A Hidden Markov Model to Detect On-Shelf Out-of-Stocks Using Point-of-Sale Data

  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.},
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... 

Figures and Tables from this paper

Predicting Out-of-Stock Using Machine Learning: An Application in a Retail Packaged Foods Manufacturing Company

For decades, Out-of-Stock (OOS) events have been a problem for retailers and manufacturers. In grocery retailing, an OOS event is used to characterize the condition in which customers do not find a

Estimating Lost Sales for Substitutable Products with Uncertain On-Shelf Availability

Problem definition: We address the problem of how to estimate lost sales for substitutable products when there is no reliable on-shelf availability (OSA) information. Academic/practical relevance: We

Applied Machine Learning in Operations Management

This work highlights how both supervised and unsupervised learning shape operations management research in both descriptive and prescriptive analyses, and identifies several exciting future directions at the intersection of machine learning and operations management.

A Social Media Infodemic-Based Prediction Model for the Number of Severe and Critical COVID-19 Patients in the Lockdown Area

An improved prediction model based on machine learning is built that can guide the Centers for Disease Control and Prevention and other clinic institutions to expand the monitoring channels and research methods concerning COVID-19 by using web-based social media data.

I Will Survive: Predicting Business Failures From Customer Ratings

This paper presents a novel method that uses online ratings to predict business failures.



Inventory estimation from transactions via hidden Markov models

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

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

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

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

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

Classification Performance for Making Decisions about Products Missing from the Shelf

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

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.