Six Sigma training is available online and in-class from a variety of reputable institutions. Professional Six Sigma training courses typically cost from $800 to $2500 per course for Project Champion, Six Sigma Green Belt and Six Sigma Black Belt Training respectively.

Certification is the path to building your career status as an effective Six Sigma practitioner, and usually requires the completion and verification of specific Six Sigma projects or course work. The certification is then registered and must be maintained with yearly training and fees.

Click the links above and below for more information on Six Sigma training and certification.

Statistical Process Control (SPC) charting software comes in a variety of packages, each with a format and cost point that is best suited for a particular business need.

SPC Software format is broken into 3 camps:

• Excel SPC Add-In - By far the least expensive type of SPC Software. Usually in the $100-$200 range. These add-ins use the power of Microsoft Excel to draw graphs and present your data in Excel standard chart format.
• SPC Stand Alone Software - Medium cost in the $800-$1500 range. Here is the real meat of the market including the most popular professional statistical analysis package; Minitab, and all its imitators.

Career Tip: Most companies use Minitab. Learning and using Minitab
is the best way to build marketable career skills that major industry
employers will value.

• SPC Web Based Integrated - Best suited for large enterprise shop floor management. Typical cost is $10K-$25K and may include subscription software support, training and maintenance. Heavy implementation cost and risk is involved. Ongoing support and maintenance is crucial.

Click the links above and below for specific information on Six Sigma SPC Software.

Lean production methods have been evolving since the beginning of the industrial revolution. Examples include Henry Ford’s creation of the first production line for the automobile industry, and in World War II where a B24 was rolling of the assembly line every 56 minutes. Lean methods have been further improved by Toyota, and other forward thinking companies with undeniable results. Toyota has been rated #1 in auto manufacturing quality for the past five years.

Lean methods include:

• 5S Programs
• Theory of Constraints
• The 7 Wastes
• Toyota Production Systems, (TPS)
• Demand Flow
• Just in Time
• Value Stream Mapping
• Transactional Mapping
• TQC
• Re-engineering

In all cases the expected outcome remains the same:

• Focus on Customer, their expectations and what they perceive as value
• A passion for Continuous Improvement in the elimination of waste
• Identification of where an organization adds value and the identification of non-value activities to enable the successful implementation of the future state vision
• Creating the ability for products or activities, (transactions) to flow through a process map in shortest amount of time possible
• Establishing disciplines to link Customer Demand directly to processes, transactions, resource, or material.

There is a strong case that can be made for a partnership between Six Sigma, which fixes processes, and lean, which fixes the connections between processes. By combining Six Sigma and Lean you will gain a broad perspective on your people, processes, and results that will yield powerful data driven solutions and grow your business.

Many companies will latch onto one or two lean methods and realize short term improvements. This is where a comprehensive Six Sigma program then steps in to assess process variation and manage the continuous improvement of expected outcomes for the long term.

DMAIC refers to a data-driven quality strategy for improving processes, and is an integral part of the company’s Six Sigma Quality Initiative. DMAIC is an acronym for five interconnected phases: Define, Measure, Analyze, Improve, and Control.

Each step in the DMAIC Process is required to ensure the best possible results. Define the Customer, their Critical to Quality (CTQ) issues, and the Core Business Process involved.

The process steps:

Design

• Define who customers are, what their requirements are for products and services, and what their expectations are
• Define project boundaries, the stop and start of the process
• Define the process to be improved by mapping the process flow

Measure 

• Measure the performance of the Core Business Process involved.
• Develop a data collection plan for the process
• Collect data from many sources to determine types of defects and metrics
• Compare to customer survey results to determine shortfall

Analyze 

• Analyze the data collected and process map to determine root causes of defects and opportunities for improvement.
• Identify gaps between current performance and goal performance
• Prioritize opportunities to improve
• Identify sources of variation

Improve 

• Improve the target process by designing creative solutions to fix and prevent problems.
• Create innovate solutions using technology and discipline
• Develop and deploy implementation plan

Control 

• Control the improvements to keep the process on the new course.
• Prevent reverting back to the “old way”
• Require the development, documentation and implementation of an ongoing monitoring plan
• Institutionalize the improvements through the modification of systems and structures (staffing, training, incentives)

Histograms show the spread, or dispersion, of your data. The customer’s upper specification limit (USL) and lower specification limit (LSL) set the boundaries or parameters that determine acceptable levels of variation  in the process. When the process is between/within the USL and LSL then the process is said to be capable. The Minitab histogram uses variable data to determine process capability. With attribute data (defects), capability assumes that the process produces zero defects.

Cp Cpk for Process Capability Analysis

Cp > 1.0 Process is capable meaning measured process requirements fall between/within the customer’s USL and LSL, 3-4 sigma (Cp=1.0 to 1.33)

Cpk > 1.0 Process is capable and centered between the LSL and USL, not shifted either direction. (Defects <-+-> Waste)

In observation 4 below we have a capable process with Cp 1.14 and Cpk 1.12 respectively.

(Minitab Output)

Cp values are not the best indicators of process capability. Cp is the ratio of the engineering tolerance (USL - LSL) to the natural tolerance (6s). The value of Cp does not take into account where the process is centered. Just knowing that a process is capable (Cp > 1.0) does not ensure that all the product or service being received is within the specifications. A process can have a Cp > 1.0 and produce no product or service within specifications. In addition, Cp values can’t be calculated for one- sided specifications. A better measure of process capability is Cpk.

Cpk takes into account where the process is centered. The value of Cpk is the minimum of two process capability indices. One process capability is Cpu, which is the process capability based on the upper specification limit. The other is Cpl, which is the process capability based on the lower specification limit. Algebraically, Cpk is defined as shown in the figure.

Cpk Equation

Both Cpu and Cpl take into account where the process is centered. The value of Cpk is the difference between the process average and the nearest specification limit divided by three times the standard deviation. It should be noted that the standard deviation is the standard deviation based on a R or s chart - not the standard deviation of the individual measurements.

Cpk values above 1.0 are desired. This means that essentially no product or service is being produced above USL or below LSL. If Cpk is less than 1.0, this means that there is some product being produced out of specification. If there is only one specification, the value of Cpk is either Cpu or Cpl, whichever is appropriate for the specification.

It should also be noted that Cp and Cpk levels (B/W Capability) should relate closely to Pp and Ppk levels (Overall Capacity) If Cp Cpk levels are substantially greater than Pp Ppk levels then your process is capable but not stable enough to reliably produce a consistent result.

If this is the case use corresponding control charts  to measure process capacity and stability in tandem with the histogram to measure capability. In most quality settings this multi chart approach would be preferable to using the histogram alone.

In Minitab the capability 6 pack offers additional views of the capability scenario. Using the 6 pack you can see the correlation of other charts to the capability analysis in question.

A Minitab control chart in its most basic form is the line chart. The line chart is used to correlate measurements over time or number of samples. Typical measurements include down time, defects, delays, people, cost, outages, returns etc…

By adding an average line to the to the line chart we create what is called a run chart. The run chart will show us how the measurements in the line chart compare to the average to illustrate variation from the average over time.

By adding upper control limits (UCL) and lower control limits (LCL) to the run chart we have the fully illustrated Minitab control chart shown here.

Using Control Charts

Control charts are used to analyze variation and process stability over time. Process parameters or customer specifications are set by the designated UCL and LCL. The movement of attributes between the average and the UCL/LCL indicate process variation trends as well as out of bounds conditions.

The C Chart above illustrates a one sided variation analysis meaning that LCL=0. In this case when measuring defects a condition of LCL=0 is optimal or equal to 0 defects.

A two sided variation analysis requires that LCL be greater than zero meaning that out of bounds conditions may exist above UCL or below LCL.

There are seven types of Minitab control charts used in six sigma.

• C Chart - Attribute or counted data (sample size constant)
• Np Chart - Attribute or counted data (sample size constant)
• P Chart - Fraction defective (sample size varies)
• U Chart - Number of defects (sample size varies)
• Individuals Chart - Subgroup size = 1
• XbarR Chart - 2 to 10 subgroups
• XbarS Chart - 2 to 50 subgroups

The use of different control charts is indicated by the type and form of data sample to be analyzed. Use the chart below to properly choose the correct control chart based on data type and form.

To recap: In six sigma we use the control charts to measure process stability over time. This is required to capture and respond to common and special causes of variation  in the process.

Combine your control chart analysis with Histogram Cp Cpk Pp Ppk  analysis to measure process capability. The combined measurements of Stability (Control Charts), and Capability (Histogram) tell the full story and give us a data driven framework for continuous improvement of the process.

Common cause variability occurs naturally in every process. Common cause variation is fluctuation caused by unknown factors resulting in a steady but random distribution of output around the average of the data. This fluctuation defines process potential, or how well the process can perform when all special cause variation is removed.

Common cause variation is also called random variation or noise. Example: Many small variations with a small impact. Common cause variation is the remaining variation after removing the special causes (non-normal causes) due to one or more of the 6Ms (Man power, Mother nature, Materials, Method, Measurements or Machine). Common cause variation is measured with control charts  as a fundamental metric of quality improvement.

Special Cause Variation

Special cause variability is also unavoidable in most every process. Special cause variation is caused by known factors that result in a non-random disruption of output. Sometimes referred to as “exceptional” or “assignable” variation. Example: earthquake, and other environmental causes or catastrophic mechanical failure. Special cause variation can be thought of as the few with major impact. Special cause variation can be accounted for directly and potentially removed. Measured continuously, identified and removed through the proper use of control charts we take this as a measure of process control.