Characteristics of Data – Central Tendency and Dispersion
February 12, 2025
Total Quality management refers to a continuous effort of management along with the employees of a particular organization to improve the quality of products and services. Businesses need to emphasize on quality of their products rather than quantity to survive the fierce competition. Remember in today’s scenario, there is no dearth of competitors in the […]
A large number of developing countries across the world are dependent upon commodities. About 135 of these developing countries were surveyed, and it turned out that more than 94 of these countries are commodity dependent. This means that primary commodities like food grains, oil, etc. account for more than 60% of their total exports. Prima […]
Global Capital, Local Resistance The previous articles in this module discussed how international businesses expand into overseas markets both for selling their products as well as to extract the resources in mineral rich countries. We discussed how the international expansion of these businesses has to equitable and follow the rules of justice. What happens when […]
Capability Maturity Models When organizations want to be certified for their quality or operational excellence, they usually turn to quality frameworks like Six Sigma, Kaizen, or TQM. These are just representative of the different quality and operational excellence models and there are many other frameworks as well. Similarly, there are process capability models like the […]
Converting Data to Information: The goal of a six sigma project is not to produce an overwhelming amount of data that ends up intimidating the concerned people. The goal is to find out as much data as possible and convert it into meaningful information that can be used by the concerned personnel to make meaningful […]
The purpose of measurement system is to validate the measurements before they are considered as factual data and used as a basis for decision making. Any experienced Six Sigma executive knows the reality that the measurement system is nowhere nearly as good as you think it is. People who conduct the measurement system analysis for the first time are surprised to find that it is not rare to find measurements that are 30% or more off the mark in the organizations measurement system. Here is a review of the causes that lead to such widespread variations:
People: Probably the single most important cause of measurement system variation is the people involved. If measurements need to be taken repetitively, it becomes a monotonous task and sometimes errors are made. Also, many times employees wilfully avoid work and fudge the measurement numbers. A system where people were entrusted the job of taking measurements is a thing of the past. Quality guru Juran has made it explicit that measurements related to process efficiency must be collected by non-human means.
Equipment: Faulty equipment is another major factor which result in getting inaccurate measurements. It is not uncommon to find machines that are worn out and/or not properly calibrated. This is true particularly at the extreme ends of the operating range of the machine. Regular maintenance and calibration of the machines is essential. If the measurements are critical to the functioning of the process, the latest and the most high tech measurement systems need to be installed. If we get what we measure, we must measure in the best possible way, isn’t it!
Computational Complexity: This reason is closely linked to the human cause of measurement systems error. However, here the error is not caused by negligence or wilful manipulation. Sometimes, complex metrics are designed. However, the design of this metric is not correctly communicated to the concerned people. Hence we get faulty software tools and/or operators with incorrect knowledge.
Lack of Standard Procedures: It is important that the important metrics of the measurement systems be defined. Such a definition should be communicated to every person that needs to know it. The definition must be reached by consensus and the procedure to arrive at the numerical value of the metric must be explicitly stated inside a manual. Failure to do so can lead to different people having different notions about the same metric. Hence, at a systemic level, you could even end up comparing apples to oranges, if you are not careful enough!
Your email address will not be published. Required fields are marked *