A multiple objective decision model for the evaluation of advanced manufacturing system technologies.

Ordinary financial measures oversimplify the evaluation of Advanced Manufacturing System Technologies (AMST). A multiple objective decision model is developed which avoids the shortcomings of the traditional evaluation methods. The model is comprised of three objectives: Pecuniary, Strategic, and Tactical. The Pecuniary objective is based upon traditional Discounted Cash Flow techniques, with the results normalized to a [-1, +1] (worst-best) scale. The Strategic and Tactical objectives are based upon the concept of qualitative flows, and a qualitative discounting method is employed to discount the qualitative costs/benefits to a present value. The three objectives are traded-off using the Composite Programming technique, resulting in a rank ordering of the alternatives which are under consideration. The three objectives of the model are broken down into attributes which define the objective, and these attributes are "mapped" into the organization of a manufacturing environment. It is shown that the model covers the entire manufacturing environment. It is shown that the model covers the entire manufacturing organization in accounting for the costs/benefits of the proposed AMST alternatives. The influence of the three objectives on the final score is analyzed using a mixture experiment. The mixture experiment provides insight into the effect of varying the importance of each objective and its effect on the final rankings. This provides the analyst a method to determine which attributes and/or objectives are critical for the AMST alternative being investigated. Realizing that the evaluation of AMST hinges on events which are to occur in the future, and these events are not known for certain, the procedure is extended to include a measure of risk. Cash flows, qualitative flows, interest rates, and project lengths are provided by the decision maker as pessimistic, most likely, and optimistic estimates. An analysis is provided where the inputs are assumed to be independent. The model is then further enhanced to allow time dependence between cash flows and qualitative flows of a single attribute. Results are provided as a mean and variance of the evaluation score, objective scores, and indices, along with frequency distributions. A case study analysis is provided which shows the application of the techniques developed in this work