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Article: New product life cycle curve modeling and forecasting with product attributes and promotion: A Bayesian functional approach

TitleNew product life cycle curve modeling and forecasting with product attributes and promotion: A Bayesian functional approach
Authors
KeywordsBayesian model
functional regression
product life cycle
sales forecasting
Issue Date1-Feb-2023
PublisherWiley
Citation
Production and Operations Management, 2023, v. 32, n. 2, p. 655-673 How to Cite?
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.


Persistent Identifierhttp://hdl.handle.net/10722/336526
ISSN
2023 Impact Factor: 4.8
2023 SCImago Journal Rankings: 3.035
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLei, D-
dc.contributor.authorHu, H-
dc.contributor.authorGeng, D-
dc.contributor.authorZhang, J-
dc.contributor.authorQi, Y-
dc.contributor.authorLiu, S-
dc.contributor.authorShen, ZJM-
dc.date.accessioned2024-02-16T03:57:28Z-
dc.date.available2024-02-16T03:57:28Z-
dc.date.issued2023-02-01-
dc.identifier.citationProduction and Operations Management, 2023, v. 32, n. 2, p. 655-673-
dc.identifier.issn1059-1478-
dc.identifier.urihttp://hdl.handle.net/10722/336526-
dc.description.abstract<p>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.</p>-
dc.languageeng-
dc.publisherWiley-
dc.relation.ispartofProduction and Operations Management-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectBayesian model-
dc.subjectfunctional regression-
dc.subjectproduct life cycle-
dc.subjectsales forecasting-
dc.titleNew product life cycle curve modeling and forecasting with product attributes and promotion: A Bayesian functional approach-
dc.typeArticle-
dc.identifier.doi10.1111/poms.13892-
dc.identifier.scopuseid_2-s2.0-85139975050-
dc.identifier.volume32-
dc.identifier.issue2-
dc.identifier.spage655-
dc.identifier.epage673-
dc.identifier.eissn1937-5956-
dc.identifier.isiWOS:000868644200001-
dc.identifier.issnl1059-1478-

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