Transforming supply chains with AI: achieving agility and efficiency

AI is revolutionizing supply chains, making them more resilient and efficient amid rising complexities.

In brief

• AI enables real-time risk identification and management in complex supply chain ecosystems.
• Geographically diversifying suppliers mitigates risks from natural disasters and geopolitical events.
• AI-driven decisions enhance efficiency, allowing focus on strategic, high-interpretation tasks.

Acompany at the end of the value chain can have as many as a thousand suppliers. This requires a control tower to identify risks, which is now made possible by Artificial Intelligence.

Companies need to carefully consider where they produce, what raw materials they use, and from whom they procure them. Over the past decade, supply chains have increasingly evolved from linear chains to complex ecosystems with various partners that all need to be managed. On top of that, there have been various disruptive events and new regulations that have made managing the value chain much more complex. During the COVID-19 crisis, it became clear that mass production in low-cost countries with minimal inventories can be dangerous. Additionally, there are geopolitical risks. Think of the trade war between the US, China, and Europe, or the Houthi rebels attacking merchant ships passing through the Suez Canal. There are also new regulations such as the 'Carbon Border Adjustment Mechanism'.
 

Geographic Diversification

Companies must therefore not only financially examine their suppliers but also geographically spread them. By working with two different Japanese suppliers, a company thought it had distributed the risk. But at the beginning of this year, an earthquake affected both companies, which are located very close to each other.

Even companies that spread their suppliers further can still face risks. Transport routes often converge at some point, such as in the port of Singapore. If something happens there that halts the trade flow, it's good to have a supplier who ships via the port of Hong Kong.
 

Control Tower

Resilience is another reason to scrutinize the supply chain. While companies used to know exactly from whom they bought their raw materials or components, there are now more suppliers. A company at the end of the value chain may deal directly and indirectly with perhaps a thousand companies. If a manufacturer of a patented component has production problems, it can have significant consequences. That's why more and more companies are mapping out critical products to develop a strategy.

The balance between a cost-efficient supply chain and its vulnerability is different for each company. For example, it may lead to taking a stake in suppliers to gain more control and thus use the strategy of further vertical integration.

Another possibility is working with multiple suppliers. Companies can source 80% of a raw material from a low-cost country and 20% from a local supplier. The latter is often more expensive, but then they have a buffer if something goes wrong internationally. It’s about finding the right balance.

There is a need for a central platform that gives organizations real-time visibility and control over an organization’s operational processes and potential risks. This can offer opportunities. By linking such a control tower, which typically focuses more on risks, to process optimization, which looks more at opportunities for improvement, you can create significant added value.

AI Co-Decision Making

The exciting thing about the times we live in today is that artificial intelligence increasingly allows for this linkage and visibility. Decisions about supply chains will become more technology-driven and autonomous.

We call this the Self-SustAInable Supply Chain. Supply chains have long ceased to be linear but are complex ecosystems that are difficult for the human brain to grasp. Artificial intelligence can make this complexity manageable and propose decisions that a human only needs to validate.

Supply Chain Planners, for example, must study a lot of data to make informed decisions. That takes time. Based on scenario analyses, AI can propose decisions to them, so they can work more efficiently and focus purely on the difficult decisions that require advanced interpretation. There are already companies that let AI make autonomous decisions about production planning.