Abstract
This chapter discusses retail inventory management. Techniques are discussed to handle the complex and fast-paced context in which many retailers are competing today. These techniques are especially applicable for retailers selling products with shorter life cycles in a complex network of stores and platforms.
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Notes
- 1.
This is formulated quite bluntly and there may be many reasons to keep a slow-rotating product in the assortment. One might be wanting to provide a certain level of service to customers by being a one-stop shop. Alternatively, it may also be the case that a single sale is highly profitable, making it interesting to keep the product in the assortment even if sales are few and far between.
- 2.
An example could be a temporary store closure for renovation works that has taken place last year. A simple model could view this as a seasonal pattern that can be expected to repeat itself in the current year.
- 3.
This is a simplification, as it is likely that demand will fall back before inventory drops to zero.
- 4.
Depending on the context, this can sometimes not be completely avoided because there is insufficient data available, or some change has occurred that makes historical data no longer representative. This does not automatically mean that an approach such as the one presented here cannot be used. While such modeling may lead to criticisms from statisticians and data scientists, it is often still better than pure guesswork. The main thing to watch out for is to ensure that the models that are used are not too complex in nature. This is a simple—but imperfect—safeguard against overfitting.
- 5.
Being able to generalize purports that the model works in new situations.
- 6.
This is getting a bit technical; do not worry about this too much—this was only included for sake of completeness for the technically inclined reader.
- 7.
This concept is somewhat different from traditional probability, but the specific reasons why this is the case are outside of the scope of this text.
- 8.
The attentive reader might have noticed that notation was switched from Pr to P; this represents a change from a discrete to continuous probability. The mechanics remain the same, so no need to worry.
- 9.
The sum of the probability equals 1.
- 10.
Theoretical footnote: the observed sales are assumed to be Poisson distributed. This is a discrete distribution that assumes that events occur independently of each other (i.e., one sale does not depend on another). The Poisson distribution is frequently used to model the number of events taking place in a given frame of time. This is very similar to modeling the number of sales in a given time period. Within the context of Bayesian inference, the combination of a gamma-distributed prior and a Poisson-distributed likelihood has the interesting property that the posterior distribution is again a gamma distribution.
- 11.
In this section the term channel is used to signify a source of customer demand. Different physical stores can be considered separate channels, as well as different geographical regions serviced by a website.
- 12.
This quote is often attributed to Eisenhower, but is likely a much older adage.
- 13.
Complex does not mean impossible, and problems such as these may be tackled using reinforcement learning techniques, the question here being if the added value of using such complex techniques weighs up against the required investment. As always the advice here is to start simple and see for how long additional complexity produces a significant return on investment.
- 14.
One exception to this can be the situation where the limited capacity for sending or receiving inventory from a single location is taken into account. This is a useful extension of this problem, but for the sake of simplicity, it is assumed that this is not an aspect that has to be taken into account.
- 15.
Drawn from a uniform distribution.
- 16.
See Sect. 4.6.4 for more on project substitution in the context of cross-price elasticities.
- 17.
Economic order quantity.
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Kerkhove, LP. (2022). Product (Re-)Distribution and Replenishment. In: Data-driven Retailing. Management for Professionals. Springer, Cham. https://doi.org/10.1007/978-3-031-12962-9_5
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