Multi-product inventory modeling with demand forecasting and Bayesian optimization

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.

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Bibliographic Details
Main Authors: Valencia-Cárdenas,Marisol, Díaz-Serna,Francisco Javier, Correa-Morales,Juan Carlos
Format: Digital revista
Language:English
Published: Universidad Nacional de Colombia 2016
Online Access:http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532016000400029
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