Manufacturing generates data faster than it can make decisions

Manufacturing organisations enter 2026 with unprecedented volumes of operational data generated across machines, production lines, enterprise systems, and supply chain platforms. Over the last decade, investments in MES, ERP, IIoT, and advanced analytics have significantly improved visibility into what is happening on the shop floor and beyond. From a technical perspective, many manufacturers now see more than they ever have before, often in near real time.

What has not improved at the same pace is the ability to act on that information in a coordinated, end-to-end manner. Despite growing data availability, decisions that span production, maintenance, and supply chain remain slow, fragmented, and heavily reliant on manual escalation. Signals move across systems, dashboards, and reports, but responsibility for acting on those signals often remains unclear or dispersed across multiple functions with competing priorities and incentives.

This growing gap between data availability and decision execution explains why many manufacturing organisations continue to experience repeated disruptions, delayed responses, and suboptimal trade-offs, even though the information required to act is technically present.

Fragmentation persists behind functional optimisation

The root cause does not lie in missing data sources or insufficient analytics capability. It lies in how manufacturing organisations are structured and governed. Over time, data landscapes evolved around functions rather than around decisions or services. Production teams optimise throughput and uptime. Maintenance teams focus on asset reliability. Supply chain teams manage availability and cost. IT owns platforms and integration layers, while OT owns machines and control systems.

Each function operates effectively within its own scope, supported by dedicated systems, metrics, and reporting structures. The problem emerges when decisions require coordination across those boundaries. Data that is meaningful within one function loses clarity when it crosses into another, because ownership, authority, and accountability no longer align in a way that supports timely action.

In practice, this means that insights frequently arrive without a clearly defined decision owner. Escalation paths remain informal or situational. Actions are delayed until issues become unavoidable rather than being addressed early, when options still exist.

IT and OT alignment remains an operating model issue

In many manufacturing environments, IT and OT alignment is still treated primarily as a technical integration challenge. Unified dashboards, data lakes, and analytics platforms are expected to bridge the gap between enterprise systems and shop floor operations. While these initiatives often improve data accessibility, they rarely improve decision effectiveness on their own.

The reason is structural rather than technical. IT and OT operate under different risk models, funding mechanisms, and success criteria. IT prioritises stability, security, and scalability. OT prioritises continuity, safety, and immediate production output. When these worlds are connected without redefining decision ownership and authority, integrated data increases complexity instead of clarity.

End-to-end decision making requires agreement not only on how data flows, but on who is authorised to act on that data when production, maintenance, and supply chain objectives collide.

Predictive initiatives stall without decision authority

Predictive maintenance, demand forecasting, and production optimisation are widely cited as priorities across manufacturing sectors, particularly in environments facing labour shortages, volatile supply chains, and rising energy costs. Technically, many organisations already possess the data and analytical capability required to support these use cases.

Operationally, outcomes remain inconsistent because predictions are rarely embedded into decision structures. Alerts and forecasts are generated, but responsibility for acting on them is unclear. Maintenance teams receive early warnings without authority to intervene in production schedules. Operations teams see risk indicators without the ability to adjust plans across plants or suppliers. Supply chain insights arrive too late to influence production decisions in a meaningful way.

Without explicit decision ownership, predictive systems remain advisory tools rather than operational mechanisms. Their impact remains limited regardless of model accuracy or data quality.

Where end-to-end decision making breaks down in practice

Across manufacturing organisations, the same structural weaknesses appear repeatedly. Decision ownership that spans production, maintenance, and supply chain remains fragmented. Data is organised around systems and functions rather than around decisions and outcomes. IT and OT accountability is separated by governance structures instead of aligned through shared objectives. Escalation relies on manual coordination rather than predefined authority. Analytics are delivered without embedded decision rights.

These issues persist precisely because they sit between organisational domains. They fall outside traditional improvement initiatives focused on efficiency, automation, or system performance within individual functions, while remaining central to how work actually gets done under pressure.

End-to-end decisions require redesign, not more data

Manufacturers that improve decision effectiveness do not start by collecting more data or deploying additional analytics tools. They start by redesigning how decisions are made. This means defining which decisions matter most under both normal and disrupted conditions, assigning clear ownership for those decisions, and aligning data availability with the moment authority is exercised.

In this model, technology supports decision execution rather than attempting to replace it. Data flows are structured around accountability rather than reporting convenience. IT and OT alignment is achieved through shared responsibility for outcomes, not through technical interfaces alone.

Without this shift, additional data and analytics increase operational noise while leaving execution fundamentally unchanged.

FAQ: Data and decision making in manufacturing

Why do manufacturers remain decision-poor despite extensive data investments?

Because data flows faster than authority. Insights are available, but ownership and decision rights across functions are unclear or misaligned.

Is this primarily a technology integration issue?

No. It is an operating model issue involving governance, accountability, and escalation, rather than tooling alone.

Why do predictive maintenance initiatives underperform?

Because predictions are not linked to clear operational authority, which delays or prevents action even when risks are identified early.

What does end-to-end decision making mean in manufacturing?

It means aligning data, responsibility, and authority across production, maintenance, and supply chain instead of optimising each function independently.

What should manufacturers prioritise in 2026?

Redesigning decision ownership and escalation paths before expanding analytics or automation investments.