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Traffic-Based Labor Planning in Retail Stores

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  • Howard Hao-Chun Chuang
  • Rogelio Oliva
  • Olga Perdikaki

Abstract

type="main" xml:id="poms12403-abs-0001"> 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.

Suggested Citation

  • Howard Hao-Chun Chuang & Rogelio Oliva & Olga Perdikaki, 2016. "Traffic-Based Labor Planning in Retail Stores," Production and Operations Management, Production and Operations Management Society, vol. 25(1), pages 96-113, January.
  • Handle: RePEc:bla:popmgt:v:25:y:2016:i:1:p:96-113
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    File URL: http://hdl.handle.net/10.1111/poms.2016.25.issue-1
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    Citations

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    Cited by:

    1. Chen, Chien-Ming & Chuang, Howard Hao-Chun, 2023. "Time to shift the shift: Performance effects of within-day cumulative service encounters in retail stores," Omega, Elsevier, vol. 119(C).
    2. Mohammad Delasay & Aditya Jain & Subodha Kumar, 2022. "Impacts of the COVID‐19 pandemic on grocery retail operations: An analytical model," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2237-2255, May.
    3. Saurabh Mishra & Sachin B. Modi & Michael A. Wiles, 2022. "Economic policy uncertainty and shareholder wealth: the role of marketing, operations, and R&D capabilities," Journal of the Academy of Marketing Science, Springer, vol. 50(5), pages 1011-1031, September.
    4. Pol Boada-Collado & Victor Martínez-de-Albéniz, 2020. "Estimating and Optimizing the Impact of Inventory on Consumer Choices in a Fashion Retail Setting," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 582-597, May.
    5. Haoyan Sun & Jianqing Chen & Ming Fan, 2021. "Effect of Live Chat on Traffic‐to‐Sales Conversion: Evidence from an Online Marketplace," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1201-1219, May.
    6. Masoud Kamalahmadi & Qiuping Yu & Yong-Pin Zhou, 2021. "Call to Duty: Just-in-Time Scheduling in a Restaurant Chain," Management Science, INFORMS, vol. 67(11), pages 6751-6781, November.
    7. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
    8. Saravanan Kesavan & Susan J. Lambert & Joan C. Williams & Pradeep K. Pendem, 2022. "Doing Well by Doing Good: Improving Retail Store Performance with Responsible Scheduling Practices at the Gap, Inc," Management Science, INFORMS, vol. 68(11), pages 7818-7836, November.
    9. Wang, Wei & Li, Gang & Fung, Richard Y.K. & Cheng, T.C.E., 2019. "Mobile Advertising and Traffic Conversion: The Effects of Front Traffic and Spatial Competition," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 84-101.
    10. Tian, Xin & Zhu, Jiayi & Zhao, Xuan & Zhou, Xiaoyang, 2024. "Unveiling insights from online shopping carnivals: A pre-vs-post analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    11. Marshall Fisher & Ananth Raman, 2022. "Innovations in retail operations: Thirty years of lessons from Production and Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4452-4461, December.
    12. Wen, Xin & Choi, Tsan-Ming & Chung, Sai-Ho, 2019. "Fashion retail supply chain management: A review of operational models," International Journal of Production Economics, Elsevier, vol. 207(C), pages 34-55.
    13. Yuqian Xu & Tom Fangyun Tan & Serguei Netessine, 2022. "The Impact of Workload on Operational Risk: Evidence from a Commercial Bank," Management Science, INFORMS, vol. 68(4), pages 2668-2693, April.
    14. Tom Fangyun Tan & Serguei Netessine, 2020. "At Your Service on the Table: Impact of Tabletop Technology on Restaurant Performance," Management Science, INFORMS, vol. 66(10), pages 4496-4515, October.
    15. Ma, Shaohui & Fildes, Robert, 2020. "Forecasting third-party mobile payments with implications for customer flow prediction," International Journal of Forecasting, Elsevier, vol. 36(3), pages 739-760.
    16. Sunil Mithas & Yanzhen Chen & Yatang Lin & Alysson De Oliveira Silveira, 2022. "On the causality and plausibility of treatment effects in operations management research," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4558-4571, December.

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