Lesson 1: Defining and Tracking Result Indicators — How to Prove That the Root Cause Has Been Eliminated

Opening: When the Numbers Don’t Tell the Full Story

Imagine this: your Kobetsu Kaizen team has just completed a three-week improvement project on a bottleneck packaging line. The root cause — a misaligned conveyor sensor causing frequent minor stoppages — has been identified, countermeasures have been implemented, and the team is confident the problem is solved. The plant manager asks one simple question: “How do we know it’s actually fixed?” The room goes quiet. The team has anecdotal evidence, a few observations from the gemba, and a gut feeling — but no structured data to prove the root cause has truly been eliminated. This is exactly the gap that well-defined result indicators are designed to close. Without them, even the most rigorous Kobetsu Kaizen effort risks becoming an exercise in activity rather than verified improvement.

Learning Objectives

• Define the difference between process indicators and result indicators in the context of Kobetsu Kaizen.
• Identify and select the right Key Performance Indicators (KPIs) aligned to the specific loss being addressed.
• Apply the SMART criteria to set measurable targets that confirm root cause elimination.
• Use operational data — including OEE metrics and the 16 Loss framework — to verify that countermeasures have delivered lasting impact.
• Structure a result tracking routine that supports the Check and Act phases of the PDCA cycle.

Step 4 of KK Is Not the Last Step — Verification Is

In the Kobetsu Kaizen methodology, the structured problem-solving process moves through four essential steps: problem representation, root cause analysis, solution implementation, and — critically — verification of the solution. This fourth step is where result indicators take center stage. According to the KK framework, every improvement action must be followed by a data-based check to confirm that the identified root cause has been permanently eliminated, not just temporarily suppressed.

The Kobetsu Kaizen Board provides the operational structure for this: it demands that results be displayed graphically, that data speak for themselves, and that the team maintain a three-month tracking horizon oriented toward zero — zero defects, zero breakdowns, zero accidents. This is not a bureaucratic requirement. It reflects a fundamental Lean principle: you cannot manage what you do not measure. The board becomes a living document that connects the problem, its cause, the countermeasure, and the verified outcome in one transparent, visual narrative.

Result indicators must be tied directly to the loss category being attacked. The 16 Loss framework — which includes eight main equipment losses (such as breakdowns, minor stoppages, set-up losses, and reduced speed), three production-related losses, five process losses, plus losses in energy, materials, and tools — gives teams a structured vocabulary for defining what to measure. When a KK project targets minor stoppages, for example, the primary result indicator is the frequency and duration of those stoppages tracked over time. When the project targets start-up losses, the indicator is the time elapsed from machine start to stable production output. The indicator must mirror the loss, not just the activity performed to address it.

Setting SMART Result Indicators Aligned to P, Q, C, D, S, M Targets

One of the most common mistakes in KK result tracking is selecting indicators that are too generic. Measuring “overall quality” when the root cause was a specific tooling wear pattern tells you very little about whether that cause has been eliminated. Effective result indicators must be SMART: Specific, Measurable, Attractive, Realistic, and Time-limited. This is not a soft HR concept — it is a technical discipline embedded in the KK methodology itself.

When setting targets, the P, Q, C, D, S, M framework provides a powerful structure. Each dimension maps to a category of operational performance:

• P (Productivity): OEE improvement, output per shift, takt time adherence — tracked using machine-level data collected at the gemba.
• Q (Quality): Defect rate, first-pass yield, rework hours — directly tied to loss categories such as waste, rework, and tool change losses.
• C (Cost): Variable cost reduction, typically with a minimum target of 10% cost reduction as a company-level KK objective.
• D (Delivery): Lead time, on-time delivery rate, line organization losses.
• S (Safety): Near-miss incidents, unsafe condition tags resolved.
• M (Morale): Operator participation rate in KK activities, number of improvement suggestions submitted.

For each KK project, the team should define at least one primary result indicator (typically in P or Q) and one secondary indicator (often in C or D) to capture both operational and financial impact. These indicators must be baselined before the countermeasure is applied. Without a clear before-state captured in data, the after-state is meaningless.

The KK methodology also reinforces the importance of installing gemba measurements as part of the improvement journey — not after it. Data acquisition happens at the machine, documentation and evaluation happen at the machine, and the KPI system reflects what is actually happening on the shop floor, not what is assumed in the office.

Practical Example: Verifying Root Cause Elimination at Meridian Plastics

Meridian Plastics, a mid-sized injection molding manufacturer, was experiencing persistent reduced speed losses on their Line 4 press. OEE data showed that the speed loss factor was consistently running at 78% of nominal, dragging overall OEE down to 61% against a target of 75%. A KK team was formed, and after a structured N5W (5-Why) analysis combined with Pareto diagram review of the machine downtime log, the root cause was identified: hydraulic oil temperature was exceeding the operating range during the second half of each shift, causing the press controller to automatically reduce cycle speed as a protective measure.

The team defined two SMART result indicators before implementing the countermeasure (installation of an auxiliary oil cooling unit):

1. Primary indicator: Speed loss factor on Line 4, measured per shift, target ≥ 95% of nominal speed maintained throughout the full shift — to be achieved within 30 days of countermeasure installation.
2. Secondary indicator: OEE on Line 4, target improvement from 61% to 72% within 60 days, tracked weekly.

The team installed gemba measurement points for hydraulic oil temperature and cycle time, updated the Kobetsu Kaizen Board with weekly trend charts, and monitored results for a full three-month period. At the 30-day check, speed loss factor had reached 97%. At 60 days, OEE had improved to 74%. The graphical results on the KK Board showed a clear, sustained trend — not a one-week spike followed by regression. The root cause had been eliminated, and the data proved it. The countermeasure was then secured through an updated operational standard and integrated into the autonomous maintenance plan, closing the PDCA loop.

What made this successful was not just the improvement itself, but the discipline of defining what success looks like in data terms before starting. The team knew exactly what they were trying to prove, and they built the measurement system to prove it.

Key Takeaways

• Result indicators must be defined before countermeasure implementation — a baseline is essential to prove that root cause elimination has actually occurred, not just that activity has taken place.
• Connect every indicator to a specific loss category from the 16 Loss framework; this ensures that what you measure directly reflects the problem you solved.
• Apply SMART criteria rigorously — vague targets such as “reduce breakdowns” are insufficient; specify the metric, the target value, and the time horizon.
• Use the KK Board as a live verification tool, displaying graphical trends