Fast high precision decision rules for valuing manufacturing flexibility

doi:10.1016/S0377-2217(98)00374-9

European Journal of Operational Research

Volume 120, Issue 1, 1 January 2000, Pages 108-117

https://doi.org/10.1016/S0377-2217(98)00374-9 Get rights and content

Abstract

The valuation of Flexible Manufacturing Systems is one of the most frequently undertaken productivity improvement activities. In practice, the introduction of an FMS into industry must be done on the basis of cost justification. Recently developed techniques for the evaluation of the value of flexibility typically include the computation of stochastic dynamic programs. However, the computational effort of stochastic dynamic programs grows combinatorially and limits application to real world problems. In this contribution we derive fast approximations to the stochastic dynamic program and compare their results to the exact solution. The proposed methods show an excellent worst case behavior (1%) for a wide range of volatility of the underlying stochastic profit margins and costs for switching the production mode. The computational effort is reduced by a factor of more than 200.

Section snippets

Motivation

Most investment decisions share three important characteristics: irreversibilty, uncertainty about future rewards and the timing (Dixit and Pindyck, 1993)). Traditionally, the value of an investment is determined by the net present value. Dixit and Pindyck have pointed out that this decision rule tends to underestimate the value of a project because it neglects options associated with an investment. Recently, researchers in the field of real options (see, e.g., Trigeorgis, 1995) have developed

Stochastic dynamic programming

Given that the owner of a project has the opportunity to change its operation mode in a persistent way, it is clear that the value of such a project and the optimal decision rule that specifies an optimal action for each possible time/environment combination have to be determined simultaneously from the solution found by a dynamic program. In particular, our model describes a flexible manufacturing system (FMS) that can produce one of M possible products at any time t (t=0,…,T). The vector of

Application to capital budgeting for FMS

Typically, the introduction of an FMS into industry must be done on the basis of cost justification (Lint, 1992). The commercial profitability of an FMS as compared to normal manufacturing centers cannot be easily proved since the investment cost exceeds the initial cost of a manufacturing center by 70–300% (Schlingensiepen, 1987). Although for a manufacturing company FMS entail higher inital costs than special-purpose machines, at least part of this cost disadvantage is compensated by the

Conclusion

Conventional capital budgeting methods used to justify the introduction of an FMS tend to understate the value of such production systems. The possibility to react to changing market conditions may increase the value of an FMS. While the net present value approach neglects this added value, real options methodology helps to decide, whether the value of flexibility is higher than the difference of the costs between a special-purpose machine and an FMS. In order to compute the value of

Acknowledgements

The authors would like to express their gratitude to Prof. Zimmermann, the editor and two anonymous reviewers for several useful comments on earlier versions of this paper.

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It also discusses the “positive” and “negative” connotations related to the use of the cash flow methodology, proposing new alternative valuation approaches (i.e., [218,211]). With respect to the two peripheral clusters identified, scheduling focuses on the development of heuristic and simulation tools to solve specific problems of day-to-day operations (i.e., [226,229]), whereas FMS mainly concentrates on analysing the impact of FMS implementation on firms´ flexibility capacity (i.e., [244,238,239]). Finally, the strategic matrix has also revealed the existence of two emergent clusters that have co-evolved: perspective and technology.
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Theory and MethodologyFast high precision decision rules for valuing manufacturing flexibility

Abstract

Section snippets

Motivation

Stochastic dynamic programming

Application to capital budgeting for FMS

Conclusion

Acknowledgements

European Journal of Operations Research

Generalized simulated annealing for function optimization

Technometrics

Valuation by approximation: A comparison of alternative valuation techniques

Journal of Financial and Quantitative Analysis

A lattice claims model for capital budgeting

IEEE Transactions on Engineering Management

Valuing the flexibility of flexible manufacturing systems

IEEE Transactions on Engineering Management

Strategic growth options

Management Science

Theory and Methodology
Fast high precision decision rules for valuing manufacturing flexibility