The “physics” of capacity and backlog management in service and custom manufacturing supply chains

Abstract

Abstract In this paper, we investigate the dynamic behavior of service supply chains in the presence of varying demand and information sharing. Each stage holds no finished goods inventory, rather only backlogs that can be managed solely by adjusting capacity. These conditions reflect the reality of many service (and custom manufacturing, such as capital equipment) supply chains. While there is a growing literature on finished goods inventory management in supply chains, relatively little research exists on managing capacity in the absence of finished goods inventory. To address this problem, we develop a capacity management model for a serial chain. At each stage in the supply chain, our model relates capacity, processing, backlog, and service delays to capture the aggregate dynamic interactions between the different stages. Using a system dynamics simulation model in an experimental design as well as a formal analysis of a simplified, yet representative, model using control theory and signal analysis techniques, we characterize the conditions under which a “bullwhip effect” (i.e., an increase in demand and backlog variability as one looks up the supply chain) can occur. We then study the impact of different management strategies and levels of information visibility on capacity and service delay variability in a two‐stage model. Conventional wisdom, derived from studies of make‐to‐stock manufacturing supply chains, strongly supports lead‐time reduction in order to mitigate the bullwhip effect. We show that lead‐time reduction can exacerbate the bullwhip effect in a service or custom manufacturing setting if it is not carefully coordinated with capacity adjustment. In particular, lead‐time reduction generally reduces backlog variance locally but often increases backlog variances at higher stages. Further, sharing end‐customer demand reduces backlog variances as in inventory supply chains but over‐reliance on it relative to local information may actually increase demand variance at higher stages. Finally, we show that the natural tendency to pursue system‐wide process improvement by imposing uniform parameter targets across the supply chain exacerbates demand, capacity, and backlog variances at higher stages. Instead, we show that a superior policy is asymmetric, holding the bulk of system backlog at the stage closest to the point of end customer demand. Copyright © 2005 John Wiley & Sons, Ltd.

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