Adjusting Replenishment Orders to Reflect Learning in a Material Requirements Planning Environment

Some manufacturing firms, particularly in the high-technology sector, have production processes which are characterized by very low initial yields followed by steady “experience” based yield improvement. Material Requirements Planning literature reveals that MRP implementations are seldom adjusted in any systematic way to account for such yield improvement. A single product, single stage MRP model is developed which incorporates learning curve behavior into conventional MRP logic. A series of experiments systematically examine the impact on mean inventory level of various combinations of environmental conditions and managerial policies. The research demonstrates that substantial reductions in mean inventory levels can be realized in low yield environments if learning is properly included in the order release logic. This finding proves to be robust with respect to modest errors in the estimation of learning rate.

• 저널Management Science · 36(12) · 1532–1547
• 토픽Supply Chain and Inventory Management · Management Information Systems
• DOI10.1287/mnsc.36.12.1532
• 저자Larry R. Dolinsky, Thomas E. Vollmann, Michael J. Maggard