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GembaWay

Welcome to Gemba Way. We're your premier destination for mastering Continuous Improvement. Whether you're diving into Kaizen, Lean Six Sigma, Process Improvements, or Operations Excellence, our content is crafted to help you excel.

Explore our comprehensive resources to boost your problem-solving skills, implement innovative business ideas, and achieve unparalleled Process Excellence. Plus, we’ll guide you through the realm of Digital Transformation and IT Tech, revealing the tools and technologies that drive today’s innovations.

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Is your process actually capable, or are you just getting lucky?
Most operations leaders track metrics like SLA compliance, average handle time, or defect rates. But these are lagging indicators—they tell you what already happened, not what your process is statistically designed to deliver.
That’s where Process Capability Analysis ($C_p$ & $C_{pk}$) comes in.

The Service Desk Reality Check:
Imagine your SLA target for resolving customer requests is between 6 and 14 minutes (Target: 10 mins).

Scenario A (High Cp, Low Cpk):
Your team consistently resolves tickets in 12 to 13 minutes.
Cp is high: The spread is tight and variation is low. The team has the potential to hit the SLA.
Cpk is poor: The process is shifted dangerously close to the 14-minute breach point. One minor spike and tickets fail.

Scenario B (High Cp, High Cpk):
Your team resolves tickets clustered tightly between 9 and 10 minutes.
The process is both consistent (low variation) and centered (on target). The risk of an SLA breach drops to near zero.

The Golden Rule for Operations Leaders:
Before trying to shrink variation (reducing Cp spread), center your process mean first (Cpk).
Shifting an average through standard operating procedures (SOPs) or removing obvious bottlenecks is usually faster and cheaper than overhauling the entire system to eliminate variation.

Want to run a baseline capability assessment on your service workflows?

At GembaWay, we help MSMEs and corporate service teams diagnose hidden waiting waste, stabilize processes, and build predictable delivery systems.

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