Optimal timing of investments in advanced manufacturing systems under competition

Large initial investment in advanced manufacturing systems (AMS) makes it imperative to develop economic justification methods. Once the new system has been properly justified, a critical issue facing the firm is the optimal timing of the installation in an industry. In this paper we use a game-theoretic model by which the optimal timing of the investments in AMSs is analysed. The existence of timing equilibria is demonstrated and simple decision rules of optimal strategy are provided. Economic interpretations of the decision rules are subsequently made clear. Finally, illustrative examples are used to demonstrate the correctness and usefulness of the analysis.     

WORKFORCE COMPOSITION AND FIRM PRODUCTIVITY: EVIDENCE FROM TAIWAN

We study the relationship between workforce composition and firm productivity based on a new employee‐employer‐matched data set, using an array of workforce characteristics and three alternative measures of firm productivity. While firm age is not essential for the performance of firms, those of smaller size and those in the steel and transportation industries outperform others. Moreover, labor quality, particularly the middle‐aged with higher education, contributes significantly to firms' productivity. Furthermore, economic incentives and market competition both play important roles in the performance of firms. Finally, there is an employer‐size premium with larger firms paying higher wages and nonwage benefits. (JEL C33, D20, J30)     

A chance-constraints linear programming model on the economic evaluation of flexible manufacturing systems

The implementation of flexible manufacturing systems (FMSs) normally entails a large initial investment under a long-term, uncertain environment. Many effects of installing a FMS will be due to improvement in throughput efficiency, quality, flexibility and the opportunity costs. However, most economic evaluations of FMSs assume the problem is deterministic, such that they fail to model accurately and capture the nature of FMSs. This paper uses stochastic variables to capture the.nature of a FMS under given resource limitations and leads to a multistage chance-constraints linear programming (LP) formulation. Finally, in order to incorporate the uncertainty of capital investment, the interest rate as a function of time is considered over the whole planning horizon and the decision model is extended under continuous and variable discounting.