Service level management of nonstationary supply chain using direct neural network controller

In recent supply chain management, as the online use of inventory data becomes available with the development of Radio Frequency Identification (RFID) technology, it is now possible to monitor the performance measures in a timely fashion. Customer service level is a key performance measure that can be computed as the percentage of times that customer orders electronically received are fulfilled by on-hand inventory. Online monitoring of the service level enables the management paradigm to progress toward the closed loop based control which keeps revising the operation policy to reach a target service level. This paper proposes a closed loop supply chain control based on a direct neural network controller. Unlike the simulation based optimizations which usually need a demand forecasting and an early warning model, our proposed approach has the strength that it can maintain the target only by using the actual ones measured online. For the direct neural network controller, an amplification function which increases the learning speed by augmenting the learning error is proposed. Simulation based experiments were performed to test the performance of the controller against two kinds of unstable customer demand curves.