DMAIC
The basic methodology consists of the following five steps:
Define process improvement goals that are consistent with customer demands and the enterprise strategy.
Measure key aspects of the current process and collect relevant data.
Analyze the data to verify cause-and-effect relationships. Determine what the relationships are, and attempt to ensure that all factors have been considered.
Improve or optimize the process based upon data analysis using techniques like Design of Experiments.
Control to ensure that any deviations from target are corrected before they result in defects. Set up pilot runs to establish process capability, move on to production, set up control mechanisms and continuously monitor the process.
DMADV
The basic methodology consists of the following five steps:
Define design goals that are consistent with customer demands and the enterprise strategy.
Measure and identify CTQs (characteristics that are Critical To Quality), product capabilities, production process capability, and risks.
Analyze to develop and design alternatives, create a high-level design and evaluate design capability to select the best design.
Design details, optimize the design, and plan for design verification. This phase may require simulations.
Verify the design, set up pilot runs, implement the production process and hand it over to the process owners.
DMADV is also known as DFSS, an abbreviation of "Design For Six Sigma".[8]
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Thursday, May 29, 2008
must read
Six Sigma--the organizational quality system made famous by GE's legendary Jack Welch--has set new standards for process improvement.
This is the first book to provide managers a basic, non-technical overview and steps...
go here
This is the first book to provide managers a basic, non-technical overview and steps...
go here
which improvement model/s to choose ?
Implementing Six Sigma: The Road Map
Identify Core Processes
Identify key customers
Define Customer requirements
Measure current performance
Improve the process or redesign the process
DMAIC is one way
but not the only way
Identify Core Processes
Identify key customers
Define Customer requirements
Measure current performance
Improve the process or redesign the process
DMAIC is one way
but not the only way
Wednesday, May 28, 2008
Monday, May 26, 2008
Sampling the process performance
Sampling (statistics)
Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference. Each observation measures one or more properties (weight, location, etc.) of an observable entity enumerated to distinguish objects or individuals. Survey weights often need to be applied to the data to adjust for the sample design. Results from probability theory and statistical theory are employed to guide practice.
The sampling process comprises several stages:
Defining the population of concern
Specifying a sampling frame, a set of items or events possible to measure
Specifying a sampling method for selecting items or events from the frame
Determining the sample size
Implementing the sampling plan
Sampling and data collecting
Reviewing the sampling process
Contents
1 Population definition
2 Sampling frame
3 Sampling method
3.1 Quota sampling
3.2 Simple random sampling
3.3 Stratified sampling
3.4 Cluster sampling
3.5 Random sampling
3.6 Matched random sampling
3.7 Systematic sampling
3.8 Mechanical sampling
3.9 Convenience sampling
3.10 Line-intercept sampling
4 Sample size
5 Types of data
5.1 Categorical and numerical
6 Sampling and data collection
7 Review of sampling process
7.1 Non-response
8 Survey weights
9 History
10 See also
11 External links
12 Notes
13 References
//
more
Process Sampling
The following is an excerpt from Chapter 11 of Pyzdek's Guide to SPC, Volume 2: Applications and Special Topics by Thomas Pyzdek, © 1992 by Quality Publishing. It may be ordered from the Quality Publishing Order Form.
Sampling to determine process control is more an art form than a science. The objective is to select subgroups such that the variation of measurements or counts within the subgroup will be produced by only common causes. The spread of the control limits will be based on only within subgroup variation. Thus, any addition variation will cause the production of subgroup statistics which fall beyond the control limits, signaling a special cause of variation.
I have always found it helpful to think about the process as a bowl of blue chips with numbers written on them. A controlled process is one where the same bowl of chips is sampled time-after-time. If the chips in the bowl have different numbers on them, there will be a variation in the sample.
However since the bowl doesn’t change, the variation will be relatively consistent from one sample to the next. After sampling the bowl numerous times we will become more and more comfortable setting up some limits on the variation we expect to see in the future samples from the same bowl. The bowl represents a controlled process, a predictable process.
Now lets say that there are two bowls, one with blue chips and one with green chips. Assume further that the number written on the blue chips are quite different than those written on the green chips. Furthermore, lets say that you don’t get to see the chips themselves; all you know is the numbers you obtained. Sometimes the sample is taken from the blue chips and sometimes from the green chips. Could you tell the difference?
The answer depends a great deal on the way you formed your subgroups. If your subgroups were formed from a mixture of blue and green chips, then the process is neither blue nor green; the process is blue + green. The subgroup variation would include the variation from both the blue + green and the difference between them.
For example, if the blue chip varied from 10 to 50 and the green varied from 60 to 100, a mixed sample of both blue and green would vary from 10 to 100. Control limits based on the mixed sample would show a greater spread, and your estimate of the process capability would indicate a less capable process than either the blue or the green alone. In other words, you would probably conclude that the blue + green process was "in control and capable of holding a tolerance of 10 to 100."
The objective of forming rational subgroups is to identify the underlying process so that departure from the underlying process can be quickly detected and corrected. The underlying process can be thought of as the performance that could be attained if all special causes of variation were eliminated and the process was operating at its best. To do this you must plan carefully to avoid mixing processes from different cause systems, which is comparable to mixing the blue chips and the green chips.
Here’s a more down- to- earth example. An o-ring is made in a mold with fifty cavities. It is known that there is a substantial difference between the cavities. It would be a mistake to form a subgroup using a o-ring from cavities known to be different because the cavity- to- cavity variation would mask the variation caused by other factors such as material, temperature, etc..
SPC methods useful for this type of data are presented in chapter 21. However, taking a longer-term perspective, you should try to modify the mold so that there is less variation between the different cavities. Eventually you would like to get the molding process so consistent that the o-rings are all alike regardless of which cavity in the mold produced them. That is the ultimate goal of SPC, to change the real world for the better not to make a control chart look better.
more
more
Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference. Each observation measures one or more properties (weight, location, etc.) of an observable entity enumerated to distinguish objects or individuals. Survey weights often need to be applied to the data to adjust for the sample design. Results from probability theory and statistical theory are employed to guide practice.
The sampling process comprises several stages:
Defining the population of concern
Specifying a sampling frame, a set of items or events possible to measure
Specifying a sampling method for selecting items or events from the frame
Determining the sample size
Implementing the sampling plan
Sampling and data collecting
Reviewing the sampling process
Contents
1 Population definition
2 Sampling frame
3 Sampling method
3.1 Quota sampling
3.2 Simple random sampling
3.3 Stratified sampling
3.4 Cluster sampling
3.5 Random sampling
3.6 Matched random sampling
3.7 Systematic sampling
3.8 Mechanical sampling
3.9 Convenience sampling
3.10 Line-intercept sampling
4 Sample size
5 Types of data
5.1 Categorical and numerical
6 Sampling and data collection
7 Review of sampling process
7.1 Non-response
8 Survey weights
9 History
10 See also
11 External links
12 Notes
13 References
//
more
Process Sampling
The following is an excerpt from Chapter 11 of Pyzdek's Guide to SPC, Volume 2: Applications and Special Topics by Thomas Pyzdek, © 1992 by Quality Publishing. It may be ordered from the Quality Publishing Order Form.
Sampling to determine process control is more an art form than a science. The objective is to select subgroups such that the variation of measurements or counts within the subgroup will be produced by only common causes. The spread of the control limits will be based on only within subgroup variation. Thus, any addition variation will cause the production of subgroup statistics which fall beyond the control limits, signaling a special cause of variation.
I have always found it helpful to think about the process as a bowl of blue chips with numbers written on them. A controlled process is one where the same bowl of chips is sampled time-after-time. If the chips in the bowl have different numbers on them, there will be a variation in the sample.
However since the bowl doesn’t change, the variation will be relatively consistent from one sample to the next. After sampling the bowl numerous times we will become more and more comfortable setting up some limits on the variation we expect to see in the future samples from the same bowl. The bowl represents a controlled process, a predictable process.
Now lets say that there are two bowls, one with blue chips and one with green chips. Assume further that the number written on the blue chips are quite different than those written on the green chips. Furthermore, lets say that you don’t get to see the chips themselves; all you know is the numbers you obtained. Sometimes the sample is taken from the blue chips and sometimes from the green chips. Could you tell the difference?
The answer depends a great deal on the way you formed your subgroups. If your subgroups were formed from a mixture of blue and green chips, then the process is neither blue nor green; the process is blue + green. The subgroup variation would include the variation from both the blue + green and the difference between them.
For example, if the blue chip varied from 10 to 50 and the green varied from 60 to 100, a mixed sample of both blue and green would vary from 10 to 100. Control limits based on the mixed sample would show a greater spread, and your estimate of the process capability would indicate a less capable process than either the blue or the green alone. In other words, you would probably conclude that the blue + green process was "in control and capable of holding a tolerance of 10 to 100."
The objective of forming rational subgroups is to identify the underlying process so that departure from the underlying process can be quickly detected and corrected. The underlying process can be thought of as the performance that could be attained if all special causes of variation were eliminated and the process was operating at its best. To do this you must plan carefully to avoid mixing processes from different cause systems, which is comparable to mixing the blue chips and the green chips.
Here’s a more down- to- earth example. An o-ring is made in a mold with fifty cavities. It is known that there is a substantial difference between the cavities. It would be a mistake to form a subgroup using a o-ring from cavities known to be different because the cavity- to- cavity variation would mask the variation caused by other factors such as material, temperature, etc..
SPC methods useful for this type of data are presented in chapter 21. However, taking a longer-term perspective, you should try to modify the mold so that there is less variation between the different cavities. Eventually you would like to get the molding process so consistent that the o-rings are all alike regardless of which cavity in the mold produced them. That is the ultimate goal of SPC, to change the real world for the better not to make a control chart look better.
more
more
Sunday, May 25, 2008
Improve your process
Identify key customers
A lot of organizations appreciate the importance of key account management but fail to identify their key accounts in a strategic fashion. This is simply because of a common misconception that “big” (company size) is also “key,” and that offering special treatment costs more. In this series of articles, I will highlight the importance and steps for identification of key accounts, an approach to segmentation and categorization of accounts, and steps for developing a key account management strategy.
Why “big” is not always “key”?
Most companies try to include big companies in their customer portfolio and it is a good strategy (unless you choose to serve only small customers for strategic reasons). Serving a fragmented base of customers generally raises the cost of doing business and customer turnover can cause severe fluctuations.
However, serving big companies too has its challenges:
Require more attention and typically do not pay for it.
Leverage their scale and market power to negotiate lower prices and often exploit suppliers by creating conditions for price wars.
Use small suppliers by giving them small orders and getting the lowest possible prices to squeeze their larger suppliers.
Salespeople learn only the hard way that many big companies rarely give them the business that they deserve based on the effort that they put in. Therefore, it is important to clearly identify what customers are “key” to your business and then serving them using a well thought out plan.Customer segmentation approach to identifying key accountsIdentification of key accounts should be a quantitative exercise rather than an emotional one based on personal preferences.
The recommended approach is suggested below.
Step 1: Group your customers into three (or more categories) by sales. For instance, more than $1 million (A accounts), $100,000 to $1 million (B accounts), and less than $100,000 (C accounts).
Step 2: Include contribution margins and direct profit (or any other financial metrics that make sense for your business).
Step 3: Identify key accounts based on the accounts that have the highest impact on company financials.
more
Now go here
Why “big” is not always “key”?
Most companies try to include big companies in their customer portfolio and it is a good strategy (unless you choose to serve only small customers for strategic reasons). Serving a fragmented base of customers generally raises the cost of doing business and customer turnover can cause severe fluctuations.
However, serving big companies too has its challenges:
Require more attention and typically do not pay for it.
Leverage their scale and market power to negotiate lower prices and often exploit suppliers by creating conditions for price wars.
Use small suppliers by giving them small orders and getting the lowest possible prices to squeeze their larger suppliers.
Salespeople learn only the hard way that many big companies rarely give them the business that they deserve based on the effort that they put in. Therefore, it is important to clearly identify what customers are “key” to your business and then serving them using a well thought out plan.Customer segmentation approach to identifying key accountsIdentification of key accounts should be a quantitative exercise rather than an emotional one based on personal preferences.
The recommended approach is suggested below.
Step 1: Group your customers into three (or more categories) by sales. For instance, more than $1 million (A accounts), $100,000 to $1 million (B accounts), and less than $100,000 (C accounts).
Step 2: Include contribution margins and direct profit (or any other financial metrics that make sense for your business).
Step 3: Identify key accounts based on the accounts that have the highest impact on company financials.
more
Now go here
FREE Beginners guide to six sigma
Implementing Six Sigma: The Road Map
Identify Core Processes
Identify key customers
Define Customer requirements
Measure current performance
Improve the process or redesign the process
Start here
Identify Core Processes
Identify key customers
Define Customer requirements
Measure current performance
Improve the process or redesign the process
Start here
Six Sigma - the Basics explained
Although much has been written touting Six Sigma and its benefits, many are still confused about what exactly Six Sigma is and why it is extremely beneficial.
Six Sigma Fundamentals cuts through the fluff of conventional Six Sigma jargon and provides the reader with a solid understanding of what defines a Six Sigma initiative and what is expected from the organization, management, and customer.
Each chapter fully addresses the concepts of the Six Sigma philosophy and explains the methodologies for real-world applications. Included with the text is a CD-ROM containing more than 75 ready-to-use Six Sigma forms. Six Sigma Fundamentals gives an overview to the entire process - from understanding the significance of customer requirements all the way to Designing for Six Sigma and implementation strategy.
The model tools, methodology, and goals are explained thoroughly, so that this powerful system may be applied to organizations that are concerned with drastic positive changes to both customer satisfaction and profitability. With a focus on both manufacturing as well as non-manufacturing organizations, Six Sigma Fundamentals demystifies the methodologies, identifies the tools needed for accurate deployment, and enables the reader to explain the design of Six Sigma within the organization.
more
Six Sigma - What is Six Sigma?
Definition of Six Sigma
Six Sigma Deployment
Is Six Sigma Just For Large Companies? No.
Is Six Sigma hype or truth?
Hype
Truth
Unsure
Six Sigma at many organizations simply means a measure of quality that strives for near perfection. Six Sigma is a disciplined, data-driven approach and methodology for eliminating defects (driving towards six standard deviations between the mean and the nearest specification limit) in any process -- from manufacturing to transactional and from product to service.
The statistical representation of Six Sigma describes quantitatively how a process is performing. To achieve Six Sigma, a process must not produce more than 3.4 defects per million opportunities. A Six Sigma defect is defined as anything outside of customer specifications. A Six Sigma opportunity is then the total quantity of chances for a defect. Process sigma can easily be calculated using a Six Sigma calculator.
The fundamental objective of the Six Sigma methodology is the implementation of a measurement-based strategy that focuses on process improvement and variation reduction through the application of Six Sigma improvement projects. This is accomplished through the use of two Six Sigma sub-methodologies: DMAIC and DMADV. The Six Sigma DMAIC process (define, measure, analyze, improve, control) is an improvement system for existing processes falling below specification and looking for incremental improvement. The Six Sigma DMADV process (define, measure, analyze, design, verify) is an improvement system used to develop new processes or products at Six Sigma quality levels. It can also be employed if a current process requires more than just incremental improvement. Both Six Sigma processes are executed by Six Sigma Green Belts and Six Sigma Black Belts, and are overseen by Six Sigma Master Black Belts.
more
Six Sigma Fundamentals cuts through the fluff of conventional Six Sigma jargon and provides the reader with a solid understanding of what defines a Six Sigma initiative and what is expected from the organization, management, and customer.
Each chapter fully addresses the concepts of the Six Sigma philosophy and explains the methodologies for real-world applications. Included with the text is a CD-ROM containing more than 75 ready-to-use Six Sigma forms. Six Sigma Fundamentals gives an overview to the entire process - from understanding the significance of customer requirements all the way to Designing for Six Sigma and implementation strategy.
The model tools, methodology, and goals are explained thoroughly, so that this powerful system may be applied to organizations that are concerned with drastic positive changes to both customer satisfaction and profitability. With a focus on both manufacturing as well as non-manufacturing organizations, Six Sigma Fundamentals demystifies the methodologies, identifies the tools needed for accurate deployment, and enables the reader to explain the design of Six Sigma within the organization.
more
Six Sigma - What is Six Sigma?
Definition of Six Sigma
Six Sigma Deployment
Is Six Sigma Just For Large Companies? No.
Is Six Sigma hype or truth?
Hype
Truth
Unsure
Six Sigma at many organizations simply means a measure of quality that strives for near perfection. Six Sigma is a disciplined, data-driven approach and methodology for eliminating defects (driving towards six standard deviations between the mean and the nearest specification limit) in any process -- from manufacturing to transactional and from product to service.
The statistical representation of Six Sigma describes quantitatively how a process is performing. To achieve Six Sigma, a process must not produce more than 3.4 defects per million opportunities. A Six Sigma defect is defined as anything outside of customer specifications. A Six Sigma opportunity is then the total quantity of chances for a defect. Process sigma can easily be calculated using a Six Sigma calculator.
The fundamental objective of the Six Sigma methodology is the implementation of a measurement-based strategy that focuses on process improvement and variation reduction through the application of Six Sigma improvement projects. This is accomplished through the use of two Six Sigma sub-methodologies: DMAIC and DMADV. The Six Sigma DMAIC process (define, measure, analyze, improve, control) is an improvement system for existing processes falling below specification and looking for incremental improvement. The Six Sigma DMADV process (define, measure, analyze, design, verify) is an improvement system used to develop new processes or products at Six Sigma quality levels. It can also be employed if a current process requires more than just incremental improvement. Both Six Sigma processes are executed by Six Sigma Green Belts and Six Sigma Black Belts, and are overseen by Six Sigma Master Black Belts.
more
Using Six Sigma toImprove Product QualityHow can Six Sigma improve product quality? The key is to reduce variation and eliminate defects. Let’s explore the basics of Six Sigma to understand how this is accomplished.
The term "sigma" refers to standard deviation, which is a measure of the variation or scatter in a process. Within business and industry, the sigma value is a metric that indicates how well a process is performing, compared to the benchmark value of Six Sigma. Sigma measures the capability of a process to perform defect-free work. A defect is anything that may result in customer dissatisfaction.
The common measurement for Six Sigma is defects-per-unit, where a unit can be virtually anything: a component, an administrative form, a piece of material, a line of software code, and so on. The sigma value is a quality measurement that indicates how often a defect is likely to occur. The higher the sigma value, the less likely a process will produce defects. As sigma increases, cycle time and cost decreases, and customer satisfaction increases.
So what does it mean to be Six Sigma? Consider a process that produces one million parts. For this process to meet a Six Sigma quality level, it must produce less than four defective parts (the actual number is 3.4) out of the million that are produced! Clearly, achieving a Six Sigma quality level represents world-class status. Let’s further examine the impact of variation on product quality. Referring to the graphic, the variation of two products is depicted and represented by the bell-shaped curves. The product produced using Six Sigma methodologies is shown with less variation, represented by the steeper slope of the curve and more narrow spread around the mean value, than the traditional product. With the customer specification limit superimposed on the curves, you can see that the shaded area under the curves on the right-hand side of the specification limit line is considerably smaller for the Six Sigma product than for the traditional product. This area corresponds to the quantity of defects that are produced for each product.
Clearly, a Six Sigma product produces far fewer defects translating into less scrap and rework costs. As a result of this reduced variation, Six Sigma methodologies often lead to the identification of product development best practices. Ultimately, exploiting these practices result in the creation of superior products.
As you can see, variation has a significant impact on product quality. Controlling variation leads to improved productivity and lower costs, which translate into a competitive advantage for the company.
The term "sigma" refers to standard deviation, which is a measure of the variation or scatter in a process. Within business and industry, the sigma value is a metric that indicates how well a process is performing, compared to the benchmark value of Six Sigma. Sigma measures the capability of a process to perform defect-free work. A defect is anything that may result in customer dissatisfaction.
The common measurement for Six Sigma is defects-per-unit, where a unit can be virtually anything: a component, an administrative form, a piece of material, a line of software code, and so on. The sigma value is a quality measurement that indicates how often a defect is likely to occur. The higher the sigma value, the less likely a process will produce defects. As sigma increases, cycle time and cost decreases, and customer satisfaction increases.
So what does it mean to be Six Sigma? Consider a process that produces one million parts. For this process to meet a Six Sigma quality level, it must produce less than four defective parts (the actual number is 3.4) out of the million that are produced! Clearly, achieving a Six Sigma quality level represents world-class status. Let’s further examine the impact of variation on product quality. Referring to the graphic, the variation of two products is depicted and represented by the bell-shaped curves. The product produced using Six Sigma methodologies is shown with less variation, represented by the steeper slope of the curve and more narrow spread around the mean value, than the traditional product. With the customer specification limit superimposed on the curves, you can see that the shaded area under the curves on the right-hand side of the specification limit line is considerably smaller for the Six Sigma product than for the traditional product. This area corresponds to the quantity of defects that are produced for each product.
Clearly, a Six Sigma product produces far fewer defects translating into less scrap and rework costs. As a result of this reduced variation, Six Sigma methodologies often lead to the identification of product development best practices. Ultimately, exploiting these practices result in the creation of superior products.
As you can see, variation has a significant impact on product quality. Controlling variation leads to improved productivity and lower costs, which translate into a competitive advantage for the company.
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