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Estimating the Impact of Understaffing on Sales and Profitability in Retail Stores
Abstract
In this paper we use hourly data on store traffic, sales, and labor from 41 stores of a large retail chain to identify the extent of understaffing in retail stores and quantify its impact on sales and profitability. Using an empirical model motivated from queueing theory, we calculate the benchmark staffing level for each store, and establish the presence of systematic understaffing during peak hours. We find that all 41 stores in our sample are systematically understaffed during a 3-hour peak period. Eliminating understaffing in these stores can result in a significant increase in sales and profitability in these stores. Also, we examine the extent to which forecasting errors and scheduling constraints drive understaffing in retail stores and quantify their relative impacts on store profits for the retailer in our study.This article is protected by copyright. All rights reserved.
... Drawing on channel selection and retailing literature on store performance, we operationalise channel member performance as the volume of sales predicted by store traffic. While there is no consensus in the retailing literature on how to measure retail performance, some studies suggest that the relationship between sales and traffic is an important indicator (Mani et al., 2015). When measuring retail performance, companies typically rely on internal data, such as sales history and counting the number of people (i.e. ...
... Perdikaki et al. (2011) explored the relationship between sales and traffic, suggesting a nonlinear connection due to decreased service quality. Mani et al. (2015) investigated the impact of understaffing on traffic, sales and labour, finding that insufficient staff diminished sales and profit. In short, store traffic has been identified as a significant predictor of sales performance, establishing its importance in channel member selection theory. ...
... Our paper proposes a new method to overcome this issue. Building upon previous literature on the relationship between store traffic and sales performance (Walters and Mackenzie, 1988;Gijsbrechts et al., 2003;Babakus et al., 2004; Verhetsel, 2005;Maxham et al., 2008;Liu et al., 2016;Mani et al., 2015;Gauri et al., 2017;Katona et al., 2018;Tian et al., 2021;Feng and Fay, 2022), we examine whether a positive correlation exists between parked cars and sales performance. While large companies use reports of the number of vehicles in their parking lots as a crucial variable in developing models to predict store earnings over a given period (Cisek et al., 2017), academia has not paid much attention to the subject. ...
Purpose
This study aims to propose a new method to predict retail store performance using publicly available satellite imagery data and machine learning (ML) algorithms. The goal is to provide manufacturers and other practitioners with a more accurate and objective way to assess potential channel members and mitigate information asymmetry in channel selection and negotiation.
Design/methodology/approach
The authors developed an open-source approach using publicly available Google satellite imagery and ML algorithms. A computer vision algorithm was used to count cars in store parking lots, and the data were processed with a CNN. Linear regression and various ML algorithms were used to estimate the relationship between parked cars and sales.
Findings
The relationship between parked cars and sales was nonlinear and dependent on the type of channel member. The best model, a Stacked Ensemble, showed that parking lot occupancy could accurately predict channel member performance.
Research limitations/implications
The proposed approach offers manufacturers a low-cost and scalable solution to improve their channel member selection and performance assessment process. Using satellite imagery data can help balance the marketing channel planning process by reducing information asymmetry and providing a more objective way to assess potential partners.
Originality/value
This research is unique in proposing a method based on publicly available satellite imagery data to assess and predict channel member performance instead of forward-looking sales at the firm and industry levels like previous studies.
... In addition to maintaining the right level of inventory at the right location and right time, retailers also need to match labor supply to incoming customer traffic to provide the right service level to their customers. Matching labor supply to customer demand can be challenging as customer traffic varies significantly across hours-ofa-day, days-of-a-week, and months-of-a-year (Mani et al. 2015). Operations management researchers play a leading role in developing sophisticated algorithms for scheduling labor to closely track variations in consumer demand (see Ernst et al. 2004 for a review). ...
... For example, the seminal research of Fisher et al. (2006) documents how inadequate store staffing levels undermine store execution and customer satisfaction and sales. Several other operations researchers have expanded this line of inquiry by investigating the impact of staffing levels on a range of markers of store performance, including conversion rate, basket value, sales, and profitability (Netessine et al. 2010, Perdikaki et al. 2012, Mani et al. 2015, Fisher et al. 2021, Musalem et al. 2021. This research clarifies the importance of having enough sales associates available to meet customers' needs while restricting labor expenses. ...
... This component also targeted improving consistency and adequacy for store associates. Managers were given modest increases in the store's labor budget to use during times analyses we conducted, based on Mani et al. (2015), indicated the store was understaffed. In our early discussions, managers explained that their labor budget was tight, with little slack for changes in workflow or employee calloffs, leading them to schedule associates for short shifts and to make changes to the schedule at the last minute. ...
We estimate the causal effects of responsible scheduling practices on store financial performance at the U.S. retailer Gap, Inc. The randomized field experiment evaluated a multicomponent intervention designed to improve dimensions of work schedules—consistency, predictability, adequacy, and employee control—shown to foster employee well-being. The experiment was conducted in 28 stores in the San Francisco and Chicago metropolitan areas for nine months between November 2015 and August 2016. Intent-to-treat (ITT) analyses indicate that implementing responsible scheduling practices increased store productivity by 5.1%, a result of increasing sales (by 3.3%) and decreasing labor (by 1.8%). Drawing on qualitative interviews with managers and quantitative analyses of employee shift-level data, we offer evidence that the intervention improved financial performance through improved store execution. Our experiment provides evidence that responsible scheduling practices that take worker well-being into account can enhance store productivity by motivating additional employee effort and reducing barriers to employees adhering to the scheduled labor plan.
This paper was accepted by David Simchi-Levi, operations management.
Funding: This research was supported by generous grants from the W.K. Kellogg Foundation, the Washington Center for Equitable Growth, the Robert Wood Johnson Foundation, the Institute of International Education in collaboration with the Ford Foundation, the Center for Popular Democracy, the Suzanne M. Nora Johnson and David G. Johnson Foundation, and Gap, Inc.
Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2021.4291 .
... Store performance is a combination of measures that might indicate if a business is prospering. Although there is no consensus in the marketing literature on which criteria should be used to measure store performance, some studies suggest that an important indicator is a relationship between sales and traffic (Gijsbrechts et al., 2003;Mani et al., 2015;Walters & Mackenzie, 1988). When measuring store performance, companies usually look at their internal data. ...
... Store performance has been a subject of interest in several marketing studies. Since the early 1980s, empirical studies have analyzed the relation between different factors and store performance (Babakus et al., 2004;Gijsbrechts et al., 2003;Lusch et al., 2015;Mani et al., 2015;Maxham et al., 2008;Walters & Mackenzie, 1988). Table 1 shows the different types of operationalizations of the concept in the most relevant papers. ...
... A critical variable of the model was the traffic of customers. Mani et al. (2015) also studied the relationship between traffic, sales, and labor. They concluded that staffing decisions based on traffic could yield higher profits since customer traffic captures the store demand potential. ...
A traditional approach for measuring store performance is to understand store traffic, where each company measures its sales performance based on its internal (i.e., customers) traffic data. However, privacy policies and the lack of internal data often hinder companies' capacity in assessing their own or their competitors' store performance. We propose a new method to estimate store performance, where Machine Learning (ML) algorithms are fed with remote sensing (satellite imagery) data. We develop a Computer Vision algorithm to count cars in parking lots using freely available Google satellite imagery. We gathered and processed images of 501 parking lots in 209 Brazilian cities, representing 25 out of the 27 states of the country. We used ML algorithms to estimate the relation between the number of cars and the sales of a major electrical manufacturer's products. The results suggest that the relationship between sales and parked cars is non-linear and different depending on the type of business (retail vs. other kinds). The conditional effect of parked cars on sales was positive for non-retail stores and negative for retail stores. The Deep Learning algorithm achieved the best predictive performance. The results suggest a counter-intuitive inverted U-shape effect of parking lot occupancy on sales.
... In addition to maintaining the right level of inventory at the right location and right time, retailers also need to match labor supply to incoming customer traffic to provide the right service level to their customers. Matching labor supply to customer demand can be challenging as customer traffic varies significantly across hours-ofa-day, days-of-a-week, and months-of-a-year (Mani et al. 2015). Operations management researchers play a leading role in developing sophisticated algorithms for scheduling labor to closely track variations in consumer demand (see Ernst et al. 2004 for a review). ...
... For example, the seminal research of Fisher et al. (2006) documents how inadequate store staffing levels undermine store execution and customer satisfaction and sales. Several other operations researchers have expanded this line of inquiry by investigating the impact of staffing levels on a range of markers of store performance, including conversion rate, basket value, sales, and profitability (Netessine et al. 2010, Perdikaki et al. 2012, Mani et al. 2015, Fisher et al. 2021, Musalem et al. 2021. This research clarifies the importance of having enough sales associates available to meet customers' needs while restricting labor expenses. ...
... This component also targeted improving consistency and adequacy for store associates. Managers were given modest increases in the store's labor budget to use during times analyses we conducted, based on Mani et al. (2015), indicated the store was understaffed. In our early discussions, managers explained that their labor budget was tight, with little slack for changes in workflow or employee calloffs, leading them to schedule associates for short shifts and to make changes to the schedule at the last minute. ...
... Our approach is related to recent attempts to obtain causal inference on managerial decisions in various industries, such as ride-sharing (Cohen et al. 2016, Castillo 2019, staffing (Mani et al. 2015), and loan markets (Costello et al. 2020); each of these papers exploits a specific institutional or technological feature that generates exogenous variation in the implemented policy. In contrast, our estimation approach uses a "behavioral reaction" of managers that can be applied potentially in many other contexts in which decisions are based on algorithmic recommendations. ...
... Staffing. A number of papers have estimated the effect an additional worker has on profits by constructing a predictive model of staffing behavior and then exploiting observed departures from it as the exogenous variation in staff levels (e.g., Mani et al. 2015 andFisher et al. 2020). Although such methods may Garcia, Tolvanen, and Wagner: Demand Estimation and Price Recommendations work well in some cases, it is generally difficult to assess the extent to which the econometrician's model is misspecified and the observed departures truly exogenous. ...
We provide a new framework to identify demand elasticities in markets where managers rely on algorithmic recommendations for price setting and apply it to a data set containing bookings for a sample of midsized hotels in Europe. Using nonbinding algorithmic price recommendations and observed delay in price adjustments by decision makers, we demonstrate that a control-function approach, combined with state-of-the-art model-selection techniques, can be used to isolate exogenous price variation and identify demand elasticities across hotel room types and over time. We confirm these elasticity estimates with a difference-in-differences approach that leverages the same delays in price adjustments by decision makers. However, the difference-in-differences estimates are more noisy and only yield consistent estimates if data are pooled across hotels. We then apply our control-function approach to two classic questions in the dynamic pricing literature: the evolution of price elasticity of demand over and the effects of a transitory price change on future demand due to the presence of strategic buyers. Finally, we discuss how our empirical framework can be applied directly to other decision-making situations in which recommendation systems are used.
This paper was accepted by Omar Besbes, revenue management and market analytics.
... Ton (2009) emphasized the role that retail store personnel play in driving sales through execution quality. Many others have looked into the effects of store-level personnel scheduling on store profits (e.g., Mani et al., 2015;Van den Bergh et al., 2013). For example, Mani et al. (2015) derived an optimal store staffing level based on queuing theory and archival data from a retailer. ...
... Many others have looked into the effects of store-level personnel scheduling on store profits (e.g., Mani et al., 2015;Van den Bergh et al., 2013). For example, Mani et al. (2015) derived an optimal store staffing level based on queuing theory and archival data from a retailer. ...
Retail inventory shrinkage, resulting primarily from employee theft and shoplifting, costs retailers nearly $70 billion annually. With brick‐and‐mortar retailers today confronting increased competition and low future growth expectations, reducing inventory shrinkage is becoming even more critical to becoming profitable. This paper analyzes a unique dataset that combines both primary survey and objective archival data from a Fortune 500 retailer to test a theoretical model associating retail inventory shrinkage, the capacity of a retail store to sense weak security breach signals, centralization of decision making, and formalization of security breach management. The analysis builds on insights from high reliability organization theory and the literature on organizational structure. Results reveal that as a retail store increases its capacity to sense weak security breach signals, it observes decreases in store‐level inventory shrinkage, with this negative association amplified (dampened) when the retail store has formalized procedures and protocols for managing security breaches (has centralized decision making within the retail store). Moreover, while the establishment of formalized procedures and protocols for managing security breaches bolsters the capacity of a retail store to sense weak security breach signals, centralizing decision making has the opposite effect. Our findings contribute to the retail operations literature by introducing a new store‐level organizational capability to guard against theft‐based retail inventory shrinkage and by offering novel insights into how and why organizational structure at the level of a retail store deters or facilitates the capacity to sense weak security breach signals. From a practical perspective, these findings advise retailers to develop the capability to become aware of and to mitigate security breaches. Further, to support this capacity, retailers are urged to decentralize decision making to retail store personnel and to invest in formalizing procedures and protocols for managing security breaches in order to deter retail thefts that shrink retail store inventory.
... They show that service quality management needs to account for both congestion effects and customer sensitivity. Building on a model motivated by queuing theory and using a large empirical data set from multiple retail stores, Mani, Kesavan and Swaminathan (2015) show that reducing understaffing may lead to significant increases in sales and profits. They underline the importance of factoring store traffic information into staffing decisions. ...
... Of particular importance in this context is the utilization of the service system. This observation resembles the results of Mani et al. (2015) , who argue that dynamic capacity management based on store traffic information is important to the optimization of profits. However, in contrast to short-term staffing decisions, adapting the optimal EAS gate configuration may be achieved rather easily. ...
The increasing adoption of omnichannel strategies in recent years has led retail companies worldwide to fundamentally rethink the future role of their network of brick-and-mortar stores. One strategic option being pursued by many retailers is the transformation of the stationary store into a “smart store,” augmented by various digital services. However, an essential prerequisite for the success of smart store services is high quality of the underlying data generated through the use of technologies for tracking products and customer behavior. As a means of investigating the use of machine learning to improve data quality, the present study considers the example of Radio Frequency Identification (RFID) as a technological infrastructure for tracking products in fashion retail. We examine electronic article surveillance and automated checkouts as practical use cases enabled by a classification model for the detection of product movements on the store floor. In order to identify an economically optimal configuration of the classifier, we develop a complementary service operations model that allows for determining the respective cost impact. In addition to the specific results for the considered use cases, the study thus points to a general and novel prescriptive analytics approach.
... The retail industry operates within a highly competitive and dynamic environment, necessitating a substantial workforce to meet the demands of customer service (Cuevas et al., 2016;Henao et al., 2015;Kesavan et al., 2014;Lam et al., 1998;Mani et al., 2015;Henao, 2015;Porto et al., 2020;2023). Various types of physical retail stores exist, including specialty, convenience, department, home improvement, and supermarkets (groceries). ...
This research examines the economic viability of night shelf stocking in the retail industry. By analyzing real data on the costs and benefits of both night and day stocking, we propose a novel integer linear optimization model to determine the optimal structure of shelf stocker shifts for individual stores. The model is tested on a standard Chilean supermarket, with a sensitivity analysis performed on key parameters including product demands, shelf capacity, and cost factors. The results reveal that a daytime shelf stocking system is impractical and costly for high-demand outlets. We recommend combining day and night stockings, as night stocking proves to be more efficient, reducing stockouts and salary costs. Additionally, an analysis incorporating semi-night shifts as an alternative to traditional shift patterns demonstrates that combining day, night, and semi-night shift structures is the most cost-effective solution, minimizing labor and stocking expenses without compromising service quality. This study highlights the economic advantages of night shelf stocking and provides valuable insights for retailers seeking to optimize their operations.
... Operating hours are the total time scheduled for work in comparison to firms' optimal capacity (Foss, 1995). In retail, where selling is the primary activity, operating hours are typically determined by historical customer traffic, type of day [1] and location (Mani et al., 2015;Ganesha et al., 2020). The unique confinement stipulations of the COVID-19 crisis and firms' reaction to intermittent movement control make this indicator imperative to appraise. ...
Purpose-Although recent literature has examined diverse measures adopted by SMEs to navigate the COVID-19 turbulence, there is a shortage of evidence on how crisis-time strategy creation behaviour and digitalization activities increase (1) sales and (2) cash flow. Thus, predicated on a novel strategy creation perspective, this inquiry aims to investigate the crisis behaviour, sales and cash flow performance of 528 SMEs in Morocco.
Design/methodology/approach-Novel links between (1) aggregate wage cuts, (2) variable operating hours, (3) deferred payment to suppliers, (4) deferred payment to tax authorities and (5) sales performance are developed and tested. A further link between sales performance and cash flow is also examined and the analysis is conducted using a non-linear structural equation modelling technique.
Findings-While there is a significant association between strategy creation behaviours and sales performance, only variable operating hours have a positive effect. Also, sales performance increases cash flow and this relationship is substantially strengthened by e-commerce digitalization and innovation.
Originality/value-Theoretically, to the best of the authors' knowledge, this is one of the first inquiries to espouse the strategy creation view to explain SMEs' crisis-time behaviour and digitalization. For practical purposes, to supplement Moroccan SMEs' propensity to seek tax deferrals, it is argued that debt and equity support measures are also needed to boost sales performance and cash flow.
... If used correctly, prediction can play a vital role in maximizing the profits by cutting down the unnecessary costs (Kim et al., 2014;Badorf and Hoberg, 2020). By finalizing the optimum predictive model, the retailers can improve planning and budgeting as the output of the model influence overstocking and understocking (Mani et al., 2015). Accurate predictions lead to better pricing, revenue and inventory planning (Ferreira et al. 2016;Williams et al. 2014). ...
in collaboration with Robert Gordon University Aberdeen , UK 2021 ii Declaration I declare that the work presented in this dissertation is my own work and to best of my knowledge acknowledgement is made for all sources of information used in this dissertation. Further, this as a whole or as parts has not been submitted previously or concurrently for a degree or any other qualification at any University or institutions of Higher Learning. …………………………. ……………………. Signature of the student Date Full Name of Student: Student Registration No: The above student carried out his/her research project under my supervision. ………………………… ……………………. Signature of the supervisor Date Name of the Supervisor: iii Abstract Predicting sales has become a vital role in consumer durable retail industry with the increased competition. The importance of this has increased due to its direct and indirect affect to the company's profitability. Managing the right inventory would reduce inventory holding costs while overstocking brings repercussions such as increasing the inventory holding cost, occurring promotional costs to flush out the excess stock etc. In this study, several machine learning techniques were used to predict the sales using the factors affecting sales. The techniques namely; K-nearest neighbor (KNN), Artificial neural network (ANN), Decision tree, Random Forest, Linear Regression and Bayesian Applied Regression. This study was conducted on four main product categories namely; Air conditioners, Fans, Refrigerator and audios. The algorithms were applied on the preprocessed sales data captured from the ERP system of the company, promotional data, event data and weather data. Once applied the algorithms, the best model was selected comparing RMSE and MAPE. KNN was identified to be the best fitting model of the seven models used for all the product lines. The analysis were done using R programming language and then the predicted values are then visualized through Power BI dashboards to present to the management. These predictions visualized through dashboards allows the retailer to take correct decisions at the right time while monitoring the progress of the actions. Moreover, the finalized data model allows the retailer to gain the cutting edge over the competitors while increasing the profits. iv
... Several review papers were published covering the workforce scheduling problems for nurse rostering, physician scheduling, and railway crew scheduling (Defraeye & Van Nieuwenhuyse, 2016;Erhard, Schoenfelder, Fügener, & Brunner, 2018;Lee & Loong, 2019;Maria Gonzalez-Neira, Montoya-Torresc, & Barrera, 2017;Ojstersek, Brezocnik, & Buchmeister, 2020). And several authors contributed to workforce scheduling in the traditional retail sector include (Álvarez, Ferrer, Muñoz, & Henao, 2020;Bürgy, Michon-Lacaze, & Desaulniers, 2019;Chen et al., 2022;Mani, Kesavan, & Swaminathan, 2015;Mou & Robb, 2019). To the best of our knowledge, no review paper on driver scheduling problems in the O2O retail context has been published. ...
During the spread of the epidemic, the home delivery service (HDS) has been quickly introduced by retailers which helps customers avoid the risk of viral infection while shopping at offline stores. However, the operation cost of HDS is a huge investment for O2O retailers. How to minimize the operating costs of HDS is an urgent issue for the industry. To solve this problem, we outline those management decisions of HDS that have an impact on operating costs, including dynamic vehicle routing, driver sizing and scheduling, and propose an integrated optimization model by comprehensively considering these management decisions. Moreover, the dynamic feature of online orders and the heterogeneous workforces are also considered in this model. To solve this model, an efficient adaptive large neighborhood search (ALNS) and branch-and-cut algorithms are developed. In the case study, we collected real data from a leading O2O retailer in China to assess the effectiveness of our proposed model and algorithms. Experimental results show that our approach can effectively reduce the operating costs of HDS. Furthermore, a comprehensive analysis is conducted to reveal the changing patterns in operating costs, and some valuable management insights are provided for O2O retailers. The theoretical and numerical results would shed light on the management of HDS for O2O retailers.
... This paper contributes to this stream of work by characterizing the volatility of worker schedules and examining how this volatility impacts voluntary employee turnover, which in turn have a negative impact on firm productivity. Furthermore, while much of the empirical work in this area has examined these topics with regard to retail store employees (Perdikaki et al., 2012;Kesavan et al., 2014;Mani et al., 2015;Yung et al., 2020;Musalem et al., 2021) and restaurant workers (Tan and Netessine, 2019;Tan and Staats, 2020;Kamalahmadi et al., 2021), we use data from an understudied yet important segment of the labor market: the nursing workforce in health care delivery settings. To this end, our work contributes to the recent literature that examines staffing and scheduling issues in post-acute care settings. ...
... The retail industry operates within a highly competitive and dynamic environment, necessitating a substantial workforce to meet the demands of customer service (Cuevas et al., 2016;Henao et al., 2015;Kesavan et al., 2014;Lam et al., 1998;Mani et al., 2015;Henao, 2015;Porto et al., 2020;2023). Various types of physical retail stores exist, including specialty, convenience, department, home improvement, and supermarkets (groceries). ...
This research examines the economic viability of night shelf stocking in the retail industry. By analyzing real data on the costs and benefits of both night and day stocking, we propose a novel integer linear optimization model to determine the optimal structure of shelf stocker shifts for individual stores. The model is tested on a standard Chilean supermarket, with a sensitivity analysis performed on key parameters including product demands, shelf capacity, and cost factors. The results reveal that a daytime shelf stocking system is impractical and costly for high-demand outlets. We recommend combining day and night stockings, as night stocking proves to be more efficient, reducing stockouts and salary costs. Additionally, an analysis incorporating semi-night shifts as an alternative to traditional shift patterns demonstrates that combining day, night, and semi-night shift structures is the most cost-effective solution, minimizing labor and stocking expenses without compromising service quality. This study highlights the economic advantages of night shelf stocking and provides valuable insights for retailers seeking to optimize their operations.
... 2018, O'Carroll 2019 and thereby boost foot traffic at the brick and mortar channel (Yantra 2005, Gannon 2019). Note that store traffic plays a crucial role to generate in-store sales (Chapados et al. 2014, Chuang et al. 2015, which can come through either impulse purchases (Stahlberg and Maila 2010) or the ability of store employees to convert store traffic into sales (Perdikaki et al. 2012, Mani et al. 2015. For example, Bae et al. (2011) indicate that at department stores, impulse purchases account for 27% to 62% of in-store sales. ...
Implementing free in-store pickup services has become increasingly widespread in the retail industry as part of an omni-channel strategy. Retailers considering a new service need to evaluate the value gained and resulting changes to customer behavior to understand if implementation should move forward. Operations literature on omni-channel retailing is growing, has generally been empirically focused, and has recently been emphasized since the COVID-19 pandemic has made services such as ship-to-store or curbside pickup necessary for retailers to continue to operate. We add to this body of work by analytically modeling the omni-channel setting, providing prescriptive optimal decisions and comparative statics across a wide range of market parameters. We analytically model a profit-maximizing retailer faced with a heterogeneous customer base and derive optimal pricing and shipping fee decisions. We further analyze how demand segments and the resulting profit change under different market conditions to understand the impact of free ship-to-store service on customer shopping behavior. We find that introducing additional ship-to-store service is always at least as profitable as offering traditional fee-based home-delivery service only. By its nature, adopting free ship-to-store service attracts customers to local stores for the pickup process, which in turn increases store foot traffic. We show that the retailers price and shipping fee decisions depend on the additional sales arising from the increased store foot traffic. Furthermore, we show that the impact of additional profit arising from the increased in-store traffic on the optimal price depends on the hassle cost difference between the home-delivery and ship-to-store services.
... The average lost sales due to understaffing during peak hours are calculated to be 8.56%. The average profitability of retailers will increase by 7.02% if it eliminates understaffing during peak hours (Mani et al., 2015). ...
Retail workforce optimization keeps store employees happy, improves customer service, and reduces opportunity costs of lost sales. As workforce compensation costs constitute one of the largest components of retailers' operating costs, there is a widespread tendency to understaff to save on those costs. In the case of workforce undersizing, when a retailer decides to increase the size of the workforce, the additional workforce not only generates incremental revenue with better sales conversion but also has a positive impact on workforce morale, as the workforce is not overstretched. It also results in higher retention of the retail workforce. Improved retail workforce retention leads to lower hiring and training costs and improved store performance. On the other hand, retail workforce oversizing results in higher payroll costs and decreased engagement of retail employees. Hence, there is a need to find the right number of store employees to provide consistent customer service even during the period of volatile store traffic and still manage compensation costs favorably. The research provides various frameworks to investigate whether retail stores are properly sized and studies the impact of optimal workforce sizing on retail workforce compensation costs and ultimately on store performance.
... Scheduling laws focus on the allocation of a valued and scarce resource: labor hours. As the US service sector has embraced lean staffing models as an approach to manage overall business costs, labor budgets have tightened to the point of generating understaffing, poor customer service, ''phantom stockouts,'' and lower sales (Mani, Kesavan, and Swaminathan 2014;Ton 2014). Many firms hold managers accountable for staying within hours, or maintaining a particular ratio between outlays for labor and markers of demand (e.g., customer traffic, sales; Lambert 2008). ...
Employment legislation intended to establish scheduling standards in hourly jobs is spreading across US cities. Yet the well-documented role that cost-focused business models play in shaping manager practices forecasts uneven compliance. Joining perspectives from labor and public policy studies, the authors examine variation in the organizational arena—local workplaces—where implementation of scheduling regulation is set to play out. Analyses draw on surveys and interviews with 52 retail and food service managers on the eve of enactment of Seattle’s Secure Scheduling Ordinance. By capturing the full range of variation in managers’ scheduling practices prior to enactment, and their distance from legal compliance, the authors contribute unique insight into the prospects of establishing universal work hour standards in service industries and the varying pathways employers will likely pursue toward regulatory compliance. Findings suggest targets for enforcement and manager training and offer insight into the implementation challenges posed by municipal-level regulation.
... The ability to match customers' demands in a timely and costeffective manner is one of the key drivers of retail store performance (Mani, Kesavan, & Swaminathan, 2015). Inefficient staffing and scheduling of workers usually result in higher understaffing and overstaffing costs for the retailers due to varying demand. ...
A three-stage iterative sequential methodology is proposed for the staffing and tour scheduling problem of a retail store. The proposed work differs from the existing works in the following ways (i) Existing studies usually considered the tour scheduling problems for a given workforce size. However, this work attempts to solve both staffing and tour scheduling problems. (ii) The proposed work incorporates a large number of shift flexibilities such as the requirement of break window, meal break assignment, day-off scheduling, multiple shift start time, and other business and regulatory constraints. Previous studies have usually focused on limited flexibility as it makes the problem formulation complex and difficult to solve. The first stage of our methodology uses deterministic finite automata (DFA) that handle the above-mentioned flexibilities well and generates all the feasible shifts. The use of DFA reduces the problem complexity, search space, and the number of constraints. The second stage formulates a mixed-integer linear programming (MILP) model considering all the feasible shifts generated in the first stage. The MILP finds the optimal workforce schedule for the given workforce demand. The third stage presents a heuristic that iteratively solves MILP by varying the workforce size and determines the optimal staffing level that minimizes the total overstaffing and understating cost. The proposed methodology is applied in a specialty store on 550 randomly generated problem instances of realistic sizes. It obtains the solution within a 2.35 percent average optimality gap in a reasonable computational time. The result shows that the proposed heuristic outperforms in terms of solution quality and runtime on the workforce demand instances generated using Poisson distribution over those generated using Uniform distribution. The percentages of overstaffing and understaffing decrease with the increase in the mean and standard deviation of hourly workforce demand.
... A higher value of the conversation rate means better understanding of the shoppers, boost sales, stealing shoppers from competitors, and improvement in brand perception. Research suggests that store sales have a concave traffic connection, with conversion rates dropped non-linearly as traffic increases (Mani and Swaminathan, 2015). Improving shopper conversion rates has become a challenge for retailers (Dave and Sondhi, 2011). ...
Purpose
The best possible way for brick-and-mortar retailers to maximise engagement with personalised shoppers is capitalising on intelligent insights. The retailer operates differently with diversified items and services, but influencing retail atmospheric on personalised shoppers, the perception remains the same across industries. Retail atmospherics stimuli such as design, smell and others create behavioural modifications. The purpose of this study is to explore the atmospheric effects on brick-and-mortar store performance and personalised shopper's behaviour using cognitive computing based in-store analytics in the context of emerging market.
Design/methodology/approach
The data are collected from 35 shoppers of a brick-and-mortar retailer through questionnaire survey and analysed using quantitative method.
Findings
The result of the analysis reveals month-on-month growth in footfall count (46%), conversation rate (21%), units per transaction (27%), average order value (23%), dwell time (11%), purchase intention (29%), emotional experience (40%) and a month-on-month decline in remorse (20%). The retailers need to focus on three control gates of shopper behaviour: entry, browsing and exit. Attention should be paid to the cognitive computing solution to judge the influence of retail atmospherics on store performance and behaviour of personalised shoppers. Retail atmospherics create the right experience for individual shoppers and forceful use of it has an adverse impact.
Originality/value
The paper focuses on strategic decisions of retailers, the tactical value of personalised shoppers and empirically identifies the retail atmospherics effect on brick-and-mortar store performance and personalised shopper behaviour.
... Our research contributes to the retail operations literature on human resource management. Previous empirical studies have found that increasing staffing leads to higher conversion of customer traffic into sales (Perdikaki et al. 2012, Mani et al. 2015 and that the impact of staffing on sales varies with store characteristics (Fisher et al. 2020b, Musalem et al. 2020. Others demonstrate the importance of employing the right mix of temporary or part-time workers (Kesavan et al. 2014), managing employee retention (Ton and Huckman 2008), and investing in employees through training, compensation, and work design (Ton 2017). ...
An important but overlooked cost of payments in retailing is the cost on checkout cashiers. This paper examines the compensating wage differential that cashiers require to handle payments in cash. First, a multicountry panel data study shows that cashier wages increase with retail cash usage, which is consistent with cashiers requiring compensation to handle cash. Second, in a discrete choice experiment where supermarket cashiers chose between collecting card and cash payments, eight of 10 cashiers preferred card to cash. Among those who preferred card, the median cashier required a wage premium of S27) a month to handle cash. The premium was lower among cashiers who are local, less risk averse, and younger. Third, in a laboratory study, subjects traded off earnings against stress. With higher frequency of cash payments, high earners experienced greater physiological stress than low earners. Earnings also increased with abilities in arithmetic and coping with stress. Collectively, these studies show that cashiers require higher wages to handle cash payments, in part due to higher stress. We offer policy, managerial, and research implications for job design, payment systems, and workplace stress.
... The average lost sales due to understaffing during peak hours are calculated to be 8.56%. The average profitability of retailers will increase by 7.02% if it eliminates understaffing during peak hours (Mani et al., 2015). Understaffing also negatively impacts store associate satisfaction and a decline in employee satisfaction has been linked to a decline in the store's financial performance (Maxham et al., 2008). ...
The retail workforce is a strategic lever of the retailer for improving sales growth, market share, and profitability. With optimal retail workforce sizing and structure, customers would get prompt sales assistance and service, shelves should be replenished in a timely manner, store employees should be neither idle nor overstretched, and compensation costs should be managed effectively. Undersizing may hurt retailers in the long run as it affects merchandising capability and customer services, which ultimately hurt store sales and profits. Retail workforce optimization keeps store employees happy, improves customer service, and reduces opportunity costs of lost sales. The research provides various frameworks that outline the impact of undersizing in retail stores on sales and profitability and provides a methodology to determine the optimal workforce size. The research also provides an illustration with various scenarios to investigate whether a retail store is understaffed and calculates the financial impact of undersizing on revenue and profitability.
... We contribute to this literature by exploring how minimum wage may impact firm's scheduling practice and its use of scheduling flexibility. More broadly, our studies are also related to the recent empirical studies exploring how scheduling discretion (Ibanez et al. 2018, Tan and Staats 2020), duration (Song et al. 2020), and modality (Buell et al. 2020) may impact service outcome, the impact of scheduling on worker retention Musalem et al. (2019) and how scheduling constraint may lead to understaffing (Mani et al. 2015). ...
Problem definition: The effect of the minimum wage is an important yet controversial topic that has received attention for decades. Our study is the first to take an operational lens and empirically study the impact of the minimum wage on firms' scheduling practices.
Methodology/results: Using a highly granular dataset from a chain of fashion retail stores, we estimate that a 1.
Managerial implications: Our study empirically identifies and highlights a new operational mechanism through which increasing the minimum wage may negatively impact worker welfare. Our further analysis suggests that the combination of the reduced hours, lower eligibility for benefits, and less consistent schedules (that resulted from the minimum wage increase) may substantially hurt worker welfare, even when the overall employment at the stores stay unchanged. By better understanding the intrinsic trade-off of firms' scheduling decisions, policy makers can better design minimum wage policies that will truly benefit workers.
The coronavirus crisis has illuminated how poorly the United States compares with other major industrialized nations in providing workers across all industries equal access to paid sick and family leave, employee-requested flexible scheduling, and reasonable work hours. Many essential workers in frontline jobs (such as those in health care, food services, and public safety) have been unable to access benefits that support work-life balance and that play a critical role in helping employees manage job stress and protect their health. At the same time, many nonessential workers (disproportionately women) who can telecommute to prevent exposure have been left juggling a demanding job while also caring for children, elders, or others at home. We propose three evidence-based national initiatives that would improve U.S. work-life policy: ensure employees have access to and the ability to use paid sick leave and family leave, mandate that employers create emergency backup staffing infrastructures, and give employees the right to request flexible and reasonable work hours. These work-life policies are based on principles of balanced flexibility that benefit employers, employees, and society as a whole.
We show that financial reporting influences consumer behavior by drawing consumer attention to announcing firms. Analyzing global positioning system (GPS) data, we document upticks in foot traffic to firms’ commercial locations immediately following their earnings announcements. This increase is more pronounced for announcements with substantial media attention, fewer concurrent announcements, heightened internet search volume, and extreme stock price jumps and earnings surprises—indicating that announcement coverage impacts consumer behavior by capturing attention. Furthermore, foot traffic increases with positive earnings for firms offering durable goods, suggesting consumers respond to news about firms’ financial prospects. Consumer attention patterns increase revenues and advertising effectiveness, ultimately suggesting that financial reporting serves a marketing function.
Data Availability: All other data are available from the public sources cited in the text.
JEL Classifications: G10; G11; G12; G14; G40; G41.
Purpose
This study aims to reveal how the organizational structure (vertical integration vs. franchising) of 308 stores in a Spanish fashion retail franchise chain affects their performance measured through two key performance indicators commonly used in this industry, namely, labor productivity and service quality ratings. We also appraise the moderating role played by the servant leadership of franchisees and managers of company-owned outlets to explore its influence on the relationship between organizational structure and store performance.
Design/methodology/approach
We have used multivariate analyses to study the research questions, with a panel dataset of quarterly store-level data for the period January–December 2022.
Findings
Vertically-integrated stores record lower labor productivity than franchised ones. This impact is lower in stores run by individuals high in servant leadership than in those run by individuals low in it. Franchised outlets also record lower ratings in service quality than vertically-integrated stores, and this negative impact is weaker in stores run by individuals high in servant leadership.
Originality/value
Nothing has thus far been published on the moderating effect of servant leadership in the relationship between the organizational structure of different stores and their outcomes in franchise systems.
Problem definition: Employers across many sectors of the economy have been fast to adopt variable work scheduling policies. The cost of this flexibility for employers is usually borne by employees, for whom unstable work schedules create several disruptions. In the context of home healthcare, we examine how employer-driven volatility in nurses’ schedules impacts their decision to voluntarily leave their job. Methodology/results: Using an instrumental variables approach, we causally identify the effect of schedule volatility on nurses’ voluntary turnover. We begin by constructing an operational measure of schedule volatility using time-stamped work log data from one of the largest home health agencies in the United States. Because this measure may be endogenous to the worker’s decision to quit, we instrument for schedule volatility using paid days off taken by other nurses in the same branch. We find that higher levels of schedule volatility substantially increase a worker’s likelihood of quitting. Specifically, a one-standard-deviation increase in schedule volatility increases the average worker’s propensity to quit on a given day by more than threefold. Translated into yearly terms, 30 days of high schedule volatility over the course of the year increases the average worker’s probability of quitting that year by 20%. Our policy simulations of counterfactual scheduling policies suggest that excess schedule volatility can explain a significant portion of voluntary turnover, and some interventions have the potential to substantially reduce workers’ daily propensity to quit. Managerial implications: This work contributes to the understanding of the extent to which employees value control over their own work schedules and are averse to volatile work schedules that are dictated by employers. Especially in the current environment where there is a growing emphasis on work-life balance and employee-driven flexibility, finding a way to support stable schedules could be important for employers to attract and retain workers.
Funding: This work was supported by the National Research Service Award Postdoctoral Fellowship, the Wharton Dean's Research Fund, the Agency for Healthcare Research and Quality [T32 Grant 5T32HS26116], and the Claude Marion Endowed Faculty Scholar Award.
Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1205 .
Understanding the drivers of market concentration in the generic pharmaceutical industry is essential to guaranteeing the availability of low-cost generics. In this paper, we develop a structural model to capture the multiple determinants governing manufacturers’ entry decisions; in particular, we focus on how manufacturing complexity and the regulatory environment for generics approval affect concentration in drug markets. We estimate the model using data collated from six disparate sources. We find that manufacturing complexity, as reflected in the number of active ingredients, for example, significantly reduces the likelihood of generics entry. Moreover, the delay in the review process for the generics applications significantly affects the number of firms entering a market. Our policy simulations suggest that a shortened drug approval review time significantly increases the average number of entrants per market and reduces the fraction of markets with no generics entry; however, a notable portion of markets would still lack any generic competition.
This paper was accepted by Jayashankar Swaminathan, operations management.
We review papers published in POM during its thirty‐year history that deal with retail operations issues with an empirical approach. The papers span a range of issues, from traditional ones like forecasting and inventory planning, to new technologies, like RFID and e‐commerce, and strategic, like links between retailing and stock market performance. This article is protected by copyright. All rights reserved
We study the problem of category space location-allocation in the retail industry. We introduce a new attractiveness factor to reflect the product-based visibility level in designing the optimal allocation policy. This factor will be determined for each aisle by the lineup of product categories allocated to that aisle and all other aisles sharing a shopping path with it. We explore how considering the classical location-based attractiveness and the proposed product-based attractiveness can improve a retailer's overall space profitability. We develop a modelling framework that integrates both location-based and product-based attractiveness factors in a mixed-integer nonlinear program. Due to the non-linearity and non-convexity of the proposed model, large-scale instances are computationally challenging to solve using the state-of-the-art commercial solvers. We thus introduce a two-stage heuristic solution method that generates a near-optimal solution in a reasonable amount of time. Using the two-stage model, we explore the optimal store design for an illustrative case study. The results couple the optimal category space allocation to customers' shopping paths and create a profitability-maximising balance between the placement of high-demand and high-impulse product categories. We show that focussing on product-based attractiveness exposes the store to congestion risks, which can be prevented by adding constraints limiting congestion in different aisles of the store.
Retail stores are geographically dispersed as a part of multi‐unit organization. In such a setting, store managers play an important role in driving store performance. To motivate them to exert effort, retailers have provided group incentives for store managers. Using data from 75 stores of a U.S.‐based retail chain that changed its incentive plan for store managers from being purely dependent on store performance to being dependent upon both store and corporate performance, we investigate the effect of this change on store performance. We also examine the moderating role of geographical proximity among stores and past performance of the focal store. To establish a causality, we identify a control group from a matched stores of another retail chain in the same industry and perform a difference‐in‐differences analysis. We find that making managers' bonus dependent on corporate goals has resulted in overall worsening of store performance. However, such effect is contingent upon both geographical proximity among stores and past performance of the focal store. The impact of the incentive change on store performance is positively moderated by the increase in stores within close geographical proximity while it is negatively moderated by the past performance of the focal store. This paper broadens our understanding of how incentives work in practice, and more importantly, documents how firms can design more effective incentive scheme for managers by considering relevant conditions, such as stores' geographical network and their past performance. This article is protected by copyright. All rights reserved
In this research note, we study the effectiveness of parking lot traffic in predicting forward-looking retailer performance (measured by Tobin's q). We obtain parking lot car count data derived from satellite images to construct a quarterly aggregate measure of parking lot traffic for 15 general merchandise retailers. To mitigate endogeneity concerns, we exploit the exogenous shocks of mass shootings that provide exogenous variations in parking lot traffic. Applying the control function approach and a panel firm fixed-effects estimator on 402 retailer-quarter observations, we find that quarterly aggregate parking lot traffic significantly predicts forward-looking retailer performance. Moreover, drawing on the perspectives of organizational competency, we find that the positive relationship between parking lot traffic and forward-looking retailer performance is moderated by factors that are related to a retailer's store management competency, namely, a retailer's existing store management competency (measured by comparable store sales), its proactivity to maintain the competency (reflected by store management intensity, i.e., the total number of store openings and closings relative to the total number of stores), and the external environment (measured by industry concentration) that affects its store management competency.
The bullwhip effect represents a major source of supply chain inefficiency. Traditional OM analytical approaches and theories view vertical integration as a countermeasure to the bullwhip effect. Despite its intuitive appeal, this relationship has yet to be rigorously examined. To be specific, a firm may choose backward integration to tighten its grip on the supply and production side, or forward integration to control over the demand and distribution side. The effect of different integration could be diverse depending on the firm’s supply chain position, but research on this link is limited. Driven by a large dataset containing 292,080 detailed business information of listed firms in China, we empirically examine the impact of forward and backward vertical integration on the bullwhip effect, as well as the moderating role of the firms’ supply chain positions. We find that: (1) forward vertical integration does reduce the bullwhip effect, and this mitigation effect is more pronounced for firms located further downstream; (2) the magnitude and direction of backward vertical integration effects are diverse. It has a strong mitigation effect on the bullwhip effect for upstream firms, and when firms are located further downstream, backward vertical integration will surprisingly increase the bullwhip effect.
This research seeks to discover how the organisational form (franchising vs. vertical integration) of 305 supermarkets belonging to a Spanish franchise chain influences unit-level performance measured through three key performance indicators commonly used in the retail literature: sales per square metre, sales per employee, and service quality scores. Additionally, we assess the moderating role of the manager's gender in each individual supermarket. We have analysed the research questions using multivariate analyses, with a panel dataset that includes quarterly establishment-level data covering the period from January 2017 to December 2019. We have found that franchised supermarkets record higher sales both per square metre and per employee than vertically integrated ones. This positive effect of franchising is lower in establishments run by females than in those run by males. The findings also reveal that franchised supermarkets record lower service quality scores than their company-owned counterparts, and this negative effect is again lower in establishments managed by females than in those managed by males.
Understanding the patterns of demand evolution for a new category is important for firms to effectively manage capacity planning, market and service operations, and research and development. Our objective is to analyze how marketing at the industry level affects the evolution of primary demand in different stages of the product life cycle. We characterize the aggregate marketing activities in two constructs: marketing breadth and competitive spread. The first construct reflects the spread of spending across different marketing instruments at the industry level, and the second construct reflects the spread of spending across different firms. Though both constructs are related to the spread of spending within a category, we find that they have qualitatively different effects on category growth. An econometric model making use of the hierarchical nature of time observations within countries is estimated for each category. First, we find that high degrees of spending breadth impede market growth when the number of competitors is small (the category is young) but accelerate market growth when the number of competitors is higher (the category is maturing). Second, we find that high levels of competitive spread decrease category growth when spending levels are relatively low. However, as spending levels increase, the negative effect of competitive spread on demand growth all but evaporates.
Operational risk has been among the three most significant types of risks in the financial services industry, and its management is mandated by Basel II regulations. To inform better labor decisions, this paper studies how workload affects banks’ operational risk event occurrence. To achieve this goal, we use a unique data set from a commercial bank in China that contains 1,441 operational risk events over 16 months. We find that workload has a U-shaped impact on operational risk error rate. More specifically, the error rate of operational risk events decreases first, as workload increases, and then increases. Furthermore, when workload is low, employees tend to make performance-seeking risks; however, when workload is high, employees tend to exhibit quality degradation due to cognitive multitasking. Based on the causal relationship between workload and operational risk events from the empirical analysis, we discuss staffing policies among branches aimed at reducing operational risk losses. We find that employing a flexible staffing rule can significantly reduce the number of operational risk events by 3.2%–10% under different scenarios. In addition, this significant performance improvement can be achieved by adding even a little bit of flexibility to the process by allowing employees to either switch their business lines in the same branch or switch branches within the same business lines on a quarterly basis.
This paper was accepted by Vishal Gaur, operations management.
Purpose
This research seeks to discover how the organisational form (franchising vs vertical integration) of 384 fashion stores belonging to a Spanish franchise chain influences unit-level performance measured through three key indicators commonly used in the retail literature: sales per square metre, sales per employee and service quality scores.
Design/methodology/approach
The authors have analysed this research question using bivariate and multivariate analyses, with a panel dataset that includes quarterly establishment-level data covering the period from January 2018 to December 2019.
Findings
The aggregated data initially reveal weaker outcomes among franchised establishments. However, after controlling for other variables related to the fashion stores and their local markets, the authors have found that franchised establishments record higher sales both per square metre and per employee than vertically integrated stores. The findings also reveal that franchised establishments record lower service quality scores than their company-owned counterparts.
Originality/value
Nothing has been published on the differences between franchising and company ownership in terms of establishment-level performance in fashion retailing.
Problem definition: Fast recovery from sourcing interruptions is a key objective for global supply chains and for business continuity professionals. In this paper, we study the impact of different supply chain strategies—supplier diversification and the use of long-term relationships—on the ability of a supply chain to recover from sourcing interruptions. Academic/practical relevance: Improving supply chains’ recovery ability has been an important focus area for both practitioners and academics. Collectively, available anecdotal evidence and theoretical analyses provide ambiguous recommendations driven by competing effects of different sourcing strategies. Our paper provides the first rigorous and large-scale empirical evidence relating the use of different supply chain strategies to the ability of a supply chain to recover from supply interruptions. Methodology: We develop a compound estimator of a supply chain’s recovery rate that can be constructed using limited available data (only the time series of firms’ actual sourcing behavior). Using more than two and half million import manifests, we extract firms’ maritime sourcing transactions and use this data to estimate recovery rates of different firm-category supply chains of publicly traded U.S. firms. Results: We find that supplier diversification is associated with slower recovery from sourcing interruptions, whereas the use of long-term relationships is associated with faster recovery. A one standard deviation decrease in the former is associated with a 16% faster recovery, and a like increase in the latter is associated with a 20% faster recovery. Managerial implications: Our paper brings important empirical evidence to the hitherto theoretical debate on the impact of sourcing strategies on faster recovery in supply chains. We therefore provide actionable advice on supply chain design for faster recovery.
Just-in-time scheduling has become ubiquitous in the service industries. Although effective in reducing staffing level, hence labor cost, the potential impact of just-in-time scheduling on workers’ productivity and the firm’s revenue is not well understood. Using a data set of 1,444,044 transactions from 25 stores of a full-service casual dining restaurant chain in the United States, we study how just-in-time scheduling impacts worker productivity. We consider two types of just-in-time schedules: (1) short-notice schedules that are assigned to servers shortly before the day of service (mostly two days in our data) and (2) real-time schedules that are assigned to servers on the day of service. We show that short-notice schedules do not harm server productivity overall, but real-time schedules do by 4.4%. Our analysis indicates this may be because servers reduce their up-selling and cross-selling efforts when working on real-time schedules. We then propose an analytical scheduling model that accounts for both the value of staffing flexibility created through just-in-time scheduling and its impact on server productivity to inform the firm how to use just-in-time scheduling to improve profitability. Through a case study, we demonstrate that with the 4.4% productivity loss during the real-time schedules, the managers should shift from the heavy use of real-time scheduling toward scheduling more servers with longer advance notice. Such a shift not only provides more predictable work schedules for the workers but can also improve restaurants’ expected profit by up to 1%, a significant number for the low-margin restaurant industry.
This paper was accepted by Vishal Gaur, operations management.
If service quality relates to retention of customers at the aggregate level, as other research has indicated, then evidence of its impact on customers’ behavioral responses should be detectable. The authors offer a conceptual model of the impact of service quality on particular behaviors that signal whether customers remain with or defect from a company. Results from a multicompany empirical study examining relationships from the model concerning customers’ behavioral intentions show strong evidence of their being influenced by service quality. The findings also reveal differences in the nature of the quality-intentions link across different dimensions of behavioral intentions. The authors’ discussion centers on ways the results and research approach of their study can be helpful to researchers and managers.
After the cost of goods sold, store labor expense is the largest cost component in the retailing industry. As a result, developing and executing labor plans is a key task for retailers. In this paper we use private data provided by a large retail chain to assess the relationship between labor planning and execution practices and the average transaction (basket) value of a retail store location. We find that consumer basket value varies greatly from store to store and that there is a strong cross-sectional association between labor practices at different stores and basket values. In particular, matching labor deployment to store traffic (rather than to the forecast of sales, as many retailers currently do) is associated with larger basket values. We further separate the task of labor management into the planning component (i.e., creating a labor plan that matches traffic patterns) and the execution components (i.e., deploying part-time employees, full-time employees and managers to match the labor plan), and we find that stores with better plans and stores with better execution of these plans for full-time employees (but not for part-time employees) demonstrate significantly higher basket values. We obtain these results after controlling for customer demographics and product variety, which are also significant in explaining basket values. Our findings suggest that modest improvements in employee scheduling and in execution of the schedule can result in a 3% sales lift at moderate, or even no, additional cost. 1 We thank the three anonymous referees and the associate editor for their constructive suggestions, and the Fishman-Davidson Research Center, P&G-Gillette Corporation and the International Commerce Institute for their generous financial support for this project. We are also grateful to Andy Buteux, Nicole DeHoratius, the participants of the 2007 COER conference in Boston, and numerous individuals interviewed for this project for their valuable insights and ideas.
If service quality relates to retention of customers at the aggregate level, as other research has indicated, then evidence of its impact on customers' behavioral responses should be detectable. The authors offer a conceptual model of the impact of service quality on particular behaviors that signal whether customers remain with or defect from a company. Results from a multicompany empirical study examining relationships from the model concerning customers' behavioral intentions show strong evidence of their being influenced by service quality. The findings also reveal differences in the nature of the quality-intentions link across different dimensions of behavioral intentions. The authors' discussion centers on ways the results and research approach of their study can be helpful to researchers and managers.
The goal of this article is to increase knowledge about the ways that front-line managers use scheduling practices to implement labor flexibility in low-skill, hourly jobs. Data come from a comparative study of 88 non-production jobs in 22 work sites in four industries (hospitality, retail, transportation, and financial services). The focus is on scheduling practices in part-time and full-time standard jobs that allow front-line managers to vary the number of hours employees work each week, the distribution of employees' hours across a week, and the number of employees scheduled for any hours week-to-week. The findings provide insight into the daily accountability requirements that press front-line managers to make quick adjustments to work schedules and the specific scheduling practices that enable them to do so. The discussion considers the extent to which these scheduling practices, like other labor flexibility practices, are implemented in ways that protect some workers from instability at the expense of others.
We use unique data from 245 stores of a UK retailer to study links among middle (store) manager skills, sales, and manager pay. We find that, of the six management practice areas surveyed, the most important is “commercial awareness,” where abler managers achieve up to 13.9% higher sales per worker. We find that many stores have poor managers on this indicator. However, the company is careful to incentivize managers, operating a scheme giving shares (approximately 20%) in both positive and negative deviations of actual sales from expected. Abler managers do not receive higher pay, implying that their skills are company specific.
We describe a methodology by which a retailer can identify action steps that are likely to increase sales and customer satisfaction and demonstrate the methodology using proprietary data from a large retailer with over 500 stores. We use monthly store-level data on a number of operational variables including in-stock rate, store staffing level as measured by payroll and store employee turnover, together with customer responses to satisfaction surveys. We develop a nested three-stage econometric model to analyze the marginal effects of various execution levers on sales, customer satisfaction and the percentage of customers who answer 'yes' to the question 'Did you find everything you were looking for?', which we term customer perceived in-stock. Our model explains approximately 75%, 97% and 71%, respectively, of the residual variation in sales, customer satisfaction, and customer perceived in-stock. We find that customer perceived in-stock is primarily driven by actual in-stock and customer rating of employee knowledge; overall customer satisfaction is primarily driven by customer perceived in-stock, payroll level, customer rating of employee knowledge and check-out efficiency; and sales is primarily driven by actual in-stock, overall satisfaction and payroll level. Finally, we estimate relative magnitudes of these effects, propose specific actions to improve sales and estimate the likely sales increase from those actions. Our results suggest that a modest reallocation of the payroll budget among stores could be expected to yield a 2-3% increase in sales with no increase in cost.
Retail store managers may not follow order advices generated by an automated inventory replenishment system if their incentives differ from the cost minimization objective of the system or if they perceive the system to be suboptimal. We study the ordering behavior of retail store managers in a supermarket chain to characterize such deviations in ordering behavior and investigate their potential drivers. Using orders, shipments, and POS data for 19, 417 item-store combinations over 5 stores, we find that store managers systematically modify automated order advices by advancing orders from peak to non-peak days. We show that order advancement is explained significantly by hypothesized product characteristics, such as case-pack size relative to average demand per item, net shelf space, product variety, demand uncertainty, and seasonality error. Our results suggest that store managers add value. They improve upon the automated replenishment system by incorporating two ignored factors: in-store handling costs and sales improvement potential through better in-stock. We test a heuristic procedure, based on our regression results, to modify order advices to mimic the behavior of store managers. Our method performs better than the store managers by achieving a more balanced handling workload with similar average days of inventory.
In many service industries, companies compete with each other on the basis of the waiting time their customers' expe-rience, along with other strategic instruments such as the price they charge for their service. The objective of this paper is to conduct an empirical study of an important industry to measure to what extent waiting time performance measures impact different firms' market shares and price decisions. We report on a large scale empirical industrial organization study in which the demand equations for fast-food drive-thru restaurants in Cook County are estimated based on so-called structural estimation methods. Our results confirm the belief expressed by industry experts, that in the fast-food drive-thru industry customers trade off price and waiting time. More interestingly, our estimates indicate that consumers attribute a very high cost to the time they spend waiting.
We review queueing-theory methods for setting staffing requirements in service systems where customer demand varies in a predictable pattern over the day. Analyzing these systems is not straightforward, because standard queueing theory focuses on the long-run steady-state behavior of stationary models. We show how to adapt stationary queueing models for use in nonstationary environments so that time-dependent performance is captured and staffing requirements can be set. Relatively little modification of straightforward stationary analysis applies in systems where service times are short and the targeted quality of service is high. When service times are moderate and the targeted quality of service is still high, time-lag refinements can improve traditional stationary independent period-by-period and peak-hour approximations. Time-varying infinite-server models help develop refinements, because closed-form expressions exist for their time-dependent behavior. More difficult cases with very long service times and other complicated features, such as end-of-day effects, can often be treated by a modified-offered-load approximation, which is based on an associated infinite-server model. Numerical algorithms and deterministic fluid models are useful when the system is overloaded for an extensive period of time. Our discussion focuses on telephone call centers, but applications to police patrol, banking, and hospital emergency rooms are also mentioned.
To date, little research has been done on managing the organizational and political dimensions of generating and improving forecasts in corporate settings. We examine the implementation of a supply chain planning process at a consumer electronics company, concentrating on the forecasting approach around which the process revolves. Our analysis focuses on the forecasting process and how it mediates and accommodates the functional biases that can impair the forecast accuracy. We categorize the sources of functional bias into intentional, driven by misalignment of incentives and the disposition of power within the organization, and unintentional, resulting from informational and procedural blind spots. We show that the forecasting process, together with the supporting mechanisms of information exchange and elicitation of assumptions, is capable of managing the potential political conflict and the informational and procedural shortcomings. We also show that the creation of an independent group responsible for managing the forecasting process, an approach that we distinguish from generating forecasts directly, can stabilize the political dimension sufficiently to enable process improvement to be steered. Finally, we find that while a coordination system—the relevant processes, roles and responsibilities, and structure—can be designed to address existing individual and functional biases in the organization, the new coordination system will in turn generate new individual and functional biases. The introduced framework of functional biases (whether those biases are intentional or not), the analysis of the political dimension of the forecasting process, and the idea of a coordination system are new constructs to better understand the interface between operations management and other functions.
Telephone call centers are an integral part of many businesses, and their economic role is significant and growing. They are also fascinating sociotechnical systems in which the behavior of customers and employees is closely intertwined with physical performance measures. In these environments traditional operational models are of great value—and at the same time fundamentally limited—in their ability to characterize system performance.
We review the state of research on telephone call centers. We begin with a tutorial on how call centers function and proceed to survey academic research devoted to the management of their operations. We then outline important problems that have not been addressed and identify promising directions for future research.
The erosion of service quality throughout the economy is a frequent concern in the popular press. The American Customer Satisfaction Index for services fell in 2000 to 69.4%, down 5 percentage points from 1994. We hypothesize that the characteristics of services---inseparability, intangibility, and labor intensity---interact with management practices to bias service providers toward reducing the level of service they deliver, often locking entire industries into a vicious cycle of eroding service standards. To explore this proposition we develop a formal model that integrates the structural elements of service delivery. We use econometric estimation, interviews, observations, and archival data to calibrate the model for a consumer-lending service center in a major bank in the United Kingdom. We find that temporary imbalances between service capacity and demand interact with decision rules for effort allocation, capacity management, overtime, and quality aspirations to yield permanent erosion of the service standards and loss of revenue. We explore policies to improve performance and implications for organizational design in the service sector.
Many retail service facilities have both front room and back room operations. The front room deals with serving customers, perhaps from a queue. The back room focuses typically on restocking of shelves and sorting and/or processing of paperwork. We model such a facility having workers who are cross-trained to do both jobs. We assume that back room work is interruptible. A manager can bring a worker from the back room to the front room when the customer checkout queue becomes "too long". A reverse assignment occurs when the number of customers is sufficiently small. We assume that the retail facility contains two customer-counting technologies. The first counts the number of shoppers in the store who are not already in checkout queues and the second counts the number of customers at checkout. The goal is to find the minimum number of workers to staff the facility subject to two performance constraints. The mean queue delay in the front room must be less than a pre-specified value, and the time-average number of workers in the back room must be greater than a pre-specified value. Once the minimum complement of workers is found, a secondary goal is to minimize mean queueing delay subject to retaining back room feasibility. The system is a continuous time Markov process having a three-dimensional state space. The (heuristic) optimization process includes state-dependent rules for switching workers between rooms.
This is a book, not a book review.
The authors test a value chain model entailing a progression of influence from retail employee job perceptions → retail employee job performances → customer evaluations → customer spending and comparable store sales growth. The authors test the model using three matched samples of 1,615 retail employees, 57,656 customers, and 306 stores of a single retail chain.
The authors find that three retail employee job perceptions (conscientiousness, perceived organizational justice, and organizational identification) have main and interactive effects on three dimensions of employee job performance (in-role performance, extra-role performance toward customers, and extra-role performance toward the organization). In turn, these performance dimensions exert influence on customer evaluations of the retailer (a satisfaction, purchase intent, loyalty, and word-of-mouth composite). The authors also show that employee perceptions exert a direct influence on customer evaluations, and that customer evaluations affect retail store performance (customer spending and comparable store sales growth).
Finally, the authors conduct some simple simulations that show: (1) how changes in employee perceptions may raise average employee performances; (2) how changes in employee performances enhance average customer evaluations; and (3) how changes in customer evaluations raise average customer spending and comparable store sales growth. The authors then show that employee job perceptions and performances “ripple thru the system” to affect customer spending and store sales growth. The authors offer implications for theory and practice.
ost performance evaluation models in the operations management literature implicitly assume that tasks possess standardized completion criteria. However, in many systems, particularly service and professional work, judgment is frequently required to determine how much time to allocate to a task. In this paper, we show that introducing discretion in task completion adds a fourth variability buffer, quality, to the well-known buffers of capacity, inventory and time. To gain insight into the managerial implications of this difference, we model the work of one- and two-worker systems with discretionary task completion as controlled queues. After characterizing the optimal control policy and identifying some practical heuristics, we use this model to examine the differences between discretionary and nondiscretionary work. We show that in systems with discretionary task completion, (i) adding capacity may actually increase congestion, and (ii) task variability in service time can improve system performance. This implies that it may be suboptimal to expect shorter delays as a result of a capacity increase, and that task variability reduction may not be an appropriate goal in systems with discretionary task completion. We also find that the benefit of queue pooling is smaller in systems with discretionary task completion than in systems with nondiscretionary task completion.
Much of prior work in the area of service operations management has assumed service rates to be exogenous to the level of load on the system. Using operational data from patient transport services and cardiothoracic surgery--two vastly different health-care delivery services--we show that the processing speed of service workers is influenced by the system load. We find that workers accelerate the service rate as load increases. In particular, a 10% increase in load reduces length of stay by two days for cardiothoracic surgery patients, whereas a 20% increase in the load for patient transporters reduces the transport time by 30 seconds. Moreover, we show that such acceleration may not be sustainable. Long periods of increased load (overwork) have the effect of decreasing the service rate. In cardiothoracic surgery, an increase in overwork by 1% increases length of stay by six hours. Consistent with prior studies in the medical literature, we also find that overwork is associated with a reduction in quality of care in cardiothoracic surgery--an increase in overwork by 10% is associated with an increase in likelihood of mortality by 2%. We also find that load is associated with an early discharge of patients, which is in turn correlated with a small increase in mortality rate.
Applications of cluster analysis to marketing problems are reviewed. Alternative methods of cluster analysis are presented and evaluated in terms of recent empirical work on their performance characteristics. A two-stage cluster analysis methodology is recommended: preliminary identification of clusters via Ward's minimum variance method or simple average linkage, followed by cluster refinement by an iterative partitioning procedure. Issues and problems related to the use and validation of cluster analytic methods are discussed.
Organizations can create volume flexibility—the ability to increase capacity up or down to meet demand for a single service—through the use of flexible labor resources (e.g., part-time and temporary workers, as compared to full-time workers). Although organizations are increasingly using these resources, the relationship between flexible labor resources and financial performance has not been examined empirically in the service setting. We use two years of archival data from 445 stores of a large retailer to study this relationship. We hypothesize and find that increasing the labor mix of temporary or part-time workers shows an inverted U-shaped relationship with sales and profit while temporary labor mix has a U-shaped relationship with expenses. Thus, although flexible labor resources can create volume flexibility for a firm along multiple dimensions, it is possible to have too much of a good thing.
This paper was accepted by Serguei Netessine, operations management.
We develop a framework for asymptotic optimization of a queueing system. The motivation is the staffing problem of large call centers, which we have modeled as M/M/N queues with N, the number of agents, being large. Within our framework, we determine the asymptotically optimal staffing level N* that trades off agents’ costs with service quality: the higher the latter, the more expensive is the former. As an alternative to this optimization, we also develop a constraint satisfaction approach where one chooses the least N* that adheres to a given constraint on waiting cost. Either way, the analysis gives rise to three regimes of operation: quality-driven, where the focus is on service quality; efficiency-driven, which emphasizes agents’ costs; and a rationalized regime that balances, and in fact unifies, the other two. Numerical experiments reveal remarkable accuracy of our asymptotic approximations: over a wide range of parameters, from the very small to the extremely large, N* is exactly optimal, or it is accurate to within a single agent. We demonstrate the utility of our approach by revisiting the square-root safety staffing principle, which is a long-existing rule of thumb for staffing the M/M/N queue. In its simplest form, our rule is as follows: if c is the hourly cost of an agent, and a is the hourly cost of customers’ delay, then N*=R+y*(a/c)R, where R is the offered load, and y*(·) is a function that is easily computable.
In a competitive market, consumers are not well informed about true service level offered by firms. Instead, consumers choose between firms based on their past experience and word of mouth (WOM) from their acquaintances. Thus, firms' market shares should depend on not only their product or service quality, but also choice behavior through consumer learning and WOM. How does this dependence affect their market shares? When should firms promote WOM? We address these questions by developing a multi-period model of consumer learning, WOM and choice behavior in response to uncertain service offered by firms. Using this model, we show that low-quality firms are overpenalized in the market share and consequently high-quality firms are overcompensated due to the effect of consumer learning on choice behavior. We show conditions in which WOM reduces or increases this difference in market shares. Our results are useful to retailers and marketers of experience or repeat purchase products.
We study the problem of setting nurse staffing levels in hospital operating rooms when there is uncertainty about daily workload. The workload is the number of operating room hours used by a medical specialty on a given day to perform surgical procedures. Variable costs consist of wages at a regular (scheduled) rate and at an overtime rate when the realized workload exceeds the scheduled time. Using a newsvendor framework, we consider the problem of determining optimal staffing levels with different information sets available at the time of decision: no information, information on number of cases, and information on number and types of cases. We develop empirical models for the daily workload distribution in which the mean and variance change with the information available. We use these models to derive optimal staffing rules based on historical data from a U.S. teaching hospital and prospectively test the performance of these rules. Our numerical results suggest that hospitals could potentially reduce their staffing costs by up to 39%--49% by deferring staffing decisions until procedure type information is available. The results demonstrate how data availability can affect a newsvendor's performance. The systematic approach of empirical modeling presented in the paper can be applied to other newsvendor problems with heterogeneous sources of demand.
Retail store managers may not follow order advices generated by an automated inventory replenishment system if their incentives differ from the cost-minimization objective of the system or if they perceive the system to be suboptimal. We study the ordering behavior of retail store managers in a supermarket chain to characterize such deviations in ordering behavior, investigate their potential drivers, and thereby devise a method to improve automated replenishment systems. Using orders, shipments, and point-of-sale data for 19,417 item-store combinations over five stores, we show that (i) store managers consistently modify automated order advices by advancing orders from peak to nonpeak days, and (ii) this behavior is explained significantly by product characteristics such as case pack size relative to average demand per item, net shelf space, product variety, demand uncertainty, and seasonality error. Our regression results suggest that store managers improve upon the automated replenishment system by incorporating two ignored factors: in-store handling costs and sales improvement potential through better in-stock. Based on these results, we construct a method to modify automated order advices by learning from the behavior of store managers. Motivated by the management coefficients theory, our method is efficient to implement and outperforms store managers by achieving a more balanced handling workload with similar average days of inventory.
We analyze a large, detailed operational data set from a restaurant chain to shed new light on how workload (defined as the number of tables or diners that a server simultaneously handles) affects servers' performance (measured as sales and meal duration). We use an exogenous shock—the implementation of labor scheduling software—and time-lagged instrumental variables to disentangle the endogeneity between demand and supply in this setting. We show that servers strive to maximize sales and speed efforts simultaneously, depending on the relative values of sales and speed. As a result, we find that, when the overall workload is small, servers expend more and more sales efforts with the increase in workload at a cost of slower service speed. However, above a certain workload threshold, servers start to reduce their sales efforts and work more promptly with the further rise in workload. In the focal restaurant chain, we find that this saturation point is currently not reached and, counterintuitively, the chain can reduce the staffing level and achieve both significantly higher sales (an estimated 3% increase) and lower labor costs (an estimated 17% decrease).
This paper was accepted by Noah Gans, special issue on business analytics.
In exemplary service organizations, executives understand that they need to put customers and frontline workers at the center of their focus. Those managers heed the factors that drive profitability in this service paradigm: investment in people, technology that supports frontline workers, revamped recruiting and training practices, and compensation linked to performance. They also express a vision of leadership in somewhat unconventional terms, referring to an organization's "patina of spirituality" and the "importance of the mundane." In this article, Heskett, Jones, Loveman, Sasser, and Schtesinger take a close look at the links in the service-profit chain, which puts hard values on soft measures so that managers can calibrate the impact of employee satisfaction, loyalty, and productivity on the value of products and services delivered. Managers can then use this information to build customer satisfaction and loyalty and assess the corresponding impact on profitability and growth. Describing the links in the service-profit chain, the authors explain that profit and growth are stimulated by customer loyalty; loyalty is a direct result of customer satisfaction; satisfaction is largely influenced by the value of services provided to customers; value is created by satisfied, loyal, and productive employees; and employee satisfaction, in turn, results from high-quality support services and policies that enable employees to deliver results to customers. By completing the authors' service-profit chain audit, companies can determine not only what drives their profit but how they can sustain it in the long term.
The service profit chain is a simple conceptual framework linking employee satisfaction and loyalty, customer satisfaction and loyalty, and financial performance. Although widely used by practitioners, the service profit chain's series of hypothesized relationships between employee, customer, and financial outcomes has not been rigorously tested using data that span all components of the model. Panel data from the branches of a large regional bank are used to test individually each of the service profit chain's constituent hypotheses. The results generally support the model, but there are some exceptions. Further work is needed to refine and simplify several critical measures and to enhance the analysis to test the service profit chain as a complete system of related hypotheses.
A series of subjectively parameterized models was developed and implemented, beginning in 1982, to aid Syntex Laboratories in deciding how large their sales force should be, and how it should be deployed. The response functions of the models were estimated by a team of knowledgeable managers and salespeople using a modified Delphi technique. The model structure and parameter estimation techniques were developed in response to constraints unique to Syntex Laboratories and its available data. The original response functions were significantly better predictors of the sales of each Syntex product for two years in the future than were the existing forecasts. Use of the models helped the corporation to decide to significantly increase its sales force size and to try to change its deployment. This decision resulted in a documented continuing $25,000,000, eight percent annual sales increase. The model had important impacts on the strategic direction of the firm, helping to change its focus to product markets with better future potential.
Why do some organizations learn at faster rates than others? Why do organizations "forget"? Could productivity gains acquired in one part of an organization be transferred to another? These are among the questions addressed in Organizational Learning: Creating, Retaining and Transferring Knowledge. Since its original publication in 1999, this book has set the standard for research and analysis in the field. This fully updated and expanded edition showcases the most current research and insights, featuring a new chapter that provides a theoretical framework for analyzing organizational learning and presents evidence about how the organizational context affects learning processes and outcomes. Drawing from a wide array of studies across the spectrum of management, economics, sociology, and psychology, Organizational Learning explores the dynamics of learning curves in organizations, with particular emphasis on how individuals and groups generate, share, reinforce, and sometimes forget knowledge. With an increased emphasis on service organizations, including healthcare, Linda Argote demonstrates that organizations vary dramatically in the rates at which they learn-with profound implications for productivity, performance, and managerial and strategic decision making. © Springer Science+Business Media New York 2013. All rights are reserved.
Applications of cluster analysis to marketing problems are reviewed. Alternative methods of cluster analysis are presented and evaluated in terms of recent empirical work on their performance characteristics. A two-stage cluster analysis methodology is recommended: preliminary identification of clusters via Ward's minimum variance method or simple average linkage, followed by cluster refinement by an iterative partitioning procedure. Issues and problems related to the use and validation of cluster analytic methods are discussed.
E. W. Paxson of Rand has suggested that linear programming methods could be used as an alternative procedure in the scheduling section of Leslie C. Edie's interesting paper on “Traffic Delays at Toll Booths” (Leslie C. Edie. 1954. Traffic delays at toll booths. J. Opns. Res. Soc. Am. 2 107.). The purpose of this note is to elaborate on this suggestion.
Operations Research, ISSN 0030-364X, was published as Journal of the Operations Research Society of America from 1952 to 1955 under ISSN 0096-3984.
This paper empirically investigates how managers choose to set and maintain their inventory levels using field data we have collected from a large nation-wide dealer network for a major industrial tractor manufacturer. We find that even though dealers receive inventory stocking recommendations from the manufacturer, a multitude of economic, organizational and behavioral factors jointly determine dealers' actual inventory policy choice and their preferred inventory levels, such as demand pattern, holding cost, competition, management score, process orientation and infrastructure. Dealers are more likely to adopt a constant base stock policy when their overall demand is low, their demand is more stable, their competitors are located farther away, and their managers are less process oriented and lack infrastructure support in inventory management. We also find that dealers tend to stock more inventory when they face more competition, and when they are more managerially capable in inventory management. Our results shed new light on inventory management and supply chain execution.
Attracting shoppers to stores and converting the incoming traffic into sales profitably are vital for the financial health of retailers. In this paper, we use proprietary data pertaining to an apparel retailer to study the relationship between store traffic, labor, and sales performance. We decompose sales volume into conversion rate (defined as the ratio of number of transactions to traffic) and basket value (defined as the ratio of sales volume to number of transactions) and analyze the impact of traffic on sales and its components. We find that store sales volume exhibits diminishing returns to scale with respect to traffic and labor moderates the impact of traffic on sales. For example, we find that for values of traffic and labor corresponding to the mean, increasing average traffic per hour by 1 unit increases average sales volume per hour by 10.00 to $11.32 when labor increases by one standard deviation. In addition, we find that conversion rate declines with increasing traffic and lower conversion rate is associated with decrease in future traffic growth. Our study underscores the importance of in-store operations in driving the financial performance of retailers.
We conduct an empirical study to analyze how waiting in queue in the context of a retail store affects customers' purchasing behavior. Our methodology combines a novel data set with periodic information about the queuing system (collected via video recognition technology) with point-of-sales data. We find that waiting in queue has a nonlinear impact on purchase incidence and that customers appear to focus mostly on the length of the queue, without adjusting enough for the speed at which the line moves. An implication of this finding is that pooling multiple queues into a single queue may increase the length of the queue observed by customers and thereby lead to lower revenues. We also find that customers' sensitivity to waiting is heterogeneous and negatively correlated with price sensitivity, which has important implications for pricing in a multiproduct category subject to congestion effects.
This paper was accepted by Martin Lariviere, operations management.
This paper reports some research, ideas, and theory about managerial decision making. The first research projects dealt with are aggregate production and employment scheduling. From this is developed the idea that management's own (past) decisions can be incorporated into a system of improving their present decisions. Decision rules are developed, with the coefficients in the rules derived from management's past decisions (rather than from a cost or value model). Half a dozen test cases are used to illustrate and test these ideas (theory). Some rationale about decision making in organizations and criteria surfaces is supplied to help interpret the major ideas presented.
The efficient operation of a salesforce is a critical element in the profitability of many firms. Three factors play key roles: the salesforce's size, its allocation and its productivity. This gives rise to the following questions: can salesforce performance be improved by (1) hiring more salespeople, (2) allocating them more effectively to the various sales districts and/or (3) improving salesperson productivity through better calling patterns in terms of consumers and product line items?
The practice of most firms and the methodology used in most of the academic literature to address salesforce design and productivity questions is a “Bottom Up” approach. This approach starts with assessments by each salesperson of the sales and effort corresponding to each customer and prospect in their territory. These assessments are then aggregated to the territory, district and national levels. This paper takes an alternative “Top Down” approach. It is based on an estimated relationship between district level sales and salesforce size, effort and other variables. This more macro level decision tool can be used by management in parallel to, and as an objective check of, the more conventional and more subjective “Bottom Up” approach.
We develop an efficient frontier methodology which allows us to estimate how total district sales respond to salesforce size, district potential and competitive activity in the firm's best performing districts. The methodology utilized is based on Data Envelopment Analysis (DEA) and yields a benchmark measure of each district's efficient frontier sales (sales assuming the district's salesforce allocates its effort as done in the best performing districts).
Based on the estimated response function we discuss the three potential sources of increased profitability: closing the inefficiency gap of each of the lower performing districts, optimally reallocating the current salesforce to the various districts, and changing the current size of the salesforce to its optimal level. The inefficiency gap issue is addressed through comparison of the parameter estimates for the best districts obtained through our methodology with those of an average district sales response function obtained using regression analysis. This comparison points to an important methodological finding. The use of multiple estimation results may lead to an improved understanding of the phenomenon being studied (in our case, the identification of the likely causes of district productivity inefficiencies). The latter two sources of increased profitability, salesforce reallocation and changes in the current salesforce size, are addressed analytically given the district level efficient frontier sales response function.
The proposed “Top Down” procedure using the efficient frontier methodology and the insights it provides are examined by evaluating the operations of two different salesforces, one selling manufacturing equipment and the other business equipment. In both cases, regression-based analysis would have resulted in a declaration that the status-quo was close to optimal, while the frontier-based analysis pointed out that strong gains were possible in certain districts. In particular, for both firms, the greatest increases in profit are obtained through improved salesforce efficiency in the lower performing districts, not through salesforce size or district allocation adjustments. At the more micro-level, a comparison of the frontier and regression parameters made it possible to identify which specific changes in the daily operations of the salesforces would allow the realization of these potential productivity gains. In our two cases this could be obtained through more emphasis on pursuing prospective accounts.
The decision problems involved in setting the aggregate production rate of a factory and setting the size of its work force are frequently both complex and difficult. The quality of these decisions can be of great importance to the profitability of an individual company, and when viewed on a national scale these decisions have a significant influence on the efficiency of the economy as a whole. This paper reports some of the findings of a research team that has been developing new methods to enable production executives to make better decisions and to make them more easily than they can with prevailing procedures. With the cooperation of a manufacturing concern, the new methods have been developed in the context of a set of concrete production scheduling problems that were found in a factory operated by the company.
The new method which is presented in this paper, involves: (1) formalizing and quantifying the decision problem (using a quadratic approximation to the criterion function) and (2), calculating a generalized optimal solution of the problem in the form of a (linear) decision rule. Like a rule of thumb, an optimal decision rule prescribes a course of action when it is applied to a particular set of circumstances; but, unlike most rules of thumb, an optimal decision rule prescribes courses of action for which the claim can be made that the decisions are “the best possible,” the meaning of “best” being clearly specified. The ultimate test, of course, must be whether the new decision methods do or do not outperform prevailing decision methods when full allowance is made for the cost of obtaining the optimal decisions.
An application of linear decision rules to production and employment scheduling was described in the last issue of this journal [Holt, C. C., F. Modigliani, H. A. Simon. 1955. A linear decision rule for production and employment scheduling. Management Sci. (October).]. The hypothetical performance of these rules represented a significant improvement over the actual company performance as measured by independent cost estimates and other managerial measures of efficiency. The quadratic cost function which was used should be applicable to production and employment scheduling decisions in many other situations. Also the general approach of approximating decision criteria with quadratic functions and obtaining linear decision rules can usefully be extended to many decision problems.
In the present paper we will demonstrate (a) how optimal (i.e., minimum expected cost) decision rules may be derived for a quadratic cost function involving inventory, overtime, and employment costs, and (b) how the numerical coefficients of the rules may be computed for any set of cost parameters.
Many factors, both obvious and subtle, influence customers’ store patronage intentions. Using videotape technology that enabled us to experimentally manipulate the number of visible store employees, number of customers, and music, we test the relative importance of wait expectations and store atmosphere evaluations on patronage intentions. These constructs are found to be critical antecedents of store patronage intentions in the context of the service-intensive retail store at which the model was tested. We also find support for the direct effects of gender on wait expectations and store atmosphere evaluations. We discuss some implications for retailing research and practice.
Although retailers acknowledge the impact of sales force scheduling decisions on store profits and customer service, current scheduling methods may fail to capture the sales potential of customers who enter their premises. These methods do not recognize the effect that the sales staff availability has upon the customer purchasing, thus leaving significant opportunity for additional sales volume unrealized. To resolve this problem, we propose a model which sets store sales potential as a function of store traffic volume, customer type, and customer response to sale force availability. We test our model for a profit maximizing sales force schedule with data providing hourly data of store sales, store traffic and staff head count. The solution for this optimal schedule indicates that the store may be seriously understaffed and that expanding the number of salespersons would both generate higher profits and provide customers with better service. Our scheduling method also provides a tool for identifying the time periods when service is most heavily demanded by customers.
In this exploratory study, a segmentation analysis of a shopping mall's customers is conducted according to the activities they performed during their visit, based on a methodology developed by Bloch et al. (J. Retailing 70 (1994) 23). This methodology is extended with measures of perceptions, emotions, and motivations. Activity-based clusters, obtained with the Variable Neighborhood Search metaheuristic applied to the P-median problem. (Hansen and Mladenović, 1997) proved to be significantly different along many psychographic dimensions (including atmospheric perceptions), and demographic variables. This profiling methodology successfully synthesizes many segmentation approaches that were used separately in previous studies. This results in a complete and distinct profile of each group that may be a useful tool for retail strategists.
The Newsvendor model captures the trade-off faced by a decision maker that needs to place a firm bet prior to the occurence of a random event. Previous research in Operations Management has mostly focused on deriving the decision that minimizes the expected mismatch costs. In contrast, we present two methods that estimate the unobservable cost parameters characterizing the mismatch cost function. We present a structural estimation framework that accounts for heterogeneity in the uncertainty faced by the newsvendor as well as in the cost parameters. We develop statistical methods that give consistent estimates of the model primitives, and derive their asymptotic distribution, which is useful to do hypothesis testing. We apply our econometric model to a hospital that balances the costs of reserving too much vs. too little operating room capacity to cardiac surgery cases. Our results reveal that the hospital places more emphasis on the tangible costs of having idle capacity than on the costs of schedule overrun and long working hours for the staff. We also extend our structural models to incorporate external information on forecasting biases and mismatch costs reported by the medical literature. Our analysis suggests that over-confidence and incentive conflicts are important drivers of the frequency of schedule overruns observed in our sample.