The Most Complete Methodology for Process Characterization, Process Optimization and Total Process Control.
We Wrote the Books
This course is the only proven methodology for machine and process characterization studies.
M/PCpS® is a step-by-step methodology for characterizing, optimizing and controlling manufacturing and production processes. This methodology places an order on how to apply statistical methods in manufacturing processes with the objective of identifying, reducing or eliminating its major sources of variability and achieving Six Sigma (Cp=2 & Cpk=2) levels of performance.
The methodology is divided into five stages:
The methodology is taught in the order of the stages with approximately one month in between sessions to allow for the application of the methods to your projects.
The most complete step-by-step documentation on how to characterize, optimize and control machines and processes.
MPCpS Training On-Site
Stage 1: Process Delineation
Define the process, its boundaries and functional characteristics, response variables, independent variables and their interrelationships.
Stage 2: Metrology Characterization
Define the measurement system needed to evaluate the responses and quantify its variation.
Stage 3: Capability Determination
Determine the current ability of the process to meet customer specifications and predict its stability, capability and Sigma performance at the process optimum levels.
Stage 4: Optimization
Reduce the sources of variation through statistically designed experimentation and optimization techniques.
Stage 5: Control
Set up a complete system of preventive control for the independent variables. Mistake-proof the process against defects and monitor the response variables.
Duration: Typically 3 to 4 weeks. Extra modules are added depending on the organization's needs.
We are Proud of Our "Hands-On" Training Approach
We take participants through lectures in which they learn the tools, methods, and concepts. Then, we help them apply the tools in their own processes.
This is, by far, the most efficient way to learn the application of statistical methods.