Matt Waller

We investigate inventory ordering decisions when decision makers anticipated a demand shock. Decision makers anticipating an event have been shown to brace for an uncertain negative outcome by overestimating the likelihood of that event. Decision makers faced with a spike in demand may incur increased holding costs because they may brace, exhibiting a judgment bias, and consequently a decision bias by over-ordering inventory. Three studies span conditions of uncertainty regarding the timing and… Show more

We investigate inventory ordering decisions when decision makers anticipated a demand shock. Decision makers anticipating an event have been shown to brace for an uncertain negative outcome by overestimating the likelihood of that event. Decision makers faced with a spike in demand may incur increased holding costs because they may brace, exhibiting a judgment bias, and consequently a decision bias by over-ordering inventory. Three studies span conditions of uncertainty regarding the timing and magnitude of a demand shock: Employing three between-subjects experiments, Study 1 investigates behavior when decision makers were faced with uncertainty in timing and in magnitude of demand at the most elemental level, manipulating holding and stock out costs. The three experimental tasks feature uncertainty about the magnitude of demand, uncertainty about the timing of demand, and uncertainty about both the magnitude and timing of demand. Study 2 uses a dynamic, multi-period replenishment task and a between-subjects manipulation regarding the uncertainty of timing and magnitude of a demand shock. Study 3 also employs a multi-period decision environment, but compares behavior under a demand shock condition with that in a condition featuring only random variability. The collective results from the three studies identify a bias toward over-ordering in response to a demand shock, relative to the optimal orders. The between-subjects manipulations in Study 2 points toward a possible remedy as
we found that providing information concerning the timing and magnitude of a shock ameliorated the bias. The primary revelation was that decision makers had more difficulty dealing with uncertain timing than with uncertain magnitude of demand. One implication is that it is critical for retailers to carefully plan and manage how they share information with upstream supply chain partners regarding when they plan to introduce store-level promotions. Show less

Despite advances in retail point-of-sale (POS) data sharing, retailers’ suppliers struggle to effectively use POS data to improve their fulfillment planning processes. The challenge lies in predicting retailer orders. We present evidence that retail echelon inventory processes translate into a long-run balance or equilibrium between orders and POS, which we refer to as the inventory balance effect, allowing for more accurate order forecasting. Based on the inventory balance effect, this… Show more

Despite advances in retail point-of-sale (POS) data sharing, retailers’ suppliers struggle to effectively use POS data to improve their fulfillment planning processes. The challenge lies in predicting retailer orders. We present evidence that retail echelon inventory processes translate into a long-run balance or equilibrium between orders and POS, which we refer to as the inventory balance effect, allowing for more accurate order forecasting. Based on the inventory balance effect, this research prescribes a forecasting approach which simultaneously uses both sources of information (retailer order history and POS data) to predict retailer orders to suppliers. Using data from a consumable product category, this approach is shown to outperform approaches based singularly on order or POS data, by up to 125%. The strength of this novel approach – significantly improved forecast accuracy with minimal additional analysis – make it a candidate for widespread adoption in retail supply chain collaborative planning and forecasting initiatives with corresponding impact on fulfillment performance and related operating costs. Show less

Previous experimental research demonstrates that inefficient replenishment decision making in the supply chain can be caused by specific judgment and decision biases. Based on the literature we use controlled experiments involving both student subjects and supply chain managers to test debiasing interventions that provide declarative knowledge, which is theorized to enhance the acquisition of procedural knowledge. We first investigate the effects of three debiasing components in a… Show more

Previous experimental research demonstrates that inefficient replenishment decision making in the supply chain can be caused by specific judgment and decision biases. Based on the literature we use controlled experiments involving both student subjects and supply chain managers to test debiasing interventions that provide declarative knowledge, which is theorized to enhance the acquisition of procedural knowledge. We first investigate the effects of three debiasing components in a single-echelon setting: knowledge of bullwhip, inventory position (IP), and use of a target order-up-to quantity. Experiment 1 (N = 1,608 decisions by 67 student subjects) using a 2 × 2 × 2 factorial design for the three components finds that the conceptual understanding of IP is salient for efficient replenishment decisions. We next examine the effects of the components in a simulated, multi-echelon, serial supply chain, which introduces the additional complexity of coordination risk. Experiment 2 (N = 3,072 decisions by 128 student subjects) using a 2 × 2 × 2 factorial design finds that although subjects benefit from training components, there is evidence of cognitive overload with an increased quantity of information. Finally we test whether these debiasing components may be an effective training program for practicing supply chain managers who can be expected to have higher levels of procedural knowledge through experience gained in the field. Experiment 3 (N = 864 decisions by 36 supply chain managers) using a 2 × 1 design investigates the effects of an instructional training intervention which includes all three debiasing components and finds the intervention to reduce costs by 14%. We provide avenues for future research and successful practice. Show less

Existing literature suggests that the increasing concentration in the retail industry is allowing powerful retailers to exploit their weaker suppliers, which causes the suppliers’ performance to suffer. This study takes a collaborative perspective of resource dependency theory and suggests that when suppliers engage in supply chain relationships with key retail account (KRA) customers, their performance may improve, depending on the varying levels of the supplier's and KRAs’ market shares. The… Show more

Existing literature suggests that the increasing concentration in the retail industry is allowing powerful retailers to exploit their weaker suppliers, which causes the suppliers’ performance to suffer. This study takes a collaborative perspective of resource dependency theory and suggests that when suppliers engage in supply chain relationships with key retail account (KRA) customers, their performance may improve, depending on the varying levels of the supplier's and KRAs’ market shares. The empirical analysis of data from two large retailers, Wal-Mart and Target, and a broad cross-section of their suppliers provides ample support for most of the hypotheses set forth in this paper: Suppliers that depend on KRAs for a significant share of their total revenues relinquish some of their leverage in the marketplace, but as the KRAs gain market share, their suppliers’ performance tends to increase. Cumulatively, these results provide evidence of collaborative supplier–KRA relationships, such that a supplier's dependency on KRAs may positively affect supplier performance. This finding supports a more positive, symbiotic view of dependency, resulting in important implications for key account management, supply chain management, and retail research and practice. Show less

Traditional inventory models fail to take into account the dynamics between the retail sales floor and the backroom, commonly used by retailers for extra storage. When a replenishment order for a given item arrives at a retail store, it may not fit on the allocated shelf space, making backroom storage necessary. In this article, we introduce the backroom effect (BRE) as a consequence of misalignment of case pack size, shelf space, and reorder point. This misalignment results from the fragmented… Show more

Traditional inventory models fail to take into account the dynamics between the retail sales floor and the backroom, commonly used by retailers for extra storage. When a replenishment order for a given item arrives at a retail store, it may not fit on the allocated shelf space, making backroom storage necessary. In this article, we introduce the backroom effect (BRE) as a consequence of misalignment of case pack size, shelf space, and reorder point. This misalignment results from the fragmented nature of inventory policy decision making in the retail industry and affects basic trade-offs in inventory models. We specify conditions under which the BRE exists, quantify the expected amount of backroom inventory, derive an optimal short-term inventory policy, and assess the impact of the BRE on the optimal inventory policy and total costs. Our results indicate that ignoring the BRE leads to artificially high reorder points and higher total costs. The paper concludes with a discussion of theoretical and managerial implications. Show less

Point-of-sale (POS) data, shared by retailers, is often touted as the solution to suppliers' ongoing challenge of accurate order forecasting. However, we find neither empirical evidence of increased order forecast accuracy from the literature, nor consistent use of POS data in suppliers' order forecasting processes. Using a sample containing weekly POS and order data for 10 ready-to-eat (RTE) cereal stock-keeping-units (SKU's), 7 yogurt SKU's, and 7 canned soup SKU's from 18 retailer… Show more

Point-of-sale (POS) data, shared by retailers, is often touted as the solution to suppliers' ongoing challenge of accurate order forecasting. However, we find neither empirical evidence of increased order forecast accuracy from the literature, nor consistent use of POS data in suppliers' order forecasting processes. Using a sample containing weekly POS and order data for 10 ready-to-eat (RTE) cereal stock-keeping-units (SKU's), 7 yogurt SKU's, and 7 canned soup SKU's from 18 retailer distribution centers (DC's) throughout the U.S., our research compares historical POS and order data as order forecasting inputs and finds that POS data does not always outperform order data in terms of order forecast accuracy. While we did find that POS data is a better forecast input in a majority of the forecasts and that on average POS data produces a lower order forecast error, we find that there remain a large number of forecasts where order data is a better predictor than is POS data. Hence, we operationalize this comparison in terms of the frequency and magnitude of order forecast improvement based on POS data. We then hypothesize affecting factors and empirically test these relationships. Show less

Errors in order forecasts are a salient source of inefficiencies in retail supply chains. Many operational decisions made by suppliers hinge on order forecasts, which typically are based solely on either order or point-of-sale (POS) history. Using a discrete-time formulation, this research demonstrates that if a supplier knows that a retailer is using a base stock policy, it should use that knowledge to forecast the retailer's orders, even if the supplier does not know the base stock level… Show more

Errors in order forecasts are a salient source of inefficiencies in retail supply chains. Many operational decisions made by suppliers hinge on order forecasts, which typically are based solely on either order or point-of-sale (POS) history. Using a discrete-time formulation, this research demonstrates that if a supplier knows that a retailer is using a base stock policy, it should use that knowledge to forecast the retailer's orders, even if the supplier does not know the base stock level and/or have access to POS data. Show less

Given that the impact of retail shelf facings and price on a product�s market share is of substantial interest to marketing managers in the retail supply chain, we examine whether these relationships may be interdependent with the firm�s supply chain activities. We offer predictions regarding the interdependence of the marketing and supply chain variables using monthly in-store observations from 62 different retail stores from five different chains, taken over a 24-month period. The in-store… Show more

Given that the impact of retail shelf facings and price on a product�s market share is of substantial interest to marketing managers in the retail supply chain, we examine whether these relationships may be interdependent with the firm�s supply chain activities. We offer predictions regarding the interdependence of the marketing and supply chain variables using monthly in-store observations from 62 different retail stores from five different chains, taken over a 24-month period. The in-store observations included price and number of facings, which is combined with data obtained from the manufacturer on case pack quantity and market share data from the ACNielsen HomeScan consumer scanner panel. Results indicate that shelf facings impact the effects of price and case pack quantity on market share. In addition, we explore the strength of relationships across retailers employing everyday low price versus HiLo pricing strategies. Generally, our findings suggest that retailers and suppliers must work to integrate marketing activities and supply chain processes both within and across firms to most effectively serve the consumer at the retail shelf and increase market share. Show less