There are many pitfalls to a “painkiller” or “Band-Aid” solution. The biggest are that you’re destined to repeat problems if you’re not addressing the root cause, and often the cost of rework outweighs the convenience of a simple ‘symptom’ fix.
Making ‘symptom’ fixes (e.g., adjusting parameters after a defect occurs) offer short-term relief but ignore systemic flaws.
By way of example, if a steelmaker repeatedly recalibrates a rolling mill to address width defects (painkiller) but fails to identify that suboptimal temperature control during casting is the root cause of material inconsistencies.
Alternatively, in chemicals, manually bypassing a faulty valve during reactor overpressure (painkiller) ignores corroded components or sensor drift.
RCA processes like '5 Whys' and 'Fishbone diagrams' are built to uncover underlying issues.
An example is when a mill experienced recurring width defects in rolled steel. The root cause was that temperature fluctuations in the Hot Strip Mill created uneven cooling rates. The Solution: ML models analyzed historical data to pinpoint cooling system inefficiencies, reducing defects by 20%.
However, traditional RCA processes like these can take a lot of time and resources, which are luxuries inside factories today where engineers are responsible for many tasks. Although the returns from RCA can be great the cost of rework is greater.
- Sustainability Impact: Steelmakers using RCA-driven ML cut material waste by 15%, directly lowering CO₂ emissions per ton produced.
- Costs of Band-Aids: A mining company saved $2M/year by replacing substandard equipment materials (root cause) instead of increasing maintenance checks (symptom fix).
But there’s a better, faster way to identify the root cause of every problem.
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Fero Diagnostics uses AI to automate RCA capabilities to quickly pinpoint root causes of problems – in addition to making recommendations on how to precisely fix it.
Even less experienced engineers are able to run multivariate analyses in seconds to identify causal relationships in your production data.
For example:
- A Fero steel client reduced slag inclusion defects by correlating furnace slag viscosity with raw material impurities (root cause), saving close to a million per year in rework.
- A Fero chemical producer eliminated batch inconsistencies by tracing variability to outdated calibration protocols, not “operator error”.
🚀Are you ready to move beyond painkillers?
Let’s discuss how Fero Diagnostics delivers AI-driven RCA to future-proof your operations.
