Supply Chain Risk Management

Supply Chain Digital; Jeddah; On-site; SAR 200,000 - 300,000; Full time

Job description

How AI & Analytics are Transforming Supply Chain Resilience

AI and predictive analytics are helping firms more intelligently mitigate supply chain disruption. Picture: Getty Images

Martin Tombs, Field CTO for the EMEA region at Qlik, says the ability to anticipate disruptions has become a defining factor in organisational success

In an era defined by volatility and unprecedented challenges, supply chains face mounting pressures from all directions.

The ability to anticipate and mitigate disruptions has become a defining factor in organisational success.

"Supply chains are more critical — and vulnerable — than ever," asserts Martin Tombs, Field CTO for the EMEA region at Qlik."From raw material shortages to geopolitical instability, shifting consumer demands and even natural disasters, disruptions can derail operations and damage trust."

Yet amidst these challenges lie significant opportunities to revolutionise supply chain management through advanced technologies, as Martin explains: "The solution lies in using AI and predictive analytics to more intelligently manage supply changes and mitigate disruption."

The growing complexity of supply chains

Today's supply chains have evolved into intricate networks spanning numerous continents, involving numerous stakeholders and touchpoints — making traditional management approaches increasingly inadequate.

"Supply chains today are increasingly complex and hard to manage," Martin notes.

This complexity creates vulnerabilities that can quickly cascade through operations when disruptions occur.

The response, Martin claims, isemerging in the form of advanced AI applications.

"AI is becoming essential to access data across entire supply chains in real-time and identify risks long before they escalate," he continues. "For example, machine learning algorithms can flag early warning signs, like raw material shortages or port congestion, and give companies a chance to intervene before bottlenecks arise."

Beyond reaction: The predictive revolution

What truly distinguishes next-generation supply chain management is the shift from reactive to proactive approaches.

This transformation is being driven by predictive capabilities that allow organisations to anticipate challenges before they materialise.

Martin Tombs, Field CTO for the EMEA region at Qlik

"AI also allows companies to model scenarios, simulate potential risks and pre-emptively design response strategies," Martin says.

"Predictive analytics takes companies beyond merely reacting to disruptions. By analysing historical and real-time data, it's possible to forecast future demand patterns and streamline operations."

This predictive capacity extends beyond known challenges to emerging threats.

Martin adds: "To go one step further, predictive AI can help to combat net-new challenges, which are emerging as climate impact becomes more unpredictable – or as we saw with the COVID-19 pandemic."

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."

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."