The cost of inflated planned lead times in MRP systems

Much of the current literature in the field of production and inventory control systems stresses the need to revise traditional forms of thinking regarding production processes, the role of inventories for work in process, and the need for reduced lead times or flow times. Group technology, manufacturing cells, and other means of incorporating repetitive manufacturing techniques into traditional job‐shop settings constitute the leading edge in system development.

Still, there is resistance to these dramatic changes, and traditional “business as usual” methods still predominate. This study attempts to illustrate graphically the cost justification associated with reduction in lead times which generally results from these new concepts. In most job shops today, lead times are much longer than they need to be due to inflation of lead time estimates. Actual lead times for the manufacture of fabricated and assembled products have been shown to be a direct consequence of the planning lead times used in the MRP planning process—a form of self‐fulfilling prophesy.

The research employs a simulation model of a factory using MRP as a planning tool in a multiproduct, multilevel production environment. Manufacturing costs constitute the dependent variable in the experiments, defined as the sum of material costs (including expedite premiums), direct labor costs (including overtime premiums), inventory carrying costs, and overhead costs. The independent variable being manipulated is the planned lead time offset used in the MRP planning process. Twenty values of planned lead time are evaluated ranging from a value that includes no slack time at all (pure assembly line) up to a value that allows 95% slack (queue) time which, unfortunately, is not uncommon in many job shops today. Stochastic variables in the model include customer demand and actual processing times—the sum of set‐up and run times.

The result of the study is a cost curve formed over the range of independent lead time variables that is constructed using nonlinear regression techniques. The conclusions from the resultant graph clearly indicate the cost consequences of long lead times, with exponential cost increases beyond the 80–90% queue time level. Total costs are 41% higher at the maximum lead time allowance compared to the minimum. Clearly, this study demonstrates the need for lead time reduction, either through downward adjustment of MRP planned lead times or by introducing new manufacturing concepts.