In a steel mill, quality issues recur on a rolling line every few weeks. The symptom is always the same: elevated dimensional deviations in the finished product, a brief production stop, replacement of a roll or adjustment of the pressure parameters and then on with the show. The actual root cause? Unresolved. No one has time to dig deeper. The expert who could is running at 150 percent capacity and has been for months.
This is not an isolated case. Across large industrial and energy companies, Root Cause Analysis (RCA) the systematic search for the underlying causes of disruptions, quality problems, or failures is a well-known method and theoretically established. In practice, however, it fails regularly. Not for lack of willingness, but because of structural barriers that compound with every unresolved problem.
Adding to the difficulty: many practitioners are uncertain which RCA method suits which problem whether 5-Why, Ishikawa diagrams, FMEA, or another technique. And even when the method is clear, the analysis frequently stumbles at the problem definition stage. “Quality deviation” is not a workable problem statement. Only a precise description exactly when, under which conditions, how often, to what extent enables a targeted investigation.
The first seven points are operational and technical barriers – the last three address organisational patterns that are often overlooked in these discussions.
A food manufacturer is struggling with sporadic quality deviations in a filling line. With Visplore, an operator compares the last three abnormal batches against twenty normal ones in under a minute automatically assembled from process data, temperature logs, and shift records. The analytics rank the most deviating tags to the top. Within 15 minutes, a hypothesis has been formulated that had gone unnoticed for months: a pressure fluctuation pattern in a feed pump that only appears at certain ambient temperatures.
Even when a root cause is identified, the work does not end there. The finding needs to be communicated, translated into a process change, implemented, and ultimately verified: Did the measure actually work? By how much has the failure frequency been reduced? What efficiency gains are measurable?
Each of these steps again requires time, suitable visualisations, and clear before-and-after comparisons. Without this closure, RCA remains a half-finished process the organisational learning curve flattens, and the investment in the analysis cannot be justified internally.
Visplore is an analysis platform developed specifically for high-resolution industrial time series data. It does not address every barrier listed above but it tackles several of them in a targeted and practical way.
A utility company notices recurring fluctuations in the efficiency of a turbine. The operations team replaces a bearing – problem temporarily resolved. Three months later, the same thing happens. A proper RCA might have revealed that a fluctuating inlet pressure from an upstream pump group was the actual cause. But no one consolidated the operational data from six comparable time periods – and even if they had tried, it would have taken days.
See how much inefficient RCA is costing your operation.
No tool replaces missing instrumentation. If relevant influencing factors are simply not being measured, even the best analysis platform cannot identify causes that are absent from the data. The same applies to IT/OT barriers that prevent data from reaching an analysis system in the first place. And for rare events with few comparable cases, every statistical method runs into its limits this is a question of data availability, not of the tool.
Organisational and cultural barriers blame-oriented thinking, unclear accountability, the absence of formal RCA requirements in the management system cannot be resolved with software either. These require leadership decisions and a deliberate investment in a culture of learning from failure. What Visplore can do is make the data foundation broader, the visualisations clearer, and the effort of conducting an analysis low enough that the question “Do we have time for this?” comes up far less often.
And causality the heart of every RCA remains a human task. What Visplore does is lower the barrier to analysis far enough that the question shifts from “Can we manage this?” to “Why didn’t we do this sooner?”
If you want to experience live how Visplore can help you with root cause analysis across your entire industrial data, book a live demo.
