Performance varied greatly, and that had to stop

One of the world’s largest steel producers was midway through a five-year programme to perform consistently at world-class level. At its largest plant, the hope was that Six Sigma would eliminate the remaining process variation. The team discovered that half of that variation came from well-intentioned interventions by the operators themselves. The pilot delivered far more consistent batch quality, and €1.8 million in annual savings.

Duration: six months

I’ve now learned that variation destroys your performance. Before the project, I spent 80% of my time managing deviations. – Unit anager

The situation

One of the world’s largest steel producers was on a rollercoaster of improving and then declining performance. It was frustrating, and it had to change. An ambitious five-year programme was set up to bring the company to world-class level for good.

Halfway through that programme, major improvements had already been achieved in delivery reliability, fixed costs, equipment utilisation and quality. Quality had in fact never been higher. The company had heard that Six Sigma was the global standard for operations, and wondered whether it could improve performance even further.

Axisto was asked to help design and implement the Six Sigma pilot at the largest steel plant. The team deliberately chose a department that already had a relatively stable operation.

What was really wrong

There were high expectations that the new statistical tools would be a kind of silver bullet that would eliminate the remaining process variation in one go. That put the pilot at risk of becoming a theoretical exercise, detached from day-to-day work.

When the team collected and analysed the data themselves, it became clear where the variation really came from. 20% was caused by the statistical computer model that set the process parameters for each batch. The measurement process added another 20%. And 50% came from the operators’ own interventions: well-intentioned actions to reduce variation that had exactly the opposite effect. That last finding came as a shock. But because the team had worked with the data themselves and reached the conclusions themselves, they could accept the outcome without being discouraged by it.

What we did together

An integrated project team: two process engineers, a production engineer, one operator from each of the five shifts, and two Axisto people. The project followed the Six Sigma phases, from selecting the project to embedding the solutions.

Looked at their own improvement track record first. Which initiatives had succeeded, and why? Which had struggled, and why? The team identified the critical success factors, and tested the performance management framework, from KPI tree and division of roles to meeting and reporting structure, on whether it would be able to hold the results.

Used setbacks to learn. Implementation had its ups and downs, and we deliberately let them happen. The team discussed what went wrong, understood and accepted it, and decided for itself how to adjust its behaviour.

Kept the analysis close to the floor. By involving operators from the shop floor and working around their daily work and problems, the project stayed practical and relevant. Production engineers, process engineers and shift operators collected, analysed and discussed the data together.

Tackled all three sources of variation. The computer model, the measurement process and operator intervention were all thoroughly addressed. The solutions were built into the performance management framework, so that they became part of day-to-day management.

At first the team was mainly focused on discovering new tools. Along the way it realised it already knew all the relevant tools, and that success lay not in the tools but in its own behaviour: how you work day in, day out in a dynamic production environment, and whom you involve.

What it delivered

  • Far more consistent batch quality, which also made downstream processes more consistent and better performing
  • A much better understanding of the steelmaking process across production, process technology and the shifts
  • Solutions embedded in the performance management framework

Financially: the investment was recouped nine times, just due to lower catalyst consumption.

What happened afterwards

The changes are firmly embedded in the way people work. More than two years later, we reviewed progress together with the client: the improvement had held up well.

Related cases

Performance varied greatly, and that had to stop

One of the world’s largest steel producers was midway through a five-year programme to perform consistently at world-class level. At its largest plant, the hope was that Six Sigma would eliminate the remaining process variation. The team discovered that half of that variation came from well-intentioned interventions by the operators themselves. The pilot delivered far…

Growth swamped the innovation department

Growing demand for new product variants and engineering changes swamped the innovation department of a fast-growing electronics company. Design errors only came to light in production, crash actions pushed aside projects that were on schedule, and hiring more engineers made things worse. Within six months of starting work with Axisto, output was reliable again, and…

The new system was actually costing the engineers time

It turned out that the Engineers had to perform a lot of repetitive, manual, clicking, copying, and pasting that suit RPA well. The implementation of RPA has saved 5.5 man-years — and the savings still continue to grow.

Related insights

Why so many Industry 4.0 projects stall before they deliver real value

Industry 4.0 projects rarely stall because of technology. They stall because organisations approach digitalisation as a technology trajectory, while real value only emerges when strategy, operations, governance, leadership and behaviour change alongside it. That is the core of the problem. And precisely why many CEOs and COOs see that the digital ambition in their organisation…

AI and Agentic Automation in 2026

From Pilots to Industrial-Scale Performance AI is no longer the constraint. Execution with clear ROI is. Across industrial organisations, AI pilots have multiplied, tools have matured, and investment continues to grow. Yet many leaders see a familiar pattern: promising experiments that don’t translate into sustained operational performance, because they never scale into the processes, roles,…

Europe’s ESG Pay Pivot: Between Purpose and Pragmatism

Europe isn’t abandoning ESG leadership—it’s refining it. The continent’s strong regulatory foundations, from CSRD to SFDR, remain intact. But implementation is now being tempered by calls for flexibility, competitiveness, and industrial relevance. This isn’t about ESG versus growth. It’s about ESG for resilient growth. And that’s not idealism. That’s smart governance.