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The process of data analysis

Unveiling the Power of Data Analysis in Lean: Enhancing Processes with Insights

Data analysis is the main point in which we turn raw data into real conclusions. Thus, under Lean, the conclusions are made according to three parameters:

  1. Speed of the process: To assess how quickly all actions are taken in this workflow.
  2. Variation in the process: To identify the variations from the standard.
  3. The number of defects in the process: The deviations are identified and fixed.

With the spirit of Lean, it is crucial to ascribe problems firstly to the process and not to performance flaws. William Edwards Deming’s pioneering studies demonstrated the remarkable statistic that as many as 94% of issues are caused by the process, whereas only 6% develop from the workers’ incompetence. In contrast, however, too much effort and capital are dedicated to monitoring personnel instead of the deficient systems.

By leveraging the insights provided by data analysis, organizations explore process performance to make it a perfect cycle attainable by repeated feedback and improvement dialogues. From a high-level perspective, the surrounding components that build this cycle are continuous measurement and proactive dialogue.

Once we reach a more detailed level afforded by data analysis, the best possibilities for improvement projects become evident to answer the following questions:

  • Is it the average process performance time that is the necessary focus?
  • Are there evident defects in the system that require immediate reaction?
  • Is excessive variation a deterrent to consistent process performance?

Wrapping Up:

Answers to these critical questions; then, light the way and focus the work on reducing variability, improving averages, or a deliberate mix of both. According to Lean improvement initiatives, the insights obtained from the data streamline the process and allow for measurable progress and continuous success.

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