Big Data and ML Applications for Supply Chain Control Tower & Digital Twin

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@article{1715996,
      author = {Taher Ali Mohammed},
      title = {Big Data and ML Applications for Supply Chain Control Tower & Digital Twin},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {727-735},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715996.pdf},
      abstract = {In today's fast-changing world of business, supply chains are becoming more complicated because of unpredictable customer needs, many layers of suppliers, and more uncertainty in how things are run. Using Big Data analytics and Machine Learning in supply chain management changes how things work by improving visibility, giving better predictions, and helping make smarter decisions. This study looks at how Big Data and Machine Learning are used in Supply Chain Control Towers and Digital Twin systems. These tools cover the entire supply chain process and assist in real-time supply chain monitoring, scenario testing, and risk management.
ERP systems, IoT devices, and logistics platforms are just a few examples of the types of sources that control towers use to gather data. They also use machine learning to predict demand, spot unusual activity, and improve how inventory and transportation work. Digital twins work with these tools by making virtual copies of parts of the supply chain, which lets you test how disruptions might affect things and check different ways to fix problems. A single approach that combines Big Data systems, machine learning predictions, and digital twin technology is introduced and shown through a real-world example in a global manufacturing and shipping network. The results show better operational transparency, more accurate predictions, stronger resilience, and quicker responsiveness. The study offers a real-world guide for companies that want to use AI-powered control towers and digital twins to make their supply chains more efficient, strong, and able to change as needed.
},
      keywords = {Supply Chain Control Tower, Digital Twin, Big Data Analytics, Machine Learning, Predictive Analytics, Supply Chain Resilience},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1715996}
  }