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.
Main Authors: | , , |
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Format: | Digital revista |
Language: | English |
Published: |
Universidad Nacional de Colombia
2016
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Online Access: | http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532016000400029 |
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