Competitiveness of the U.S. manufacturing industry has declined sharply in the last decade. Countries such as Japan have been more active in investing in Advanced Manufacturing System Technologies (AMST), including robotics and Flexible Manufacturing Systems, and have experienced higher growth rates. U.S. manufacturers rely heavily on discounted cash flow methods with high hurdle rates when attempting to justify investments in new technology. Significant strategic and tactical advantages of the AMST are undervalued and risk is associated only with investment, ignoring the erosion of competitiveness experienced by do-nothing decisions. In this chapter a methodology is presented for integrating financial, strategic, and tactical factors into a single investment decision model. A model of the firm is used to divide all relevant effects on enterprise performance into a set of attributes in the financial (pecuniary), strategic, and tactical areas. An analysis is then performed for each decision alternative. Financial effects are handled through traditional discounted cash flow methods. Additionally, a qualitative assessment is performed on each of the attributes in the strategic and tactical areas. Project life and attributes may be treated as random variables. All attribute evaluations are discounted to the current time and then integrated using Composite Programming. An estimate of the distribution is obtained for each alternative. The methodology allows decision makers to quantify intangible benefits such as lower marginal cost and employee development, and integrate these with the cash flows in a single decision model. Treating the pecuniary, strategic and tactical dimensions as factors in a mixture experiment, we describe how sensitivity analysis can be performed on model parameters. The entire procedure is demonstrated with a case study.
|Original language||English (US)|
|Number of pages||35|
|Journal||Manufacturing Research and Technology|
|State||Published - Jan 1 1992|
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering