A Visual Analytics Model Using Power BI to Improve Decision-Making in Supply Chain Operations

@article{1709619,
      author = {Opeyemi Morenike Filani, John Oluwaseun Olajide, Grace Omotunde Osho, Patience Okpeke Paul},
      title = {A Visual Analytics Model Using Power BI to Improve Decision-Making in Supply Chain Operations},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {2},
      pages = {286-298},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709619.pdf},
      abstract = {In today?s data-intensive global economy, the ability to translate complex datasets into actionable insights is crucial for supply chain optimization. Visual analytics tools such as Microsoft Power BI have emerged as powerful platforms to support real-time monitoring, pattern recognition, and informed decision-making across supply chain operations. This literature-based journal article introduces a Visual Analytics Model (VAM) using Power BI to enhance transparency, responsiveness, and efficiency within supply chains. Drawing upon over 100 scholarly sources in data visualization, business intelligence, and supply chain management, the paper constructs a conceptual model that leverages Power BI?s capabilities in interactive dashboards, dynamic filtering, and KPI tracking. Structured into five sections introduction, literature review, model design, discussion, and recommendations, the article provides a theoretical and practical foundation for integrating visual analytics into supply chain decision-making frameworks.},
      keywords = {visual analytics, Power BI, supply chain, decision-making, business intelligence, dashboard design},
      month = {August},
  }