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Turn supply chain exposure into business opportunity through data

How trusted data and insights can drive business growth and resilience.

How trusted data and insights can drive business growth and resilience.

For businesses the world over, relying on a complex global supply chain can unwittingly be sailing close to an iceberg. Icebergs are a known source of risk and uncertainty, but it can seem impossible to fully understand or map them. Now, the confluence of data availability and technical capabilities is changing the supply chain risk management game. And learning the new rules can open up extraordinary opportunities.

Why does innovation happen? Often, breakthroughs are the result of a “perfect storm”: A combination of external pressures causing a crisis, technology and human ingenuity coming together at the right time to present a solution. The development of the catalytic converter in the automotive industry is a clear illustration of this. Innovation is crucial as we navigate the complex and increasingly pressing challenges of our changing world. We are on the verge of innovation of similar scale and significance in the insurance industry. A paradigm shift is coming.

In today’s global economy, that complexity is embodied in increasingly complex supply chains. Since 2020, the global supply chain management market has grown from $15.58 billion to $24.6 billion, and experts expect the global market to be worth $30.91 billion by 2026. This global trend percolates down into specific industries, too. The average auto manufacturer has a staggering 18,000 suppliers across their full value chain. These multitiered, interconnected ecosystems represent a confluence of many types of risk – from geopolitical and environmental challenges, to financial and digital disruptions, to structural risks associated with single points of failure or geographical concentration. In 2011, for example, floods in Thailand coincided with the aftermath of a catastrophic earthquake and tsunami in Japan. This crippled much of the automotive supply chain and caused disruption on a global scale.

For business leaders, these external pressures are an ever-present concern. Mapping complex supply chains and trying to predict where a crisis might occur is a constant challenge, and one which has often been labelled as simply ‘too difficult’ or ‘too costly’ to solve.

Some companies rely on supply chains so opaque that they don’t know a shipment will be missed until it has already happened. Others are under pressure from regulators to show they are compliant across their operations – but a lack of visibility means they can’t undertake proper due diligence and move to a more proactive posture that anticipates disruption and manages it before impacts are felt. This also makes educated foresight nearly impossible. A business that can’t fully map and understand its value chain is unable to take a proactive and considered approach to enhancing its resilience.

But the game has changed. New artificial intelligence (AI) tools are reshaping the way we identify and assess risk. AI is often touted as a silver bullet to solve all kinds of complex problems including supply chain risk. The truth is that it is just a tool, but an exceptionally powerful one. Knowing how to put the right tool to work on the right problems, and understanding how to extract and create meaning from the output is what creates the breakthrough. Putting these tools in the hands of trained, experienced experts and directly embedding their thought process into the risk management systems organizations will create a real breakthrough.

The need for innovation

Supply chain mapping traditionally tends to involve a manual process, with organizations approaching their immediate suppliers and asking them who, in turn, supplies them. This laborious approach comes with no assurance that the information provided would be complete, accurate, supplied in good time – or indeed would be supplied at all. What’s more, there is a good chance that, by the time it is provided, the information is already out of date.

Until now, this was the best available approach – even as supply chains became infinitely more complex. For many business leaders, however, the problem of unseen – and therefore hard to mitigate – risk compounded itself year-on-year as current loss statistics didn’t pick up on it. Many of today’s risks can be insured against – as long as there is visibility of exposures and how these aggregate at different levels across an organization’s ecosystem.

This is where AI-based innovations are changing the supply chain risk management game. Take for example, a manufacturing company that used innovative AI tools to map its supply chain. Where previously it only had a limited view of its second and third tier suppliers, it uncovered an additional 900 sites, including over 300 upstream, in under 48 hours. What’s more, this company could determine with accuracy when the last shipment took place and what type of goods were exchanged. Crucially, these sites were identified based on observed relationships, not supplier reporting. All insights were up-to-date and stored in a single, centralized repository that identified connections at the product and portfolio level.

As a result, this company became aware of previously unknown suppliers in Asia and, most importantly, of a geographic concentration in Vietnam. In fact, 50% of its suppliers around Hanoi were exposed to the same river flood risk. This made the company vulnerable to disruption in the manufacturing of four key components across two major product lines. Higher up the supply chain, it also uncovered a significant bottleneck in Mexico. None of this was previously known to this company in its mainly Europe-centric view of supply chain risk.

As the global business landscape becomes more unpredictable, access to trusted data that empowers organizations to cut through the noise and actionable insight will be key. Companies navigating choppy waters with complex value chains will need full visibility of their organization’s network so they can understand where risk exists, how it flows through their processes and whether it is absorbed or compounded. This information will reveal patterns, potential blind spots and trouble areas, and allow stress testing to be undertaken using a range of plausible ‘what if’ scenarios.

The business case is clear. Oversight of this depth and breadth, powered by AI innovations, could help minimize losses, prevent downtime, free up capital, and generally boost productivity and efficiency across the entire supply chain.

Collaborating to action

Obtaining this kind of information is only half the battle, however. How can organizations move from insight to action?

Although having a full understanding of the lay of the land can be a revelation, companies must use this visibility to arm a new data-driven risk management process. This must become an integrated part of a company’s technology landscape.

Perhaps most importantly, this visibility brings up data insights that empowers insurance and risk experts to focus on what really matters. In turn, collaborating closely with these experts will enable decision makers to unlock a whole new level of value for their business. That is because, by deriving meaning from the data, they can answer business-critical questions that are long overdue: Where do we need to put in place contingency planning? Which of our suppliers requires a different approach?

The combination of actionable data and expert guidance enables companies worldwide to become more nimble, adaptable, and resilient. With new AI technology tools, insurers and brokers can combine countless data points, stories, and insights to create game-changing perspectives and impact for their clients. By harnessing this new data on supply chain risk and pairing it with expert interpretation, companies can replace exposure and vulnerability with resilience.

Our people

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James Crask

Head of Strategic Risk Consulting, Marsh

Jonathan Lee

Jonathan Lee

Partner

John Davies

John Davies

Commercial Director, Marsh McLennan Sentrisk

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