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

Ting Zhang, Xiaowei Zhu () and Qinglong Gou
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Ting Zhang: School of Management, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China2College of Business, City University of Hong Kong, Kowloon, SAR, Hong Kong
Xiaowei Zhu: College of Business and Public Affairs, West Chester University of Pennsylvania, West Chester, PA 19383, USA
Qinglong Gou: School of Management, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2017, vol. 34, issue 02, 1-26

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 (N), Information Sharing (I), and Retailer Forecasting (R). 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.

Keywords: Forecasting; information sharing; stackelberg game; store brand; pricing (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0217595917400188

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