Supply chains today face unprecedented challenges as global disruptions reshape economic landscapes. However, advancements in technology, particularly artificial intelligence (AI) and digital twins, provide a pathway toward greater resilience. Recently, I sat down with Darcy MacClaren, Chief Revenue Officer at SAP Digital Supply Chain, to explore how these transformative technologies are revolutionizing supply chain operations and fostering profitability.

MacClaren believes that AI and machine learning are crucial in providing predictability within supply chains. Past data, when combined with real-time insights, enables businesses to react promptly to ongoing challenges. “Senior leaders and supply chain leaders recognize the need to adopt AI. The real dilemma is knowing where to start,” he notes. One effective approach is to identify a specific area in need of improvement, assess its impact, set measurable goals, and initiate change.

For instance, enterprises commonly begin with integrated business planning, known for its high value. By validating data and operational rules while empowering teams, organizations not only enhance efficiency but also build faith in AI capabilities. “The early results can be quite impressive,” MacClaren asserts, emphasizing the importance of showcasing tangible outcomes to foster trust among stakeholders.

One powerful innovation arising from AI advancements is the concept of a digital twin. Essentially, a digital twin creates a virtual representation of a physical supply chain, providing real-time insights into operations, from material flow to manufacturing and logistics. MacClaren explains how machine learning enables organizations to adjust operational strategies based on gathered data. “The digital twin allows you to run simulations—what if scenarios that evaluate changes in performance,” he says, identifying potential pitfalls, adjustments for maintenance schedules, or enhancements to throughput.

For example, a manufacturing facility can use predictive analytics to understand how different environmental conditions (such as barometric pressure) affect the productivity of machines. Such information allows companies to make informed decisions, enhancing efficiency while also reducing overall operational costs.

Moreover, the interconnected nature of today’s supply chain networks plays a pivotal role in managing disruptions. MacClaren describes a scenario where losing a trucking route from San Francisco to Colorado might necessitate alternative transportation methods. With a sophisticated network, businesses gain deeper visibility and can respond swiftly to shifting circumstances. This ability to pivot not only improves operational agility but also reflects successful resource management, particularly relevant in terms of sustainability and carbon footprint reduction.

MacClaren cites a compelling case involving a large paper products manufacturer. Initially heavily reliant on spreadsheets, this organization recognized the value of solid demand planning. They initiated their AI journey by ensuring that stakeholders understood internal processes—collaborating on demand forecasting and developing supply plans. They adapted AI technologies to analyze data across their supply chain, focusing on areas needing improvement while also measuring AI’s performance.

These initiatives didn’t merely optimize existing practices. They led to transparent algorithms that offered insights into AI recommendations. Companies employing SAP’s Joule AI copilot can query how AI generated specific solutions, which substantiates trust within the organization. This transparency is critical, as it mitigates fears associated with AI’s “black box” nature and fosters deeper engagement with the data.

To develop a resilient, intelligent supply chain that exhibits anti-fragility—a term referring to systems that thrive under stress—MacClaren highlights three fundamental components:

1. Connected Supply Chain Processes: It is imperative that every aspect of the supply chain, from product design and manufacturing to delivery operations, is seamlessly integrated. This connectivity fosters collaborative responses to challenges.

2. Ecosystem Connectivity: Stakeholders in the supply chain ecosystem—including suppliers, logistics partners, and workers—must be fully connected. This network allows for efficient information exchange, which ultimately enhances responsiveness and operational coherence.

3. Contextualized Data: With vast amounts of data flowing into organizations, it is crucial to determine who needs specific information and for what purpose. Understanding the implications of delays or disruptions helps prioritize communications and actions effectively.

Currently, many product-centric companies are still optimizing their supply chains, employing established algorithms to manage costs. Conversely, around 30% are advancing quickly into adaptive phases, leveraging analytics for enhanced operational strategies. Notably, newer industries like electric vehicle manufacturing are embracing AI tools at a rapid pace, aiming for a more autonomous supply chain model.

As organizations navigate through these phases, the consistent goal remains operational efficiency and responsiveness. AI and digital twins are not merely buzzwords but valuable tools shaping the future of supply chains. By employing these technologies, companies can thrive in a climate of uncertainty and build robust systems capable of withstanding the test of time.