Developing a Predictive Analytics Framework for Supply Chain Resilience: Enhancing Business Continuity and Operational Efficiency through Advanced Software Solutions

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@article{1704008,
      author = {Favour Uche Ojika, Osazee  Onaghinor, Oluwafunmilayo Janet Esan, Andrew Ifesinachi Daraojimba, Bright Chibunna Ubamadu},
      title = {Developing a Predictive Analytics Framework for Supply Chain Resilience: Enhancing Business Continuity and Operational Efficiency through Advanced Software Solutions},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {7},
      pages = {517-526},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704008.pdf},
      abstract = {In today's dynamic business environment, supply chain resilience has emerged as a critical determinant of organizational success, particularly amid increasing uncertainties and disruptions. This paper presents a comprehensive predictive analytics framework to enhance supply chain resilience, improve business continuity, and optimize operational efficiency. Through an extensive literature review, the study identifies key trends in supply chain management and predictive analytics, establishing a solid theoretical foundation for the proposed framework. The methodology outlines the framework development process, encompassing data sources, analytical techniques, and implementation strategies. Following the framework's application, empirical findings from case studies demonstrate significant improvements in forecast accuracy, inventory turnover, lead times, and customer service levels. Evaluation metrics indicate substantial cost savings and enhanced decision-making capabilities, underscoring the framework's practical implications for organizations. The discussion highlights the importance of proactive risk management and continuous improvement in fostering supply chain resilience. Finally, the paper concludes with recommendations for future research, including exploring advanced analytical techniques, sustainability integration, and the influence of organizational culture on analytics adoption. The findings contribute to the existing body of knowledge and provide actionable insights for businesses seeking to navigate the complexities of modern supply chains.},
      keywords = {Predictive Analytics, Supply Chain Resilience, Business Continuity, Operational Efficiency, Risk Management},
      month = {January},
  }