I was once told that determining KPIs and collecting data to establish a base line takes too long and to just make certain changes and then determine if the change worked. We’ve all been there – relying on intuition for process changes. But in today’s data-driven world, gut feelings often fall short. Panic then sets in and management ends up making multiple changes, which affect moral of the production workers and diminishes trust in Management.

Here’s why data analysis must occur in order to improve processes:

Objectivity Over Bias: Our instincts can be influenced by past experiences or personal preferences. Data provides a neutral lens, revealing the true impact of changes.

Quantifiable Results: Data allows you to measure the effectiveness of process tweaks. Track key metrics and see the tangible impact on efficiency, cost, or quality.

Uncover Hidden Inefficiencies: Data analysis can expose bottlenecks and areas for improvement you might miss with a cursory glance. It reveals the “why” behind process issues.

Data-Driven Predictions: By analyzing historical data, you can predict future trends and proactively make adjustments before problems arise.

Sure, intuition can spark initial ideas, but data analysis is the key to:

Validating those hunches: Does your gut feeling hold water? Data analysis provides the evidence to back up your suspicions.

Refining the approach: Data reveals the most impactful areas to focus on, ensuring your process improvements are targeted and effective.

Just make sure you are not affected by analysis paralysis. Use the tools you have, such as PFMEA, process flow maps, and value stream maps.

What are your thoughts? Share your experiences with data analysis and process improvement in the comments.