Artificial Intelligence in Network Analytics for Supply Chain Optimization: Forecasting Demand and Preventing Disruptions

Authors

  • oghenemarho karieren CICS, Ball State University, Muncie, Indiana, USA Author https://orcid.org/0009-0008-5348-405X
  • Oluwaseni CICS Ball State University, Muncie, Indiana, USA Author
  • Balogun CICS Ball State University, Muncie, Indiana, USA Author
  • Oluwadamilare Bankole CICS, Ball State University, Muncie, Indiana, USA Author

DOI:

https://doi.org/10.64044/a01sfj03

Keywords:

Artificial Intelligence, Supply Chain Optimization, Network Analytics, Digital Supply Chain Twin, Demand Forecasting

Abstract

The current supply chain operates in a turbulent, unpredictable environment characterized by volatility, uncertainty, complexity, and ambiguity (VUCA), and thus requires a higher level of analytical skills than conventional statistical techniques. The objective of this article is to merge artificial intelligence into supply chain network analytics, focusing primarily on demand prediction and disruption reduction. The article is based on present-day documentation and technological implementations, which makes it clear how the machine learning algorithms used, namely Long Short-Term Memory (LSTM) networks and Random Forests, respectively, succeed in better forecasting and offer predictive risk management. The article proposes a model of AI-assisting network analytics and investigates consequences for resilience and operational efficiency

Author Biographies

  • oghenemarho karieren, CICS, Ball State University, Muncie, Indiana, USA

    Cloud & Network Engineer with certifications in CCNA, AWS solutions architect and CCNP. Skilled in enterprise networking, routing & switching, automation, and cloud infrastructure. Passionate about designing secure, scalable systems and solving real-world connectivity challenges.

  • Oluwaseni, CICS Ball State University, Muncie, Indiana, USA

    UI/UX Designer with a strong focus on creating intuitive, user-centered digital experiences. Graduate Student Assistant at Ball State University, passionate about product design, usability research, and problem-solving through thoughtful design.

  • Balogun, CICS Ball State University, Muncie, Indiana, USA

    Software Engineer with 5+ years of experience building scalable, high-performing applications. Skilled in full-stack development, cloud infrastructure, and Agile delivery. Passionate about creating robust solutions that improve performance and user experience.

  • Oluwadamilare Bankole, CICS, Ball State University, Muncie, Indiana, USA

    Solution Architect and Software Engineer focused on building scalable systems, optimizing processes, and driving digital transformation. Passionate about using technology to streamline workflows and deliver impactful, user-centered solutions across fintech and business environments.

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Published

02/01/2026 — Updated on 02/01/2026

Issue

Vol. 2 No. 1 (2026): AI-Driven Enterprises: Challenges of Governance and Transformation

Section

Research Article

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Copyright (c) 2026 oghenemarho karieren, Oluwaseni, Balogun, Oluwadamilare Bankole (Author)

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