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Operations Research and Analytics Research

University of Oxford_061522D
[University of Oxford]

- Overview

Operations research (OR) is an exciting field of applied mathematics that combines mathematics, statistics, computer science, physics, engineering, economics and social sciences to solve real-world business problems. Many companies in the industry require OR professionals to apply mathematical techniques to a wide range of challenging problems.

OR can be defined as the science of decision making. It successfully provides a systematic and scientific approach to a variety of government, military, manufacturing, and service operations. 

Please refer to the following for more information:

- Operations Research Model

Operations research has evolved into a standard framework that's used for identifying and solving problems. 

The seven stages of OR are:

  • Orientation: This step's primary objective is to address the problem and ensure that all team members have a clear picture of the relevant issues.
  • Problem definition: To formulate an operations research problem, a suitable measure of performance must be devised, various possible courses of action defined, and relevant uncontrolled variables identified.
  • Data collection: This is one of the seven sequential steps of the operations research approach.
  • Model formulation: This involves constructing a model to represent the system under study.
  • Solution: This involves deriving a solution from the model.
  • Model validation and output analysis: This involves testing the model and the solution derived from it.
  • Implementation and monitoring: This involves establishing controls over the solution and implementing the solution.


- The Process of Operations Research

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

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

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


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


Beverly Hills_CA_053122A
Beverly Hills, California - Civil Engineering Discoveries]

- Characteristics of Operations Research

Operations research (OR) is a method of problem-solving and decision-making that uses mathematical analysis to break down problems into basic components. It's useful for managing organizations. 

There are three primary characteristics of all OR efforts:

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

- Importance of Operations Research (OR)

The field of operations research (OR) provides a more powerful approach to decision making than ordinary software and data analytics tools. Employing OR professionals can help companies achieve more complete datasets, consider all available options, predict all possible outcomes and estimate risk. 

Additionally, OR can be tailored to specific business processes or use cases to determine which techniques are most appropriate to solve the problem.

Operations research (OR) can help companies: 

  • Remove subjective bias
  • Increase predictability
  • Consider all available options
  • Predict all possible outcomes
  • Estimate risk
  • Balance constraints and objectives
  • Implement more comprehensive and thorough solutions to problems
  • Understand how to analyze similar problems in the future
  • Eliminate uncertainties


OR uses proven methods and modeling techniques. It can provide decision-makers with more detailed and insightful analysis. 

OR originated during World War II. It was defined as "a scientific method of providing executive departments with a quantitative basis for decisions regarding the operations under their control". 


- Uses of Operations Research in Decision-Making

Business managers face countless complex problems every day. They have to make decisions about financing, where to build a factory, how much product to produce, how many people to hire, and so on. Often, the factors that make up a business problem are complex and can be difficult to understand. OR is one way to solve these difficult problems.

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

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


- Operation Research and Analytics

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.  

OR is defined as the scientific process of transforming data into insights to making better decisions. 

Analytics is the application of scientific & mathematical methods to the study & analysis of problems involving complex systems. There are three distinct types of analytics:

  • Descriptive Analytics gives insight into past events, using historical data.
  • Predictive Analytics provides insight on what will happen in the future.
  • Prescriptive Analytics helps with decision making by providing actionable advice.


[More to come ...]



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