Anticipation of lead time performance in supply chain operations planning

Whilst being predominantly used in practice, linear and mixed integer programming models for Supply Chain Operations Planning (SCOP) are not well suited for modeling the relationship between the release of work to a production unit and its output over time. In this paper we propose an approach where the SCOP model is decomposed into a deterministic materials coordination model and a stochastic lead time anticipation model. The sub-models are linked through planned lead times and workload targets. A novel algorithm is presented for rescheduling aggregate workload such that planned lead times can be met by the production unit. The approach that we present is more general than the closely related clearing function concept. It can be applied to multi-item resources without making assumptions on the order of processing, it accurately captures the development of workload over time, and it yields a more reliable production planning. Simulation experiments show that our approach yields reductions in inventory holding costs up to 20% compared to a deterministic materials and resource coordination model