An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain
Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/9067367 |
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| author | Zeynep Hilal Kilimci A. Okay Akyuz Mitat Uysal Selim Akyokus M. Ozan Uysal Berna Atak Bulbul Mehmet Ali Ekmis |
| author_facet | Zeynep Hilal Kilimci A. Okay Akyuz Mitat Uysal Selim Akyokus M. Ozan Uysal Berna Atak Bulbul Mehmet Ali Ekmis |
| author_sort | Zeynep Hilal Kilimci |
| collection | DOAJ |
| description | Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting system is developed. This improved model is based on the analysis and interpretation of the historical data by using different forecasting methods which include time series analysis techniques, support vector regression algorithm, and deep learning models. To the best of our knowledge, this is the first study to blend the deep learning methodology, support vector regression algorithm, and different time series analysis models by a novel decision integration strategy for demand forecasting approach. The other novelty of this work is the adaptation of boosting ensemble strategy to demand forecasting system by implementing a novel decision integration model. The developed system is applied and tested on real life data obtained from SOK Market in Turkey which operates as a fast-growing company with 6700 stores, 1500 products, and 23 distribution centers. A wide range of comparative and extensive experiments demonstrate that the proposed demand forecasting system exhibits noteworthy results compared to the state-of-art studies. Unlike the state-of-art studies, inclusion of support vector regression, deep learning model, and a novel integration strategy to the proposed forecasting system ensures significant accuracy improvement. |
| format | Article |
| id | doaj-art-66afdde054714514af675076e791b16c |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-66afdde054714514af675076e791b16c2025-02-03T07:25:29ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/90673679067367An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply ChainZeynep Hilal Kilimci0A. Okay Akyuz1Mitat Uysal2Selim Akyokus3M. Ozan Uysal4Berna Atak Bulbul5Mehmet Ali Ekmis6Department of Computer Engineering, Dogus University, Istanbul, TurkeyDepartment of Computer Engineering, Dogus University, Istanbul, TurkeyDepartment of Computer Engineering, Dogus University, Istanbul, TurkeyDepartment of Computer Engineering, Istanbul Medipol University, Istanbul, TurkeyDepartment of Computer Engineering, Dogus University, Istanbul, TurkeyOBASE Research & Development Center, Istanbul, TurkeyOBASE Research & Development Center, Istanbul, TurkeyDemand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting system is developed. This improved model is based on the analysis and interpretation of the historical data by using different forecasting methods which include time series analysis techniques, support vector regression algorithm, and deep learning models. To the best of our knowledge, this is the first study to blend the deep learning methodology, support vector regression algorithm, and different time series analysis models by a novel decision integration strategy for demand forecasting approach. The other novelty of this work is the adaptation of boosting ensemble strategy to demand forecasting system by implementing a novel decision integration model. The developed system is applied and tested on real life data obtained from SOK Market in Turkey which operates as a fast-growing company with 6700 stores, 1500 products, and 23 distribution centers. A wide range of comparative and extensive experiments demonstrate that the proposed demand forecasting system exhibits noteworthy results compared to the state-of-art studies. Unlike the state-of-art studies, inclusion of support vector regression, deep learning model, and a novel integration strategy to the proposed forecasting system ensures significant accuracy improvement.http://dx.doi.org/10.1155/2019/9067367 |
| spellingShingle | Zeynep Hilal Kilimci A. Okay Akyuz Mitat Uysal Selim Akyokus M. Ozan Uysal Berna Atak Bulbul Mehmet Ali Ekmis An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain Complexity |
| title | An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain |
| title_full | An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain |
| title_fullStr | An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain |
| title_full_unstemmed | An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain |
| title_short | An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain |
| title_sort | improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain |
| url | http://dx.doi.org/10.1155/2019/9067367 |
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