Definition of Error:« Back to Glossary Index
If you are looking to have a Sigma-level organization, it is important to understand errors and how to avoid them.
Overview: What is an error?
An error is most easily described, in work applications, as a deviation from accuracy. In order for a company to reach desired Sigma levels, its processes need to not exceed a defined number of errors per million opportunities.
3 drawbacks of errors
There are major drawbacks for your company in having too many errors. Here are just a few:
1. Customer dissatisfaction
If there are too many errors in your processes, it is only a matter of time before that affects your relationships with your customer base. Too many errors can cause your customers to receive products or services in an untimely manner, receive defective merchandise, and eventually erode their trust in you.
2. High costs
Dealing with errors can be time-consuming and costly. It takes a lot of resources to regularly offset a high number of errors.
3. Sigma advancement
If you are trying to have your company reach Sigma performance levels, having too many errors will prevent this from being able to happen.
Why are errors important to understand?
For the following reasons, it is absolutely vital to understand errors:
1. Being able to prevent them
If you have an understanding of the potential errors in your processes, you are more likely to be able to prevent them.
Knowing what errors to look out for, gives you the ability to properly train staff to adequately monitor for them.
3. Continuous improvement
Knowing how to recognize the errors in your processes allows you to root them out and continually improve your processes.
An industry example of errors
For a company to reach Sigma Performance Levels, they need to not exceed a certain number of errors or defects per million opportunities. This is expressed as DPMO. For example, to reach Sigma Level 1, an organization can have 691,462 errors per million opportunities. This works out to a yield of 30.85% delivered or produced correctly. It is then a sizeable jump to Sigma Level 2, which requires a DPMO of 308,538. This works out to a percentage of 69.146 delivered or produced correctly. Sigma Level 3 requires a DPMO of 66,807, Sigma Level 4 is a DPMO of 6,210, and Sigma Level 5 is a DPMO of 233 with a percentage of 99.9767 delivered or produced correctly. In order for a company to reach Sigma Level 6, it would take a DPMO of 3.4, which brings the percentage of correctly produced or delivered to 99.9997%.
3 best practices when thinking about errors
Here are some fundamental practices to keep in mind when it comes to errors:
1. Some errors are unavoidable
There are some types of errors that are likely unavoidable and unfixable without completely uprooting your processes. As long as you handle the ones you can actually control, the unavoidable ones may not need short-term addressing.
2. Learn from your mistakes
Do everything you can to avoid having the same errors over and over again. Make adjustments, within reason, so that you are not repeating mistakes.
Prioritize addressing your errors. Manage the ones that are causing the most significant issues first.
Frequently Asked Questions (FAQ) about errors
1. What are some common processing errors?
Some common types of processing errors are sequencing errors, data errors, communication errors, and messaging errors.
2. Are all errors equally an issue?
All errors are not created equally. One popular example is that customers would much rather tolerate a flight delay than an incident upon landing.
3. What is an error in statistics?
In statistics, an error refers to the estimated difference between the observed/calculated value of a quantity with its true value.
Errors in your business
You are going to encounter errors in your processes. It is just part of doing business. What is important is being able to address the errors that you can control and strive for continuous improvement. With diligence and dedication to excellence, you will be able to cut down significantly on errors.« Back to Dictionary Index