Supply Chain Digital Twin Application: Control Tower

Sean Jefferson and Rami Musa
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Sean Jefferson: Johnson & Johnson
Rami Musa: Johnson & Johnson

Chapter Chapter 4 in Optimizing Supply Chains Through Digital Twins, 2025, pp 43-57 from Springer

Abstract: Abstract The ability to visually track and manage supply chain operations in real time is a long-awaited dream for supply chain practitioners. By utilizing control towers, which leverage the vast amount of data available today and advancements in computational and storage technologies, we can proactively address operational issues before they escalate. This chapter explores the concept of the supply chain control tower and its core engine being a digital twin, outlining how we can develop and enhance this indispensable solution to fundamentally change the way supply chain professionals manage continuity of supply to meet demand. We delved into the various applications of control towers across supply chain functions (source, make, deliver, and plan). Four main use cases are described in detail: (1) visualizing the supply chain and it corresponding key metrics, (2) detecting metric violations through flags and events, (3) conducting what-if analysis to address multiple issues collectively, and (4) utilizing optimization techniques and machine learning for supporting with mitigation solutions to be adjusted manually or automatically reach to the level of supply chain self-healing. Additionally, we share practical best practices, such as tailoring the control tower to specific roles (personas), employing agile development practices, deploying Generative AI (GenAI), and managing change effectively. The selection of enabling technologies is also discussed, along with the challenges that may arise during implementation and how to mitigate associated risks. We conclude with insights and summary of the chapter and future trends of using digital twin in supply chain.

Keywords: Supply chain; Digital twin; Control tower; Near real-time supply chain management; Predictive analytics; Scenario simulation; Optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-08147-6_4

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