Forecasting With Temporally Aggregated Demand Signals in a Retail Supply Chain

Abstract

S uppliers of consumer packaged goods are facing an increasingly challenging situation as they work to fulfill orders from their retail partners' distribution facilities. Traditionally these suppliers have generated forecasts of a given retailer's orders using records of that retailer's past orders. However, it is becoming increasingly common for retail firms to collect and share large volumes of point-of-sale (POS) data, thus presenting an alternative data signal for suppliers to use in generating forecasts. A question then arises as to which data produce the most accurate forecasts. Compounding this question is the fact that forecasters often temporally aggregate data for consolidation or to produce forecasts in larger time buckets. Extant literature prescribes two countervailing statistical effects, information loss and variance reduction, that could play significant roles in determining the impact of temporal aggregation on forecast accuracy. Utilizing a large set of paired order and POS data, this study examines these relationships.Keywords: retail; forecasting; temporal aggregation; S&OP INTRODUCTIONDemand planning is increasingly recognized as fundamental to efficient supply chain operations and overall firm profitability. This is particularly true for manufacturers of consumer packaged goods (CPG) in retail supply chains. CPG manufacturers are faced with the challenge of forecasting how much inventory their retailer customers will order. Since most large retailers maintain a network of distribution centers (DCs) to fulfill stores, and those DCs place orders directly to suppliers, "it is the orders placed by the retail DC (DC orders) which suppliers find difficult to forecast accurately" (Williams and Waller 2010, 231). This is because the variance of DC orders is typically much more erratic than that of consumer demand (Lee et al. 1997).To assist with this task, many retailers now share end sales, or point-of-sale (POS) data, with suppliers, so that suppliers have access to a less variable demand signal than order history from which to generate their order forecasts (Williams and Waller 2010). Suppliers without access to POS data, or the necessary capabilities to effectively utilize POS data, frequently employ other variance reduction strategies in their demand planning processes, such as cross-sectional (e.g., geographic locations, products) demand aggregation. This results in a smoother customer demand signal, and therefore, potentially more accurate demand forecasts (Williams and Waller 2011).Recently, the notion of temporal aggregation as a variance reduction strategy for forecasting has emerged in the supply chain management (SCM) literature (Rostami-Tabar et al. 2013). Temporal aggregation refers to the aggregation of higher frequency demand data (e.g., weekly) to lower frequency data (e.g., monthly). Intuitively, it seems that the lower frequency data will reduce demand variance and subsequently lead to lower forecast error. However, in this research, we contend that this intuition may be based on ...

Tendi: Tensor Disaggregation from Multiple Coarse Views

“…Note that both store-level and DC orders data are used for demand forecasting, and especially store-level data are vital in predicting future orders [20]. Hence, many retailers share data with their suppliers to assist in the forecasting task and avoid shortage or excess in inventory [9]. In a more restricted scenario, the second source collects sales of each category of items rather than each item individually.…”

Demand and order‐fulfillment planning: The impact of point‐of‐sale data, retailer orders and distribution center orders on forecast accuracy

“…Our work relates to studies by Williams and Waller (2010), and Jin, Williams, Tokar, and Waller (2015 that compare POS-and order-based forecasting for orderfulfillment planning at the supplier echelon of a CPG supply chain. Using empirical data from cereal, soup, and yogurt product categories, Williams and Waller (2010) evaluate a supplier's POS-and order-based forecast accuracy when forecasting weekly demand for each individual DC it serves.…”

“…The authors call for additional research using a wider variety of product categories. Jin et al (2015) apply temporal demand aggregation to reduce demand variance and improve the supplier's orderfulfillment forecast for each DC it serves. The research considers a 13-week forecast horizon, and weekly and monthly time buckets using data from cereal and yogurt product categories.…”

“…A key finding is that order-based forecast accuracy is negatively associated with temporally aggregated demand, while POS-based forecast accuracy is positively associated with temporal aggregation. Thus, temporally aggregated demand may skew the relative performance of POS-and order-based forecasts (Chen & Lee, 2012;Jin et al, 2015).…”

Demand and Revenue Management of Deteriorating Inventory on the Internet: An Empirical Study of Flash Sales Markets

“…The inventory and marketing management literatures have long acknowledged the importance of demand and revenue management to improve the effective provisioning of inventory to consumers (Talluri and Van Ryzin 2006;Waller et al 2008) through a better matching of supply and demand (Spulber 1996;Rabinovich 2004;Williams and Waller 2011;Jin et al 2015). Demand and revenue management are of particular interest for firms selling fixed and limited amounts of deteriorating inventories that lose their value after finite selling periods (Ferguson and Koenigsberg 2007;Bakker et al 2012).…”