UPSTREAM VOLATILITY IN THE SUPPLY CHAIN: THE MACHINE TOOL INDUSTRY AS A CASE STUDY

Edward G. Anderson · Charles H. Fine · GEOFFREY G. PARKER

157 citations0/0 files storedProduction and Operations Management

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Supply Chain and Inventory Management Quality and Supply Management Supply Chain Resilience and Risk Management

Abstract

Cyclicality is a well-known and accepted fact of life in market-driven economies. Less well known or understood, however, is the phenomenon of amplification as one looks “upstream” in the industrial supply chain. We examine the amplification phenomenon and its implications through the lens of one upstream industry that is notorious for the intensity of the business cycles it faces: the machine tool industry. Amplification of demand volatility in capital equipment supply chains, e.g., machine tools, is particularly large relative to that seen in distribution and component parts supply chains. We present a system dynamics simulation model to capture demand volatility amplification in capital supply chains. We explore the lead-time, inventory, production, productivity, and staffing implications of these dynamic forces. Several results stand out. First, volatility hurts productivity and lowers average worker experience. Second, even though machine tool builders can do little to reduce the volatility in their order streams through choice of forecast rule, a smoother forecasting policy will lead companies to retain more of their skilled work force. This retention of skilled employees is often cited as one of the advantages that European and Japanese companies have had relative to their U.S. competitors. Our results suggest some insights for supply chain design and management: downstream customers can do a great deal to reduce the volatility for upstream suppliers through their choice of order forecast rule. In particular, companies that use smoother forecasting policies tend to impose less of their own volatility upon their supply base and may consequently enjoy system-wide cost reduction.

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References

2006The Core Competence of the Corporation 1990The Fifth Discipline 1997Information Distortion in a Supply Chain: The Bullwhip Effect 1989Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment 2012Industrial Dynamics: A Major Breakthrough for Decision Makers 1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition 1999Quantitative Models for Supply Chain Management 1939Interactions between the Multiplier Analysis and the Principle of Acceleration

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