Ordering Behavior in Retail Stores and Implications for Automated Replenishment

Donselaar, Karel H. van; Gaur, Vishal; Woensel, Tom Van; Broekmeulen, R. A. C. M.; Fransoo, JC Jan

doi:10.1287/mnsc.1090.1141

Cited by 183 publications

(102 citation statements)

References 30 publications

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“…Industry-specific papers include those by Olivares and Cachon (2009) and Cachon and Olivares (2010), which identify the key drivers that explain the variation in the finished goods inventory within the automotive distribution system. There is also work that investigates the factors that affect inventory record accuracy (DeHoratius and Raman 2008) and when managers decide to deviate from the inventory recommendations of an automated ordering system (van Donselaar et al 2010).…”

Section: Supply Chain Management (Onlinementioning

confidence: 99%

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OM Forum—Causal Inference Models in Operations Management

Lim

Reza

et al. 2017

M&SOM

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Abstract. Operations management (OM) researchers have traditionally focused on developing normative mathematical models that prescribe what managers and firms should do. Recently, there has been increased interest in understanding what managers and firms actually do and the factors that drive these decisions. To advance this understanding, empirical investigation using causal inference models is critical. However, in many contexts, the ability to obtain causal inferences is fraught with the challenges of endogeneity and selection bias. This paper describes five empirical tools that have been widely used in economics to address these challenges and how they can be adopted by OM researchers. We also present an example that illustrates how the various attributes of big data-variety, velocity, and volume-can be useful in addressing the endogeneity bias.

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Section: Supply Chain Management (Onlinementioning

confidence: 99%

“…In addition, access to a greater velocity of data will allow for the construction of more precise variables. For instance, van Donselaar et al (2010) explain how access to more accurate inventory data would have improved the construction of their key dependent variable.…”

Section: Supply Chain Management (Onlinementioning

confidence: 99%

OM Forum—Causal Inference Models in Operations Management

Lim

Reza

et al. 2017

M&SOM

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“…In empirical work, with careful field data it may be possible to explore how individuals update their beliefs based on new information. As suggested by Bowman (1963) by examining the decision-updating process in the field, it may be possible to identify not only more detail about how the process works and potential biases that exist, but also situations where managers' local knowledge permits them to make better decisions than the system (van Donselaar et al 2010). In addition, understanding other drivers of belief updating would be worthwhile.…”

Section: Discussionmentioning

confidence: 99%

“…As such, decision makers observe the consequences of a particular action and then must use this information to determine their next action. Illustrative examples can be seen in a wide variety of contexts, including labor scheduling (Kesavan, Staats and Gilland 2014;Tan and Netessine 2014), production tool choice (Upton 1997;Ramdas et al 2010), job shop scheduling (Fryer 1975), local capacity decisions (Campbell and Frei 2011), inventory ordering (van Donselaar et al 2010), pricing (Phillips, Şimşek and Ryzin 2015), research and development investment (Chao, Kavadias and Gaimon 2009), and Bayesian models (Brown, Lu and Wolfson 1964) more generally. In these and other models, individuals are assumed to use Bayesian updating to generate their beliefs, which ultimately determine their next action.…”

Section: Introductionmentioning

confidence: 99%

Blinded by Experience: Prior Experience, Negative News and Belief Updating

Staats

Gino³

2015

SSRN Journal

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Traditional models of operations management involve dynamic decision-making assuming optimal (Bayesian) updating. However, behavioral theory suggests that individuals exhibit bias in their beliefs and decisions. We conduct both a field study and two laboratory studies to examine the phenomena in the context of health. In particular, we examine how an individual's prior experiences and the experiences of those around them alter the operational decisions that the individual makes. We draw on an exogenous announcement of negative news by the Food and Drug Administration (FDA) and explore how this affects an operational decision -production tool choice -of interventional cardiologists deciding between two types of cardiac stents. Analyzing 147,000 choices over 6 years, we find that individuals do respond to negative news by using the focal production tool less often. However, we find that both individual's own experience and others' experience alter their responses in predictable ways. Moreover, although individual and other experience act as substitutes prior to negative news, the two types of experience act as complements following the negative announcement -leading to even greater use of the same production tool. Two controlled lab studies replicate our main findings and show that behavioral biases, not rational expectations, drive the effect. Our research contributes not only to operations management research, but also to the practice of healthcare and operations more generally.

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“…(2) Ordering decision Since ordering decisions impact store operations, thorough reorder planning is crucial for retailers (see e.g., Fisher 2009;Zelst et al 2009;Donselaar et al 2010;DeHoratius and Zeynep 2015). Kuhn and Sternbeck (2013) use qualitative interviews to identify that space management and in-store logistics are not yet well aligned, and that this constitutes a new area of research.…”

Section: Conceptual Background and Decision Problemmentioning

confidence: 99%

Effect of replenishment and backroom on retail shelf-space planning

Hübner

Schaal

2017

Bus Res

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Shelf-space optimization models support retailers in making optimal shelf-space decisions. They determine the number of facings for each item included in an assortment. One common characteristic of these models is that they do not account for in-store replenishment processes. However, the two areas of shelf-space planning and in-store replenishment are strongly interrelated. Keeping more shelf stock of an item increases the demand for it due to higher visibility, permits decreased replenishment frequencies and increases inventory holding costs. However, because space is limited, it also requires the reduction of shelf space for other items, which then deplete faster and must be reordered and replenished more often. Furthermore, the possibility of keeping stock of certain items in the backroom instead of the showroom allows for more showroom shelf space for other items, but also generates additional replenishment costs for the items kept in the backroom. The joint optimization of both shelf-space decisions and replenishment processes has not been sufficiently addressed in the existing literature. To quantify the cost associated with the relevant in-store replenishment processes, we conducted a time and motion study for a German grocery retailer. Based on these insights, we propose an optimization model that addresses the mutual dependence of shelf-space decisions and replenishment processes. The model optimizes retail profits by determining the optimum number of facings, the optimum display orientation of items, and the optimum order frequencies, while accounting for space-elasticity effects as well as limited shelf and backroom space. Applying our model to the grocery retailer's canned foods category, we found a profit potential of about 29%. We further apply our model to randomly generated data and show that it can be solved to optimality within very short run times, even for large-scale problem instances. 123Business Research (2017) 10:123-156 DOI 10.1007 replenishment cost on retail profits and solution structures. Based on the insights gained from the application of our model, the grocery retailer has decided to change its current approach to shelf-space decisions and in-store replenishment planning.

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Ordering Behavior in Retail Stores and Implications for Automated Replenishment

Cited by 183 publications

References 30 publications

OM Forum—Causal Inference Models in Operations Management

OM Forum—Causal Inference Models in Operations Management

Blinded by Experience: Prior Experience, Negative News and Belief Updating

Effect of replenishment and backroom on retail shelf-space planning

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