Inventory policies and information sharing in multi-echelon supply chains

Abstract

The aim of this article is to show how to modify a replenishment rule in relation to the operational information shared by suppliers. More specifically, we present a model of an Automatic Pipeline Variable Inventory and Order-Based Production Control System rule for a multi-echelon supply chain characterised by different increasing levels of shared information. A numerical study is presented to underline the performance differences for three variants of the smoothing order rule in terms of bullwhip reduction, inventory stability and operational and customer responsiveness. Results show how the effectiveness of a smoothing replenishment rule depends on the level of information sharing.

Keywords:

• order-up-to
• periodic review
• inventory control
• lumpy demand
• synchronisation
• collaboration
• coordinated order policies
• simulation

Acknowledgements

The authors thank the anonymous reviewers for their helpful comments on the earlier version of this article.

Notes

1. The APIOBPCS archetype has been used to extend the understanding of the dynamic behaviour of supply chains, and its analysis is not only theoretical but it has been applied in industry (Wikner et al. 2007). Although it may be embedded within commercial software, it is often developed and implemented on an ad hoc basis (Disney and Towill 2006).

2. For an extended guide to optimal parameter setting for proportional controller, see Disney and Towill (2003b, c, 2006) and Boute et al. (2007).

3. The selection of an optimal set of parameters is not within the scope of this article. As underlined by Dejonckheere et al. (2004) in its study on information enriched supply chains through control theory, more or less variability reduction could be obtained by selecting other vectors. In this study, we aim to emphasise the dynamics of the decision rule in multi-echelon supply chains when supported by data sharing across the stages.

4. Zero replenishment cannot be viewed as a stand-alone supply chain performance metric and will be analysed together with a customer service level measure: apparently, null or low zero replenishment values could be indicative of optimal operations and lot sizing, but this is true only when, at the same time, the system ensures a high customer service level. Otherwise, a poor customer service level associated with a null or low zero replenishment reflects poor system responsiveness.