Optimal supply planning in MRP environments for assembly systems with random component procurement times

This paper examines supply planning in an MRP environment for assembly systems under lead time uncertainties. Indeed, inventory control in a supply chain is crucial for companies who wish to satisfy their customer demands on time as well as controlling costs. A common approach is to use the MRP techniques. However, these techniques are based on the supposition that lead times are known. In an actual supply chain the lead times are often random variables. Therefore, we develop an efficient exact model to aid in MRP parameterization under lead time uncertainties, more precisely to calculate planned lead times when the component procurement times are random. The aim is to find the values of planned lead times which minimize the sum of the average component holding cost and the average backlogging cost. The developed approach is based on a mathematical model of this problem with discrete decision variables and a branch and bound algorithm.