Valentine LETZELTER Patricia LHOTE DE NEEFF
With 15 years of experience as a Black Belt in Lean Six Sigma, we’ve seen firsthand how critical the Define and Measure phases are to successful process improvement using the DMAIC (Define, Measure, Analyze, Improve, Control) methodology. While the urge to jump directly to the “Improve” stage is strong, neglecting these foundational steps often leads to wasted effort and suboptimal results. In this article, We’ve distilled the best advice and common pitfalls we’ve encountered across countless process improvement initiatives, focusing specifically on mastering these two crucial initial phases.
The Perils of a Wandering Path: The Define Phase
One common pitfall is feeling overwhelmed by a plethora of potential improvement opportunities. As Lean Six Sigma practitioners, we often develop an eye for inefficiency, both at work and in our daily lives! However, attempting to tackle everything at once is a recipe for disaster.
Let’s revisit a timeless exchange from Alice in Wonderland:
“Would you tell me, please, which way I ought to go from here?”
“That depends a good deal on where you want to get to,” said the Cat.
“I don’t much care where–” said Alice.
“Then it doesn’t matter which way you go,” said the Cat.
“–so long as I get SOMEWHERE,” Alice added as an explanation.
“Oh, you’re sure to do that,” said the Cat, “if you only walk long enough.”
This illustrates the need to specify your improvement goals clearly. Without a defined destination, you risk wandering aimlessly, lost in a forest of data, as highlighted in my previous article. It’s not about asking “What can we improve?” but “What do we need to improve?”, recognizing our limited time and resources.
Key Takeaway #1: Clearly Define Your Improvement Objective.
Another mistake is attempting to improve a process exhibiting significant instability and variability. Process stability is paramount. It provides the baseline against which you’ll measure any subsequent improvements. An unstable process, whose variation from one period to another is unpredictable, needs to be stabilized before embarking on any improvement projects.
Strategies for reducing process variation include:
• Standardizing and documenting key process steps.
• Ensuring thorough employee training on the standardized process.
• Identifying significant sources of variation and implementing checks and warnings to catch errors early.
Key Takeaway #2: Ensure Process Stability Before Attempting Improvement.
Consider a full value chain perspective, rather than trying to optimize a single isolated element. This approach yields more powerful and sustainable results. The utilization of data from diverse enterprise systems (PLM, CRM, ERP), coupled with a digital twin (a virtual replica of the physical process), allows for simulating various process segments and fostering a holistic view. This digital twin enables testing improvements in a later stage, mimicking the behaviour, performance, and characteristics of the real-world process in real-time.
Measuring What Matters: The Measure Phase
Once you’ve clearly defined the process, established its stability, and formulated the improvement objective, you can move into the Measure phase. This phase centers on determining current process performance and quantifying the problem at hand.
A common mistake in this phase is simply relying on existing metrics. It’s crucial to question whatperformance measures are needed. Are the chosen metrics truly critical to process performance and are they accurate? Here, expert judgment is invaluable. While data may be abundant, carefully selecting the right metrics and ensuring their relevance is paramount.
Focus on:
• Process Effectiveness: How well do process outputs meet customer needs and expectations (e.g., quality, speed, customer satisfaction)?
• Process Efficiency: How effectively are resources utilized to produce process outputs (e.g., cost, turnaround time)?
I have personally encountered situations where I had to define a completely new set of measures because the existing ones were inadequate for capturing customer expectations. Fortunately, years of historical operational data enabled us to reconstruct the baseline and continue monitoring with the new metrics.
Key Takeaway #3: Critically Evaluate and Select the Right Performance Metrics.
Another common mistake is using raw data without proper preparation. Understanding the importance of a stable process becomes clear here. Unstable processes generate meaningless data that cannot serve as a foundation for improvement.
You may encounter exceptional variation due to specific, explainable events (e.g., power outages, technical malfunctions). These events, also called special causes, should be identified and removed from the dataset through data cleansing. Data cleansing involves removing incorrect, duplicate, and incomplete data, as well as addressing structural errors and exceptional events. AI can greatly assist in this cleansing process, but human validation remains essential. AI can also propose extrapolations for incomplete datasets, based on data scientist-defined guidelines, accelerating data collection and the Measure phase, especially when cycle times are lengthy.
Key Takeaway #4: Cleanse and Prepare Data Before Analysis.
When selecting the set of measures, avoid solely focusing on the measures you intend to improve. Identify:
• Primary Measures: The measures directly targeted for improvement and used to gauge the success of improvement efforts.
• Secondary Measures: Measures used for ongoing monitoring to ensure that improvements in primary measures don’t negatively impact other aspects of the process.
For example, I led a project focused on improving process quality, with the number of errors and customer satisfaction as our primary measures. We monitored the process cost as a secondary measure to ensure that quality improvements didn’t lead to increased expenses.
These measures can be displayed in real-time on digital dashboards within a Digital Twin, enabling users to monitor and analyze performance across various dimensions. When connected to systems across the entire process chain (CRM, ERP, etc.), these dashboards provide a holistic view of process performance.
In Conclusion: Build on a Strong Foundation
Just as a building requires a solid foundation, successful process improvement relies on well-executed Define and Measure phases. By avoiding these common mistakes and incorporating these best practices, you’ll significantly increase your chances of achieving impactful and sustainable results.
Stay tuned for our next article, where we’ll dive into the Analyze, Improve, and Control (AIC) phases of DMAIC. Before you jump into analyzing, improving, and controlling, are you sure your process is even capable of being improved? We’ll uncover the key criteria for determining if a process is stable enough to be a good candidate for DMAIC – saving you time, resources, and potential frustration.
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