01.Project description

After the decision to build a unified, region-wide platform for collecting and analyzing industry data, our team was tasked with defining a scalable data-visualization design system that standardizes how insights are presented across products and teams. The objective was to ensure that every user—based on role, responsibilities, and access level—can quickly find the right metrics, understand performance at a glance, and drill into underlying drivers when deeper investigation is needed.

We began by surveying as many prospective users of the new system as possible, focusing on the distinct needs of each role to identify a solution that would support the broadest set of workflows. This approach was critical because early on there was a risk that conflicting requirements could skew the design toward one primary user group, limiting the system’s value for everyone else.

03. Findings

Whereas most of our findings broadly aligned with initial expectations, they were still extremely valuable because they helped us build a more complete, end-to-end understanding of how the system would be used across roles, contexts, and priorities. They also validated which assumptions were safe to keep, and which required refinement before we committed to structure, terminology, and interaction patterns.

Just as importantly, the research surfaced nuances that would have been easy to overlook—edge cases, informal workflows, and small frictions that rarely appear in high-level requirements but strongly influence day-to-day efficiency. By connecting these details into a holistic picture, we were able to spot gaps in our early thinking, anticipate points of confusion, and identify opportunities to design for consistency without ignoring role-specific needs.

Naturally, the research pointed to two primary user groups with distinct goals and decision cycles.

Engineers (operators)

Engineers work closest to the machines and need reliable, real-time information that is immediately accessible at the point of action. Their priorities are speed, clarity, and confidence: current status, active alarms, short-term trends, and the ability to quickly confirm whether an intervention improved or worsened performance. Equally important, the data must be easy to share in horizontal workflows—engineer to engineer—so teams can align on the current situation, escalate issues, and collaborate on resolution without losing time to manual explanations.

Performance and planning stakeholders

The second group is focused on long-term performance and operational optimization rather than immediate troubleshooting. They need stable, trustworthy trends over time, consistent definitions of metrics, and the ability to compare periods, lines, or sites to evaluate progress and identify systemic issues. Drill-down remains essential, but primarily as an on-demand capability: they start from aggregated views to understand direction and impact, then zoom into contributing factors only when anomalies, risks, or opportunities require deeper analysis—supporting forecasting, capacity planning, and modeling future output.

01.Proposal

The proposed solution aligns with the ISA‑95 Enterprise–Control System Integration standard and supports hierarchical drill-down across the organization (e.g., Enterprise → Site → Area → Line/Cell → Machine/Device).

At each level of the hierarchy, users can access multiple dashboard views tailored to the decisions and KPIs relevant at that scope. Visibility of specific datasets and modules is controlled through role-based permissions, ensuring users can view what they need to perform their responsibilities at each drill-down step—while restricting access to information outside their remit.

The lowest levels of the hierarchy are designed as real-time data views, enabled by machines that are already equipped with real-time sensors.

Engineers can also switch from live monitoring to short-term analysis—reviewing performance for today, this week, or this month—and comparing it with historical baselines. These comparisons often reveal early deviations in machine behavior, enabling earlier troubleshooting and helping reduce long-term maintenance costs.

In addition, metric thresholds and alerts were introduced to warn users before values exceed recommended ranges. Over time, this supports proactive operation, reduces unnecessary wear, and helps minimize downtime caused by maintenance.

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Managerial and financial views are designed for aggregated, decision-oriented monitoring rather than second-by-second operation, giving leaders a reliable picture of performance across sites, areas, and lines. These levels prioritize consistent KPIs and clear comparisons between periods so managers can quickly identify where performance is improving, stagnating, or drifting off target.

Managers can move from summaries to focused analysis by drilling down into the drivers behind changes—such as downtime categories, throughput constraints, quality losses, or utilization by shift/team—before launching corrective actions. This supports earlier intervention, better prioritization of improvement initiatives, and clearer accountability without requiring constant exposure to machine-level detail.

On the financial side, dashboards translate operational signals into business impact by connecting performance with cost and value indicators (e.g., cost of downtime, scrap cost, energy spend, maintenance spend, and budget adherence). Thresholds and alerts can be set around cost-related metrics (for example, abnormal scrap-cost spikes or rising energy intensity), helping stakeholders spot financial risk early and validate the ROI of process changes.

To support planning, managerial graphs also include an expected output prediction layer alongside actual performance. This makes it easy to see whether production is tracking above or below forecast, estimate end-of-period outcomes, and proactively adjust staffing, scheduling, or resource allocation when the projected trajectory indicates a risk to targets.