Multi-product inventory modeling with demand forecasting and Bayesian optimization

Marisol Valencia-Cárdenas, Francisco Javier Díaz-Serna, Juan Carlos Correa-Morales

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


The complexity of supply chains requires advanced methods to schedule companies’ inventories. This paper presents a comparison of model forecasts of demand for multiple products, choosing the best among the following: autoregressive integrated moving average (ARIMA), exponential smoothing (ES), a Bayesian regression model (BRM), and a Bayesian dynamic linear model (BDLM). To this end, cases in which the time series is normally distributed are first simulated. Second, sales predictions for three products of a gas service station are estimated using the four models, revealing the BRM to be the best model. Subsequently, the multi-product inventory model is optimized. To define the policies for ordering, inventory, costs, and profits, a Bayesian search integrating elements of a Tabu search is used to improve the solution. This inventory model optimization process is then applied to the case of a gas service station in Colombia.

Translated title of the contributionModelo de inventario multi-producto, con pronósticos de demanda y optimización Bayesiana
Original languageEnglish
Pages (from-to)236-244
Number of pages9
JournalDYNA (Colombia)
Issue number198
Publication statusPublished - 1 Sept 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The author; licensee Universidad Nacional de Colombia.

All Science Journal Classification (ASJC) codes

  • General Engineering


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