Quantifying Supply Chain Trade-offs Using Six Sigma, Simulation, and Designed Experiments to Develop a Flexible Distribution Network
Operations and Supply Chain Management
Date of this version
designed experiments, discrete-event simulation, distribution network, DMAIC, DOE, inventory buffer, six sigma, supply chain, WITNESS
This case study quantifies the trade-off between customer service and inventory in a multimode supply chain while assessing system performance. An integrated and generalized modeling framework in used that incorporates define, measure, analyze, improve, control (DMAIC) methodology and designed experiments. Many traditional models require normality in demand and supply, but this is often not representative of reality. Therefore, this study leverages discrete-event simulation to replicate and understand the variation in a real-world supply chain at a large multi-national corporation. The goal of this modeling application is to provide dynamic decision support to facilitate effective supply chain design. The study uses an innovative three-stage analytical approach to study a multi-echelon distribution network. The first stage uses DMAIC to highlight areas of variability in the process, enabling identification of key high-risk inputs affecting system performance. The next stage, discrete-event simulation, utilizes DMAIC results in the development of a validated model that represents the real-world variability present in the supply chain. The third and final stage, designed experiments, is used to analyze the simulation output and quantify the factors that drive supply chain performance, specifically inventory levels and customer service (fill rate) at various stages of the supply chain. To more effectively respond to stochastic behavior of customers, study results impact decision making and facilitate inventory replenishment policy changes. The analysis also helped in the proper allocation of resources to manage the various stock-keeping units (SKU) classes and customer categories. This proactive modeling approach is robust and readily replicated in any supply chain.