Make-or-buy decision analysis: a Monte Carlo simulation

In this paper a Monte Carlo simulation approach as a support system for make-or-buy decision is presented. Generally, in make-or-buy analysis the input data concerning costs and profits are frequently characterized by a given level of imprecision. Therefore, the traditional techniques for the make-or-buy selection do not provide a satisfactory result. To improve the result of the analysis it is possible use propagation of errors techniques. But even here, this approach does not eliminate the problem of imprecision, because the results produced are characterized by a high level of uncertainty. A new approach based on Monte Carlo simulation is proposed to overcome these limitations thus making it possible to perform probabilistic make-or-buy decisions. Using the methodology proposed is possible to evaluate the make-or-buy likelihood distribution. A case study is provided in which the proposed Monte Carlo simulation is applied to a food analysis and testing laboratory for the drink industry. The application aims at analysing the feasibility study of a testing-laboratory.