{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T08:08:59Z","timestamp":1775808539316,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,7,28]],"date-time":"2024-07-28T00:00:00Z","timestamp":1722124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Gulf University for Science and Technology (GUST), Kuwait"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>In today\u2019s dynamic business environment, the accurate prediction of sales orders plays a critical role in optimizing Supply Chain Management (SCM) and enhancing operational efficiency. In a rapidly changing, Fast-Moving Consumer Goods (FMCG) business, it is essential to analyze the sales of the products and accordingly plan the supply. Due to low data volume and complexity, traditional forecasting methods struggle to capture intricate patterns. Domain Adversarial Neural Networks (DANNs) offer a promising solution by integrating transfer learning techniques to improve prediction accuracy across diverse datasets. This study presents a new sales order prediction framework that combines DANN-based feature extraction and various machine learning models. The DANN method generalizes the data, maintaining the data behavior\u2019s originality. The approach addresses challenges like limited data availability and high variability in sales behavior. Using the transfer learning approach, the DANN model is trained on the training data, and this pre-trained DANN model extracts relevant features from unknown products. In contrast, Machine Learning (ML) algorithms are used to build predictive models based on it. The hyperparameter tuning of ensemble models such as Decision Tree (DT) and Random Forest (RF) is also performed. Models like the DT and RF Regressor perform better than Linear Regression and Support Vector Regressor. Notably, even without hyperparameter tuning, the Extreme Gradient Boost (XGBoost) Regressor model outperforms all the other models. This comprehensive analysis highlights the comparative benefits of various models and establishes the superiority of XGBoost in predicting sales orders effectively.<\/jats:p>","DOI":"10.3390\/bdcc8080081","type":"journal-article","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T11:11:25Z","timestamp":1722337885000},"page":"81","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Improving Machine Learning Predictive Capacity for Supply Chain Optimization through Domain Adversarial Neural Networks"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9039-5733","authenticated-orcid":false,"given":"Javed","family":"Sayyad","sequence":"first","affiliation":[{"name":"Department of Robotics and Automation, Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, Maharashtra, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5768-2649","authenticated-orcid":false,"given":"Khush","family":"Attarde","sequence":"additional","affiliation":[{"name":"Department of Robotics and Automation, Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, Maharashtra, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2954-1217","authenticated-orcid":false,"given":"Bulent","family":"Yilmaz","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Gulf University for Science and Technology (GUST), Hawally 32093, Kuwait"},{"name":"GUST Engineering and Applied Innovation Research Center (GEAR), Gulf University for Science and Technology (GUST), Hawally 32093, Kuwait"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1108\/09600030210437960","article-title":"Implementing collaborative forecasting to improve supply chain performance","volume":"32","author":"McCarthy","year":"2002","journal-title":"Int. J. Phys. Distrib. Logist. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/0377-2217(95)00136-0","article-title":"Exploiting timely demand information to reduce inventories","volume":"92","author":"Bourland","year":"1996","journal-title":"Eur. J. Oper. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100026","DOI":"10.1016\/j.sca.2023.100026","article-title":"A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality","volume":"3","author":"Tadayonrad","year":"2023","journal-title":"Supply Chain Anal."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.tre.2017.04.001","article-title":"Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice","volume":"114","author":"Arunachalam","year":"2018","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7776","DOI":"10.1109\/ACCESS.2017.2696365","article-title":"Machine learning with big data: Challenges and approaches","volume":"5","author":"Grolinger","year":"2017","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.crfs.2021.03.009","article-title":"Deep learning and machine vision for food processing: A survey","volume":"4","author":"Zhu","year":"2021","journal-title":"Curr. Res. Food Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1080\/03036758.2019.1609052","article-title":"A survey on evolutionary machine learning","volume":"49","author":"Bi","year":"2019","journal-title":"J. R. Soc. N. Z."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1111\/1541-4337.12492","article-title":"Application of deep learning in food: A review","volume":"18","author":"Zhou","year":"2019","journal-title":"Compr. Rev. Food Sci. Food Saf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"114820","DOI":"10.1016\/j.eswa.2021.114820","article-title":"Machine Learning for industrial applications: A comprehensive literature review","volume":"175","author":"Bertolini","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"113480","DOI":"10.1016\/j.jbusres.2022.113480","article-title":"Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning","volume":"156","author":"Hu","year":"2023","journal-title":"J. Bus. Res."},{"key":"ref_11","first-page":"2203322","article-title":"Cross-border e-commerce platform logistics and supply chain network optimization based on deep learning","volume":"2022","author":"Guo","year":"2022","journal-title":"Mob. Inf. Syst."},{"key":"ref_12","first-page":"57","article-title":"A prediction model for automobile sales in Turkey using deep neural networks","volume":"31","author":"Kaya","year":"2020","journal-title":"End\u00fcstri M\u00fchendisli\u011fi"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"565","DOI":"10.3390\/forecast4020031","article-title":"Deep learning for demand forecasting in the fashion and apparel retail industry","volume":"4","author":"Giri","year":"2022","journal-title":"Forecasting"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9067367","DOI":"10.1155\/2019\/9067367","article-title":"An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain","volume":"2019","author":"Kilimci","year":"2019","journal-title":"Complexity"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/00207543.2020.1733125","article-title":"Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor","volume":"58","author":"Chien","year":"2020","journal-title":"Int. J. Prod. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1287\/mnsc.2022.4564","article-title":"A practical end-to-end inventory management model with deep learning","volume":"69","author":"Qi","year":"2023","journal-title":"Manag. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"237","DOI":"10.5267\/j.dsl.2023.9.003","article-title":"An integrated approach for modern supply chain management: Utilizing advanced machine learning models for sentiment analysis, demand forecasting, and probabilistic price prediction","volume":"13","author":"Amellal","year":"2024","journal-title":"Decis. Sci. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alzoubi, H.M., Alshurideh, M.T., and Ghazal, T.M. (2024). Empowering Supply Chain Management System with Machine Learning and Blockchain Technology. Cyber Security Impact on Digitalization and Business Intelligence: Big Cyber Security for Information Management: Opportunities and Challenges, Springer International Publishing.","DOI":"10.1007\/978-3-031-31801-6_21"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106610","DOI":"10.1016\/j.cie.2020.106610","article-title":"Accelerating supply chains with Ant Colony Optimization across a range of hardware solutions","volume":"147","author":"Dzalbs","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_20","unstructured":"Constante, F., Silva, F., and Pereira, A. (Mendeley Data, 2019). DataCo smart supply chain for big data analysis, Mendeley Data, Version 5."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Siniosoglou, I., Xouveroudis, K., Argyriou, V., Lagkas, T., Goudos, S.K., Psannis, K.E., and Sarigiannidis, P. (2023, January 28\u201330). Evaluating the effect of volatile federated timeseries on modern DNNs: Attention over long\/short memory. Proceedings of the 2023 12th International Conference on Modern Circuits and Systems Technologies (MOCAST), Athens, Greece.","DOI":"10.1109\/MOCAST57943.2023.10176585"},{"key":"ref_22","unstructured":"Wasi, A.T., Islam, M., and Akib, A.R. (2024). SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112171","DOI":"10.1016\/j.measurement.2022.112171","article-title":"Retentive multimodal scale-variable anomaly detection framework with limited data groups for liquid rocket engine","volume":"205","author":"Zhang","year":"2022","journal-title":"Measurement"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"122191","DOI":"10.1016\/j.eswa.2023.122191","article-title":"A Viewpoint Adaptation Ensemble Contrastive Learning framework for vessel type recognition with limited data","volume":"238","author":"Zhang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3657300","article-title":"Domain Generalization in Time Series Forecasting","volume":"18","author":"Deng","year":"2024","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hayashi, S., Tanimoto, A., and Kashima, H. (2019, January 14\u201319). Long-term prediction of small time-series data using generalized distillation. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851687"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s13677-023-00576-7","article-title":"Time series forecasting model for non-stationary series pattern extraction using deep learning and GARCH modeling","volume":"13","author":"Han","year":"2024","journal-title":"J. Cloud Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Javeri, I.Y., Toutiaee, M., Arpinar, I.B., Miller, J.A., and Miller, T.W. (2021, January 23\u201326). Improving neural networks for time-series forecasting using data augmentation and AutoML. Proceedings of the 2021 IEEE Seventh International Conference on Big Data Computing Service and Applications (BigDataService), Oxford, UK.","DOI":"10.1109\/BigDataService52369.2021.00006"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101020","DOI":"10.1016\/j.jocs.2019.07.007","article-title":"Generalization in fully-connected neural networks for time series forecasting","volume":"36","author":"Borovykh","year":"2019","journal-title":"J. Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3275","DOI":"10.1109\/ACCESS.2022.3140377","article-title":"Time series forecasting by generalized regression neural networks trained with multiple series","volume":"10","author":"Rivera","year":"2022","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.neunet.2006.01.012","article-title":"Machine learning approaches for estimation of prediction interval for the model output","volume":"19","author":"Shrestha","year":"2006","journal-title":"Neural Netw."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Awad, M., and Khanna, R. (2015). Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, Apress.","DOI":"10.1007\/978-1-4302-5990-9"},{"key":"ref_33","first-page":"2323","article-title":"Decision tree regressor compared with random forest regressor for house price prediction in mumbai","volume":"10","author":"Reddy","year":"2023","journal-title":"J. Surv. Fish. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"116217","DOI":"10.1016\/j.eswa.2021.116217","article-title":"Empirical investigation of hyperparameter optimization for software defect count prediction","volume":"191","author":"Nevendra","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Weber, F., and Sch\u00fctte, R. (2019). A domain-oriented analysis of the impact of machine learning\u2014The case of retailing. Big Data Cogn. Comput., 3.","DOI":"10.3390\/bdcc3010011"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.indmarman.2017.12.019","article-title":"Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice","volume":"69","author":"Syam","year":"2018","journal-title":"Ind. Mark. Manag."},{"key":"ref_37","unstructured":"Marr, B. (2019). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems, John Wiley & Sons."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/8\/81\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:25:23Z","timestamp":1760109923000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/8\/81"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,28]]},"references-count":37,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["bdcc8080081"],"URL":"https:\/\/doi.org\/10.3390\/bdcc8080081","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,28]]}}}