[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121951-en":3,"doc-seo-121951-105":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":20,"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},121951,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Complex Problem-Solving in Enterprises with Machine Learning Solutions","The paper explores how machine learning (ML) addresses complex, multi-constraint problems across enterprises in different industries. It reviews prior research and proposes a theoretical model that integrates ML into business processes to strengthen operational efficiency, improve customer experience, and support risk management. Findings show measurable gains in manufacturing quality control and predictive maintenance, reducing costs while raising productivity. In addition, ML enhances personalized marketing and customer support, and improves financial stability through fraud detection and credit risk assessment.","COMPLEX PROBLEM-SOLVING IN ENTERPRISES WITH MACHINE  \nLEARNING SOLUTIONS  \nDOI: 10.5937/JEMC2401033D UDC: 005.334:004 .85  \nOriginal Scientific Paper  \nLuka ĐORĐEVIĆ1, Borivoj NOVAKOVIĆ2, Mića ĐURĐEV3, Velibor PREMČEVSKI4,  \nMihalj BAKATOR5  \n1University of Novi Sad, Technical Faculty ”Mihajlo Pupin”, 23000 Zrenjanin, Đure Đakovića bb, Republic of Serbia ORCID ID ([https://orcid.org/0000-0003-4578-9060](https://orcid.org/0000-0003-4578-9060))  \n2University of Novi Sad, Technical Faculty ”Mihajlo Pupin”, 23000 Zrenjanin, Đure Đakovića bb, Republic of Serbia ORCID ID ([https://orcid.org/0000-0003-2816-3584](https://orcid.org/0000-0003-2816-3584))  \n3University of Novi Sad, Technical Faculty ”Mihajlo Pupin”, 23000 Zrenjanin, Đure Đakovića bb, Republic of Serbia ORCID ID ([https://orcid.org/0000-0002-1825-2754](https://orcid.org/0000-0002-1825-2754))  \n4University of Novi Sad, Technical Faculty ”Mihajlo Pupin”, 23000 Zrenjanin, Đure Đakovića bb, Republic of Serbia ORCID ID ([https://orcid.org/0000-0002-2883-6956](https://orcid.org/0000-0002-2883-6956))  \n5University of Novi Sad, Technical Faculty ”Mihajlo Pupin”, 23000 Zrenjanin, Đure Đakovića bb, Republic of Serbia [Corresponding author. E-mail:](Corresponding author. E-mail:mihalj.bakator@tfzr.rs)[mihalj.bakator@tfzr.rs](Corresponding author. E-mail:mihalj.bakator@tfzr.rs)  \nORCID ID ([https://orcid.org/0000-0001-8540-2460](https://orcid.org/0000-0001-8540-2460))  \nPaper received: 19.04.2024.; Paper accepted: 28.05.2024.  \nThis paper explores the application of machine learning (ML) in solving complex problems within enterprises across various industries. By leveraging ML, businesses can enhance operational efficiency, customer experience, and risk management. The study reviews existing literature to develop a theoretical model that integrates ML applications into business processes. Key findings indicate that ML significantly improves quality control and predictive maintenance in manufacturing, leading to reduced costs and increased productivity. Additionally, ML-driven personalized marketing and customer support enhance customer satisfaction and loyalty. In financial management, ML enhances fraud detection and credit risk assessment, contributing to financial stability and security. The paper provides suggestions for effectively implementing ML strategies to optimize business performance and addresses the implications for future business operations in a rapidly evolving technological landscape.  \nKeywords: Complex problem solving; Machine learning solutions; Enterprises; Improvement;  \nCompetitiveness.  \nINTRODUCTION  \nModern business conditions are characterized by rapidly changing market dynamics and an increasingly competitive landscape. Advances in technology, globalization, and the rise of digital platforms have transformed traditional business environments, making them more interconnected and complex (Cvjetković et al., 2021) . Enterprises today must navigate a multitude of challenges, including evolving consumer preferences, disruptive innovations, and the entry of new competitors from around the globe. These factors necessitate a more agile and responsive approach to business strategy. One of the critical aspects of modern business is the accelerated pace of technological advancement. Innovations such as  \nartificial intelligence, big data, and the Internet of Things (IoT) are reshaping industries, offering new opportunities for growth and efficiency. However, they also pose significant risks for businesses that fail to keep up with the rapid pace of change. Companies must invest in continuous learning and development, ensuring that their workforce is equipped with the necessary skills to leverage new technologies effectively (Spasojević-Brkić et al., 2020) .  \nMachine learning (ML) has become a fundamental component of contemporary technology, allowing systems to extract insights from data patterns and make decisions with minimal human input. This groundbreaking techn","cbCaioslk5cOvkBq","https://ap.wps.com/l/cbCaioslk5cOvkBq","pdf",555544,1,17,"English","en",105,"# Introduction\n## Machine learning in enterprise environments\n## ML applications across industries\n## Handling multifaceted and dynamic problems\n## Examples in urban planning and environmental science","[{\"question\":\"What does the paper focus on regarding machine learning in enterprises?\",\"answer\":\"It focuses on applying machine learning to solve complex problems across enterprises, supporting efficiency, customer experience, and risk management.\"},{\"question\":\"Which enterprise areas are highlighted as benefiting from ML according to the findings?\",\"answer\":\"Manufacturing quality control and predictive maintenance, personalized marketing and customer support, and financial management tasks such as fraud detection and credit risk assessment.\"},{\"question\":\"How does the proposed approach in the paper connect ML to business processes?\",\"answer\":\"The study reviews existing literature and develops a theoretical model that integrates ML applications into enterprise business processes for improved performance.\"}]","Complex Problem-Solving in Enterprises with Machine Learning Solutions | 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