After completing and validating an improvement to a process, a Belt (referring to a Six Sigma Belt, such as a Green Belt or Black Belt) and the Project Team create a Control Plan. The Control Plan is a crucial document in process improvement and quality management, as it outlines the steps to maintain the gains achieved during the improvement process and ensures continued compliance with the desired process changes.
Batching is the practice of making or processing large lots of a particular item or task at once. It is a common approach used in many industries to gain economic efficiencies, especially in traditional mass production systems.
Fractional Factorial Design, Factorial Design, and Response Surface Method are all types of planned experiments commonly used in various fields of science, engineering, and research.
Control Charts were indeed developed by Dr. Walter A. Shewhart in the 1920s to track data over time and to detect Special Cause variation in processes. Control Charts are a fundamental tool in Statistical Process Control (SPC), which is used to monitor and maintain process stability and control.
Special Cause Variation, also known as Assignable Cause Variation, does indeed fall into the category of "Assignable" variation in Statistical Process Control (SPC). However, "Pattern" is not a category of variation in SPC.
In Fractional Factorial Designs, Resolution V designs are not desired when controlling the costs of experimentation. Instead, Resolution V designs are used when the focus is on obtaining main effects and two-way interactions while reducing the number of experimental runs.
All the elements affecting a process's costs collectively constitute a Critical to Price (CTP). To determine the entire cost of producing a product, component costs, assembly expenses, and shipping costs must be taken into account.
Collinearity can be a problematic issue in DOE because it can lead to difficulties in estimating the true effects of individual independent variables on the response variable. When collinearity is present, it becomes challenging to distinguish the separate contributions of each variable, and it can lead to unstable or unreliable estimates of the regression coefficients.
PDCA (Plan-Do-Check-Adjust), also known as the Deming Cycle or Shewhart Cycle. However, it was not developed by Dr. Deming but by Dr. Walter A. Shewhart, a pioneer in statistical quality control. Dr. Deming, a prominent figure in the quality management field, did extensively promote and popularize the PDCA cycle as part of his teachings on continuous improvement.
A waste is a loss that has no added value. Prior to project completion, waste should ideally be eliminated or reduced as much as possible.
Orthogonality is an essential property of experimental designs, particularly in factorial experiments, to ensure efficient estimation of factors' effects and prevent confounding.
The three consequential measures are decreased cycle time, scrap time, and set-up time.
Data collected at regular intervals are displayed on an R-chart. It calculates dispersion by tracking the sample range over time. At least 20 subgroups of observed values, each containing 3 to 6 observations, must be present in the R-chart. It is convenient to observe whether the variation falls between the upper and lower control boundaries with this bare minimum of observations.
Following the completion of a Lean Six Sigma (LSS) project, the Belt (Six Sigma Belt, such as Green Belt or Black Belt) may create both a Control Plan and a Response Plan. These plans are essential components of the project's implementation and sustainability phases, ensuring that the improvements achieved during the project are effectively maintained and that the team knows how to respond to deviations from the desired performance.
Six sigma's capability and complexity analysis aids in defining, measuring, and controlling both the capabilities and complexity of goods and services. What-if analysis can be used to optimize process mapping and modeling using multiple variables thanks to the advanced capability-complexity analysis.
By achieving flow in processes, organizations can improve throughput, reduce lead times, increase efficiency, and enhance customer satisfaction. Flow is a central concept in Lean thinking, and its realization requires ongoing efforts to streamline processes, eliminate waste, and continuously improve the system's performance.
