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Difference between Quality Management and Six Sigma |
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If you have any questions about our Quality Matrix, or wish to make any comments, please feel free to send a message to us at quality@artige.com. |
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| Overview |
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This analysis is just one of many comparisons that are offered as part of the Artige Quality Matrix, which can be seen here in its original form. The definitions that are used in these comparisons are the ones that we at the Artige Company use internally and with our clients, derived from the research that we perform as a matter of due course. These definitions are derived from natural laws of physics and statistics, in order to screen our work from the effects of the business press. The original article where these terms are discussed appears here. In other words, we like to think that this work will withstand the scourges of time and not be categorized as "management du jour". |
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| Quality Management |
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Definition |
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We like to use this category as a catch-all for the quality methods that are not founded by obvious cause and effect methods. That is, those methods that are driven more by personalities than particulars. We also like to lump in the traditional quality control methods, as it can be shown that the quality management practices are a result of the original "measure and reject" philosophy of quality control. |
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Quality management is based upon heuristic and ad hoc methods, based on previous experience. Note that there is nothing wrong with that, and it can be deployed very successfully. It is just that certain individuals like a predictable and deterministic methodology, and an ad hoc method does not fit such a constraint. The quality management methodology has its roots in traditional quality control in production, where conformance to requirements is the major point of interest. |
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We use a waterfall method to explain where quality management comes from, which is based upon quality control and quality assurance. This is described by the three definitions below: |
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| QC |
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The main task that the quality control methodology sets out to accomplish is to insure that only good items were acceptable for further production, and bad parts were rejected. To have the concept of good and bad parts means that standards of some type must be in place, so comparisons can be made. The fact that good and bad parts could exist was not the main concern (and is still not) of quality control. Any activity that is related to measuring product parameters against criteria and passing judgment on whether the product can be used or sold based on meeting the criteria is considered a quality control task. This covers final goods, intermediate work-in-progress or services being delivered. |
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| QA |
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Obviously, a regime of rejection will only get a business so far, and could result in an unprofitable situation if most parts are rejected. So the obvious step to take is to prevent the rejections, which is typically done by assessing the processes that are causing the rejects in the first place. This method is called quality assurance. This practice considers all aspects of production, insuring that raw materials are fit for consumption, equipment is operating as desired, as well as maintained properly, and that all the constraints from customers have been collected, documented and integrated back into the constraints used for production. So, activities related to preventing rejected parts or services would be considered part of a quality assurance practice. |
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| QM |
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The next obvious step for the enterprise that is able to prevent rejections is to tighten the constraints, which should result in better financial performance, owing to the fact that fewer resources are being used, with a resulting drop in costs. This is considered quality improvement, and the practice that handles this would be considered quality management. Whereas the two previous practices of quality control and quality assurance have a cause and effect relationship, the prediction and improvement of defects enters an area that is not amenable to a cause and affect analysis. No one set of rules exists that one can deploy to improve a process. |
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This category of process design is extremely interesting, in that it is the reason why there are so many other methodologies to handle the process design and quality needs of an enterprise. We cannot emphasize more clearly, "no one set of rules exists that one can deploy to improve a process". All of the methodologies listed in this report are an attempt to overcome this shortcoming. Since there is no cause and effect relationship that can point one in the one and only direction to improve their processes, all of these other processes are acceptable, and can never be proven invalid or unsound in every case. All that one can do is determine which of these processes fits one's way of operating and still be able to meet the criteria set by the customer. |
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Since this is the methodology that brings up the point that there is no cause-and-effect method to improve processes, we like to use quality management as the moniker to point to the ad hoc and people-oriented methods. That is because as the quality improvement concept advanced, this method would be the first that recognized human intervention was needed to improve process outcome. Most of these process improvement ideas came about in an ad hoc manner, just from trial-and-error and previous experience. Since previous experience is such a great factor in promoting prevention and improvement, the practitioners of quality management will always be focused upon people-oriented methods that pull out best practices from the experienced workers. |
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Quality management is also where that concept of announcing the company financials to production workers came about in a big way. The idea here is that education and knowledge of the workforce is important, in order for them to understand where their paycheck comes from, and that they have an input in the processes that allow them to maintain their cash flow. This focus on financial measures then proves to the organization membership that ignoring quality holds a price for non-conformance. |
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Note that the workforce education component is a tricky area. There are two facets to this factor. On one hand one could just announce the data and expect that the workforce will consume the data to everyone's advantage. This also allows certain executives to claim they are open and honest with their workforce. There is another step that is probably necessary. One will need to explain, and even tutor, what the meaning is behind the raw data. In other words, how will the workforce gain knowledge from the financial data? The first option to just announce the data opens the door for misunderstanding and mistrust, which is why the second option is preferred. |
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All of the above tasks would be categorized as incremental in terms of implementing change. |
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| Six Sigma |
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Definition |
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Six Sigma has come to mean two things. First, it is a focal point or slogan used as a means to coach a company into improving its performance. For example, one firm's Six Sigma program is "a highly disciplined process that helps us focus on developing and delivering near-perfect products and services". Second, Six Sigma is a designation for a regulated program that a firm might elect to use to establish a quality management system in an effort to improve the quality of products produced or services delivered, and then desires to maintain that improved level of performance. The latter definition is the one referenced most often in the popular business press. |
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| General Systems Theory |
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For both definitions, Six Sigma draws upon the general system theory and relies heavily upon statistics, especially statistical process control (SPC), and requires quantitative parameters that can be measured on an on-going basis if it is deployed as a quality management system. This methodology utilizes traditional process control at its best, making Lord Kelvin proud. Process control is the practice of operating a system, measuring externally available parameters, and modifying the process based upon the measurements. The calculations are not random, but based upon statistics, especially standard deviations. |
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Actually, Six Sigma gets its name from the table of probabilities for the normal (Gaussian) distribution that is used in many statistical calculations. The standard deviation variable is typically symbolized by the small Greek letter sigma. A standard deviation in this context is the amount of the population of samples that are expected to be perfect. The higher the standard deviation, the fewer rejects are expected. The amount becomes exponentially smaller as the number of standard deviations increases, which indicates that it will be more difficult to maintain a process within higher levels of standard deviation. |
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| Sigma = 2 Std Dev |
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Back in the good old days when SPC was common practice, a process was considered to be in control when it ran with +/- three standard deviations, or three sigma. Note that number sums up to six total standard deviations. Today, one is not satisfied unless the process runs within six-sigma deviation, leaving little room for error or defects. To give some numbers to these sigma values, one could consider the number of defects one could expect in the different scenarios. For three sigma, one could expect 2.6 defects per thousand units. For six sigma, the rate would be one half defect per billion units. However, there is an additional factor that needs to be taken into account, that of the drift in the process being measured. SPC takes that into consideration, so the typical defect rate realized with six sigma processes increases to 3.4 defects per million units. Note that these values are typical, and a properly run SPC regime will measure the true defect rates. |
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| DMAIC |
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As you can see from the previous paragraph, it is possible to deploy a Six Sigma program with steadiness and purpose. However, SPC is only one portion of a Six Sigma program. Essentially, Six-Sigma extends the process control concepts to process design and improvement. It requires that one take a system view of the business or manufacturing processes and treat them in a systemic manner. An acronym associated with Six Sigma is DMAIC, which stands for the continuous improvement process of Define, Measure, Analyze, Improve and Control. This is a circular process, where the results of the first pass are used to run the second iteration. |
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The hardest part of Six Sigma is defining the system that describes the business process. It is completely up to the business or process owner to select the best places for splitting an enterprise into monitorable systems. The first two parameters of Define and Measure are the numerically manageable parameters. Metrics and goals need to be defined, seeking out those that can be measured and consistently reported upon, that reflect upon some sort of process output. As the process is operated, process measurements are collected and recorded on a periodic basis. |
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The final three parameters of Analyze, Improve and Control act upon the metrics that were recorded, and are of a more qualitative nature. Here one compares the results against the self-determined boundary conditions and goals. The process is investigated when the boundary conditions are exceeded, and problem solving is engaged in an attempt to determine what went wrong and what could be done to improve the process. The metrics also allow for one to be proactive, and start problem solving based on trends that are observed before the boundary conditions are crossed. Main point is that the DMAIC process is never halted, otherwise complacency will set in. |
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The Six-Sigma methodology requires human intervention, as this process occurs around and about the business processes. The data collection aspect can be automated, but does not have to be. The analysis and improvement aspects cannot be automated at this time. The requirement for human intervention, along with its inefficiencies, brings along indirect issues, such as group dynamics and process ownership. To address these inescapable issues, many Six-Sigma methodologies incorporate personnel practices, summarized through the use of mentoring and granting of titles to the practitioners, based upon colored belts. |
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Note that there is difference of opinion on the effectiveness of hierarchical organizations, and quality organizations typically run flat. On top of that the notion that the practitioners of Six-Sigma are limited by the level of expertise that they possess and are only able to draw upon is curious for a quality organization to pursue. Nonetheless, discipline is strongly promoted, as the tighter the control limits (higher the number in front of sigma), the more tedious the effort to maintain control will be. For the most part, Six-Sigma is an incremental methodology. |
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| The Difference |
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The Differences and / or Similarities |
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At first glance, one would think that Quality Management and Six Sigma have a few things in common, in addition to the fact that they both deal with the topic of quality. They are both methodologies that can be used to implement some sort of regime which could potentially instill or improve quality in a firm's offerings. The two methodologies take different approaches, resulting in different outcomes. |
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Six Sigma offers a direct approach to improving an existing system, through the disciplined application of continuous improvement, using a statistical approach applied using general systems theory. Six Sigma does provide a framework in which to apply the continuous improvement, the DMAIC approach. While not presenting exact details for every specific situation, it does offer a consistent methodology. This makes this approach well suited for large organizations that need a firm structure and documented tactics in order to institute process improvements. One could consider this method a direct attempt at improving the lot of an organization. |
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Alternatively, Quality Management takes an indirect approach to improving the quality of an organization. It is based upon the premise that deploying Quality Control, and then regulating the Quality Control regime with a Quality Assurance regime, plus improving the Quality Assurance principles with a Quality Management process will instill an environment of quality in the enterprise. The latter relies solely on the experience and knowledge of the enterprise workers to drive the process changes and improvements. The more knowledgeable the workers, the better the chance to improve quality. Note too, that the ad hoc nature of Quality Management is typically more acceptable in small firms. |
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So on one hand with Six Sigma we have a methodology that brings us a set of continuous improvement ideas that have an SPC component and a process assessment component, which will integrate the results of the SPC testing. This means that Six Sigma includes a manual component, which will require discipline to maintain. Also, Six Sigma may not be simple or trivial to implement. One must realize that any time process redesign is suggested, effort must be exerted, and probably capital funds will need to be spent. On the other hand, Quality Management uses direct methods to maintain quality and indirect methods to possibly improve the quality of business processes. Both methodologies should result in changes to the underlying operating processes. |
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In summary, Six Sigma and its continuous improvement methodologies offer a direct method that has a fighting chance to improve quality of business processes. Quality Management uses indirect methods to possibly improve the quality of business processes. The other major difference is that Quality Management is open ended with the improvements that could be implemented, while Six Sigma relies heavily on its statistical-based DMAIC approach. |
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Note that in the Quality Management methodology the outcome will be an indirect result of deploying the management processes. With its ad hoc basis, there is nothing to mandate what level of quality is expected or desired. Note too that a Quality Management regime could adopt some of Six Sigma practices as part of the improvement process. If this occurs, it will do so as a result of trial and error, not as part of a planned course of action that dictates lean principles must be part of the business process design. |
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