Setting Retail Staffing Levels: A Methodology Validated with Implementation

Marshall Fisher (), Santiago Gallino () and Serguei Netessine ()
Additional contact information
Marshall Fisher: The Wharton School, The University of Pennsylvania, Philadelphia, Pennsylvania 19104
Santiago Gallino: The Wharton School, The University of Pennsylvania, Philadelphia, Pennsylvania 19104
Serguei Netessine: The Wharton School, The University of Pennsylvania, Philadelphia, Pennsylvania 19104

Manufacturing & Service Operations Management, 2021, vol. 23, issue 6, 1562-1579

Abstract: Problem definition : How should retail staffing levels be set? While cost of labor is well understood, the revenue implications of having the right staffing level are hard to estimate. Moreover, these implications vary by store; hence, staffing levels should vary as well. Academic/practical relevance : We provide a novel method for setting store associate staffing at the individual store level. We discuss a field implementation that tested this methodology. Methodology : We use historical data on revenue and planned and actual staffing levels by store to estimate how revenue varies with the staffing level at each store. We disentangle the endogeneity between revenue and staffing levels by focusing on randomly occurring deviations between planned and actual labor. Using historical analysis as a guide, we validate these results by changing the staffing levels in a few test stores. We implement the results chain-wide and measure the impact in a large specialty retailer. Results : We find that the implementation validates predictions of the historical analysis. The implementation in 168 stores over six months produces a 4.5% revenue increase and a nearly $7.4 million annual profit increase. The impact of staffing level on revenue varies greatly by store. Managerial implications : Our paper makes three contributions to academic literature and to retail practice. First, we describe a process by which retailers can improve the most common industry practice: set store labor to be proportional to forecasted store revenue. Our proposed approach systematically sets the labor level in each store. Second, we demonstrate the effectiveness of that process via a field test and then via chain-wide implementation over a six-month time period. Finally, most retailers set store labor at the same level across stores, proportionate to revenue. We show that this is not the best approach because the revenue impact of store labor varies by store. The stores in our study that could benefit from relatively more labor were those with high potential demand, closely located competition for that demand, and experienced store managers. Overall, we provide the first simple but rigorous, field-tested approach that any retailer can use to increase revenue and profitability through better labor management.

Keywords: retail operations; business analytics; empirical operations management (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2020.0917 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:23:y:2021:i:6:p:1562-1579

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().