New product life cycle curve modeling and forecasting with product attributes and promotion: A Bayesian functional approach. Issue 2 (17th October 2022)

• Record Type: Journal Article
• Authors: Lei, Dazhou; Hu, Hao; Geng, Dongyang; Zhang, Jianshen; Qi, Yongzhi; Liu, Sheng; Shen, Zuo‐Jun Max
• Is Part Of: Production and operations management. Volume 32:Issue 2(2023)
• Page Start: 655
• Page End: 673
• Publication Date: 2022-10-17
• Subjects: Bayesian model -- functional regression -- product life cycle -- sales forecasting; Production management -- Periodicals; 658.505
• DOI: 10.1111/poms.13892
• Languages: English
• ISSNs: 1059-1478

Abstract

Abstract: New products are highly valued by manufacturers and retailers due to their vital role in revenue generation. Product life cycle (PLC) curves often vary by their shapes and are complicated by promotional activities that induce spiky and irregular behaviors. We collaborate with JD.com to develop a flexible PLC curve forecasting framework based on Bayesian functional regression that accounts for useful covariate information, including product attributes and promotion. The functional model treats PLC curves as target variables and includes both scalar and functional predictors, capturing time‐varying promotional activities. Harnessing the power of basis function transformation, the developed model can effectively characterize the local features and temporal evolution of sales curves. Our Bayesian framework can generate initial curve forecasts before the product launch and update the forecasts dynamically as new sales data are collected. We validate the superior performance of our method through extensive numerical experiments using three real‐world data sets. Our forecasting framework reduces the forecasting error by 5.35%–30.76% over JD.com's current model and outperforms alternative models significantly. Furthermore, the estimated promotion effect function provides useful insights into how promotional activities interact with sales curves.