From Visibility to Predictability: MES on the Ground

KEYWORDS: Manufacturing Execution System, Artificial Intelligence, Manufacturing, Infor

Executive Summary

This perspective is inspired by a recent webinar featuring Infor MES expert Matt Barber and ARC Advisory Group’s Jan Burian, combining practitioner insights with broader market analysis. Manufacturing is entering a phase where the challenge is no longer just efficiency, but consistency under growing complexity. What used to be manageable through better execution is now shaped by variability, with smaller batch sizes, more customized products, fragmented demand patterns, and tighter operational constraints. This shift is particularly visible in mature markets, as highlighted by ARC research. Large, stable production runs are gradually giving way to more dynamic and less predictable order patterns, especially in Europe. As a result, the shopfloor is no longer operating in a stable environment, but under constant change.

"By the time an issue appears on a dashboard, the opportunity to prevent it has often passed.” 
-- Matt Barber, Infor MES
 

In this context, improving individual processes is no longer enough. Stability cannot be engineered locally. It has to emerge system wide. Planning, execution, materials, and logistics must remain aligned because even small deviations propagate quickly. What starts as a minor issue often escalates into a broader operational disruption. Yet many organizations still approach the problem as if it were local. They optimize machines, deploy dashboards, or digitize isolated workflows without addressing systemic misalignment. As both Infor’s field experience and ARC’s research consistently show, this leads to more visibility, but not less volatility.

The Limits of Visibility

Over the past decade, manufacturers have heavily invested in visibility, and in many cases, this goal has been achieved. Operations teams can now monitor production in real time, track orders, and detect issues as they occur. Yet this picture is uneven. Many manufacturers have yet to take even this first step, still running the shop floor on paper, spreadsheets, and whiteboards.

But visibility does not prevent disruption. It only makes it visible. As Matt Barber emphasized during the webinar, by the time an issue appears on a dashboard, the opportunity to prevent it has often passed. Organizations may react faster, but they are still reacting. The real shift is toward predictability. Being able to anticipate disruptions, whether in production, materials, or supply chains, changes when decisions are made. It allows organizations to act while there is still time to influence outcomes. This is where operations become structurally more resilient, not just operationally more responsive.

Why Most AI Efforts Stall

There is strong demand across the industry to leverage AI, particularly in manufacturing operations. However, as both Infor’s implementation experience and ARC’s advisory work show, many initiatives fail to scale. The root cause is not a lack of algorithms. It is a lack of context. Manufacturing data is typically fragmented and disconnected across systems. Even when accessible, it often lacks the structure needed to reflect real operational conditions. As a result, predictions may be technically correct but operationally irrelevant.

This is why many AI use cases never move beyond pilot stages. They do not integrate into decision-making, and they do not earn trust. To address this, the focus must shift toward building contextualized data environments. As highlighted in ARC research, semantic models, unified data layers, and knowledge graphs are becoming essential, not because they are technologically advanced, but because they make data usable in practice. Infor’s approach reflects this reality by embedding operational context directly into its platforms, ensuring that data is not just collected, but understood in relation to processes, materials, and constraints.
 

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