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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Universidad Nacional de Colombia
2016
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oai:scielo:S0012-735320160004000292017-04-23Multi-product inventory modeling with demand forecasting and Bayesian optimizationValencia-Cárdenas,MarisolDíaz-Serna,Francisco JavierCorrea-Morales,Juan Carlos Dynamic Linear Models Inventory Models Forecasts Bayesian Statistics 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.info:eu-repo/semantics/openAccessUniversidad Nacional de ColombiaDYNA v.83 n.198 20162016-09-01info:eu-repo/semantics/articletext/htmlhttp://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532016000400029en10.15446/dyna.v83n198.51310 |
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Colombia |
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English |
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Digital |
| author |
Valencia-Cárdenas,Marisol Díaz-Serna,Francisco Javier Correa-Morales,Juan Carlos |
| spellingShingle |
Valencia-Cárdenas,Marisol Díaz-Serna,Francisco Javier Correa-Morales,Juan Carlos Multi-product inventory modeling with demand forecasting and Bayesian optimization |
| author_facet |
Valencia-Cárdenas,Marisol Díaz-Serna,Francisco Javier Correa-Morales,Juan Carlos |
| author_sort |
Valencia-Cárdenas,Marisol |
| title |
Multi-product inventory modeling with demand forecasting and Bayesian optimization |
| title_short |
Multi-product inventory modeling with demand forecasting and Bayesian optimization |
| title_full |
Multi-product inventory modeling with demand forecasting and Bayesian optimization |
| title_fullStr |
Multi-product inventory modeling with demand forecasting and Bayesian optimization |
| title_full_unstemmed |
Multi-product inventory modeling with demand forecasting and Bayesian optimization |
| title_sort |
multi-product inventory modeling with demand forecasting and bayesian optimization |
| description |
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. |
| publisher |
Universidad Nacional de Colombia |
| publishDate |
2016 |
| url |
http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532016000400029 |
| work_keys_str_mv |
AT valenciacardenasmarisol multiproductinventorymodelingwithdemandforecastingandbayesianoptimization AT diazsernafranciscojavier multiproductinventorymodelingwithdemandforecastingandbayesianoptimization AT correamoralesjuancarlos multiproductinventorymodelingwithdemandforecastingandbayesianoptimization |
| _version_ |
1755932672024117248 |