Optimizing Demand Forecasting Method with Support Vector Regression for Improved Inventory Planning
Problems arising from suboptimal production planning can cause inventory management to be less effective and efficient in the company. The lack of integrated presentation of information also causes less efficiency in making decisions. This study aims to obtain the best kernel function forecasting mo...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Andalas
2025-01-01
|
| Series: | Jurnal Optimasi Sistem Industri |
| Subjects: | |
| Online Access: | https://josi.ft.unand.ac.id/index.php/josi/article/view/83 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832575486914461696 |
|---|---|
| author | Tryantomo Lokhilmahful Palgunadi Rina Fitriana Anik Nur Habyba Yun-Chia Liang |
| author_facet | Tryantomo Lokhilmahful Palgunadi Rina Fitriana Anik Nur Habyba Yun-Chia Liang |
| author_sort | Tryantomo Lokhilmahful Palgunadi |
| collection | DOAJ |
| description | Problems arising from suboptimal production planning can cause inventory management to be less effective and efficient in the company. The lack of integrated presentation of information also causes less efficiency in making decisions. This study aims to obtain the best kernel function forecasting model by predicting ground rod sales using the Support Vector Regression (SVR) method in order to determine the level of forecasting accuracy and the results of ground rod forecasting in the future which are presented in an optimal data visualization. This problem-solving is done with the Support Vector Regression method, which consists of linear kernel functions, polynomial kernel functions, and radial basis function (RBF) kernel functions with the Grid Search Algorithm. Based on the results of the best parameter search that has been done using the grid search algorithm, it can be concluded that the best kernel function forecasting model is a linear kernel function with a value of C = 100 and ε = 10-3. The accuracy of this forecasting model has a MAPE value of training data and testing data of 2.048% and 1.569%, where this value is the smallest MAPE value compared to the MAPE value of the other two functions. After getting the best model, forecasting was carried out within five months, obtaining an average of 6,647 monthly pieces. The results of forecasting and historical sales are reviewed in a visualization of Business Intelligence data so that it is well exposed, where the forecasting shows an increase from every month. |
| format | Article |
| id | doaj-art-e507a474b5a843ebade0893606a91e0b |
| institution | Kabale University |
| issn | 2088-4842 2442-8795 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Andalas |
| record_format | Article |
| series | Jurnal Optimasi Sistem Industri |
| spelling | doaj-art-e507a474b5a843ebade0893606a91e0b2025-02-01T06:52:12ZengUniversitas AndalasJurnal Optimasi Sistem Industri2088-48422442-87952025-01-0123214916610.25077/josi.v23.n2.p149-166.202483Optimizing Demand Forecasting Method with Support Vector Regression for Improved Inventory PlanningTryantomo Lokhilmahful Palgunadi0Rina Fitriana1Anik Nur Habyba2Yun-Chia Liang3Universitas TrisaktiUniversitas TrisaktiUniversitas TrisaktiYuan Ze UniversityProblems arising from suboptimal production planning can cause inventory management to be less effective and efficient in the company. The lack of integrated presentation of information also causes less efficiency in making decisions. This study aims to obtain the best kernel function forecasting model by predicting ground rod sales using the Support Vector Regression (SVR) method in order to determine the level of forecasting accuracy and the results of ground rod forecasting in the future which are presented in an optimal data visualization. This problem-solving is done with the Support Vector Regression method, which consists of linear kernel functions, polynomial kernel functions, and radial basis function (RBF) kernel functions with the Grid Search Algorithm. Based on the results of the best parameter search that has been done using the grid search algorithm, it can be concluded that the best kernel function forecasting model is a linear kernel function with a value of C = 100 and ε = 10-3. The accuracy of this forecasting model has a MAPE value of training data and testing data of 2.048% and 1.569%, where this value is the smallest MAPE value compared to the MAPE value of the other two functions. After getting the best model, forecasting was carried out within five months, obtaining an average of 6,647 monthly pieces. The results of forecasting and historical sales are reviewed in a visualization of Business Intelligence data so that it is well exposed, where the forecasting shows an increase from every month.https://josi.ft.unand.ac.id/index.php/josi/article/view/83support vector regressionforecastinggrid search algorithmkernel functionsbusiness intelligence |
| spellingShingle | Tryantomo Lokhilmahful Palgunadi Rina Fitriana Anik Nur Habyba Yun-Chia Liang Optimizing Demand Forecasting Method with Support Vector Regression for Improved Inventory Planning Jurnal Optimasi Sistem Industri support vector regression forecasting grid search algorithm kernel functions business intelligence |
| title | Optimizing Demand Forecasting Method with Support Vector Regression for Improved Inventory Planning |
| title_full | Optimizing Demand Forecasting Method with Support Vector Regression for Improved Inventory Planning |
| title_fullStr | Optimizing Demand Forecasting Method with Support Vector Regression for Improved Inventory Planning |
| title_full_unstemmed | Optimizing Demand Forecasting Method with Support Vector Regression for Improved Inventory Planning |
| title_short | Optimizing Demand Forecasting Method with Support Vector Regression for Improved Inventory Planning |
| title_sort | optimizing demand forecasting method with support vector regression for improved inventory planning |
| topic | support vector regression forecasting grid search algorithm kernel functions business intelligence |
| url | https://josi.ft.unand.ac.id/index.php/josi/article/view/83 |
| work_keys_str_mv | AT tryantomolokhilmahfulpalgunadi optimizingdemandforecastingmethodwithsupportvectorregressionforimprovedinventoryplanning AT rinafitriana optimizingdemandforecastingmethodwithsupportvectorregressionforimprovedinventoryplanning AT aniknurhabyba optimizingdemandforecastingmethodwithsupportvectorregressionforimprovedinventoryplanning AT yunchialiang optimizingdemandforecastingmethodwithsupportvectorregressionforimprovedinventoryplanning |