Real-world success stories
The transformative impact of AI and predictive analytics is already evident in organisations that have embraced these technologies.

Martin highlights Penske as a compelling example: “Penske, a leader in logistics and supply chain management, is a great example of how data analytics can support supply chain management.

“Penske faced the challenge of integrating data from disparate sources — fleet management systems, logistics platforms and customer demand data — to improve decision-making. With Qlik’s analytics platform, Penske has consolidated all its data into a single, actionable view.”

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The results have been substantial: “AI-driven predictive analytics helped Penske anticipate issues before they occurred, whether it was flagging vehicles in need of maintenance, predicting delivery delays or preparing for demand spikes. These insights enabled Penske to optimise routes, reduce operational costs and improve delivery times—which helped the business to become more resilient and keep customers happy.”

Another success story comes from the food industry in the form of Whitworth’s, a major UK supplier of dried fruit and nuts, which has been using data analytics to manage its supply chain and mitigate risk.

Martin goes on: “Real-time insights have helped Whitworth’s respond proactively to disruptions, pool inventory to meet demand during peak times and decided the most effective manufacturing locations.”

The strategic imperative
The adoption of AI and predictive analytics in supply chain management is rapidly transitioning from competitive advantage to competitive necessity.

“Being able to understand and respond to events that impact supply chains is no longer a luxury; it’s a business imperative,” Martin emphasises. “Companies that embrace AI and predictive analytics now will be better equipped to weather future storms – some literal – while those that rely on outdated, reactive methods risk being left behind.”

It seems clear that the future of supply chain management must be focused on anticipating, as opposed to reacting to, disruption.

For organisations beginning their journey toward AI-enhanced supply chain management, Martin offers some practical guidance: “Start small but think big. Identify a critical pain point in your supply chain where predictive insights could make a tangible difference. Build from there, ensuring that your team has the tools and training they need to fully leverage these capabilities.”