Artificial intelligence for forecasting sales of agricultural products: A case study of a moroccan agricultural company
This paper presents a study focused on the analysis of phytosanitary treatment sales in the Souss Massa region of Morocco. The objective of the study is to predict the sales of agricultural products, particularly crop protection solutions, aiming to optimize supply chain operations and meet customer...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Journal of Open Innovation: Technology, Market and Complexity |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2199853123002913 |
| _version_ | 1797217129308618752 |
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| author | Nebri Mohamed-Amine Moussaid Abdellatif Bouikhalene Belaid |
| author_facet | Nebri Mohamed-Amine Moussaid Abdellatif Bouikhalene Belaid |
| author_sort | Nebri Mohamed-Amine |
| collection | DOAJ |
| description | This paper presents a study focused on the analysis of phytosanitary treatment sales in the Souss Massa region of Morocco. The objective of the study is to predict the sales of agricultural products, particularly crop protection solutions, aiming to optimize supply chain operations and meet customer demand effectively. Data for this study are collected from multiple sources, including the Enterprise Resource Planning (ERP) system called Microsoft Dynamics AXAPTA used by a leading agricultural company operating in the region. Information such as the date of sale, farming type, climate, and specific sales locations within the Sous Massa region is gathered. Machine learning techniques are applied for forecasting. Various regression models, including the Gradient Boosting Regressor algorithm, are employed to determine the most accurate predictor. Evaluation of the models reveals promising results, with a Mean Absolute Error (MAE) of 0.0035 and a Root Mean Square Error (RMSE) of 0.0066. The results obtained by applying various regression models, including the Gradient Boosting Regressor algorithm, demonstrate promising prediction scores. These findings contribute to the field of sales prediction in the agricultural industry while considering the impact of climate conditions, farming practices, and regional factors. |
| first_indexed | 2024-03-08T22:31:44Z |
| format | Article |
| id | doaj.art-d570aadb719c4c1eae2b171a74a6a005 |
| institution | Directory Open Access Journal |
| issn | 2199-8531 |
| language | English |
| last_indexed | 2024-04-24T11:56:57Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Open Innovation: Technology, Market and Complexity |
| spelling | doaj.art-d570aadb719c4c1eae2b171a74a6a0052024-04-09T04:12:48ZengElsevierJournal of Open Innovation: Technology, Market and Complexity2199-85312024-03-01101100189Artificial intelligence for forecasting sales of agricultural products: A case study of a moroccan agricultural companyNebri Mohamed-Amine0Moussaid Abdellatif1Bouikhalene Belaid2Laboratory LIMATI, Department of Mathematics and Informatics, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco; Corresponding author.ENSIAS, Mohammed V University in Rabat, Rabat 10000, MoroccoLaboratory LIMATI, Department of Mathematics and Informatics, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, MoroccoThis paper presents a study focused on the analysis of phytosanitary treatment sales in the Souss Massa region of Morocco. The objective of the study is to predict the sales of agricultural products, particularly crop protection solutions, aiming to optimize supply chain operations and meet customer demand effectively. Data for this study are collected from multiple sources, including the Enterprise Resource Planning (ERP) system called Microsoft Dynamics AXAPTA used by a leading agricultural company operating in the region. Information such as the date of sale, farming type, climate, and specific sales locations within the Sous Massa region is gathered. Machine learning techniques are applied for forecasting. Various regression models, including the Gradient Boosting Regressor algorithm, are employed to determine the most accurate predictor. Evaluation of the models reveals promising results, with a Mean Absolute Error (MAE) of 0.0035 and a Root Mean Square Error (RMSE) of 0.0066. The results obtained by applying various regression models, including the Gradient Boosting Regressor algorithm, demonstrate promising prediction scores. These findings contribute to the field of sales prediction in the agricultural industry while considering the impact of climate conditions, farming practices, and regional factors.http://www.sciencedirect.com/science/article/pii/S2199853123002913Machine learningSales predictionPhytosanitaryEnterprise resource planningClimateOpen innovation |
| spellingShingle | Nebri Mohamed-Amine Moussaid Abdellatif Bouikhalene Belaid Artificial intelligence for forecasting sales of agricultural products: A case study of a moroccan agricultural company Journal of Open Innovation: Technology, Market and Complexity Machine learning Sales prediction Phytosanitary Enterprise resource planning Climate Open innovation |
| title | Artificial intelligence for forecasting sales of agricultural products: A case study of a moroccan agricultural company |
| title_full | Artificial intelligence for forecasting sales of agricultural products: A case study of a moroccan agricultural company |
| title_fullStr | Artificial intelligence for forecasting sales of agricultural products: A case study of a moroccan agricultural company |
| title_full_unstemmed | Artificial intelligence for forecasting sales of agricultural products: A case study of a moroccan agricultural company |
| title_short | Artificial intelligence for forecasting sales of agricultural products: A case study of a moroccan agricultural company |
| title_sort | artificial intelligence for forecasting sales of agricultural products a case study of a moroccan agricultural company |
| topic | Machine learning Sales prediction Phytosanitary Enterprise resource planning Climate Open innovation |
| url | http://www.sciencedirect.com/science/article/pii/S2199853123002913 |
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