Workload-dependent capacity control in production-to-order systems

The development of job intermediation and the increasing use of the Internet allow companies to carry out ever quicker capacity changes. In many cases, capacity can be adapted rapidly to the actual workload, which is especially important in production-to-order systems, where inventory cannot be used as a buffer for demand variation. A set of Markov chain models is introduced that are able to represent workload-dependent capacity control policies. Two analytical approaches to evaluate the policies' due date performance based on a stationary analysis are presented. One provides an explicit expression of throughput time distribution, the other is a fixed-point iteration method that calculates the moments of the throughput time. The due date performance, capacity, capacity switching and lost sales costs are compared to select optimal policies. Insights into situations in which a workload-dependent policy can be beneficial are presented. The results can be used by manufacturing and service industries when establishing a static policy for dynamic capacity planning.