In recent years, global supply chains have been shaped by a multitude of disruptive occurrences, including the US-China trade war, the surge in consumer goods demand during the Covid-19 pandemic, and the conflict between Russia and Ukraine. And, although 2023 presented us with a period of normalization of supply chains, recent events in the Red Sea underscore the notion that sustained tranquility may be a fleeting expectation.

In other words: Moving forward, supply chain disruptions will hit companies, again. Today it is the Red Sea, tomorrow another Black Swan event. To safeguard their organizations, executives need to anticipate and mitigate unforeseen developments and explore innovative pathways to avoid unnecessary delays and drive the supply chain forward.

The need to continuously anticipate and mitigate unforeseen developments has increased the volume, velocity and complexity of the decisions that organizations need to make on an almost daily basis. To address this, major corporations have turned to decision intelligence — the digitization, augmentation and automation of decision-making using artificial intelligence— to dramatically increase the agility and responsiveness to the disruptions organizations find themselves faced with. 

This article is written by Bart Ladru ([email protected]) and Jaouad Bantal ([email protected]). Bart and Jaouad are both part of RSM Netherlands Business Consulting Services with a specific focus on Sustainability and Supply Chain Management.

What supply chain disruptions do companies have to navigate today?   

The recent attacks in the Red Sea are what comes to mind first, as it has put serious pressure on a vital route handling approximately 12 percent of global trade, and has forced companies to make challenging decisions. Opting for the Red Sea route involves the risk of airborne strikes and increased insurance costs, while avoiding the route results in expensive delays. Since mid-December, maritime freight prices have surged, more than tripling on the Asia-to-Europe route.

European companies will be the first to feel the impact, especially those relying on the Red Sea for transporting goods from Asia to Europe. Their goods now cost more to ship and take longer to arrive. The repercussions may extend to manufacturing, particularly affecting companies employing just-in-time production methods, where parts arrive precisely when needed, leaving little room for shipping delays. The automotive industry, a notable user of this production approach, has already witnessed disruptions, with Tesla and Volvo temporarily halting production in Europe.

Furthermore, there is a not to be underestimated latent risk of the conflict escalating further, potentially drawing the Middle East into even greater turmoil, destabilizing the region, and potentially having dire consequences on energy prices and the like. 

Aside from this major supply chain disruption, other current risks include excess inventories, drought in the Panama Canal, as well as the increased pressure regulators have put on businesses to provide transparency into their supply chains and root out human rights abuses (by means of regulations such as the German Supply Chain Due Diligence Act, or the EU Corporate Sustainability Reporting Directive, or the Corporate Sustainability Due Diligence Directive). 

In what ways can AI applications safeguard the continuity of global supply chains of companies?

AI applications can help companies limit their exposure to the risks highlighted above and play a crucial role in fortifying the efficient continuity of global supply chains in several ways. 
For one, AI can support supply chain professionals by automating routine decisions, as well as by providing clear insights from value chain data, augmenting their ability to make correct decisions quickly, and allowing them to achieve a new level of operational agility and efficiency. 

AI can also enhance supply chain visibility by providing real-time tracking and monitoring of inventory, production, and transportation, both with their own operations, as with their direct and indirect suppliers, enabling companies to proactively identify and address potential disruptions. AI-driven analytics can also be utilized to analyze vast amounts of data to predict fluctuations in supply and demand, allowing companies to optimize inventory levels and production schedules in response to changing market conditions.

Generative AI brings this one step further, by simulating potential disruptions and risks in the supply chain. By assessing port congestion, shipment routes, and tier-n supplier mapping, AI can be used to predict risks, their corresponding impact on operations, and recommend actions to mitigate those risks. This allows supply chain managers to proactively implement mitigation strategies, develop contingency plans, and improve overall resilience. Organizations that implement such measures and invest in an analytics-driven approach to supply chain design, can better assess the trade-offs between cost, service levels, resilience and sustainability.

What opportunities and threats implementing the use of AI in supply chains introduces

AI integration poses a risk related to data bias and ethical guidelines. Human involvement and oversight are crucial to address this concern. For instance, training data and AI-generated insights often carry biases, requiring humans to select pertinent data for LLM training and ensure ethical compliance. Additionally, AI may lack understanding of global supply chain contexts, or if appropriate measures are not in place, might forego certain sustainability or human rights considerations in its goal to achieve optimal supply chain efficiency. Such risks highlight the necessity of using human interpretation to evaluate the appropriateness of its recommendations. The principles of responsible AI-governance from the EU AI Act offer a solid foundation for the development of appropriate measures to ensure ethical incorporation of AI systems in supply chains. Still, to be able to properly address these challenges, it would be wise for forward-thinking organizations to incorporate roles such as research scientists, chatbot developers, and AI ethics analysts in their supply chain teams. 

Another point of caution relates to the data you input into third-party AI algorithms for supply chain management, as it involves the analysis of supplier and customer information. GDPR mandates safeguarding EU citizens' personal data, imposing restrictions on transferring data outside the EU. Using a non-EU company's AI application for analysis may lead to GDPR concerns if data is transferred outside the EU. Organizations must ensure rigorous processing and protection of data to mitigate potential issues.

Companies should also consider the opportunities that AI integration offers in regard to the legislative transparency requirements in supply chains. AI allows companies to achieve the visibility that is needed to make informed decisions when facing supply chain disruptions, but can simultaneously provide companies with the information they need about their supply chains to get a better grip on ESG conditions in their whole value chain. This information empowers them to address value chain issues, and report transparently, aligning with frameworks like CSRD and the CSDDD, while also strengthening their ability to show their commitment to being an organization that has a positive influence.

Forward Thinking

In the face of frequent supply chain disruptions, the importance of anticipating and addressing unforeseen challenges has grown significantly. One particularly effective solution for companies looking to lower their exposure to supply chain hazards is artificial intelligence. By automating and augmenting decision-making, offering real-time visibility, predicting fluctuations in supply and demand, and simulating potential disruptions, AI enables informed mitigation strategies, contingency plans, and overall resilience improvement. 

However, as with any emerging technology, AI still has its teething problems. Often, algorithm development is very expensive, so entrepreneurs often grab an algorithm "off the shelf" developed by another party. The biggest risk lies in not knowing how and with what data this algorithm was trained. Regardless of the input into the algorithm, the output could possibly lead to undesirable results such as reputational damage, a halt in business operations or even catastrophic consequences in the value chain. It is therefore advisable not to simply adopt another party's algorithm. Apart from legal liability, decisions made by these algorithms may simply not produce the desired results as thought beforehand. 

Despite challenges, forward-thinking organizations are urged to integrate AI algorithms into their supply chain management. Doing so allows companies to elevate the efficiency, resilience, and transparency of their supply chain, and, perhaps more importantly, attain stability in an increasingly unstable world.

RSM is Thought leader in the field of Supply Chain and Sustainability consulting. We offer frequent insights through training and sharing of thought leadership that is based on a detailed knowledge of regulatory obligations and practical applications in working with our customers. If you want to know more, please reach out to one of our consultants.