Process Capability Analysis

The process capability analysis module builds on the basics of statistics. Here you will gain a deeper insight into data types and distribution forms.
In this module, you will learn, among other things, how to present and interpret the performance of processes in various key figures. You will gain a basic understanding of whether the statements in your data are statistically relevant or not. You will learn to interpret diagrams correctly and to think and act in terms of probabilities.

Quick Info

Contents

  • Data acquisition
  • Types of diagrams
  • Normal distribution
  • Binomial distribution
  • Poisson distribution
  • Weibull distribution
  • Sigma level
  • z-value table
  • Process performance
  • Specification limits
  • Accuracy
  • Precision
  • Cp & Cpk
  • z-value vs. Cpk
  • Process capability DPMO
  • DPU
  • Ppm
  • Long-term & short-term capability
  • Sigma Shift
  • Test for normal distribution
  • Duration:

Duration on request

  • Upcoming Start Dates:
  • Live webinar dates in:

Dates on request

  • Alternatives:
  • Individual Coaching
  • In-House
  • Small Group Session
  • In the Languages:
Request offer

Key information

Process capability analysis is a statistical method used to evaluate the ability of a process to produce products or results within defined specification limits. This tool is used to measure and improve the consistency and reliability of production processes by evaluating data on process performance and comparing it with the specified tolerances.

Beneftits

  • Ensure the reliability of processes
  • Reduction of waste
  • Erhöhen der Kundenzufriedenheit
  • Identify the causes of fluctuations
  • Reduction of error costs

Risks

  • Precise data acquisition required
  • Possession of expertise in statistics
  • Misinterpretation of the results

With more than 4,000 projects and case studies, Alphadi is a leader in the field of Lean Six Sigma and Sales Process Engineering. We advise you holistically so that your goals are achieved sustainably and in the long term.

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