Applying Analytical Methods to Decision-making

Operations Research

Summary/Abstract

The term OR is somewhat unfortunate or even funny, since OR is not (any more) concerned with operations, nor does its application involve any research in the traditional sense. Some good Operations Research definitions are:

Operations Research is the discipline of applying advanced analytical methods to help make better decisions. By using techniques such as mathematical modeling to analyze complex situations, operations research gives executives the power to make more effective decisions and build more productive systems.
O.R. is the professional discipline that deals with the application of information technology for informed decision-making. It aims to provide a rational bases for decision making by seeking to understand and structure complex situations and to use this understanding to predict system behavior and improve system performance. Much of this work is done using analytical and numerical techniques to develop and manipulate mathematical and computer models of organizational systems composed of people, machines, and procedures.
O.R. draws upon ideas from engineering, management, mathematics, and psychology to contribute to a wide variety of application domains; the field is closely related to several other fields in the "decision sciences" -- applied mathematics, computer science, economics, industrial engineering, and systems engineering.
O.R. is the science of rational decision making and the study, design and integration of complex situations and systems with the goal of predicting system behavior and improving or optimizing system performance. It encompasses managerial decision making, mathematical and computer modeling and the use of information technology for informed decision-making.

As a science, O.R. traces its roots back to World War II, as military planners such as Frederick Lanchester and Patrick Blackett looked for ways to bring scientific calculations to Allied warfare against Nazi Germany.

Typical OR methods include:

Simulation - Giving you the ability to try out approaches and test ideas for improvement
Optimization - Narrowing your choices to the very best when there are virtually innumerable feasible options and comparing them is difficult
Probability and statistics - Helping you measure risk, mine data to find valuable connections and insights, test conclusions, and make reliable forecasts
Mathematical models
(Complex) algorithms
Visualization
Neural networks
Pattern recognition
Data mining, Data warehousing

O.R. can be used for supporting an indefinite number of business decisions. However, typical applications of OR are:

Capital budgeting
Asset allocation
Portfolio selection
Fraud prevention, Anti-Money Laundering
Benchmarking
Marketing channel optimization, Customer segmentation
Direct marketing campaigns, Predicting customer response, Campaign optimization
Supply chain planning
Distribution, Routing, Scheduling, Traffic flow optimization
Resource allocation, Staff allocation
Inventory planning
Retail planning, Merchandize optimization
Product mix and blending, Industrial waste reduction

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