The Third Principle of SPC is extension of the second one. In the last post on Second Principle of SPC, I mentioned that we notice a Feature if the measurements of the output are analysed.
If we want to see the pattern, all we need to do is plot the individual data points or the measures taken onto a tally form. we will see definite pattern begin to form after several measurements are plotted.
An easy way to demonstrate this is to roll a pair of dice about 50 times or more and record them on a tally sheet. the pattern we see is a frequency distribution.
We can make a frequency distribution curve by enclosing the tally marks in a curved line. The curve you see will have more measurements at the middle and fewer as we go away from middle. It can be seen that the curve looks like a bell.
Whenver one takes a group of measurements, a frequency distribution curve appears.
This would be explaind by the fourth basic principle of Statistical Process Control (SPC)
Originally posted 2012-03-03 01:01:00.
We have already discussed about the same thing done by us giving different output in the first principle of SPC. The second principle is based on the first principle and states that the variation in the process can be measured.
Some Variation is always inherent to our job and this is acceptable to some extent so far as the variation is within the Tolerance. However, the Variation tends to increase over a period of time. We need to measure and monitor our job to see that the variation is well within the normal expectations. If we donot make an effort to do so, we land up in trouble and the consequences add to the costs.
Even though it is always desirable to Measure the output of a process, it becomes necessary to measure the output of the process or operation to know when the trouble is brewing.
The measurements can be on the characteristics of the output. It can be the Continuous Variables dimensions, or attribute Variables like colour, shape, finish etc.
After collecting the information as described above, we must analyse to see if things are OK. When we check the output of the feature, we will quickly notice a Feature. This feature noticed is the basis of third principle of Statistical Process control – Things Vary according to a definite pattern
Originally posted 2012-02-28 01:36:00.
From the past experience of many generations, we can clearly understand that things are never exactly
alike. All you can find is two similar
things. Even the “peas of pod” which look alike, may show some differences among them when we have a closer look. The peas are different in size, shape, colour, softness, or some thing else.
If we apply this to a product, say some products, parts or components, we know that no two manufactured parts are exactly
alike each other. In one way or the other, these parts will be slightly different in Size, Shape, or finish.
If two parts are looking alike, the differences can be found if the resolution of the measurement. The more precise you are in measuring, the differences are more clearly understood.
This is a basic problem, which will get us into trouble for making parts interchangeable, which the main aim of mass production. To work around the problem, we use tolerances.
However, our aim is to keep the variation between the parts to be minimum and as small as possible.
The above discussion is the first principle of SPC. Based on the above discussion, we can go to the second principle of Statistical Process Control (SPC) – Variation in a process or product can be measured.
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Originally posted 2012-02-28 01:12:00.
The six principles below are the Basic Principles of Statistical Process Control (SPC). These can be clearly understood using Frequency Distributions.
The principles are listed below. The explanation is linked to each sentence
- No two things are exactly alike
- Variation in a product or process can be measured
- Things Vary according to a definite pattern
- Whenever things of the same kind are measured, a large group of the measurements will tend to cluster around the middle.
- It is possible to determine the shape of the distribution curve for the measured Output (Parts produced, transaction) by any process
- Variation is due to assignable causes tend to distort the Normal distribution Curve.
To Understand more on these principles, we can study the data from the output of the process. These foundation principles will be useful for all types of processes.
Originally posted 2012-02-27 01:53:00.
Leaders of team responsible for measuring, analyzing, improving and controlling key processes that influence customer satisfaction and/or productivity growth. Black Belts are full-time positions.
Similar to Black Belt but not a full-time position.
Master Black Belt
First and foremost teachers. They also review and mentor Black Belts. Selection criteria for Master Black Belts are quantitative skills and the ability to teach and mentor. Master Black Belts are full-time positions.
Any internal or external person/organization who receives the output (product or service) of the process; understanding the impact of the process on both internal and external customers is key to process management.
Process owners are exactly as the name sounds – they are the responsible individuals for a specific process. For instance, in the legal department there is usually one person in charge – maybe the VP of Legal – that’s the process owner. There may be a Director of Marketing at your property – that’s the process owner for marketing, and for the Check-in process, the process owner is typically the Front Office Manager.
For DMAIC projects, the team leader is usually the Black Belt. For Quick Hit and iDMAIC projects, it is typically the Sponsor or Process Owner. For large DMAIC projects with more than one BB or MBB, the Team leader is the main point of contact for the project.
An active member of a Six Sigma Project team (DMAIC or iDMAIC),heavily involved in the measurement, analysis and improvement of a process. To be effective, team memberships require a minimum of 10% time commitment to a phase of the project. He/she also helps fosters the Six Sigma culture within the organization by informing /educating fellow Associates about Six Sigma tools and processes.
Transfer Team Leader (Process Owner/Department Head)
A person selected by the GM and property SIXSIGMA Council to lead an iDMAIC project based primarily on proximity and decision-making authority relative to the process involved. This person has primary responsibility for implementing the project, leading the team, and interacting with others to gather information and understanding necessary to succeed. Often, the transfer team leader will be the department head or process owner of the process being improved with the best practice. The ability to lead the team and to anticipate clear barriers are important characteristics for a person in this role.
Transfer Team Member
Associates selected by the Transfer Team Leader and Six Sigma Council to serve on the iDMAIC project based on their knowledge of key aspects of the process, experience with the current process, enthusiasm for improvement, and ability to champion change. Other key factors in selecting transfer team members include time availability and representation from relevant functions. All members will be provided training on the skills and tools used in the transfer process.
This member of the executive committee is a strong advocate of the project and can assist with barriers that may come up. He or she is accountable for the project’s success and can therefore explain to Six Sigma Council members and everyone in the property the business rationale for the transfer project and assist with cross-functional collaboration efforts. He or she will remain up to date on key aspects of the project by regularly meeting with the team leader and members.
The project sponsor:
- Is a member of the Executive committee
- Is accountable for project success
- Addresses cross-functional or other barriers
- Reviews and tracks progress with team leader
- Advocates for necessary resources
Originally posted 2012-02-26 03:50:00.
Tabulation of data is the most commonly used method for presentation or organization of data. The table has distinct features which are explained here. The data collected by using various methods like Surveys, Interviews, In-field studies etc will give you the raw data. You may not be able to draw any conclusions using this data. This data need to be organized and then only it can talk(yes. the data talks if it is collected properly) to you. Also this data will talk for you only if your present it in such a way that the user can receive.
There are various methods by which you can organize and present the data.The selection of the method depends upon the purpose and the target audience.
The most commonly used method is tabulation. Normally we see many tables daily. However, if your presentation or analysis is to be relevant, the table shall contain the relevent details.
A good table is used to condense the data and present in a useful form. It is the most common method and easily understood method of presenting data.
A good table will have the following details.
- The Table must have a heading.
- The Table should present the data clearly, highlighting important details.
- The Table should save space but attractively designed.
- The table number and title of the table should be given.
- Row and Column headings must explain the figures therein.
- Averages or percentages used in the table should be close to the data.
- Units of the measurement should be clearly stated along the titles or headings.
- Abbreviations and symbols should be avoided as far as possible.Sources of the data should be given at the bottom of the data.
- In case irregularities creep in table or any feature is not sufficiently explained, references and foot notes must be given.The rounding of figures should be unbiased.
- Wherever notes is required, they should be given below the table with relevent references.
If a table contains all the above, it can be said a good table. Even though these are present is the normal table, the classifications is good to verify if the table is good or not.
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Originally posted 2012-01-23 02:14:00.
Control Charts is a running record of the process performance. It is a recod of results of periodic sampling inspections.
A chart becomes a control chart when it has control limits based on inherent process variation. Process control limits are boundaries on a control chart within which the process can operate to a standard. These limits are based on natural variation of the process without the influence of assignable causes. each time the job is checked, the results are compared with the control limits. If the results are within the control limits, then the process is to be left alone. But if a point on a control chart falls outside the control limits, or any other indications of an out of control process, it indicates that there is some change which is happened and the process in no lnger operating normally.
In other words, Control Limits are warning singnals that tell us
1. When to take action
2. When to leave the process alone.
Taking action on a process operating within control limits is not only eneconomcal but may also increase the variation.
There are two general types of Control Charts
1. Variables Chart – This type of chart is used where a dimentsion of a charecteristin is meaeasured and the result is a value.
Popularly used Variables charts are
X-Bar – R Charts
X-Bar – S – Charts
2. Attributes Chart. – This type of chart is used where a product quality is assessed by sensory means or the data is in terms of count of defectives of count of defects.
Teh popularly used Attributes Charts are
In addition to above there are some adapations to the control charts which are a combination of the above. These are called Special Contorl Charts which will be discussed later.
Originally posted 2011-12-04 05:40:00.