfare and wellbeing or simply to develop on a personal and professional level.

Whatever your query, big or small, there are a range of people you can speak to who will be happy to help.  

Department librarians   – they will be able to help you navigate the library and maximise its resources during your studies. 

Accommodation service  – they can offer advice on living in halls and offer guidance on private accommodation related queries.

Class teachers and seminar leaders  – they will be able to assist with queries relating to specific courses. 

Disability and Wellbeing Service  – they are experts in long-term health conditions, sensory impairments, mental health and specific learning difficulties. They offer confidential and free services such as  student counselling,  a  peer support scheme  and arranging  exam adjustments.  They run groups and workshops.  

IT help  – support is available 24 hours a day to assist with all your technology queries.   

LSE Faith Centre  – this is home to LSE's diverse religious activities and transformational interfaith leadership programmes, as well as a space for worship, prayer and quiet reflection. It includes Islamic prayer rooms and a main space for worship. It is also a space for wellbeing classes on campus and is open to all students and staff from all faiths and none.   

Language Centre  – the Centre specialises in offering language courses targeted to the needs of students and practitioners in the social sciences. We offer pre-course English for Academic Purposes programmes; English language support during your studies; modern language courses in nine languages; proofreading, translation and document authentication; and language learning community activities.

LSE Careers  ­ – with the help of LSE Careers, you can make the most of the opportunities that London has to offer. Whatever your career plans, LSE Careers will work with you, connecting you to opportunities and experiences from internships and volunteering to networking events and employer and alumni insights. 

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Philipp Loick - MSc Operations Research & Analytics 2017-18

Having a background in finance and economics, I aimed for a Masters programme where I could develop mathematical and programming skills to solve industry problems in operations research and data science. Enrolling in the Operations Research and Analytics programme at LSE was the right choice for this goal.

The programme features a diverse student body with the majority of students having majored in mathematics with some engineering and finance students. Even though only a one-year programme, the programme achieved a good balance between theoretical foundations and industry applications and allowed us to study topics such as combinatorial optimization, advanced statistics or algorithmic techniques for data mining.

The high academic level and relevance of the programme is due to the academic staff, who have excellent academic credentials, partially have worked for renowned industry companies and are well connected in the academic community. Graduating from the programme, I had an offer from BCG Gamma, the advanced analytics team of BCG, which I rejected for a PhD in discrete mathematics.

Alexander Saftschuk - MSc Operations Research & Analytics 2017-18

I came to the LSE with the main goal of improving my quantitative problem-solving skills, and subsequently landing a job in investment banking. The School and societies provided extremely good network opportunities, which really helped to land the job that I aimed at. After only two months at the LSE I landed a job offer with one of the top global investment banks. However, upon finishing the Operations Research & Analytics programme I quickly realised that I would rather pursue a career in data science, and once again the university's reputation opened doors for me last minute. Currently I work as a Data Analyst in the Telenor Digital data science team in Norway. There I code various machine learning algorithms in R, all of which I have all learned during this degree. 

Overall I can say that coming from a non-quantitative, business background I have learned more in this one-year Masters than I did in my entire three years of my bachelor degree. The programme was challenging but manageable. In particular, I highly appreciated how much face time I received from all of my professors, as well as the professor who supervised my thesis. The decision to come to the LSE and studying Operations Research & Analytics was one of the best I have made so far and I can highly recommend LSE and the degree. 

Kate Lavrinenko - MSc Operations Research & Analytics 2017-18

Studying this Masters was my third MSc, after studying Applied Mathematics and Economics, four years of experience in Economics and Finance, moving country, two kids, and four years at home with them. It was a challenging experience to find myself among young, inspired and able students from around the world. It also took some time to get used to the pace of study, and to network with people and share skills and knowledge. I needed some psychological help at the start of the journey and I had an opportunity to get it at LSE, which makes me feel grateful. 

I liked that the programme was flexible in what courses you could choose in order to make it fit your personal interests and academic goals. I encourage students to research and think hard about their course choices before starting the programme. Also, it is useful to have an understanding of which direction you wish to head in (e.g. academic or business) so you can utilise LSE’s resources properly. 

I found the careers events to be very valuable in my experience here. For example, I met a member of the Data Science team from Deloitte and after many rounds and following my MA425 Project there in the summer, I found myself with a full time job after finishing the course.

I enjoyed my journey, my job, and my experience with LSE. Whenever I get a new research heavy task, I start dreaming whether I could eventually turn it into a PhD, so my journey is not over.

Preliminary reading

You are not  required to do any preliminary reading in advance of this programme, but if you wish to read some material before arriving, we can make a few suggestions. 

If you do not have experience of  computer programming, you could learn the language R, which you will use in ST447 Data Analysis and Statistical Methods . Once you learn any language it is easy to learn others, and programming will be useful in your career. Programming will also give you a sense of what computers can and cannot do, that will be useful in all algorithmic courses. Good starting points are Introductory Statistics with R  by Peter Dalgaard, and the Coursera course .

Linear algebra plays a major role in several key courses and in the field of OR generally. It is expected that you are comfortable with the basic notions (linear independence, rank, determinants, solutions of systems of equations, eigenvalues and eigenvectors). These will not be reviewed in the course; you can review this material independently. There are many good textbooks to choose from; a suitable one is Linear Algebra by Martin Anthony and Michele Harvey. 

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• What Is Operations Research And Its Best Practices

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When I mention the word research, what comes to mind? For most people, research has a lot to do with a team of strict-looking intelligent individuals going over piles and piles of documentation, going to various places to obtain data, and undergoing numerous processes and activities to analyze the data they were able to gather.

That just goes to show how little we know about research. Chances are high that, even if you are knee-deep in the operations side of things in your job, day in and day out, you still don’t fully know or understand the concept of operations research, or what others also refer to as operational research. This is our opportunity to correct that.

WHAT IS OPERATIONS RESEARCH?

Decision-making is one of the most vital processes in management since decisions are made in order to achieve something. In the context of business management, managerial and organizational goals are what most managers seek to achieve. And that’s what they are driving towards with every decision they make.

But decision-making is not something to be taken lightly. Considering what’s being aimed for, and what’s at stake in the grand scheme of things, you can’t just randomly pick one among a few options. (Well, technically, you can do that, but considering the risks involved, especially if you end up making a wrong choice, you’d want to spend more time thinking about it and weighing your options before deciding.)

With that being said, describing decision-making as a process is accurate. You have to undergo several steps or phases before you can confidently arrive at a decision. The same thing applies with problem-solving. There are also several steps to pass through before you can get to a viable solution to a problem. There are certainly more than two ways to go about the decision-making and problem-solving process, and operations research is one of them.

Have you ever heard of the phrases “management science” and “industrial engineering”? They are terms that also apply to operations research. If you hear about management using business analytics, marketing analysis, logistics planning and even the more broad-sounding decision support, it is basically the application of operations research.

“Operations research”, or simply OR, is described as an analytical method of problem-solving and decision-making used in managing businesses or organizations. It involves the application of advanced quantitative techniques in order to arrive at a decision or solution to a problem, so we’re talking about using mathematical and numerical techniques here.

What sets OR apart from other types of decision-making processes is how mathematical analysis plays a central role. The identified problem is broken down into its most basic components, and mathematical methods and techniques are employed to solve them.

The application of OR is widespread. In fact, all businesses can never be completely free from having to apply OR in their own business environments, regardless of the nature of their business operations or the size and scale. Retail businesses and service providers, which have pretty much straightforward business processes, will still need OR in their decision making processes.

With that said, the application of OR is more necessary on the larger and more complex operations, such as companies involved in highly technical industries such as information technology, biotechnology, engineering, military operations, and telecommunications.

The fact that all businesses have to perform functions and processes such as financial planning , manpower and resource allocation, and risk management means that they all require the practice of OR.

Here are some examples of OR applied or used in a real-world business settings:

• Forecasting and planning , such as in the determination of production capacity, manpower and resources allocation, and establishing the economic order (and reorder) quantity.
• Scheduling , such as sequencing in a supply and procurement chain, or processing orders in manufacturing assembly line.
• Marketing , such as in customer profiling and implementation of sale promotions and other campaigns.
• Facility planning or layouting , such as when designing an online processing system or the floor plan of a manufacturing building.

THE IMPORTANCE OF OPERATIONS RESEARCH

If you’re wondering why there is a need for OR, the most obvious answer is in order to facilitate business decision-making. After all, OR is a huge part of planning.

The decision-making we are referring to in this specific context has to do with optimization. Therefore, we can say that OR is very important because it enables businesses to “do things best under the given circumstances”.

But that’s too broad of an answer, and does not really explain in detail why you should use OR in decision-making and problem-solving and in managing your business organization in general. Let’s drill in.

• OR simplifies the business environment . Now you might be wondering, how is that possible? Wouldn’t it be even more complicated if we threw in some mathematical elements in the mix? Well, yes and no, but that mainly depends on how you go about the OR process. In a business environment, numbers and figures often provide the most reliable information. Quantification gives more room for objectivity, so business decisions can be made objectively, since there numbers say so.
• OR maximizes the usefulness of data . Depending on the size of the business operations, there are a lot of data that have to be dealt with on a daily basis. Larger operations are faced with millions of bits of information, and going through each and every piece of data can be tedious, time-consuming and, therefore, counter-productive. Through the use of OR techniques and analytical methods, there is a way to handle all those volumes and volumes of data in significantly less time. Obviously, this will lead to being able to make better decisions, faster.
• OR aids in the optimization of resources . Resources are scarce, so businesses have to find ways to make the best use of the resources that are currently available to them, while ensuring that they are of high quality or, at least, with quality that meets the expectations of the end users.
• OR ensures effective and efficient delivery of products or services to the end users . By applying OR in decision-making, the process becomes more systematic, so that you are able to provide the high quality products or services to the customers or end users when and where they are needed. Having high-quality products will be of no use if you are unable to deliver them to the end users when you’re supposed to.

BEST PRACTICES IN OPERATIONS RESEARCH

Follow a systematic and logical approach. for that, we suggest the seven steps of or, which we will get into more detail below..

Other literature broadly described OR to involve only three steps. First, you identify the potential answers or solutions to the problem, and they will then be analyzed and narrowed down into the most feasible or viable options. The third step involves further analysis, this time using more specific analytical tools.

When we talk of OR, however, there is the “Operations Research Approach”, which is composed of seven sequential steps. Let us walk through it together.

The Seven-Step Operations Research Approach

This approach is represented in this  diagram . We’ll be taking a look at the activities involved in each step.

Step 1. Orientation

OR is not a one-man activity. It takes a team, with members equipped with various skills and specializations assigned with various tasks and functions, depending on their strengths or the areas they excel at. Therefore, there are two things that must be done in this step.

• Form the team that will conduct the OR study . Take into account the multifunctional nature of OR when choosing the members of the team. You want them to be qualified to conduct OR, so you don’t have to start pulling in just any random person from the other departments midway through the study for the simple reason that the existing members turn out to be unable to do the job. It is advised that the areas or divisions that are directly or even indirectly affected by, or related to, the OR be represented in the team. If the OR is on product design, you’d want to include the engineering, assembly and quality control divisions to be represented, along with someone from finance and marketing, specifically those that are involved in customer and market research. Install a team leader who will be able to steer the team in the right direction, and one with the ability to manage both the work and the members of the OR team.
• Ensure that all members of the team fully understand the issues at hand , specifically on the matter regarding the OR. What are they supposed to study, and what should they pay attention to? For what reason are they conducting this specific activity, and how will it benefit the organization? These are only a few of the primary questions that you must address before the members of the team so that they won’t be “flying blind”.

Bringing them into the loop will also motivate them to do the best they could in the OR study. It is also important that you are able to instill an appreciation within the team for the objectives of the activity and for what have been done so far (if there are).

Step 2. Problem Definition

Most processes – even the scientific method – puts this on the first step, and it is considered by most to be the most difficult part of the entire process, since it will set the tone for the rest of the activities or tasks that will follow. If you don’t know what the problem is, then you will simply be spinning your wheels and going nowhere.

If, on the other hand, you were able to identify a problem, but it’s not the actual or real problem, then you will also end up wasting a lot of time and resources, and you might even end up making the wrong decisions.

In defining the problem, you have to clearly identify its scope and the results that you desire or expect to have at the end. This time, you will be more specific. Instead of saying that you want to improve the company’s product design system, you will have a more targeted objective, such as “to lower the unit production cost of the product”.

Once you’ve identified the specifics, delve deeper into it.

• Identify the specific factors that will affect your objective , clearly distinguishing those that are within your control from those that are not, and determine all possible alternative courses of action that may be taken. Say that you want to lower the unit production cost of the product, so the factors may include the flexibility of product design, factors of production used (e.g. direct materials, direct labor, overhead).
• Identify the constraints on the courses of action . There are bound to be limits that all decision-makers in business have to operate within. It is possible that the nature of the product and even government regulations and legislation do not provide enough room for flexibility in product design. Availability of resources – especially the alternative resources should you decide to change some of the inputs into the product – is also another constraint.

Step 3. Data Collection

In this step, there are two things you should take note of before you can go about successful data collection. Of course, this is under the assumption that you already know what type of data you should collect.

• Sources of data. There are many identifiable sources of data, depending on the data type you need. Generally, we look to existing standards, such as current and historical trends and set values. Another source is the system or process that is being studied, particularly on how it works in actuality.
• Methods and tools for data collection. Observation remains to be one of the most commonly used methods of data collection and, thanks to automation and computerization, combined with the flexibilities brought on by the internet, data collection is greatly facilitated. What used to take businesses years to collect data and process it into valuable information is now doable in just a matter of hours, days even.

Step 4. Model Formulation

Modeling is what sets OR apart from other decision-making processes. Where other approaches would directly look into the system and analyze it, OR goes about it by formulating a model, or a representation of the system, and using that model for its analysis.

Modeling allows the researchers to simplify the system while maintaining its accuracy and faithfulness to the original. Besides, it is much easier – and less costly – to analyze the model instead of the actual system.

The team conducting OR may create different types of models, and there are four general types of models that are often formulated and employed.

• Analog models. These are models with physical properties that are significantly smaller than the actual system being studied, and having similar characteristics with the latter. These similarities make the model and the original analogous, even if they are not identical.
• Simulation models. This involves the approach where the behavior of individual elements within the system is mimicked or mirrored. In other words, a model of a real-life situation is created, and that’s where techniques such as sampling and experimentation, if need be, are conducted. This method is usually favored as it allows testing for future improvement. Through simulation, you can analyze even complex systems by coming up with estimates of statistical measures. Values are inputted and, with every replication, you can observe the response of the system. In this day and age, when technology plays a very important role in almost all businesses, computer simulation is often applied. This allows you to look for areas of improvement, specifically in an automated business environment.
• Mathematical models. OR is considered one of the many branches of mathematics, so do not be surprised when you find yourself having to apply many mathematical methods in your OR. Without going into the most intricate details, let us list down the various logical methods employed in OR, which were also cited by Springer . The preference for usage of mathematical models is how they effectively map out all the variables and describe their relationships with each other.
• Physical models. As the name implies, this is a tangible model, which is basically a copy of the original system, but scaled down appropriately. Unlike the analogic models, which are simply made to be analogous to the original system, these scaled down versions are smaller replicas of the original. Among the four model categories, this is the hardest to pull off, especially in the case of complex systems.

Step 5. Model Solution

This is where you will attempt to solve the problem; in other words, it’s the analysis stage. Needless to say, this is the part where the OR team will spend the most amount of time and resources, employing a variety of analytical methods and techniques on the models formulated in the previous step.

Briefly, the most commonly used techniques are:

• Simulation techniques, for the analysis of simulation models. These techniques often come part and parcel with several statistical techniques. That’s right. If you though that resorting to simulation will save you from dealing with numbers, you can’t fully get away from it, since statistical computations will still hound you.
• Mathematical analysis techniques , which dominantly utilizes statistical methods, such as regression analysis, variance analysis, queuing, and statistical inference.
• Optimization techniques , where you will try to determine the best values or the optimum levels that will affect decision-making. That involves the application of various mathematical programs and statistical methods. Mathematical programming techniques often used include linear and non-linear programming, integer programming and network theory.

At the end of this step, you should have obtained a solution, after considering the results of the analytical tasks you used.

Step 6. Validation and Output Analysis

Does the process end once you’ve identified the solution? No, it doesn’t. You still have to make sure that the model you used in your analysis is, indeed, an accurate representation of the system. This is the validation part.

And that’s not all. If you thought you’re done with the analysis bit, there’s still more analysis to be done. In this case, you’ll be going over various “what if” scenarios, where you will consider the possible outcomes if the solutions obtained are implemented.

Step 7. Implementation and Monitoring

Finally you settled on the best solution or recommendation and made a decision. It is time to implement that decision.

Of course, you need to still have control over the implementation, which is why there should be a team in place to be in charge of the implementation. It is highly recommended that you place some members of the OR team in the implementing team.

Monitoring is a must, since you want to ensure that the solution decided upon is the one actually being implemented. This is also a way to remain on your toes, since unforeseen circumstances might lead to some aspects of the solution needing some tweaking along the way.

Use only the relevant data.

Out of a million pieces of data, you’re probably going to need only a fraction of it. Wading through all that data may be all right with you, but SHOULD YOU? Think of the resources you will be wasting if you do that. It will also take a lot of time, which you can devote to other core functions, instead of poring over data that won’t have an impact – even if indirect – to the matter at hand.

One of the reasons that you use advanced analytical methods is so that you can maximize data and handle as much of it as you can at one time. But that does not mean that you should analyze 100% of the data, even if 50% of it are not relevant to the problem you are solving or the decision you are trying to arrive at.

But do not focus solely on the quantity of data; you also have to count quality. Having too much data is not the only problem; having poor quality data is also just as problematic. In fact, researchers prefer having a small amount of high quality data, instead of having too much data of poor quality or no relevance at all.

Maintain close collaboration between managers and the researchers.

A certain degree of independence is encouraged when it comes to the people directly conducting OR, or the researchers. This is so that they can maintain a level of objectivity in their analysis.

But that does not mean that they should be completely removed from the management. Management support is vital if you want your OR to be successful. After all, at the end of the day, it is the management that will make the decision, and will see to its implementation. By striking a partnership with the researchers, the process will be smoother.

In fact, it is recommended that researchers work alongside the managers, or those who are directly involved in the process being analyzed.

Establish policies or a framework for the conduct of OR.

One way to give OR a strong presence in the organization is to institutionalize it. How can you go about that?

• Create a policy framework, providing details about OR – even if they are couched in general terms – which will then serve as a guide for staff who will later on conduct research. This is also one way to impress on the members of the organization the important role of OR.
• Create a reference document containing the policies or the framework , and disseminate it to the members of the organization. It won’t make any sense if you have a framework, and it is well-documented, but it remains inside the office of select few members of top management.

Make Operation Research an integral part of your business processes.

In other words, do not treat it as just a minor function that you can just assign to whoever has free time. Research activities, in general, take time and certain level of commitment on the part of the researchers, so treating it as a throwaway task is not a good idea.

• Assign staff members to focus on OR. Some businesses, refusing to spend on OR, make research as an additional task for its managers. This may be workable, but if the managers are already overworked, chances are high that they won’t devote as much time and attention to the research side of things. Oh, and one other thing: do not forget to assign someone to manage the research activities carried out. Pick someone to lead the team, so as to maintain some supervision and cohesiveness in the unit.
• Specify a dedicated time to conduct research activities. Again, it’s not a good idea to request your staff to “do their research whenever they have free time or during breaks”, or even demand that they render overtime specifically for research activities. Doing that will only make OR seem like an afterthought, instead of the important business process that it actually is. Maybe you can schedule at least one day per week for staff to do their research. This way, they will also be able to maintain focus when they’re on the job.
• Make room in your budget for OR. Yes, you need funding to conduct research successfully. Conducting OR means you will have to spend on salaries and compensation of the researchers, and other expenses incidental to the conduct of the research activities.
• Pick the right people. You have to make sure that the researchers understand what they are supposed to do, and they have the skills required to carry out the research successfully. You may have to conduct some on-the-job trainings, if necessary.
• Equip your people with the right tools. Arm them with the things they need in conducting a successful OR. If you think they will benefit more by undergoing training and workshops on OR, then send them to those activities. And we’re also talking about providing the staff with the hardware they’d need to carry out the many OR techniques that they have to use.

At the end of the day, the best practice that a company can apply in operations research is to fully commit itself to actually doing it. OR is an indispensable process in managing a business, and you’d do well to keep that in mind, if you plan on taking your business to greater heights.

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Operation Research: Definition, Scope and Techniques

After reading this article you will learn about:- 1. Meaning and Definition of Operation Research 2. Phases in Operation Research Study 3. Scope 4. Characteristics 5. Methodology 6. Models 7. Techniques 8. Applications 9. Limitations.

Meaning and Definition of Operation Research :

It is the method of analysis by which management receives aid for their decisions. Though the name of this method, Operation Research (O.R.) is relatively new, but the method used for this is not a new one. Operation Research is concerned with the application of the principles and the methods of science to the problems of strategy.

The subject of operation research was born during Second World War in U.K., and was used for military strategy. During World War II, a group of scientists, having representatives from mathematics, statistics, physical and social sciences were entrusted to the study of various military operations. This team was very success­ful and greatly contributed to the meticulous handling of entire operation and related problems of the operation.

The need for assigning such studies for operations arose because military strategies and their decisions become so important and costly and therefore, the best scientists, under the sponsorship of military organs were grouped together to provide quantitative information’s by adopting scientific techniques and methods for facilitating in taking decisions.

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After the World War II, it was started applying in the fields of industry, trade, agriculture, planning and various other fields of economy.

The operation research can be defined as:

Definitions:

(i) It is the application of scientific methods, techniques and tools to problems involving the operations of a system so as to provide those in the control of the system with optimum solutions to the problems.

(ii) Operation Research is a tool for taking decisions which searches for the optimum results in parity with the overall objectives and constraints of the organisation.

(iii) O.R. is a scientific method of providing executive department with a quantitative basis of decisions regarding the operations under their control.

(iv) O.R. is a scientific approach to problem solving for management.

(v) O.R. is an aid for executive in making his decisions by providing him with the needed quantitative information’s based on the scientific method of analysis.

(vi) O.R. is the application of modern methods of mathematical science to complex prob­lems involving management of large systems of men, machines, materials, and money in industry, business, government and defence. The distinctive approach is to develop a scientific model of the system incorporating measurement of factors such as chance and risk, to predict and compare the outcome of alternative decisions, strategies or controls.

(vii) It is the application of the scientific methods by scientists and subject specialists to the study of the given operation. Its purpose is to give administration, a basis for predicting quantitatively the most effective results of an operation under given set of variable conditions and thereby to provide a sound basis for “decision-making”.

In fact in Operation Research, research techniques and scientific methods are employed for the analysis and also for studying the current or future problems. Thus, Operation Research offers alternative plans for a problem to the management for decisions.

Although it is very clear that operation research never make decisions for the man­agement, instead the method presents management with a careful scientific and quantitative analysis of problem so that the management will be in a better position to make sounder deci­sions.

It can be used for solving different types of problems, such as:

i. Problems dealing with the waiting line, the arrival of units or persons requiring ser­vice.

ii. Problems dealing with the allocation of material or activities among limited facilities.

iii. Equipment replacement problems.

iv. Problems dealing with production processing i.e., production control and material ship­ment.

But it may be remembered that operation research never replaces a manager as decision maker. The ultimate and full responsibility for analysing all factors and making decision will be of the manager.

In the more wide sense, operation research does not deal with the everyday problems such as output by the one worker or machine capacity; instead it is concerned with the overall aspect of business operation such as something as the relationship between inventory, sales, production and scheduling. It may also deal with the overall flow of goods and services from plants to consumers.

The team doing operation research may have statisticians, psychologists, labour specialists, mathematicians and others depending upon the requirement for the problems.

Phases in Operation Research Study :

Since, the main objective of operation research is to provide better quantitative information’s for making decision. Now our aim is to learn how we can have better decisions.

The procedure for making decisions with the OR study generally involves the following phases:

(i) Judgment Phase:

i. Determination of operation.

ii. Determination of objectives.

iii. Determination of effectiveness of measures.

iv. Determination of type of problem, its origin and causes.

(ii) Research Phase:

i. Observation and data collection for better understanding of the problem.

ii. Formulation of relevant hypothesis and models.

iii. Analysis of available information and verification of hypothesis.

iv. Production and generation of results and consideration of alternatives.

(iii) Action Phase:

i. Recommendations for remedial action to those who first posed the problem, this includes the assumptions made, scope and limitations, alternative courses of ac­tion and their effect.

ii. Putting the solution to work: implementation.

Without OR, in many cases, we follow these phases in full, but in other cases, we leave important steps out. Judgment and subjective decision-making are not good enough. Thus industries look to operation research for more objective way to make decisions. It is found that method used should consider the emotional and subjective factors also.

For example, the skill and creative labour are important factors in our business and if management wants to have a new location, the management has to consider the personal feeling of the employees for the location which he chooses.

Scope of Operation Research:

In its recent years of organised development, O.R. has solved successfully many cases of research for military, the government and industry. The basic problem in most of the develop­ing countries in Asia and Africa is to remove poverty and hunger as quickly as possible. So there is a great scope for economist, statisticians, administrators, politicians and technicians working in a team to solve this problem by an O.R. approach.

On the other hand, with the explosion of population and consequent shortage of food, every country is facing the problem of optimum allocation of land for various crops in accordance with climatic conditions and available facilities. The problem of optimal distribution of water from a resource like a canal for irrigation purposes is faced by developing country. Hence a good amount of scientific work can be done in this direction.

In the field of Industrial Engineering, there is a claim of problems, starting from the pro­curement of material to the despatch of finished products. Management is always interested in optimizing profits.

Hence in order to provide decision on scientific basis, O.R. study team con­siders various alternative methods and their effects on existing system. The O.R. approach is equally useful for the economists, administrators, planners, irrigation or agricultural experts and statisticians etc.

Operation research approach helps in operation management. Operation management can be defined as the management of systems for providing goods or services, and is concerned with the design and operation of systems for the manufacture, transport, supply or service. The operating systems convert the inputs to the satisfaction of customers need.

Thus the operation management is concerned with the optimum utilisation of resources i.e. effective utilisation of resources with minimum loss, under utilisation or waste. In other words, it is concerned with the satisfactory customer service and optimum resource utilisation. Inputs for an operating system may be material, machine and human resource.

O.R. study is complete only when we also consider human factors to the alternatives made available. Operation Research is done by a team of scientists or experts from different related disciplines.

For example, for solving a problem related to the inventory management, O.R. team must include an engineer who knows about stores and material management, a cost ac­countant a mathematician-cum-statistician. For large and complicated problems, the team must include a mathematician, a statistician, one or two engineers, an economist, computer program­mer, psychologist etc.

Some of the problems which can be analysed by operations research are given hereunder:

1. Finance, Budgeting and Investment:

i. Cash flow analysis, long range capital requirement, investment portfolios, divi­dend policies,

ii. Claim procedure, and 

iii. Credit policies.

2. Marketing:

i. Product selection, competitive actions,

ii. Number of salesmen, frequencies of calling on, and 

iii. Advertising strategies with respect to cost and time.

3. Purchasing:

i. Buying policies, varying prices,

ii. Determination of quantities and timing of purchases,

iii. Bidding policies,

iv. Replacement policies, and 

v. Exploitation of new material resources.

4. Production Management:

i. Physical distribution: Location and size of warehouses, distribution centres and retail outlets, distribution policies.

ii. Facilities Planning: Number and location of factories, warehouses etc. Loading and unloading facilities.

iii. Manufacturing: Production scheduling and sequencing stabilisation of produc­tion, employment, layoffs, and optimum product mix.

iv. Maintenance policies, crew size.

v. Project scheduling and allocation of resources.

5. Personnel Management:

i. Mixes of age and skills,

ii. Recruiting policies, and 

iii. Job assignments.

6. Research and Development:

i. Areas of concentration for R&D.

ii. Reliability and alternate decisions.

iii. Determination of time-cost trade off and control of development projects.

Characteristics (Features) of Operation Research :

Main characteristics of operations research (O.R.) are follows:

(i) Inter-Disciplinary Team Approach:

This requires an inter-disciplinary team includ­ing individuals with skills in mathematics, statistics, economics, engineering, mate­rial sciences, computer etc.

(ii) Wholistic Approach to the System:

While evaluating any decision, the important interactions and their impact on the whole organisation against the functions originally involved are reviewed.

(iii) Methodological Approach:

O.R. utilises the scientific method to solve the problem

(iv) Objective Approach:

O.R. attempts to find the best or optimal solution to the prob­lem under consideration, taking into account the goals of the organisation.

Methodology of Operation Research :

Operation Research, is a scientific approach for decision-making, and therefore must follow following steps:

1. Formulating the Problem:

The problem must be first clearly defined. It is common to start the O.R. study with tentative formulation of the problem, which is reformulated over and again during the study. The study must also consider economical aspects.

While formulating the O.R. study, analyists must analyse following major components:

(i) The environment:

Environment involves physical, social and economical factors which are likely to affect the problem under consideration. O.R. team or analysts must study the organisation contents including men, materials, machines, suppliers, consumers, competitors, the government and the public.

(ii) Decision-makers:

Operation analyst must study the decision-maker and his rela­tionship to the problem at hand.

(iii) Objectives:

Considering the problem as whole, objectives should be defined.

(iv) Alternatives:

The O.R. study determines as to which alternative course of action is most effective to achieve the desired objectives. Expected reactions of the competitors to the alternative must also be considered.

2. Deriving Solution:

Models are used to determine the solution either by simulation or by mathematical analysis. Mathematical analysis for deriving optimum solution includes ana­lytical or numerical procedure, and uses various branches of mathematics.

3. Testing the Model and Solution:

A properly formulated and correctly manipulated model is useful in predicting the effect of changes in control variables on the overall system effectiveness. The validity of the solution is checked by comparing the results with those ob­tained without using the model.

4. Establishing Controls over the Solution:

The solution derived from a model remains effective so long as the uncontrolled variables retain their values and the relationship. The solution goes out of control, if the values of one or more variables vary or relationship between them undergoes a change. In such circumstances the models need to be modified to take the changes into account.

5. Implementing the Solution:

Solution so obtained should be translated into operating procedure to make it easily understandable and applied by the concerned persons. After apply­ing the solution to the system, O.R. group must study the response of the system to the changes made.

Operation Research Models :

Operation Research model is an idealised representation of the real life situation and repre­sents one or more aspects of reality. Examples of operation research models are: a map, activity charts balance sheets, PERT network, break-even equation, economic ordering quantity equation etc. Objective of the model is to provide a means for analysing the behaviour of the system for improving its performance.

Classification of Models :

Models can be classified on the basis of following factors:

1. By degree of Abstraction:

i. Mathematical models.

ii. Language models.

2. By Function:

i. Descriptive models.

ii. Predictive models.

iii. Normative models for repetitive problems.

3. By Structure:

i. Physical models.

ii. Analogue (graphical) models.

iii. Symbolic or mathematical models.

4. By Nature of Environment:

i. Deterministic models.

ii. Probabilistic models.

5. By the Time Horizon:

i. Static models.

ii. Dynamic models.

Characteristics of a Good Model :

i. Assumptions should be simple and few.

ii. Variables should be as less as possible.

iii. It should be able to asscimilate the system environmental changes without change in its framework.

iv. It should be easy to construct.

Constructing the Model :

A mathematical model is a set of equations in which the system or problem is described. The equations represent objective func­tion and constraints. Objective function is a mathematical expressions of objectives (cost or profit of the operation), while constraints are mathematical expressions of the limitations on the fulfillment of the objectives.

These expressions consist of controllable and uncontrollable variables.

The general form of a mathematical model is:

O = f (x i , y i )

where O = Objective function

x i = Controllable variables

y i = Uncontrollable variables

f = Relationship between O, and x i , y i .

Since model is only an approximation of the real situation, hence it may not include all the variables.

Simplification in Operation Research Models :

While constructing the model, efforts should be made to simplify them, but only up to the extent so that there is no significant loss of accuracy.

Some of the common simplifications are:

i. Omitting certain variables.

ii. Aggregating (or grouping) variables.

iii. Changing the nature of variables e.g., considering variables as constant or continuous.

iv. Changing relationship between variables i.e., considering them as linear or straight line.

v. Modify constraints.

Techniques of Operation Research :

Important techniques of Operation Research are being described hereunder:

(i) Inventory Control Models:

Operation Research study involves balancing inventory costs against one or more of the following costs:

i. Shortage costs.

ii. Ordering costs.

iii. Storage costs.

iv. Interest costs.

This study helps in taking decisions about:

i. How much to purchase.

ii. When to order.

iii. Whether to manufacture or to purchase i.e., make and buy decisions.

The most well-known use is in the form of Economic Order Quantity equation for finding economic lot size.

(ii) Waiting Line Models:

These models are used for minimising the waiting time and idle time together with the costs associated therewith.

Waiting line models are of two types:

(a) Queuing theory, which is applicable for determining the number of service facilities and/or the timing of arrivals for servicing.

(b) Sequencing theory which is applicable for determining the sequence of the servicing.

(iii) Replacement Models:

These models are used for determining the time of replacement or maintenance of item, which may either:

(i) Become obsolete, or

(ii) Become inefficient for use, and

(iii) Become beyond economical to repair or maintain.

(iv) Allocation Models:

These models are used to solve the problems arising when:

(a) There are number of activities which are to be performed and there are number of alternative ways of doing them,

(b) The resources or facilities are limited, which do not allow each activity to be performed in best possible way. Thus these models help to combine activities and available resources so as to optimise and get a solution to obtain an overall effectiveness.

(v) Competitive Strategies:

Such type of strategies are adopted where, efficiency of deci­sion of one agency is dependent on the decision of another agency. Examples of such strategies are game of cards or chess, fixing of prices in a competitive market where these strategies are termed as “theory”.

(vi) Linear Programming Technique:

These techniques are used for solving operation problems having many variables subject to certain restrictions. In such problems, objectives are—profit, costs, quantities manufactured etc. whereas restrictions may be e.g. policies of government, capacity of the plant, demand of the product, availability of raw materials, water or power and storage capacity etc.

(vii) Sequencing Models:

These are concerned with the selection of an appropriate sequence of performing a series of jobs to be done on a service facility or machine so as to optimise some efficiency measure of performance of the system.

(viii) Simulation Models:

Simulation is an experimental method used to study behaviour over time.

(ix) Network Models:

This is an approach to planning, scheduling and controlling complex projects.

Applications of Operation Research :

These techniques are applied to a very wide range of problems.

Here only some of the common applications are being mentioned:

(i) Distribution or Transportation Problems:

In such problems, various centres with their demands are given and various warehouses with their stock positions are also known, then by using linear programming technique, we can find out most economical distribution of the products to various centres from various warehouses.

(ii) Product Mix:

These techniques can be applied to determine best mix of the products for a plant with available resources, so as to get maximum profit or minimum cost of produc­tion.

(iii) Production Planning:

These techniques can also be applied to allocate various jobs to different machines so as to get maximum profit or to maximise production or to minimise total production time.

(iv) Assignment of Personnel:

Similarly, this technique can be applied for assignment of different personnel with different aptitude to different jobs so as to complete the task within a minimum time.

(v) Agricultural Production:

We can also apply this technique to maximise cultivator’s profit, involving cultivation of number of items with different returns and cropping time in different type of lands having variable fertility.

(vi) Financial Applications:

Many financial decision making problems can be solved by using linear programming technique.

Some of them are:

(i) To select best portfolio in order to maximise return on investment out of alternative investment opportunities like bonds, stocks etc. Such problems are generally faced by the managers of mutual funds, banks and insurance companies.

(ii) In deciding financial mix strategies, involving the selection of means for financing firm, projects, inventories etc.

Limitations of Operations Research :

i. These do not take into account qualitative and emotional factors.

ii. These are applicable to only specific categories of decision-making problems.

iii. These are required to be interpreted correctly.

iv. Due to conventional thinking, changes face lot of resistance from workers and some­times even from employer.

v. Models are only idealised representation of reality and not be regarded as absolute.

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• Industrial Marketing Research: Concept and Nature and Scope| Business Marketing

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Analytical and Operations Research Methods and Techniques in Lean Management

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• Marc Helmold   ORCID: orcid.org/0000-0001-9759-9002 7 ,
• Ayşe Küçük Yılmaz   ORCID: orcid.org/0000-0001-5240-1023 8 ,
• Triant Flouris 9 ,
• Thomas Winner 7 ,
• Violeta Cvetkoska 10 &
• Tracy Dathe   ORCID: orcid.org/0000-0002-9671-6489 11  

Part of the book series: Management for Professionals ((MANAGPROF))

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If a few decades ago, companies faced the problem of not having enough data from which to analyse and extract valuable information that would help them make better decisions, today, they are facing a new challenge. Today’s companies have access to a tremendous amount of data, referred to as “big data”, which is growing at a remarkable pace each day. A variety of sources can be used to gather the data, including but not limited to company databases, surveys, social media, the Internet, transactions, sensors etc. The data can appear in a structured format as reports, or mainly unstructured (more than 80%) as images, videos and audio. The big data contains hidden information, and only by knowing how to extract the information will the companies be able to improve their performance, gain a competitive advantage and survive in the market in the long term. Digital transformation imposes the need for business transformation, i.e. investing in advanced information technologies and systems and, above all, in finding staff who have the necessary competencies to discover the hidden potential in data and to invest in the existing intellectual capital in gaining analytics and operations research skills. Analytics provides the answer to the question of how to extract information from raw data that will create values such as higher profit, increased customer and employee satisfaction, increased efficiency, productivity, quality, etc. for the companies. Operations research is the application of analytical methods, techniques and tools that are used to solve real complex problems and help the decision-makers in making faster and fact-based decisions. According to the Institute for Operations Research and the Management Sciences (INFORMS), “Operations Research and Analytics enable organizations to turn complex challenges into substantial opportunities . They transform data into information, and information into insights for making better decisions and improving results ” (INFORMS, n.d.). As the amount of data that companies faced yesterday is smaller than the one they will face tomorrow, operations research and analytics have become the most important ingredient for companies to operate successfully.

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Badiezadeh, T., Saen, R. F., & Samavati, T. (2018). Assessing sustainability of supply chains by double frontier network DEA: A big data approach. Computers and Operations Research, 98 , 284–290. https://doi.org/10.1016/j.cor.2017.06.003

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Metropolitan College, Athens, Greece

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Helmold, M., Küçük Yılmaz, A., Flouris, T., Winner, T., Cvetkoska, V., Dathe, T. (2022). Analytical and Operations Research Methods and Techniques in Lean Management. In: Lean Management, Kaizen, Kata and Keiretsu. Management for Professionals. Springer, Cham. https://doi.org/10.1007/978-3-031-10104-5_11

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Operations Research Analyst Career

Job Description: Formulate and apply mathematical modeling and other optimizing methods to develop and interpret information that assists management with decisionmaking, policy formulation, or other managerial functions. May collect and analyze data and develop decision support software, services, or products. May develop and supply optimal time, cost, or logistics networks for program evaluation, review, or implementation.

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Techniques in Operations Research (MAST30013)

Undergraduate level 3 Points: 12.5 Dual-Delivery (Parkville)

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This subject introduces some major techniques and algorithms for solving nonlinear optimisation problems. Unconstrained and constrained systems will be considered, for both convex and non-convex problems. The methods covered include: interval search techniques, Newton and quasi-Newton methods, penalty methods for nonlinear programs, and methods based on duality. The emphasis is both on being able to apply and implement the techniques discussed, and on understanding the underlying mathematical principles. Examples involve the formulation of operations research models for linear regression, multi-facility location analysis and network flow optimisation.

A significant part of the subject is the project, where students work in groups on a practical operations research problem.

Intended learning outcomes

On completion of this subject students should develop:

• skills in setting up operations research models;
• a knowledge of the most important techniques for solving nonlinear optimisation problems;
• an understanding of the role of algorithmic thinking in the solution of operations research problems;
• competence in the use of computer packages in operations research;
• an understanding of the factors and restrictions involved in building and using models for planning and management problems;

Generic skills

In addition to learning specific skills that will assist in their future careers in science, students will have the opportunity to develop generic skills that will assist them in any future career path. These include:

• problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
• analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
• collaborative skills: the ability to work in a team;
• time-management skills: the ability to meet regular deadlines while balancing competing commitments;
• computer skills: the ability to use mathematical computing packages.

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