How to Identification Lean Six Sigma Project in a Process?
Posted: Sep 25, 2020
Identification of right kind of Lean Six Sigma project is very important with respect to Organization’s strategic plan, goals and Objectives. Let go through key Inputs required for Project identification.
Firstly the project identified should have some key characteristics, like it should be in line with business objective, should be process and customer focused and should have well defined scope with potential to generate significant benefits.
Secondly we need to understand that project identification involves several important participants like Master Black Belt, a facilitator, and senior leaders. To ensure a successful Lean Six Sigma Project, it is very important that we involve Senior Leadership from the very beginning. This will ensure that there interest, commitment, involvement and visibility remain throughout the Project cycle.
Next we need to choose some of the popular tools and sources for generating Project ideas:
- Nominal Group Technique
- Value Driver Tree Analysis
- Voice of Customer
- Audits Feedbacks ( Regulatory agency, Customer, first party, second party etc.,)
- Warranty Data
- Competitor Analysis
- Cost of Quality Data
- Gap Analysis
- Other Lean Six Sigma Projects
The capability indices can be calculated manually, although there are several software packages available that can complete the calculations and provide graphical data illustrating process capability. For the example in this section, we will utilize a popular statistical software package. For our example, we will utilize data from randomly collected measurements of a key characteristic of a machined part. To better represent the population values, the sample data must be randomly collected, preferably over time from a large production run. A few things to keep in mind:
- Our data is quantitative and variable
- Our data consists of 100 measurements
- The target dimension is 25.4 mm
- USL (Upper Specification Limit) = 25.527 mm
- LSL (Lower Specification Limit) = 25.273 mm
- Range = 0.254 mm
First, we will examine our data with a simple histogram to determine if it could fit a normal distribution. In addition, we can generate a probability plot evaluating our data’s best fit to a line further indicating we are 95% confident that our data fits a normal distribution.
Now let us examine the Process capability report:
- Cp (Process Capability = 1.68
- Cpk (Process Capability Index) = 1.66
Using the graph, we can further evaluate process capability by comparing the spread or range of the product specifications to the spread of the process data, as measured by Six Sigma (process standard deviation units).
Through examination of the reports, we can determine that our example process is in a state of statistical control. All the data points fall well within the specification limits with a normal distribution. A process where almost all the measurements fall inside the specification limits is deemed a capable process. Process capability studies are valuable tools when used properly. As previously mentioned the information gained is generally used to reduce waste and improve product quality. In addition, by knowing your process capabilities, the design team can work with manufacturing to improve product quality, and processes that are "not in control" may be targeted for improvement. During a typical Kaizen event or other quality improvement initiatives, Process Capability is calculated at the start and end of the study to measure the level of improvement achieved. Accurate knowledge of process capability enables management to make decisions regarding where to apply available resources based on data.
- Every time someone makes a decision – such as, "Is this the right candidate?" – it is critical that the decision-maker would select the same choice again and that others would reach the same conclusion. Attribute agreement analysis measures whether or not several people making a judgment or assessment of the same item would have a high level of agreement among themselves.
- Helps to characterize the quality of the data
- Determines the area of non-agreement
- Helps in calibrating appraisers, judges, or assessors for a higher level of agreement
- Easy to analyze with statistical software or a specialized worksheet
- How to Use
- Step 1. Set-up a structured study where a number of items will be assessed more than once by more than one assessor. Have the items judged by an expert, which will be referred to as the "standard" (can be one person or a panel – see table below).
- Step 2. Conduct the assessment with the assessors in a blind environment. They do not know when they are evaluating the same items and they do not know what the other assessors are doing.
- Step 3. Enter the data in a statistical software package or an Excel spreadsheet already set up to analyze this type of data (built-in formula).
- Step 4. Analyze the results: Is there good agreement between appraisers? Each appraiser vs. the standard? All appraisers vs. the standard?
- Step 5. Draw your conclusions and decide on the course of actions needed if the level of agreement is below a set threshold. Usually> 80 percent is considered to be a good level of agreement.
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