How supply chain managers can accelerate time-to-decision when hit by disruptions
Supply chain leaders today are grappling with unprecedented complexity and volatility. Fragmented data sources, siloed systems, and the persistent lag between insight and action continue to slow down decision-making at critical moments.
In an environment where disruptions are frequent and costly, the ability to accelerate time-to-decision has become a defining competitive advantage. This requires more than connected systems – it demands unification of data to provide a single view that supply chain managers can rely on for better decisions. Real-time unified data enables the shift from visibility alone to enable AI ready data for true decision intelligence that helps leaders model scenarios, anticipate disruption, and act pre-emptively.
The difficulties of pulling together many types of data
In many enterprises, however, the sheer number of data sources makes achieving a single view of the supply chain very difficult without a change in data architecture.
If we consider the data sources an enterprise-level supply chain organization should unify, the list includes the platforms of multiple suppliers and partners, internal CRM, ERP systems, and warehouse or transportation management systems.
Other organizations want to use data from networks of IoT devices, and from news, weather, and business data feeds supplying minute-by-minute market information. The list can be extremely long, but an organization must make sense of it all very quickly if decision-making is to be as effective as possible when faced with disruption.
Unfortunately, this information is in many different formats and includes significant duplication. A global organization may have as many as 30 different ERP systems that give multiple versions of data about a single SKU, for example. All this information must first be cleansed and harmonized before supply chain managers use it to make swift calls on how to resolve a bottleneck.
New data architecture to increase decision speed
Organizations can, however, achieve significant gains in speed and agility if they adopt a new type of data architecture – a data gateway – that is able to cleanse and unify data without the need to replace existing systems. This approach is a collection of different technologies that are brought to the data, rather than the other way round, as with conventional approaches. The effect is to simplify the complexity of the systems already in place.
From the unified, single data plane, managers can see right along the supply chain, spotting costly bottlenecks to improve end-to-end efficiency. A supply chain planner, for example, gains accelerated access to customer orders and transport schedules in a single place. Forecasting and demand management are far more accurate because of the unified data behind the applications that managers use.
In the FMCG and CPG markets, for example, where demand can be highly volatile, logistics managers have the real-time insight they need for quick decisions – reducing lead times and costs while improving customer satisfaction.
AI for faster decisions
One of the advantages of a data gateway is faster implementation for AI applications. Siloed supply chain data becomes more easily available, including through low-code or no-code access, which reduces dependence on internal IT capabilities. Generative AI is constantly evolving and is fast becoming a technology that enables supply chain professionals to solve their business problems using natural language rather than computer code.
AI or machine learning (ML) capabilities become simpler to deploy since they can draw from a single, reliable source of truth. With these technologies in place, predictive analytics alert teams to potential problems, allowing them to reroute shipments or adjust production before minor setbacks become major crises. Simultaneously, prescriptive analytics present options about what to do next, guiding teams towards solutions that have the highest chance of success.
If we take a disruption like the shocking Baltimore bridge collapse of 2024 which closed a major port, a manager with access to unified data via a gateway could use generative AI to work out almost immediately which customers would be affected, allowing fast action to reduce the business impact.
The single view of supply chain data provided by data gateway architecture reduces human error, supports better forecasting, and speeds up decision-making. It is as simple as that.
If organizations adopt it, they will find their supply chain professionals spend less time wrestling with poorly integrated systems. They will be able to make the best data-driven calls that enable a supply chain to adapt to many kinds of volatility. Their actions will be fast, effective, but not furious, ensuring minimal disruption and maximum benefit to the bottom line. Being AI ready is the key foundation for real-time decision intelligence.
By Mark Holmes
Mark Holmes is Global Head of Supply Chain Market Strategy at InterSystems, where he helps supply chain organisations unify data to improve visibility, agility, and resilience. He works with retailers and logistics providers worldwide to turn real time data and AI into practical improvements in decision making and performance across complex supply chains.