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

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.

Screening Items in an Aging Chain With a Co-Flow Structure: An Application to the Product Pipeline Management Problem

“…The manner in which our model has been set up differs from inventory/ service supply chain models (Sterman 1989, Anderson andMorrice 2005) both in terms of stock/flow and policy structures.…”

“…The limitation of managers' ability to account for the supply line and backlogs has been documented extensively in the inventory/services management context (Sterman 1989, Anderson andMorrice 2005). A related avenue for research, within the product innovation context, is to generate policy guidelines about the dynamics of capacity, resource utilization and backlog management while accounting for behavioral biases related to product innovation Calantone 2002, Gino andPisano 2005).…”

“…The structure of stocks and flows in PPM can be compared to the structure of a service supply chain model (Anderson et al 2005) as shown in figure 2. The PPM problem is a special case of service supply chains where some projects are terminated across stages based on their value.…”

“…91, Girotra et al 2005). Capacity utilization has been identified as a key construct that drives the performance of aging chains (Sundaramoorthy et al 2012;Anderson and Morrice 2005). In order to know which fraction of the stock of projects and of the stock of value shall have to be terminated, it is necessary to know which percentage of projects has a value lower than the predetermined threshold.…”

A Framework of Sustainable Service Supply Chain Management: A Literature Review and Research Agenda

“…However, in the existing literature, supply chain management in the manufacturing industry is far more studied than supply chain management in the service supply chain (Aleda and Larry, 2003 [8] [31] analyzed the bullwhip effect in a service supply chain, deriving from studies of make-to-stock manufacturing supply chains. Wei et al (2013) [32] studied a two-stage game problem on pricing, ordering and allocation in a service supply chain on the basis of news vendor model of manufacturing supply chain.…”