A Hidden Markov Model to Detect On-Shelf Out-of-Stocks Using Point-of-Sale Data
@article{Montoya2019AHM, title={A Hidden Markov Model to Detect On-Shelf Out-of-Stocks Using Point-of-Sale Data}, author={Ricardo Montoya and Carlos Gonz{\'a}lez}, journal={Manuf. Serv. Oper. Manag.}, year={2019}, volume={21}, pages={932-948} }
We propose a hidden Markov model (HMM) approach to identifying on-shelf out-of-stock (OOS) by detecting changes in sales patterns resulting from unobserved states of the shelf. We calibrate our mod...
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References
SHOWING 1-10 OF 52 REFERENCES
Inventory estimation from transactions via hidden Markov models
- Business, Computer Science
- 2015
This work solves the problem of inventory tracking in the retail industry using Hidden Markov Models by looking at the sequence of sales as a time-series, and finds that under appropriate assumptions, exact stock recovery is possible for all time.
A Hidden Markov Model of Customer Relationship Dynamics
- BusinessMark. Sci.
- 2008
This research constructs and estimates a nonhomogeneous hidden Markov model to model the transitions among latent relationship states and effects on buying behavior, and uses a hierarchical Bayes approach to capture the unobserved heterogeneity across customers.
Inventory Control in a Fluctuating Demand Environment
- EconomicsOper. Res.
- 1993
An inventory model, where the demand rate varies with an underlying state-of-the-world variable that can represent economic fluctuations, or stages in the product life-cycle, for example, is presented.
Demand Estimation from Censored Observations with Inventory Record Inaccuracy
- BusinessManuf. Serv. Oper. Manag.
- 2015
A systematic downward bias in demand estimation under typical assumptions on the distribution of inventory record inaccuracies is characterized and a heuristic prescription is proposed that relies on a single error statistic and that sharply reduces this bias.
Investigating Effects of Out-of-Stock on Consumer Stockkeeping Unit Choice
- Business
- 2012
Out-of-stock (OOS) is commonly observed in the retail environment with consumer packaged goods, but there have been few empirical studies of the effects of OOS on consumer product choice, because…
A decision support system for detecting products missing from the shelf based on heuristic rules
- Business, Computer ScienceDecis. Support Syst.
- 2009
Classification Performance for Making Decisions about Products Missing from the Shelf
- Computer Science, BusinessAdv. Decis. Sci.
- 2011
This work employs two different classification algorithms, C4.5 and naive Bayes, in order to build a mechanism that makes decisions about whether a product is available on a retail store shelf or not and identifies certain approaches for the development and introduction of such a mechanism in different retail contexts.
Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability
- BusinessMark. Sci.
- 2010
A two-stage approach for dynamically allocating detailing and sampling activities across physicians to maximize long-run profitability is presented and it is found that detailing is most effective as an acquisition tool, whereas sampling is mosteffective as a retention tool.