A Study on Evolutionary Reactive Ordering Systems

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In order to develop an agile ordering system for multi-stage production system, this research proposed a reactive controller of buffer inventory for reacting to the unstable changes in demand. In the controller, the unstable changes in demand, not only the mean but also the variance, can be detected, and the optimal level of buffer inventory is determined as a reaction to the changes using the exponentially weighted moving average (EWMA) charts. In addition to the controller using EWMA charts, a controller using a neural network is proposed for two types of just-in-time (JIT) ordering systems, the Kanban system and the concurrent ordering system. The effectiveness of the proposed system is clarified by the simulation experiments under various unstable demand conditions. As a result of simulation experiments, it is shown that the proposed controller can react to the unstable changes in demand, not only the mean but also the variance, with satisfying the required level for the mean waiting time of product demand. Furthermore, it can be shown that the proposed controller using neural network reduces the mean work-in-process inventories of the system much more than the previous controller using EWMA charts.