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題名 Fixing shelf out-of-stock with signals in point-of-sale data
作者 莊皓鈞
Chuang, Howard Hao-Chun
貢獻者 資訊管理學系
關鍵詞 Decision support systems; Shelf out-of-stock; Point-of-sale; Audits; Data analytics
日期 2017
上傳時間 29-一月-2018 12:29:50 (UTC+8)
摘要 Shelf out-of-stock (OOS) is a salient problem that causes non-trivial profit loss in retailing. To tackle shelf-OOS that plagues customers, retailers, and suppliers, we develop a decision support model for managers who aim to fix the recurring issue of shelf-OOS through data-driven audits. Specifically, we propose a point-of-sale (POS) data analytics approach and use consecutive zero sales observations in POS data as signals to develop an optimal audit policy. The proposed model considers relevant cost factors, conditional probability of shelf-OOS, and conditional expectation of shelf-OOS duration. We then analyze the impact of relevant cost factors, stochastic transition from non-OOS to OOS, zero sale probability of the underlying demand, managers’ perceived OOS likelihood, and even random fixes of shelf-OOS on optimal decisions. We also uncover interesting dynamics between decisions, costs, and probability estimates. After analyzing model behaviors, we perform extensive simulations to validate the economic utility of the proposed data-driven audits, which can be a cost-efficient complement to existing shelf inventory control. We further outline implementation details for the sake of model validation. Particularly, we use Bayesian inference and Markov chain Monte Carlo to develop an estimation framework that ensures all model parameters are empirically grounded. We conclude by articulating practical and theoretical implications of our data-driven audit policy design for retail managers.
關聯 European Journal of Operational Research,
資料類型 article
DOI https://doi.org/10.1016/j.ejor.2017.10.059
dc.contributor 資訊管理學系
dc.creator (作者) 莊皓鈞zh_TW
dc.creator (作者) Chuang, Howard Hao-Chunen_US
dc.date (日期) 2017
dc.date.accessioned 29-一月-2018 12:29:50 (UTC+8)-
dc.date.available 29-一月-2018 12:29:50 (UTC+8)-
dc.date.issued (上傳時間) 29-一月-2018 12:29:50 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/115640-
dc.description.abstract (摘要) Shelf out-of-stock (OOS) is a salient problem that causes non-trivial profit loss in retailing. To tackle shelf-OOS that plagues customers, retailers, and suppliers, we develop a decision support model for managers who aim to fix the recurring issue of shelf-OOS through data-driven audits. Specifically, we propose a point-of-sale (POS) data analytics approach and use consecutive zero sales observations in POS data as signals to develop an optimal audit policy. The proposed model considers relevant cost factors, conditional probability of shelf-OOS, and conditional expectation of shelf-OOS duration. We then analyze the impact of relevant cost factors, stochastic transition from non-OOS to OOS, zero sale probability of the underlying demand, managers’ perceived OOS likelihood, and even random fixes of shelf-OOS on optimal decisions. We also uncover interesting dynamics between decisions, costs, and probability estimates. After analyzing model behaviors, we perform extensive simulations to validate the economic utility of the proposed data-driven audits, which can be a cost-efficient complement to existing shelf inventory control. We further outline implementation details for the sake of model validation. Particularly, we use Bayesian inference and Markov chain Monte Carlo to develop an estimation framework that ensures all model parameters are empirically grounded. We conclude by articulating practical and theoretical implications of our data-driven audit policy design for retail managers.en_US
dc.format.extent 1735687 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) European Journal of Operational Research,
dc.subject (關鍵詞) Decision support systems; Shelf out-of-stock; Point-of-sale; Audits; Data analyticsen_US
dc.title (題名) Fixing shelf out-of-stock with signals in point-of-sale dataen_US
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1016/j.ejor.2017.10.059
dc.doi.uri (DOI) https://doi.org/10.1016/j.ejor.2017.10.059