Adaptive Logistic Controller (ALC): A simulation-based feedback control, integrated production-inventory algorithm for scalable design of integrated supply chains

In a traditional single product, serial line, given demand supply chain of Original Equipment Manufacturers (OEMs) each stage makes its own decision regarding how many parts to order and when to order these parts. Such decisions, although good for each individual stage adversely affect the overall performance of the complete supply chain. Since each stage of the OEM supply chain has little or no knowledge of the current inventory or the order quantity of the other stages, each stage tries to keep a safety stock in order to prevent stock-outs and to meet future demands. This gives rise to bullwhip effect, which causes demand amplification as we move up the supply chain. This research describes a simulation based feedback control algorithm called Adaptive Logistics Controller (ALC) which simultaneously decides the order quantities for each stage of the OEM supply chain in order to minimize the total WIP in the entire supply chain for a given demand. In this approach, simulation is used to provide the feedback to the ALC controller leading to an iterative numerical computational approach. Computational experiments comparing AWIP with the traditional centralized (Q,r) policy model show that the order quantities calculated by ALC are much superior in terms of total overall WIP and hence result in lesser total costs for the entire supply chain.