OPERATION RESEARCH

Operation Research (Inventory Models)



Operation Research (Inventory Models)

Introduction

The ambiguous term operations research (O.R.) was coined during World War II when the British military management called upon a group of scientists together to apply a scientific approach in the study of military operations to win the battle. The main objective was to allocate scarce resources in an effective manner to various military operations and to the activities within each operation. The effectiveness of operations research in military spread interest in it to other government departments and industry.

Due to the availability of faster and flexible computing facilities and the number of qualified O.R. professionals, it is now widely used in military, business, industry, transportation, public health, crime investigation, etc. It is concerned with co-ordinating and controlling the operations or activities within an organization. O.R. can be regarded as use of mathematical and quantitative techniques to substantiate the decisions being taken. O.R takes tools from subjects like mathematics, statistics, engineering, economics, psychology, etc. and uses them to know the consequences of possible alternative actions (Levitt, 2002).

Numerous synonyms for operations research are in common use. The British like operational research and the Americans like management science, but a preferable term to describe this subject is decision analysis.

Advantages

Better Control: The management of large organizations recognize that it is a difficult and costly affair to provide continuous executive supervision to every routine work. An O.R. approach may provide the executive with an analytical and quantitative basis to identify the problem area. The most frequently adopted applications in this category deal with production scheduling and inventory replenishment.

Better Systems: Often, an O.R. approach is initiated to analyze a particular problem of decision making such as best location for factories, whether to open a new warehouse, etc. It also helps in selecting economical means of transportation, jobs sequencing, production scheduling, replacement of old machinery, etc.

Better Decisions: O.R. models help in improved decision making and reduce the risk of making erroneous decisions. O.R. approach gives the executive an improved insight into how he makes his decisions.

Limitations

Dependence on an Electronic Computer: O.R. techniques try to find out an optimal solution taking into account all the factors. In the modern society, these factors are enormous and expressing them in quantity and establishing relationships among these require voluminous calculations that can only be handled by computers.

Non-Quantifiable Factors: O.R. techniques provide a solution only when all the elements related to a problem can be quantified. All relevant variables do not lend themselves to quantification. Factors that cannot be quantified find no place in O.R. models.

Distance between Manager and Operations Researcher: O.R. being specialist's job requires a mathematician or a statistician, who might not be aware of the business problems. Similarly, a manager fails to understand the complex working of O.R. Thus, there is a gap between the two.

Money and Time Costs: When the basic data are subjected to frequent changes, incorporating them into the O.R. models is a costly affair. Moreover, a fairly good solution at present may be ...