Resumo

VALENCIA-CARDENAS, Marisol; DIAZ-SERNA, Francisco Javier  e  CORREA-MORALES, Juan Carlos. Multi-product inventory modeling with demand forecasting and Bayesian optimization. Dyna rev.fac.nac.minas [online]. 2016, vol.83, n.198, pp.235-243. ISSN 0012-7353.  https://doi.org/10.15446/dyna.v83n198.51310.

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.

Palavras-chave : Dynamic Linear Models; Inventory Models; Forecasts; Bayesian Statistics.

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