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‘Visibility’ is often cited as a crucial goal in supply chain management. No doubt this is true. But when we say visibility, what do we mean? What kind of visibility are we talking about? Supply chains, despite being critical to nearly every business and product we rely on, have typically lagged behind in adopting modern technologies.
Traditional supply chain management often involves using manual processes, historical data and static planning models. In this context, you could have decent visibility on metrics like route performance that have taken place over time. But when historical data exists in isolation - without the ability to contextualise it with real-time variables like live carrier updates, current weather patterns, or emerging geopolitical events - it limits a company's ability to adapt quickly when conditions diverge from historical norms.
Modern supply chains are fast-moving and ever changing. Therefore, modernising these networks is essential to navigate the constant complexity and disruption the industry faces. The most forward-thinking companies are moving beyond ‘traditional visibility’ and towards AI-driven automation and integrated intelligence. In doing so, they are helping to build the supply chains of tomorrow.
Many supply chains are still reliant on manual data entry, freight tracking and documentation processes. Given supply chains involve countless carriers, suppliers, manufacturers, distributors, regions and routes, this manual dependency prevents supply chain teams from having the complete, contextualised view needed to make well-informed decisions. Data can be spread across various systems, and a lack of automation means this information remains siloed, which prevents teams from connecting the dots across their supply chain network.
Therefore, the first step to gaining this complete, contextualised view is to build unified supply chain data. This requires adopting supply chain software that uses API technology to integrate with a host of business-critical systems, partners’ data and external data sources. But even when you get to a position where you have all of this data housed in one location, how you continually update this data in real time, analyse it, and then make predictions are all key pain points to overcome.
Traditional demand forecasting, for example, illustrates these limitations. It can rely on historical sales and trends data without accounting for sudden market shifts or disruption to travel routes. Even with all this unified in one place, teams need the capability to continuously integrate real-time variables and generate predictive insights - not just static visibility of what's already happened.
Instead of solely providing visibility, AI supply chain workspaces can automate workflows and streamline operations across regions, partners and suppliers. These workspaces automatically collect live data such as freight tracking milestones from carriers and documents from forwarders and then store and share information with relevant stakeholders. In turn, this automation enables integrated intelligence, with AI analysing all of the integrated supply chain data to provide real-time insights and suggestions.
These insights are able to draw on both live and historical data to provide predictive analytics that anticipate factors such as potential supplier delays or unexpected costs like demurrage fees. For example, real-time data on port congestion could be combined with data on a route’s performance over the last year to identify the best course of action to take with a shipment. These capabilities turn decision-making into a proactive, adaptable exercise.
With this infrastructure established, supply chain managers can then explore the possibility of using agentic AI. AI agents are able to act autonomously and carry out tasks with little or no human input. So, instead of just providing insights on the best route to use for a shipment, for instance, an AI agent could automatically select the route and arrange the shipment on behalf of the supply chain team. Evidently, the benefits of using this technology could be transformative to a team’s agility and efficiency.
Supply chains have never been more vulnerable to disruption. Geopolitical risks are continually changing, extreme climate events are becoming more frequent, and the threat of cyberattacks grows by the day. These uncontrollable factors make traditional visibility and supply chain processes limited in their ability to help teams efficiently coordinate shipments across a global network of carriers and distributors.
The widespread use of AI in everyday life is raising expectations – users are aware they should be able to access key insights without much manual input. AI-driven automation and integrated intelligence empowers supply chain leaders to quickly react to disruption and, crucially, effectively pre-empt it. But making the jump from traditional visibility to AI intelligence in supply chains requires careful thought: teams must make a deliberate effort to connect their data and integrate the technology into their day-to-day workflows.
Once established, they can build holistic AI supply chain workspaces that enhance the resilience and agility of their supply chains.
Credits: Fraser Robinson, Co-Founder and CEO of Beacon