English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88645/118187 (75%)
Visitors : 23496645      Online Users : 277
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/120809
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/120809


    Title: Fixing shelf out-of-stock with signals in point-of-sale data
    Authors: Chuang, Howard Hao-Chun
    莊皓鈞
    Contributors: 資管系
    Keywords: Decision support systems;Shelf out-of-stock;Point-of-sale;Audits;Data analytics
    Date: 2018-11
    Issue Date: 2018-10-29 17:23:19 (UTC+8)
    Abstract: 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. (C) 2017 Elsevier B.V. All rights reserved.
    Relation: EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 270(3), 862-872
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.ejor.2017.10.059
    DOI: 10.1016/j.ejor.2017.10.059
    Appears in Collections:[資訊管理學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    862.pdf1706KbAdobe PDF147View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback