132 require real-time end-to-end visibility by 2030 but argues that visibility has not solved a fundamental problem: companies can see disruption without necessarily being able to quantify its consequences, simulate alternatives or respond quickly enough. “The next wave is decision intelligence and autonomous execution sitting on top of that visibility layer,” says Rodriguez. That means enabling AI to reason across competing priorities rather than optimise a single metric. Rodriguez points to policy-aware trade-off analysis that considers cost, service, cash and risk simultaneously; multi-scenario simulation that can show the cascading effects of a disruption and rank potential responses; and self-healing workflows that can detect exceptions and resolve them within defined guardrails. For Rodriguez, one of the clearest signs an organisation remains stuck in the visibility era is the way it manages exceptions. “The tell that a company is still stuck in the visibility era is simple: exceptions are still handled by email and phone, with manual overrides nobody can explain afterwards,” he says. “That’s where the value is trapped.” Unlocking that value requires manufacturers to connect functions that have traditionally operated separately. Supply chain decisions rarely affect only the supply chain: a change to a production plan can alter margins, an inventory policy affects working capital, while a sourcing decision can carry a carbon cost depending on the region and sourcing path. “This is the problem we spend most of our time on, because supply chain decisions are financial decisions that happen to have an operational expression,” says Rodriguez. He identifies two foundations for making cross-functional optimisation possible. The first is a unified data platform with a semantic layer and supplier systems. This allows AI agents to reason within the appropriate business context rather than simply analysing raw, uncontextualised data. The second is multi-agent orchestration, allowing planning, sourcing, logistics, warehouse and risk agents to collaborate instead of each optimising its own function. The impact becomes particularly visible at board level through integrated business planning (IBP). Rodriguez argues that many manufacturers still cannot run product, demand, supply, financial and executive reviews using one connected set of numbers. AI has the potential to compress that cycle, connecting operational plans with financial outcomes with greater accuracy and agility. “None of this requires rip-and-replace,” says Rodriguez. “We orchestrate across the systems customers already own. The foundation supports incremental adoption without requiring wholesale replacement of existing systems.” This is where Microsoft’s partner ecosystem becomes important. Rodriguez points to Blue Yonder and o9 Solutions as examples of how expertise can be combined with Microsoft’s technology foundation. Blue Yonder has built its next-generation applications on Microsoft Azure, helping customers move from siloed configurations towards an intelligent operating network. One example comes from a global ingredients manufacturer, which reduced inventory levels by 16 per cent while increasing service levels by 15 per cent. For a global food manufacturer, o9 Solutions used an enterprise knowledge graph representing products, plants, suppliers, carriers and their relationships, running on Azure to support forecasting, scenario planning and digital IBP. This resulted in a 10 per cent improvement in short-term forecast accuracy at itemlocation level and a 25 per cent reduction in excess inventory. FEATURE “ Supply chain decisions are financial decisions that happen to have an operational expression”
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