A new inventory level APIOBPCS-based controller

Modern companies have realized that reducing inventory levels as much as possible without losing sales opportunities is an effective way to reduce costs and to have more profitability. This fact is true not only for large companies but also for mid-size and small companies on account of the high maintenance and opportunity costs associated with large inventory stocks. In this paper we want to introduce into the inventory management field advanced methodologies and tools from de industrial automation and modern control theory. In this way, a new approach to the automatic pipeline feedback order-based production control system (APIOBPCS) is presented. The proposed control system structure add to the APIOBPCS a PID (proportional, integrative and derivative) controller as well as an extended Kalman filter, acting as demand predictor. The main objective of this controller is to stabilize and to regulate the inventory level around a desired set-point value, in spite of a demand with cyclic and stochastic components. Along this work, the dynamics and delays of the productive process were modeled as a pure delay. The Kalman filter estimates de parameters of a Volterra time-series model to forecast futures values of the demand in order to compensate production delays. A control error analysis for the proposed controller is also presented in order to obtain bounds for the control error and to probe controller stability. This analysis is also useful to make decisions about the desired inventory level for a given demand prediction error. Finally, the inventory control system is tested by simulations showing a good performance and better results than those achieved by using traditional techniques.