Demand Information Sharing in a Supply Chain of Durable Goods with Pricing Decisions

Abstract

Problem definition: We study a supply chain of durable good product consisted of a retailer with demand forecast information and a manufacturer. The retailer's demand information is about the popularity of the product, which can help the supply chain members to better price the product. We examine how the retailer's policy to share his forecasts with the manufacturer depends on the durability and the production cost of the product. Academic/Practical relevance: In a supply chain, the retailer usually has better demand forecasts than the manufacturer. The demand forecast of the retailer can help the supply chain in many contexts including optimal pricing of a new product. Consequently, in supply chain management, demand information sharing has received substantial attention. This paper studies how the retailer's forecast sharing decision depends on "durability" of the product. Methodology: We employ an analytical microeconomic model for our analysis. Results: We show that when there is medium dispersion in the popularity of the product, the durability of the product has a large impact on the optimal information sharing policy of the retailer. In such cases, the retailer should share his information with the manufacturer when (i) the production cost is large and the product is not very durable, or (ii) when the production cost is small and the product is very durable, or (iii) the production cost is intermediate and the durability of the product is intermediate. Managerial implications: The previous literature analyzed forecasting sharing without durability. We demonstrate the importance of durability on forecasting sharing which impacts the supply chain performance. The insights from the paper helps the retail practitioners' to decide in which context they should share their demand information with their upstream partners, and guides the upstream firms to know when to consider working with the retailers to elicit their forecast information.