[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120498-en":3,"doc-seo-120498-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120498,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Forecasting Accuracy through Machine Learning in Supply Chain Management - Article","Machine learning techniques are increasingly used in economic and financial forecasting to strengthen prediction accuracy and robustness. This paper evaluates the performance of support vector machines, random forests, and deep learning models for forecasting economic variables, financial market trends, and macroeconomic indicators. Results show deep learning can outperform classical econometric approaches such as ARIMA and VAR, particularly in complex nonlinear settings. The study also addresses interpretability, overfitting, and data-quality limitations and suggests ways to mitigate them.","International Journal of Supply Chain Management 2518 (Online)  \nVol.9, Issue 6, No.2, pp  \nForecasting Accuracy through Machine Learning in Supply Chain Management  \nIrshadullah Asim Mohammed & Joydeb Mandal  \nInternational Journal of Supply Chain Management ISSN 2518-4709 (Online)  \nVol.9, Issue 6, No.2, pp 8-24, 2024  \n[www.iprjb.org](www.iprjb.org)  \nForecasting Accuracy through Machine Learning in Supply Chain Management  \nIrshadullah Asim Mohammed and Joydeb Mandal  \n Article History  Received 13th August 2024  \nReceived in Revised Form 12th September 2024  \nAccepted 15th October 2024  \nHow to cite in APA format:  \nMohammed, I., & Mandal, J. (2024) . Forecasting Accuracy through Machine Learning in Supply Chain Management. International Journal of Supply Chain  \nManagement, 9(6), 8–24.  \n[https://doi.org/10.47604/ijscm.3074](https://doi.org/10.47604/ijscm.3074)  \nAbstract  \nPurpose: The use of machine learning (ML) techniques in economic and financial forecasting has gained significant attention due to their potential to improve the accuracy and robustness of predictions. This paper explores the application of various ML algorithms such as support vector machines,random forests, and deep learning models in forecasting economic variables, financial market trends, and macroeconomic indicators.  \nMethodology: We assess the forecasting accuracy of these models relative to traditional econometric approaches, including ARIMA and VAR models.  \nFindings: The analysis revealed that ML techniques, particularly deep learning, outperform classical methods in terms of predictive accuracy, especially in complex, nonlinear environments. We also discuss challenges associated with model interpretability, overfitting, and data quality, providing insights into how these limitations can be addressed.  \nUnique Contribution to Theory, Practice and Policy: The findings contribute to a deeper understanding of how advanced machine learning can enhance forecasting methodologies, with implications for both theoretical modeling and practical applications in economic policy, risk management, and financial decision-making.  \nKeywords: Forecasting and Prediction Models, Machine Learning, ML Techniques, Financial Forecasting  \nInternational Journal of Supply Chain Management ISSN 2518-4709 (Online)  \nVol.9, Issue 6, No.2, pp 8-24, 2024  \n[www.iprjb.org](www.iprjb.org)  \nINTRODUCTION  \nIn today's hyper-connected and fast-paced global economy, supply chains have evolved into intricate networks that involve multiple stakeholders, ranging from raw material suppliers to end consumers. For organizations aiming to stay competitive, reduce costs, and improve customer satisfaction, accurately forecasting demand has become an increasingly challenging task. Traditional forecasting methods, which rely heavily on historical data, simplistic statistical models, and intuition, need to be reconsidered in the face of modern supply chain complexities. Factors such as fluctuating consumer preferences, global disruptions, seasonal trends, and rapid technological advancements can all dramatically affect demand patterns, making precise forecasting a daunting challenge.  \nAgainst this backdrop, machine learning (ML) has emerged as a transformative tool in supply chain management. By harnessing vast amounts of data and sophisticated algorithms, ML enables organizations to dramatically improve forecasting accuracy. Unlike conventional methods, which struggle to account for non-linear relationships and complex datasets, ML models excel at identifying intricate patterns and correlations that may not be immediately apparent. This ability allows businesses to respond dynamically to changing market conditions and evolving consumer behaviors, enhancing overall decision-making processes. The urgency for enhanced forecasting accuracy has been underscored by recent global events, such as the COVID-19 pandemic, which caused significant disruptions in supply chains worldwide. Companies r","cbCaiu26XYj0bOXr","https://ap.wps.com/l/cbCaiu26XYj0bOXr","pdf",464756,1,19,"English","en",105,"# INTRODUCTION\n## Demand forecasting challenges in modern supply chains\n## Why machine learning improves forecasting accuracy\n## Data volume, adaptability, and resilience","[{\"question\":\"What problem does the paper address in supply chain management?\",\"answer\":\"The paper addresses the difficulty of accurately forecasting demand in complex, fast-changing supply chains influenced by consumer behavior, disruptions, seasonality, and technological change.\"},{\"question\":\"Which forecasting approaches are compared in the paper?\",\"answer\":\"The paper compares machine learning algorithms (such as support vector machines, random forests, and deep learning models) against traditional econometric approaches including ARIMA and VAR models.\"},{\"question\":\"What are the main findings regarding model performance?\",\"answer\":\"The analysis finds that machine learning, especially deep learning, achieves higher predictive accuracy than classical methods in complex nonlinear environments.\"}]","Forecasting Accuracy through Machine Learning in Supply Chain Management - Article | PDF",1785730369,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"forecasting-accuracy-through-machine-learning-in-supply-chain-management-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forecasting-accuracy-through-machine-learning-in-supply-chain-management-article/120498/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in supply chain management?","Question",{"text":75,"@type":76},"The paper addresses the difficulty of accurately forecasting demand in complex, fast-changing supply chains influenced by consumer behavior, disruptions, seasonality, and technological change.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which forecasting approaches are compared in the paper?",{"text":80,"@type":76},"The paper compares machine learning algorithms (such as support vector machines, random forests, and deep learning models) against traditional econometric approaches including ARIMA and VAR models.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main findings regarding model performance?",{"text":84,"@type":76},"The analysis finds that machine learning, especially deep learning, achieves higher predictive accuracy than classical methods in complex nonlinear environments.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]