Model Predictive Control of Supply ChainsKaushik Subramanian
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 for it over multiple locations.
Traditionally, the supply chain has been viewed as a individual nodes, and all interactions among the nodes as disturbances. This view of supply chains, leads to an sub-optimal performance, as the eventual aim, is to maximize the profit of the entire supply chain.
A systems-oriented approach to supply chains, is one which emphasizes the process, the operation support and the interactions as major components of the supply chain system, which needs to be optimized for better performance.
From a systems viewpoint, the supply chain is a set of nodes which interact with each other, and is driven by the customer demands for the products at one end (where these demands flow towards the manufacturer) and the production at the other end (where the 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 the 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 be conflicting each other, thus driving to a sub-optimal performance. This leads to an alternative viewpoint, where each node, takes its local decisions, but information is being shared among the nodes, so that it has a global picture as well. This is the distributed or decentralized approach with information sharing viewpoint.
More recently, Control theory has been suggested as an approach to obtain optimal scheduling policy for supply chains. In this research, applicability of Model Predictive Control and the communication based MPC schemes for Supply chains will be investigated.