Creating a SIPOC Chart
February 12, 2025
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In customer centric business environment, it is critical to have an effective plan to manage production capacity, materials availability and shipment schedules. The planning functionality of an ERP system provides organizations the means of meeting such customer centric approach. Planners are able to simulate alternate scenario planning wherefrom they determine which assemblies and components to […]
Adoption of quality frameworks like Six Sigma, ISO 9000 and Baldridge has ensured that companies derive a source of competitive advantage in the way in which they produce superior products with minimal loss of quality. Though these frameworks have been adopted in the manufacturing sector much earlier, the services sector has not lagged behind in […]
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In previous articles, we looked at how the BPO phenomenon has played itself out in India and other Asian countries that have derived humungous benefits to their economies. In this article, we look at the other side of the coin i.e. the benefits that accrue to the economies of the Western countries as they outsource […]
One of the greatest criticisms that have been mounted against the six sigma methodology is the fact that there is a possibility that the entire system is built on fudged numbers. Statisticians have claimed that the name six sigma is misleading. Here are the reasons why:
The statistical term Six Sigma actually refers to a process in which there will be 2 defects per billion times the process is run. However, the definition of Six Sigma accepted by modern day practitioners is a much easier to follow, 3.4 defects per million. Although even achieving efficiency of 3.4 defects per million, makes the process achieve near zero and therefore negligible defects, the statistical name 6 sigma is misleading. The values 3.4 defects per million, in reality, correspond to 4.5 sigma levels. The balance is accounted for by the 1.5 sigma shift.
The logic behind the 1.5 sigma shift is rooted in empirical studies. Empirical studies have shown that processes tend to fare better in the short term than they actually do in the long term. This is because in the short term, there is only normal process variation that needs to be dealt with. However in the long term cases of special process variation also occur. This results in the process performing at 6 sigma levels in the short run but at 4.5 sigma levels in the long run.
The long term variation in the process variation is accounted for by one of the two reasons:
As a result of either of the above reasons, or a combination of both, the process fails to meet its Six Sigma objectives. This phenomenon is called long term dynamic mean variation.
Now, we know that the Six Sigma criteria are not met because of long term dynamic mean variation. But how do we know that we need to remove 1.5 sigma from both sides of the normal curve. Well, it isn’t a statistical reality but just an industry convention.
Motorola was the pioneer of Six Sigma methodology worldwide. They have made empirical studies about the processes that they have improved and concluded that a 1.5 sigma shift occurs. While many statisticians have called this 1.5 sigma shift arbitrary, the industry wants to go the Motorola Way and 3.4 defects per million which define 4.5 Sigma have become an industry wide accepted definition of a Six Sigma process.
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