• A product or process that has been improved multiple times, yet the complaints keep coming
• A team that disagrees on whether a KPI shift is real or just noise
• A root-cause analysis that concluded with “unclear” or pointed in three directions at once
• An improvement project that delivered results in the short term, but couldn’t sustain them

FactWise brings disciplined problem-solving structure and statistical thinking to situations where repeated actions have failed to deliver clarity.

In practice, this means:

• Defining the problem in measurable terms, so the investigating doesn’t drift
• Visualising process behaviour over time, using control charts to distinguish real change from routine noise
• Testing assumptions statistically, rather than accepting the loudest opinion in the room
• Identifying root causes through structured factor analysis,  narrowing candidates based on data, not gut feel
• Verifying corrective actions before full implementation, to confirm they actually work

Crucially, FactWise first verifies whether the available data truly supports the working assumptions. Before time, money and resources are committed to corrective actions.

Depending on the situation, this typically involves stratification analysis, hypothesis testing, SPC or regression. Chosen for what the problem actually needs, not applied by default.

The result is a far more reliable basis for corrective actions that actually hold.

Every engagement follows a clear structure:

1. Problem intake & scoping We define the problem precisely, align on what data is available, and agree on the goal. No assumptions, no shortcuts.

2. Analysis & root-cause investigation FactWise analyses the available data using the appropriate statistical methods. You’re kept in the loop throughout, not handed conclusions at the end.

3. Findings & recommendations report You receive a clear written report with the verified root cause(s), the supporting evidence, and concrete recommendations for corrective action — ready to share with your team, quality department or customer.

Engagements typically run between one and four weeks, depending on the complexity of the problem and the data available. The starting point is always a short intake conversation — no commitment required

✅ A clear picture of what the data actually shows — not what the team assumed before the analysis started

✅ Fewer unnecessary interventions — because routine variation is no longer mistaken for a signal to act

✅ Root causes that hold up under scrutiny — verified before corrective actions are committed to

✅ Process performance that stays improved — not just better for a few weeks after the last project

✅ Decisions your team, quality department, or customer can stand behind — backed by evidence, not consensus

This service is particularly relevant in:

• Food & Beverage — where contamination, spoilage or complaint patterns require traceable, defensible root-cause evidence
• Pharma & Medical — where deviations must be investigated to regulatory standards and corrective actions need statistical backing
• Industrial Manufacturing — where recurring defects or process instability erode quality KPIs and customer confidence

If your environment runs on data and expects accountability, statistical problem solving belongs in your toolkit.

If you are dealing with an unresolved complaint pattern, recurring quality issue or unclear KPI behaviour, I’m happy to look at your situation and assess whether this approach is the right fit.

Let’s look at what the data is telling you — and what it isn’t.

Not ready to dive in yet? The Understanding Variation Masterclass is a good place to start.