Implementing Enterprise Risk Management (ERM) is pivotal for organizations striving to navigate and excel within increasingly complex and volatile business environments. This paper explores the core principles of ERM and underscores its crucial role in achieving organizational objectives by enhancing decision-making, risk awareness, and resilience. It highlights the benefits and challenges of ERM implementation, with a strong emphasis on the empowering role of leadership in this process. It also emphasizes the necessity for strategic resource allocation, and effective integration into organizational processes. Additionally, the paper examines key factors that contribute to the success of an ERM program, such as adaptability and continuous improvement. Through real-world case studies, the paper illustrates how successful ERM implementation can significantly benefit organizations, demonstrating quantifiable improvements in operational performance and strategic outcomes. These discussions aim to provide a comprehensive understanding of the importance of ERM in modern business practices, advocating for its widespread adoption and continuous evolution to meet emerging business challenges.
{"title":"INTEGRATING ENTERPRISE RISK MANAGEMENT (ERM): STRATEGIES, CHALLENGES, AND ORGANIZATIONAL SUCCESS","authors":"Md Rasel Ul Alam, Asif Shohel, Mahmudul Alam","doi":"10.62304/ijbm.v1i2.130","DOIUrl":"https://doi.org/10.62304/ijbm.v1i2.130","url":null,"abstract":"Implementing Enterprise Risk Management (ERM) is pivotal for organizations striving to navigate and excel within increasingly complex and volatile business environments. This paper explores the core principles of ERM and underscores its crucial role in achieving organizational objectives by enhancing decision-making, risk awareness, and resilience. It highlights the benefits and challenges of ERM implementation, with a strong emphasis on the empowering role of leadership in this process. It also emphasizes the necessity for strategic resource allocation, and effective integration into organizational processes. Additionally, the paper examines key factors that contribute to the success of an ERM program, such as adaptability and continuous improvement. Through real-world case studies, the paper illustrates how successful ERM implementation can significantly benefit organizations, demonstrating quantifiable improvements in operational performance and strategic outcomes. These discussions aim to provide a comprehensive understanding of the importance of ERM in modern business practices, advocating for its widespread adoption and continuous evolution to meet emerging business challenges.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"13 s1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141030244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Authors Md. Nazmul Haque, Rafsan Mahi Muhammad Tipu Sultan
This study delves into integrating sustainability practices within industrial operations, uncovering this endeavor's pivotal motivations, strategies, challenges, and critical success factors. Through qualitative case studies across varied sectors, the research reveals a complex interplay of intrinsic motivations—including environmental stewardship, economic incentives, and stakeholder pressures—that drive companies towards adopting sustainable practices. Operationalizing sustainability emerges as a multifaceted effort, with companies employing diverse strategies that range from incremental improvements to radical transformations aligned with circular economy principles. However, persistent barriers such as entrenched operational practices, financial considerations, and supply chain complexities underscore the significant challenges. The key to overcoming these obstacles is the unwavering commitment of leadership and fostering cross-functional collaboration, highlighting the essential role of strategic vision and organizational alignment in successful sustainability integration. This investigation enhances our understanding of sustainability in industrial contexts and sets the stage for further exploration into effective integration strategies, offering valuable insights for academics, practitioners, and policymakers alike.
{"title":"THE INTEGRATION OF SUSTAINABLE PRACTICES AND PRINCIPLES IN INDUSTRIAL OPERATIONS AND SUPPLY CHAIN MANAGEMENT","authors":"Authors Md. Nazmul Haque, Rafsan Mahi Muhammad Tipu Sultan","doi":"10.62304/ijse.v1i2.134","DOIUrl":"https://doi.org/10.62304/ijse.v1i2.134","url":null,"abstract":"This study delves into integrating sustainability practices within industrial operations, uncovering this endeavor's pivotal motivations, strategies, challenges, and critical success factors. Through qualitative case studies across varied sectors, the research reveals a complex interplay of intrinsic motivations—including environmental stewardship, economic incentives, and stakeholder pressures—that drive companies towards adopting sustainable practices. Operationalizing sustainability emerges as a multifaceted effort, with companies employing diverse strategies that range from incremental improvements to radical transformations aligned with circular economy principles. However, persistent barriers such as entrenched operational practices, financial considerations, and supply chain complexities underscore the significant challenges. The key to overcoming these obstacles is the unwavering commitment of leadership and fostering cross-functional collaboration, highlighting the essential role of strategic vision and organizational alignment in successful sustainability integration. This investigation enhances our understanding of sustainability in industrial contexts and sets the stage for further exploration into effective integration strategies, offering valuable insights for academics, practitioners, and policymakers alike.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"40 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141043392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md Arif Hossain, Md Samiul Alam Mazumder, Md Hasanujamman Bari, Rafsan Mahi
Urban centers face the mounting challenge of balancing resource demands with sustainable practices in the face of population growth and environmental concerns. Machine learning (ML) has emerged as a transformative technology with the potential to optimize resource efficiency and management within urban environments. This article investigates the multifaceted impact of ML algorithms on enhancing resource management and the associated challenges and considerations. It delves into successful ML applications in vital urban sectors, including smart grids, water conservation, and intelligent transportation systems. Through the analysis of case studies, the article quantifies improvements in resource efficiency and highlights the contributions of ML to data-driven decision-making. Crucially, it emphasizes the need for a holistic approach, addressing computational costs, data bias, privacy concerns, and ethical considerations to ensure the responsible and equitable deployment of ML. The article concludes by underscoring the ongoing evolution of ML and its pivotal role in shaping sustainable and resilient urban futures.
面对人口增长和环境问题，城市中心在平衡资源需求和可持续发展实践之间面临着日益严峻的挑战。机器学习（ML）已成为一种变革性技术，具有优化城市环境中资源效率和管理的潜力。本文探讨了 ML 算法对加强资源管理的多方面影响，以及相关的挑战和注意事项。文章深入探讨了 ML 在智能电网、水资源保护和智能交通系统等重要城市领域的成功应用。通过对案例的分析，文章量化了资源效率的提高，并强调了 ML 对数据驱动决策的贡献。最重要的是，文章强调需要采取综合方法，解决计算成本、数据偏差、隐私问题和道德考虑等问题，以确保负责任地、公平地部署人工智能。文章最后强调了人工智能的不断发展及其在塑造可持续和有弹性的城市未来中的关键作用。
{"title":"IMPACT ASSESSMENT OF MACHINE LEARNING ALGORITHMS ON RESOURCE EFFICIENCY AND MANAGEMENT IN URBAN DEVELOPMENTS","authors":"Md Arif Hossain, Md Samiul Alam Mazumder, Md Hasanujamman Bari, Rafsan Mahi","doi":"10.62304/ijbm.v1i2.129","DOIUrl":"https://doi.org/10.62304/ijbm.v1i2.129","url":null,"abstract":"Urban centers face the mounting challenge of balancing resource demands with sustainable practices in the face of population growth and environmental concerns. Machine learning (ML) has emerged as a transformative technology with the potential to optimize resource efficiency and management within urban environments. This article investigates the multifaceted impact of ML algorithms on enhancing resource management and the associated challenges and considerations. It delves into successful ML applications in vital urban sectors, including smart grids, water conservation, and intelligent transportation systems. Through the analysis of case studies, the article quantifies improvements in resource efficiency and highlights the contributions of ML to data-driven decision-making. Crucially, it emphasizes the need for a holistic approach, addressing computational costs, data bias, privacy concerns, and ethical considerations to ensure the responsible and equitable deployment of ML. The article concludes by underscoring the ongoing evolution of ML and its pivotal role in shaping sustainable and resilient urban futures.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"8 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141055776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study analyzes the temperature, rainfall, and humidity dynamics of Khulna city from 1981 to 2020. The average rainfall changes are 0.0018 mm, with an increase in daytime temperatures of 0.0223°C each year. Nighttime temperatures also show an increasing trend, with an increase in minimum temperatures of 0.0342°C each year. The overall humidity decreases, indicating less humid weather. The decadal average rainfall ranges from 5.13 mm from 1981 to 2000, with a drop to 4.65 mm between 1991 and 2000. The maximum temperature ranges from 30.94°C to 31.31°C, with a slight increase to 22.17°C between 2011 and 2020. The decadal average humidity ranges from 81.28% from 1991–2000 to 80.72% from 2001–2010. Pre-monsoon average rainfall declines by 5.13 mm, indicating a drier season. The monsoon season has an inclining trend of 0.0239 mm, with a promising increment of rain resulting in a wetter monsoon. Post-monsoon average rainfall increases by 0.0121 mm, resulting in a wetter season each year. The winter season has a slight decline of -0.0043 mm at 6°C. The variability of temperature, rainfall, and humidity patterns in Khulna city reveals a correlation between rainfall and temperature, which indirectly impacts crop yield. Better observational rainfall, humidity, and temperature data are necessary for effective agriculture and crop production. The estimated value for the average temperature (maximum) from 1981 to 2020 is 0.023°C, suggesting that if the year increases by one year, the average maximum temperature increases by 0.023°C.
{"title":"ASSESSING THE DYNAMICS OF CLIMATE CHANGE IN KHULNA CITY: A COMPREHENSIVE ANALYSIS OF TEMPERATURE, RAINFALL, AND HUMIDITY TRENDS","authors":"Md Mazharul Islam, Abdullah-Al Abid, Laila Tul Zannat Arefin Siddikui & Nahida Sultana","doi":"10.62304/ijse.v1i1.118","DOIUrl":"https://doi.org/10.62304/ijse.v1i1.118","url":null,"abstract":"The study analyzes the temperature, rainfall, and humidity dynamics of Khulna city from 1981 to 2020. The average rainfall changes are 0.0018 mm, with an increase in daytime temperatures of 0.0223°C each year. Nighttime temperatures also show an increasing trend, with an increase in minimum temperatures of 0.0342°C each year. The overall humidity decreases, indicating less humid weather. The decadal average rainfall ranges from 5.13 mm from 1981 to 2000, with a drop to 4.65 mm between 1991 and 2000. The maximum temperature ranges from 30.94°C to 31.31°C, with a slight increase to 22.17°C between 2011 and 2020. The decadal average humidity ranges from 81.28% from 1991–2000 to 80.72% from 2001–2010. Pre-monsoon average rainfall declines by 5.13 mm, indicating a drier season. The monsoon season has an inclining trend of 0.0239 mm, with a promising increment of rain resulting in a wetter monsoon. Post-monsoon average rainfall increases by 0.0121 mm, resulting in a wetter season each year. The winter season has a slight decline of -0.0043 mm at 6°C. The variability of temperature, rainfall, and humidity patterns in Khulna city reveals a correlation between rainfall and temperature, which indirectly impacts crop yield. Better observational rainfall, humidity, and temperature data are necessary for effective agriculture and crop production. The estimated value for the average temperature (maximum) from 1981 to 2020 is 0.023°C, suggesting that if the year increases by one year, the average maximum temperature increases by 0.023°C.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140664842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-21DOI: 10.62304/ijmisds.v1i1.116
The integration of AI-powered analytics offers transformative potential in optimizing supply chains within the manufacturing sector. This study adopts a qualitative, case study methodology to explore the specific ways manufacturers utilize AI-powered solutions in areas such as demand forecasting, inventory management, logistics planning, and predictive maintenance. Findings indicate substantial gains in efficiency, cost savings, and improved supply chain resilience. Additionally, the study highlights how AI-driven optimizations lead to an enhanced customer experience through increased product availability, reduced lead times, and a more responsive supply chain. Through detailed analysis of real-world implementations, the study provides practical guidance for manufacturers seeking to leverage AI to transform their supply chain operations.
{"title":"OPTIMIZING SUPPLY CHAIN EFFICIENCY IN THE MANUFACTURING SECTOR THROUGH AI-POWERED ANALYTICS","authors":"","doi":"10.62304/ijmisds.v1i1.116","DOIUrl":"https://doi.org/10.62304/ijmisds.v1i1.116","url":null,"abstract":"The integration of AI-powered analytics offers transformative potential in optimizing supply chains within the manufacturing sector. This study adopts a qualitative, case study methodology to explore the specific ways manufacturers utilize AI-powered solutions in areas such as demand forecasting, inventory management, logistics planning, and predictive maintenance. Findings indicate substantial gains in efficiency, cost savings, and improved supply chain resilience. Additionally, the study highlights how AI-driven optimizations lead to an enhanced customer experience through increased product availability, reduced lead times, and a more responsive supply chain. Through detailed analysis of real-world implementations, the study provides practical guidance for manufacturers seeking to leverage AI to transform their supply chain operations.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"102 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140678936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.62304/ijmisds.v1i1.113
The traditional approach to textile quality control, predominantly reliant on manual inspection, is fraught with precision, speed, and reliability challenges. This case study explores the deployment of an Internet of Things (IoT) based system, incorporating sophisticated image processing and machine learning techniques, aimed at automating fabric defect detection in a mid-sized textile manufacturing setting. The study reveals a notable enhancement in the accuracy of defect detection and considerable improvements in inspection speed and operational efficiency. Implementing this IoT system resulted in a marked reduction in manual labor requirements and provided a compelling cost-benefit ratio, underscoring the system's financial viability. Furthermore, the case study details significant operational benefits, such as a 94.25% accuracy in defect detection and a reduction in inspection time from 10.78 to 2.47 minutes per unit. These outcomes affirm the transformative potential of IoT technologies in refining textile quality control processes, advocating for a shift towards more sustainable, quality-focused, and efficient manufacturing paradigms.
{"title":"ENHANCING TEXTILE QUALITY CONTROL WITH IOT SENSORS: A CASE STUDY OF AUTOMATED DEFECT DETECTION","authors":"","doi":"10.62304/ijmisds.v1i1.113","DOIUrl":"https://doi.org/10.62304/ijmisds.v1i1.113","url":null,"abstract":"The traditional approach to textile quality control, predominantly reliant on manual inspection, is fraught with precision, speed, and reliability challenges. This case study explores the deployment of an Internet of Things (IoT) based system, incorporating sophisticated image processing and machine learning techniques, aimed at automating fabric defect detection in a mid-sized textile manufacturing setting. The study reveals a notable enhancement in the accuracy of defect detection and considerable improvements in inspection speed and operational efficiency. Implementing this IoT system resulted in a marked reduction in manual labor requirements and provided a compelling cost-benefit ratio, underscoring the system's financial viability. Furthermore, the case study details significant operational benefits, such as a 94.25% accuracy in defect detection and a reduction in inspection time from 10.78 to 2.47 minutes per unit. These outcomes affirm the transformative potential of IoT technologies in refining textile quality control processes, advocating for a shift towards more sustainable, quality-focused, and efficient manufacturing paradigms.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140694643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.62304/ijmisds.v1i1.115
This paper examines integrating baseline security requirements within an Enterprise Risk Management (ERM) framework, specifically focusing on cloud computing environments. As organizations increasingly migrate their operations to the cloud, the necessity for a robust security posture that aligns with comprehensive risk management practices has never been more critical. Through a systematic review of existing literature and analysis of case studies, this study identifies key strategies for implementing security measures that address the unique risks posed by cloud computing. The findings highlight the importance of continuous risk assessment, compliance and governance standards adherence, and resilient incident response and business continuity plans. The research further explores the dynamic relationship between cloud service models (IaaS et al.) and ERM strategies, offering insights into best practices for mitigating risks while capitalizing on the cloud's scalability and flexibility. The paper concludes with recommendations for organizations seeking to enhance their security and risk management practices in cloud environments, emphasizing the need for an integrated approach that supports business objectives and drives technological innovation.
{"title":"BASELINE SECURITY REQUIREMENTS FOR CLOUD COMPUTING WITHIN AN ENTERPRISE RISK MANAGEMENT FRAMEWORK","authors":"","doi":"10.62304/ijmisds.v1i1.115","DOIUrl":"https://doi.org/10.62304/ijmisds.v1i1.115","url":null,"abstract":"This paper examines integrating baseline security requirements within an Enterprise Risk Management (ERM) framework, specifically focusing on cloud computing environments. As organizations increasingly migrate their operations to the cloud, the necessity for a robust security posture that aligns with comprehensive risk management practices has never been more critical. Through a systematic review of existing literature and analysis of case studies, this study identifies key strategies for implementing security measures that address the unique risks posed by cloud computing. The findings highlight the importance of continuous risk assessment, compliance and governance standards adherence, and resilient incident response and business continuity plans. The research further explores the dynamic relationship between cloud service models (IaaS et al.) and ERM strategies, offering insights into best practices for mitigating risks while capitalizing on the cloud's scalability and flexibility. The paper concludes with recommendations for organizations seeking to enhance their security and risk management practices in cloud environments, emphasizing the need for an integrated approach that supports business objectives and drives technological innovation.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":" 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140691463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-15DOI: 10.62304/ijmisds.v1i1.112
This study investigates into the integration of Machine Learning (ML) with Prescriptive Analytics, showcasing the enhancement of decision-making processes in business through this combination. By analyzing contemporary methodologies and practical applications, it delves into how ML algorithms significantly improve the precision, efficiency, and forecasting capabilities of prescriptive analytics. Highlighting case studies across a variety of sectors, the research underscores the competitive edge businesses can gain by adopting these sophisticated analytical tools. Moreover, it addresses the array of technical and organizational hurdles that arise with the implementation of ML-enhanced prescriptive analytics, such as challenges in data handling, system integration, and the demand for specialized skills. Leveraging the latest advancements and insights from experts, the paper offers a compilation of best practices and strategic methodologies to effectively overcome these obstacles. Conclusively, it emphasizes the critical role of continuous innovation in ML and prescriptive analytics, encouraging firms to adopt these cutting-edge technologies to maintain a competitive stance in the fast-evolving, data-centric business landscape.
本研究探讨了机器学习（ML）与描述性分析（Prescriptive Analytics）的结合，展示了通过这种结合增强业务决策过程的效果。通过分析当代方法论和实际应用，本研究深入探讨了机器学习算法如何显著提高规范性分析的精度、效率和预测能力。该研究重点介绍了各行各业的案例研究，强调了企业通过采用这些先进的分析工具可以获得的竞争优势。此外，研究还探讨了在实施 ML 增强型规范性分析过程中出现的一系列技术和组织障碍，如数据处理、系统集成和专业技能需求方面的挑战。本文利用最新进展和专家见解，汇编了有效克服这些障碍的最佳实践和战略方法。最后，它强调了持续创新在 ML 和规范性分析中的关键作用，鼓励企业采用这些尖端技术，以便在快速发展、以数据为中心的商业环境中保持竞争优势。
{"title":"THE IMPACT OF MACHINE LEARNING ON PRESCRIPTIVE ANALYTICS FOR OPTIMIZED BUSINESS DECISION-MAKING","authors":"","doi":"10.62304/ijmisds.v1i1.112","DOIUrl":"https://doi.org/10.62304/ijmisds.v1i1.112","url":null,"abstract":"This study investigates into the integration of Machine Learning (ML) with Prescriptive Analytics, showcasing the enhancement of decision-making processes in business through this combination. By analyzing contemporary methodologies and practical applications, it delves into how ML algorithms significantly improve the precision, efficiency, and forecasting capabilities of prescriptive analytics. Highlighting case studies across a variety of sectors, the research underscores the competitive edge businesses can gain by adopting these sophisticated analytical tools. Moreover, it addresses the array of technical and organizational hurdles that arise with the implementation of ML-enhanced prescriptive analytics, such as challenges in data handling, system integration, and the demand for specialized skills. Leveraging the latest advancements and insights from experts, the paper offers a compilation of best practices and strategic methodologies to effectively overcome these obstacles. Conclusively, it emphasizes the critical role of continuous innovation in ML and prescriptive analytics, encouraging firms to adopt these cutting-edge technologies to maintain a competitive stance in the fast-evolving, data-centric business landscape.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Integrating the Internet of Things (IoT) and big data analytics revolutionizes supply chain management across industrial engineering sectors, offering unprecedented opportunities for enhancing efficiency, responsiveness, and competitive advantage. This study employs a qualitative research design, leveraging expert interviews to explore the multifaceted impact of these technologies on supply chain performance. Findings underscore the critical importance of strategic alignment, leadership support, and a clear focus on business objectives for successful technology implementation. Enhanced real-time visibility, improved decision-making, and operational efficiency are identified as consistent benefits across sectors. However, the specific outcomes and applications vary according to industry-specific challenges and priorities. Despite the rich insights gained, the study acknowledges the limitations inherent in its qualitative approach. It suggests avenues for future research, including quantitative analyses and deeper dives into sector-specific implementations. This research contributes to a better understanding of how IoT and big data analytics can be effectively integrated into supply chains, providing a foundation for organizations seeking to navigate the complexities of digital transformation in an interconnected global marketplace.
{"title":"INTEGRATING IOT AND BIG DATA ANALYTICS FOR ENHANCED SUPPLY CHAIN PERFORMANCE IN INDUSTRIAL ENGINEERING SECTORS: A CROSS-MARKET STUDY","authors":"","doi":"10.62304/ijse.v1i1.108","DOIUrl":"https://doi.org/10.62304/ijse.v1i1.108","url":null,"abstract":"Integrating the Internet of Things (IoT) and big data analytics revolutionizes supply chain management across industrial engineering sectors, offering unprecedented opportunities for enhancing efficiency, responsiveness, and competitive advantage. This study employs a qualitative research design, leveraging expert interviews to explore the multifaceted impact of these technologies on supply chain performance. Findings underscore the critical importance of strategic alignment, leadership support, and a clear focus on business objectives for successful technology implementation. Enhanced real-time visibility, improved decision-making, and operational efficiency are identified as consistent benefits across sectors. However, the specific outcomes and applications vary according to industry-specific challenges and priorities. Despite the rich insights gained, the study acknowledges the limitations inherent in its qualitative approach. It suggests avenues for future research, including quantitative analyses and deeper dives into sector-specific implementations. This research contributes to a better understanding of how IoT and big data analytics can be effectively integrated into supply chains, providing a foundation for organizations seeking to navigate the complexities of digital transformation in an interconnected global marketplace.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"94 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140706874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-09DOI: 10.62304/ijmisds.v1i1.107
This thesis explores the integration of Artificial Intelligence (AI) in project management practices to improve efficiency and decision-making processes. As organizations increasingly rely on project management methodologies to execute tasks, deliverables, and achieve objectives, the role of AI in enhancing these processes becomes pivotal. Through an examination of existing literature, case studies, and theoretical frameworks, this thesis investigates the potential benefits, challenges, and implications of incorporating AI technologies in project management. It aims to provide insights into how AI can optimize project planning, scheduling, resource allocation, risk management, and stakeholder communication. Additionally, the thesis explores the ethical considerations and societal impacts associated with the adoption of AI in project management. By analyzing real-world applications and theoretical perspectives, this research contributes to the understanding of how AI can be effectively utilized to streamline project management practices and drive organizational success in diverse industries.
{"title":"Artificial Intelligence in Project Management: Enhancing Efficiency and Decision-Making","authors":"","doi":"10.62304/ijmisds.v1i1.107","DOIUrl":"https://doi.org/10.62304/ijmisds.v1i1.107","url":null,"abstract":"This thesis explores the integration of Artificial Intelligence (AI) in project management practices to improve efficiency and decision-making processes. As organizations increasingly rely on project management methodologies to execute tasks, deliverables, and achieve objectives, the role of AI in enhancing these processes becomes pivotal. Through an examination of existing literature, case studies, and theoretical frameworks, this thesis investigates the potential benefits, challenges, and implications of incorporating AI technologies in project management. It aims to provide insights into how AI can optimize project planning, scheduling, resource allocation, risk management, and stakeholder communication. Additionally, the thesis explores the ethical considerations and societal impacts associated with the adoption of AI in project management. By analyzing real-world applications and theoretical perspectives, this research contributes to the understanding of how AI can be effectively utilized to streamline project management practices and drive organizational success in diverse industries.","PeriodicalId":518594,"journal":{"name":"Global Mainstream Journal","volume":"248 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140723211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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