Measuring demand chasing behavior

Abstract

Chasing behavior is an example of the anchoring and adjustment heuristic that occurs in many business contexts. In inventory management settings, decision makers often engage in demand chasing by adjusting order quantities toward recent demand observations. This can result in lower profit for the firm and increased variability across the supply chain. Prior research suggests that demand chasing can be measured by regression or correlation. Using two empirical datasets, we show that the observed costs associated with chasing are better captured using regression. We complement our empirical findings through comprehensive simulations across a range of order generating functions, supporting the robustness of regression models. Finally, we discuss behavioral differences between approaches and explain why certain models are better equipped for measuring demand chasing.

Cognitive biases as impediments to enhancing supply chain entrepreneurial embeddedness

“…Given the roles of numbers and analytics in driving decision making within supply chains, it is not surprising that adjustment and anchoring is prevalent. This bias has been found to operate within many supply chain processes including buyer-supplier contract negotiations (Davis & Hyndman, 2019), forecasting (Fildes et al, 2009), consumer-facing activities (Doyle et al, 2021), and inventory management (Kirshner & Moritz, 2021). Relative to the latter, for example, managers tend to fixate on the most recent past period when making inventory management decisions; a phenomenon commonly referred to as demand chasing.…”

“…Relative to the latter, for example, managers tend to fixate on the most recent past period when making inventory management decisions; a phenomenon commonly referred to as demand chasing. Managers' propensity to be “inappropriately influenced by the most recent observation” may “lead to significant costs for organizations and supply chains” (Kirshner & Moritz, 2021: 1264). If the most recent demand point is excessively high, the adjustment managers make in reaching an inventory decision will likely be too small, making the possession of costly excess inventory in the future more likely.…”

For the future and from afar: Psychological distance and inventory decision‐making

“…11Some behavioral irregularities influencing ordering decisions include prospect theory (Long & Nasiry, 2015; Schweitzer & Cachon, 2000; Uppari & Hasija, 2019), demand chasing (Moritz et al., 2013; Kirshner & Moritz, 2021), risk preferences (Becker‐Peth et al., 2018; de Véricourt et al., 2013), and aspects of problem framing (Becker‐Peth et al., 2013; Chen et al., 2013; Schultz et al., 2018; Tokar et al., 2016). We refer the reader to Zhang and Siemsen (2019) for a meta‐analysis of behavioral newsvendor and Becker‐Peth and Thonemann (2018) and Cui and Wu (2018) for comprehensive reviews of how biases and heuristics influence newsvendor ordering.…”

Reference‐dependent preferences in flat penalty service‐level contracts

“…We first investigated the role of this heuristic from an aggregate‐level perspective. We ran a second linear mixed‐effects regression with robust standard errors following the methods proposed in Kirshner and Moritz (2020, 2021), where the robustness of regression models in measuring demand chasing and its costs is demonstrated. We use the regression specification where is the random effect for subject , which captures the unobserved individual heterogeneity, and is the error term.…”

“…Several papers have developed behavioral models to explain actual inventory behavior in different supply chain contracts, such as buyback contracts (Becker‐Peth et al, 2013); two‐part tariff and quantity discount contracts (Ho & Zhang, 2008); and revenue‐sharing contracts (Becker‐Peth & Thonemann, 2016). Past studies in other inventory settings point to demand chasing (Kirshner & Moritz, 2021), mean‐anchoring and insufficient adjustments (Katok & Wu, 2009), and even overstocking inventory (Tokar et al, 2014) as explanations for decision makers' observed behavioral regularities. However, these heuristics do not fully explain the actual decision‐making process and individual differences (De Vericourt et al, 2013; Moritz et al, 2013; Niederhoff & Kouvelis, 2016, 2019).…”