Demand Forecasting and Pricing Decision with the Entry of Store Brand under Various Information Sharing Scenarios

Abstract

In this research, we discuss three different approaches to generate demand forecasting and pricing decision for mix of national brand and store brand products in the era of big data. We derive the equilibrium wholesale price and retail price for the national brand products, and the equilibrium retail price for the store brand products based on demand forecast under three different information scenarios, including Noninformation Sharing ([Formula: see text]), Information Sharing ([Formula: see text]), and Retailer Forecasting ([Formula: see text]). We comprehensively discuss how information collection, information sharing, forecast accuracy under era of big data affect firms’ prices and profits. Our numerical experiments illustrate and verify our analytical findings and provide further managerial insights and interpretations.