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Limitations of OR

OR is an extremely powerful tool in the hands of a decision-maker and to that extent the advantage of OR techniques are immense. Some of there are:

(a)             It helps in optimum use of resources. LP techniques suggest many methods of most effective and .efficient ways of optimally using the production factors.

(b)             Quality of decision can be improved by suitable use of OR techniques. If a mathematical model representing the real life situation is well formulated representing the real life situation, the computation tables give a clear picture of the happenings (changes in the various elements i.e. variables) in the model. The decision-maker can use it to his advantage, specially if
computerised software can be used to make changes in variables as per requirement.

The limitations of OR emerge only out of the time and cost involved as also the problem of formulating a suitable mathematical model, otherwise, as suggested above, it is a very powerful medium of getting the best out of limited resources. So, the problem is its application rather than its utility, which is beyond doubt. Some of the limitations are :-

  1. Large number of cumbersome computations. Formulation of mathematical models which takes into account all possible factors which define reallifeproblem is difficult. Because of this, the computations involved in developing relationships invery large variables needs the help of computers. This discourages small companies and other organisations from getting the best out of OR techniques.
  2. Quantification of problems. All the problems cannot be qualified properly as there are a large number of intangible factors, such as human emotions, human relationship and so on. If these intangible elements/variables are excluded from the problem even though they may be more important than the tangible ones, the best solution cannot be determined.
  3. Difficult to conceptualize and use by the managers. OR applications is a specialists job, these persons may be mathematicians or statisticians who understand the formulation of models, finding solution and recommending the implementation. The managers really do not have the hang of it. Those who recommend a particular OR technique may not understand the problem well enough and those who have to use may not understand the ‘why’ of that recommendation. This creates a ‘gap’ between the two and the results may not be optimal.