The Relationship Between Abnormal Inventory Growth and Future Earnings for U.S. Public Retailers

In this paper we examine the relationship between inventory levels and one-year ahead earnings of retailers using publicly available financial data. We use benchmarking metrics obtained from operations management literature to demonstrate an inverted-U relationship between abnormal inventory growth and one-year ahead earnings per share for retailers. We also find that equity analysts do not fully incorporate the information contained in abnormal inventory growth of retailers in their earnings forecasts resulting in systematic biases. Finally, we show that an investment strategy based on abnormal inventory growth yields abnormal returns of 11.8% (p<0.001)

Estimating Demand Uncertainty Using Judgmental Forecasts

Measuring demand uncertainty is a key activity in supply chain planning. Of various methods of estimating the standard deviation of demand, one that has been employed successfully in the recent literature uses dispersion among expertsâ forecasts. However, there has been limited empirical validation of this methodology. In this paper we provide a general methodology for estimating the standard deviation of a random variable using dispersion among expertsâ forecasts. We test this methodology using three datasets, demand data at item level, sales data at firm level for retailers, and sales data at firm level for manufacturers. We show that the standard deviation of a random variable (demand and sales for our datasets) is positively correlated with dispersion among expertsâ forecasts. Further we use longitudinal datasets with sales forecasts made 3-9 months before earnings report date for retailers and manufacturers to show that the effects of dispersion and scale on standard deviation of forecast error are consistent over time.Operations Management Working Papers Serie

Estimating the Impact of Understaffing on Sales and Profitability in Retail Stores

In this paper we use micro-level 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. We show how traffic data can be leveraged in making staffing decisions through use of a structural model that captures the relationship between traffic, sales and labor. Assuming that store managers aim to maximize profits, we estimate the contribution of labor to sales and impute the cost of labor for each store in our sample. We find significant heterogeneity in the contribution of labor to sales as well as imputed cost of labor across these stores and across time. Using the estimated parameters, we establish the presence of systematic understaffing during peak hours. Aligning staffing levels with changing traffic patterns can result in a 6.15% savings in lost sales and a 5.74% improvement in profitability. We describe a pilot implementation of our approach at another large retailer where we identify periods of understaffing in their stores and document the impact on conversion rate and lost sales

A New Roll and Pitch Control Mechanism for an Underwater Glider

In this paper, a new roll and pitch control mechanism for an underwater glider is described. The mechanism controls the glider’s pitch and roll without the use of a conventional buoyancy engine or movable mass. It uses water as trim mass, with a high flow rate water pump to shift water from water bladders located at the front, rear, left, and right of the glider. By shifting water between the left and right water bladder, a roll moment is induced. Similarly, pitch is achieved by shifting water between the front and rear water bladders. The water bladders act not only as a means for roll and pitch control but as a buoyancy engine as well. This eliminates the use of a dedicated mechanism for pitch and roll, thereby improving gliding efficiency and energy consumption, as the glider's overall size is decreased since the hardware required is reduced. The dynamics of the system were derived and simulated, as well as validated experimentally. The glider is able to move in a sawtooth pattern with a maximum pitch angle of 43.5˚, as well as a maximum roll angle of 43.6˚ with pitch and roll rates increase with increasing pump rate

Stable Scheduling Increases Productivity and Sales

Variable schedules are now the norm for part-time workers in a variety of industries including retail, where schedules typically change every day and every week, with three to seven days' notice of the next week's schedule. In recent years, these scheduling practices have come under increasing scrutiny in state attorney general offices, state and local legislatures, and the media. In retail, unstable schedules for employees have been considered an inevitable outcome of stores' need for profitability. Operations researchers have found that matching labor to incoming traffic is a key driver of retail store profitability (Perdikaki et al., 2012). At the same time, social scientists have studied the deleterious effects of variable schedules on employee wellbeing (Henly & Lambert, 2014). What has been lacking is evidence that schedules in service-sector jobs can be improved in ways that benefit both employers and employees

Graphene-Based Nanocomposites for Energy Storage

Since the first report of using micromechanical cleavage method to produce graphene sheets in 2004, graphene/graphene-based nanocomposites have attracted wide attention both for fundamental aspects as well as applications in advanced energy storage and conversion systems. In comparison to other materials, graphene-based nanostructured materials have unique 2D structure, high electronic mobility, exceptional electronic and thermal conductivities, excellent optical transmittance, good mechanical strength, and ultrahigh surface area. Therefore, they are considered as attractive materials for hydrogen (H2) storage and high-performance electrochemical energy storage devices, such as supercapacitors, rechargeable lithium (Li)-ion batteries, Li–sulfur batteries, Li–air batteries, sodium (Na)-ion batteries, Na–air batteries, zinc (Zn)–air batteries, and vanadium redox flow batteries (VRFB), etc., as they can improve the efficiency, capacity, gravimetric energy/power densities, and cycle life of these energy storage devices. In this article, recent progress reported on the synthesis and fabrication of graphene nanocomposite materials for applications in these aforementioned various energy storage systems is reviewed. Importantly, the prospects and future challenges in both scalable manufacturing and more energy storage-related applications are discussed

Estimating the impact of understaffing on sales and profitability in retail stores. Production and Operations Management forthcoming

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

Do Inventory and Gross Margin Data Improve Sales Forecasts for U.S. Public Retailers?

Firm-level sales forecasts for retailers can be improved if we incorporate cost of goods sold, inventory, and gross margin (defined by us as the ratio of sales to cost of goods sold) as three endogenous variables. We construct a simultaneous equations model, estimated using public financial and nonfinancial data, to provide joint forecasts of annual cost of goods sold, inventory, and gross margin for retailers using historical data. We show that sales forecasts from this model are more accurate than consensus forecasts from equity analysts. Further, the residuals from this model for one fiscal year are used to predict retailers for whom the relative advantage of model forecasts over consensus forecasts would be large in the next fiscal year. Our results show that historical inventory and gross margin contain information useful to forecast sales, and that equity analysts do not fully utilize this information in their sales forecasts.sales forecasting, retail, inventory, empirical

Estimating the impact of understaffing on sales and profitability in retail stores. Production and Operations Management forthcoming

Abstract In this paper we use micro-level 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. We show how traffic data can be leveraged in making staffing decisions through use of a structural model that captures the relationship between traffic, sales and labor. Assuming that store managers aim to maximize profits, we estimate the contribution of labor to sales and impute the cost of labor for each store in our sample. We find significant heterogeneity in the contribution of labor to sales as well as imputed cost of labor across these stores and across time. Using the estimated parameters, we establish the presence of systematic understaffing during peak hours. Aligning staffing levels with changing traffic patterns can result in a 6.15% savings in lost sales and a 5.74% improvement in profitability. We describe a pilot implementation of our approach at another large retailer where we identify periods of understaffing in their stores and document the impact on conversion rate and lost sales