Time compression and supply chain management ‐ a guided tour

TLDR

Supply chains must balance rapid response, high customer service, and low inventory, leading to a conflict among marketing, production, and materials management, and time compression—rooted in early system dynamics insights—has emerged as a key strategy to resolve these tensions. Industrial studies confirm that shortening cycle times enhances competitiveness, and simulation‑based time‑compression strategies reliably predict performance gains.

Abstract

Modern supply chains are expected to respond rapidly, effectively and efficiently to changes in the marketplace. Simultaneously there is the drive to achieve world class customer service levels coupled with minimum reasonable inventory (MRI). We thus have the classic conflict of interests between marketing, production and materials management. Marketing wants the complete product range available off‐the‐shelf; production is still, all too often, looking to manufacture in economic batch quantities so as to achieve economies of scale; and materials management is trying to minimize storage and distribution costs which, in turn, requires that a total systems MRI policy be adopted. Time compression at all stages in the chain is seen as the way to respond to these challenges. The ground rules for effective supply chain design were co‐incidentally established in 1961, when Jay Forrester showed that medium period demand amplification was a system dynamics phenomenon which could be tackled by reducing and eliminating delays and the proper design of feedback loops. In the same year, via his “five rules to avoid bankruptcy” directed at smoothing material flow, Burbidge showed that short period demand amplification was due to multi‐phased, multi‐period ordering policies. Confirms that, on the basis of industrial studies, collapsing cycle times drive the business into a more competitive scenario. This means that time compression strategies based on model simulation may be confidently used to predict improvements in supply chain performance.