A closed-loop approach to efficient and stable supply-chain coordination in complex stochastic manufacturing systems

Modern manufacturing productivity and competitiveness is undoubtedly time-based. As a result, the design of production facilities (product cells) and their operation (just in time, zero in process, lean etc. (R. Suri, 1998; K. Suzaki, 1987)) aim at reducing lead times and inventories of either work in process or finished goods. The MRP practice is a serious impediment to further productivity gains. Indeed, significant gains are possible when lead time dynamics of individual supply chain links are accounted for in the overall manufacturing supply chain coordination through synergistic decentralized production planning. Better production planning algorithms that exploit information sharing are capable of taking the industry to the next big step in productivity gains. The paper argues that time scale decomposition for enhanced information sharing and better deterministic fluid model approximations capturing the essence of the underlying stochastic dynamics can indeed provide these productivity gains