Supply Chain Technology and Process Interplay: Breaking Down Data Silos

Miebach and OMP are convening at MODEX 2026 and other events to discuss how planning systems, data, and operating models must come together to support faster, more confident decisions in supply chains. Christine Barnhart joined Miebach as head of industry engagement and alliances for the U.S. and Canada, bringing 25 years of end-to-end supply chain expertise, while Andrew Driscoll serves as OMP's global commercial lead for consumer and life sciences.

Data silos in supply chain operations increase costs through delayed decision-making, missed shortages, manual data reconciliation, and poor cross-site coordination. Legacy IT systems, which were not designed for real-time data sharing, force production, procurement, and logistics to run on separate, disconnected systems. This disconnection forces organizations into constant firefighting when supply chain processes are isolated, leaving decision-makers waiting on manual updates.

Miebach brings deep expertise in operating models, process design, and transformation, while OMP delivers Unison Planning—an AI-driven platform that enables faster, more resilient decisions across every level, from strategic to operational. Firms like o9 are enabling large, complex organizations to break down data silos and run AI-powered planning models directly against a single, governed source of truth across supply chain, commercial, and financial domains. SAP executives note that in 2026, leading organizations will move from firefighting to true orchestration, connecting planning, logistics, procurement, manufacturing, and the extended business network on a common, real-time data foundation.

While 83% of supply chain leaders believe AI agents and automation will accelerate the breakdown of traditional functional silos, only 27% have fully embedded an AI strategy across business units. AI is becoming essential for supply chain planning, forecasting, and exception management—but it cannot deliver meaningful insights when the data feeding it is delayed, inconsistent, or incomplete, with most exceptions today stemming from gaps in connectivity, not intelligence.

Note: This summary draws on SupplyChainBrain's publicly visible headline + subhead + opening paragraph and on sector background on supply chain technology and the breaking of data silos.

Key Takeaways:
1. Miebach and OMP emphasize that data, planning, and execution must work together—not in silos—for supply chain success
2. Data silos increase costs through delayed decision-making, manual reconciliation, and weak coordination
3. AI-powered integration platforms unify supply chain, commercial, and financial processes into a single source of truth
4. In 2026, leading organizations are shifting from firefighting to real-time orchestration
5. For AI to perform effectively, closing connectivity gaps and ensuring data quality are essential