Model Predictive Control of Supply Chains
The supply chain may be defined as a system which runs from raw material procurements, through production, inventory and warehousing, distribution and delivery transportation, order fulfillment as well as customer service and demand. The supply chain is thus an interconnected system of nodes consisting of the manufacturing facility, the suppliers for raw material for the manufacturing facility, the warehouses and distribution centers for the finished products and the retailers who interact with the customer. Each node interacts with the other nodes through material (raw or finished product) flow and information (about orders and demands). These supply chains will be highly interconnected for companies that have multiple products and demands over multiple locations.
Traditionally, the supply chain has been viewed as individual nodes, with all interactions among the nodes treated as disturbances. This view of supply chains leads to suboptimal performance, as the eventual aim is to maximize the profit of the entire supply chain.
A systems-oriented approach to supply chains emphasizes the process, the operation support, and the interactions as major components of the supply chain system which need to be optimized for better performance.
From a systems viewpoint, the supply chain is a set of nodes which interact with each other, driven by customer demands at one end (where demands flow towards the manufacturer) and production at the other end (where finished goods flow towards the customer). These flows have to be optimized (for a performance objective like maximizing profit), subject to constraints at each node. To achieve this, the systems viewpoint can look at a centralized objective, where the whole supply chain is considered as one big process and internal flows are optimized accordingly. The alternate view is to consider each node in the chain separately and make decisions to maximize the performance objective of that node. This is the decentralized operation. However, for most industrial supply chains, neither of the two approaches may yield optimal results. In the first case, the supply chain may not be completely owned by the same company, and hence a centralized model may be infeasible. In the second case, the local performance objectives for two nodes may conflict, driving to suboptimal performance. This leads to an alternative viewpoint where each node takes its local decisions but information is shared among the nodes so that it has a global picture as well. This is the distributed approach with information sharing.
More recently, control theory has been suggested as an approach to obtain optimal scheduling policy for supply chains. In this research, the applicability of model predictive control and communication-based MPC schemes for supply chains will be investigated.