Non-stationary Stochastic Demand Research Articles

Evaluation of Lead Time in Production/Inventory Systems with Non-Stationary Stochastic Demand

Evaluation of Lead Time in Production/Inventory Systems with Non-Stationary Stochastic Demand

Evaluation of Lead Time in Production/Inventory Systems with Non-stationary Stochastic Demand

For the past decade, new techniques for production management–such as kanban (pull-through) systems, internal set-ups, zero inventory, etc–have gained tremendous interest. However, research indicates that the production environment is more important than specific techniques. One aspect of this environment is the production lead time. This paper examines the impact of lead time, in conjunction with the characteristics of the demand process, on costs through the use of a periodic-review production/inventory model with a non-stationary stochastic demand process. Our results indicate that costs are affected by a combination of production lead time and demand variances. While the demand means have no effect on costs, both the shapes of the demand distributions and the relative magnitude of the penalty costs do have an effect.

A demand-driven method for scheduling optimal smooth production levels

This paper is concerned with demand-driven production scheduling in a commercial environment where smoothed production plans generation over a rolling horizon is desirable as new observations of demand are received through time. Demands are assumed to be normally distributed and dependent on the previous observed levels. The method of chance constraint of Charnes and Cooper is extended to multi-product production planning with variable workforce, back-ordered inventory, and nonstationary stochastic demand process. Bayesian procedures for revising the chance constraints and several variants of linear-programming-based production planning models are presented. In all cases the proposed methodology ensures that demands are satisfied, at a given level of confidence, while achieving smooth production.