4.6 Article Proceedings Paper

Coordinating a three-level supply chain with learning-based continuous improvement

Journal

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
Volume 127, Issue 1, Pages 27-38

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.ijpe.2010.04.010

Keywords

Learning; Forgetting; Set-up reduction; Lot-sizing; Product quality; Supply chain coordination; Quantity discounts

Ask authors/readers for more resources

Learning curve theory has been widely used as a managerial tool to describe and model product and process improvement. This paper investigates a three-level supply chain (supplier-manufacturer-retailer) where the manufacturing operations undergo a learning-based continuous improvement process. Improvements in the manufacturer's operation are characterized by enhanced capacity utilization, reductions in set-ups times, and improved product quality through the elimination of rework. As a result of these continuous improvements, the manufacturer can justify a production policy that is based on more frequent, smaller lot size production. For this production policy to be practical and not sub-optimal to the supply chain, the manufacturer must integrate its lot-sizing models with the replenishment policies of its upstream raw material suppliers and the demand requirements of its downstream customers (retailers). Mathematical models that achieve chain-wide lot-sizing integration are developed and solution procedures for the models are illustrated by numerical examples. The results demonstrate that learning-based improvements in set-up time and rework allow retailers to order in progressively smaller lot sizes as the manufacturer offers larger discounts and profits and that the entire supply chain benefits from implementing learning-based continuous quality improvements. The results also demonstrate that forgetting effects lead to increases in supply chain costs. (C) 2010 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available