Reducing the effects of demand uncertainty in single-newsvendor multi-retailer supply chains

Abstract

Due to fierce competition in today’s global market, businesses are forced to provide customers with high service levels. Typically, vendors produce or order sufficient quantities at the beginning of a selling season to ensure reasonable service levels for the whole season. However, due to the probabilistic nature of demand, high service levels at the beginning of a selling season does not guarantee appropriate service levels during the course of consuming the item. Thus, revision of service levels during a selling season is important and ignoring such revision may lead to serious consequences for businesses like profit loss due to cancelled orders and reduction of the market share of the company. In this paper, we propose a model for a newsvendor supply chain with single vendor and multiple retailers where the vendor has two-ordering opportunities. At the beginning of a selling season, the retailer orders from a vendor a quantity such that a predetermined service level is achieved. At the second-ordering instant, the retailer learns more about the demand pattern and uses the new available demand data to update the coming demand using Bayesian approach. Based on the updated demand, the retailer evaluates the new service level for the remaining portion of the selling season. If this service level is lower than a specific value, a second batch is ordered. We develop the model for general demand distribution and determine the optimal quantities at the beginning of the selling season and at the second-ordering opportunity.

Keywords:

• inventory sharing
• newsvendor
• service level
• demand updating
• supply chain

Acknowledgement

The authors are grateful to the anonymous referees for their constructive comments and helpful suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors would also like to acknowledge Kuwait University for supporting this research under Research Project No.: EI01/15.