The ultimate control tower is the gateway to more dynamic supply chain agility

Mark Holmes
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Mark Holmes: InterSystems, USA

Journal of Supply Chain Management, Logistics and Procurement, 2023, vol. 5, issue 3, 212-219

Abstract: Strains on global supply chains have exposed more than ever the need for global businesses to have greater visibility, control and a chameleon-like ability to adapt to any new situation rapidly. Organisations need an in-built ability to reconfigure to unforeseen disruptions arising from anything from geo-political events to a sudden change in consumer taste. Over the years, companies have tried different approaches to this challenge, hoping to gain that elusive level of consistency in a unified view of their supply chain data that will support all their needs. Each has run into problems, with the current shortage of data science expertise a difficulty all must overcome. This paper explains how the ultimate control tower has emerged as a highly effective approach, providing the connective tissue that global organisations need. It enables them to exploit data from their dynamic supply chain to better-inform decisions with immediate positive impact. Based on the four pillars of see, understand, optimise and act, this approach is already delivering for major companies with extended global supply chains. Using real-world examples, the paper explains how organisations deploying the ultimate control tower are achieving resilience and shape-shifting agility that enable them to excel in highly dynamic markets amid many challenges. Organisations are able to move from linear to much more supple digital and collaborative models. They can embed on-demand analytics capabilities and machine learning (ML), making it faster and easier to gain new insights and intelligent predictive and prescriptive capabilities.

Keywords: ultimate control tower; supply chain analytics; connective tissue; predictive analytics; smart data fabric (search for similar items in EconPapers)
JEL-codes: L23 M11 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:aza:jscm00:y:2023:v:5:i:3:p:212-219

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