The intent is to use advanced mathematical methods to gather information and form it into quantitative matter for decision making. The nature of the techniques used require mathematical modeling, statistical analysis, and mathematical optimization. Aside from raw operational data, engineering,  psychology and organizational  science are also also taken into consideration upon determination of optimal solutions to complex problems. Discovery of efficiencies to operations study the processes within manufacturing, engineering, logistics, business process management, business process reengineering, lean manufacturing, and  six sigma.


Examples of real world problems are how to [maximumize profit] via performance or yield and [minimize loss] due to risk or excessive cost.  Some mathematical tools or models include the following:


Computing and information technologies

Financial engineering

Manufacturing, service sciences

 Supply chain management

Marketing Engineering

Policy modeling and public sector work

Revenue management


Stochastic models (function of one argument in order to understand a process which can only develop in one way thus exists as a single system or within a larger and interconnected system)

Probability and statistics (two related but separate academic disciplines)


Within this section are overlapping areas of methodology that help form quantitative data  for decision making other than operational research. These include the following: