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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/115444


    Title: 以來店人數為基礎的零售服飾業人力規劃
    Authors: 莊皓鈞
    Contributors: 資訊管理學系
    Keywords: 零售業營運;人力配置;店家績效;資料分析
    retail operations;staffing;store performance;data analytics
    Date: 2015
    Issue Date: 2017-12-26 17:48:54 (UTC+8)
    Abstract: 零售服務業者每年花在店內員工薪水的金額高達數十億元，人力配置決策對於零售服務營運成本與效益十分重要，然而人力配置不當(過多或過少)的現象在實務上時常發生，而不足的人力常造成前述「貨架品項缺漏」和「存貨記錄錯誤」。為了改善人力配置決策與店家績效，我們利用某高端女性服飾連鎖零售業者的資料來發展一套人力規劃的架構。首先我們設計一個銷售反應函數，有別於先前的相關文獻，我們提出的函數考量了人力適當性(labor adequacy)，也就是員工對顧客的人數比；同時此函數也有個在零售服務環境中較合適的性質—可變動生產要素替代彈性(variable elasticity of substitution)。我們進而將此函數轉為追蹤資料模型，並用銷售金額、來店人數和工作時數的資料來估計銷售反應函數，追蹤資料讓我們能有效利用跨店家而非單店的資訊得到更堅實的函數參數估計值。利用估得的銷售反應函數，我們提出一個利潤最大化模型，推導出最佳人力配置的封閉解，從最佳解發展出一個以來店人數資料為基礎的人力配置法則。此簡單易用的配置法則除經實證資料分析驗證，更免除了預測來店人數的需求，因此能夠被廣泛地應用於營運實務上，幫助零售服務業者優化人力配置決策。
    Staffing decisions are crucial for retailers since staffing levels affect store performance and labor-related expenses constitute one of the largest components of retailers’ operating costs. With the goal of improving staffing decisions and store performance, we develop a labor-planning framework using proprietary data from an apparel retail chain. First, we propose a sales response function based on labor adequacy (the labor to traffic ratio) that exhibits variable elasticity of substitution between traffic and labor. When compared to a frequently used function with constant elasticity of substitution, our proposed function exploits information content from data more effectively and better predicts sales under extreme labor/traffic conditions. We use the validated sales response function to develop a data-driven staffing heuristic that incorporates the prediction loss function and uses past traffic to predict optimal labor. In counterfactual experimentation, we show that profits achieved by our heuristic are within 0.5% of the optimal (attainable if perfect traffic information was available) under stable traffic conditions, and within 2.5% of the optimal under extreme traffic variability. We conclude by discussing implications of our findings for researchers and practitioners.
    Relation: 執行起迄：2015/08/01~2016/07/31
    104-2410-H-004-134
    Data Type: report
    Appears in Collections:[資訊科學系] 國科會研究計畫

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