On the Charts: A Conversation with David Laney – Minitab.
This is an excellent discussion on the new P’ and U’ control charts. Not just new but also improved!
On the Charts: A Conversation with David Laney – Minitab.
This is an excellent discussion on the new P’ and U’ control charts. Not just new but also improved!
The Seven Basic QC (Quality Control) Tools are a given set of graphical techniques identified as being helpful in troubleshooting issues related to quality[1]. These seven are called basic because they can be used easily by anyone to solve the vast majority of quality-related issues. Many quality professional believe these were originated by Dr. Ishikawa, a world renowned quality professional. But, he would tell you that he was inspired by the “Seven Famous Weapons of Benkei[2] . The designation as the “Seven Basic Tools of Quality” arose in postwarJapan.
These diagrams are tools that organize a group or persons knowledge about the causes of a problem or issue and display the information graphically.
It was originally created and used by Dr. Kaoru Ishikawa and is sometimes called an Ishikawa Diagram. Also, because of its shape it is called a Fishbone Diagram. In general what you do is brainstorm ideas (causes) then group them in to categories. Those categories become the many branches of the Cause and Effect diagram.
This is another simple but powerful tool. Check Sheets are lists of items and the frequency that the item occurs. They can be made in so many different ways that many times, we don’t think of them as a list, but they are. below are two, one that kind of looks like a list the other not so much. On the shoe the defects are marked with an “x” in the location it was found.
They are use to answer many important questions such as:
They are often used to remind individuals doing complex tasks of what to do and in what order. They are also used many times in conjunction with other tools to help quantify or validate information.
Control charts are the most difficult of the seven tools to use. They are seldom the method of choice. When a process step is important, we would prefer that the step not vary at all. ONLY when this can not be accomplished in an economical way does one choose to use a control chart. Below is an “XBar-R Chart” also called an “Average and Range Control Chart”.
Control charts are only useful if the step (operation or function), over time, exhibits measurable random variation. Control charts display the data over time (Time is on the x axis above listed as sample). Control Limits (the red lines) are displayed on control charts, where data falling within the control limits are considered “normal” variation. Any point outside the control limits are considered “special caused” variation and need to be look at and corrected through an action plan. If you create a control chart, you must also have with it an action plan.
Besides control limits for control charts, there are several other type of trends (runs) that can indicate an out-of-control process.
What I have shown above is only one type a control chart and one of the simplest to use but there are several others (not so simple to use). Below is a Decision Tree Diagram of the different type and there use. Be sure you understand the application of each control chart or get help if you plan to use one of these.
Histograms are a “picture” of a set of data (or information). It is created by grouping the data you collect in to “Cells” or “Bins” (Bars in the chart below).
Histograms take your data and give it a shape (Distribution). With this, you can see the data sets spread, central tendencies, and if it meets requirements. As you can see, it is a valuable troubleshooting tool. You can take it a compare differences between machines, people, suppliers etc. Never use a histogram alone always also plot it in a time ordered plot (run chart).
Pareto Charts are a specialized Histogram of count data. It arranges the Bins or Cells in largest to smallest counts and gives you an accumulation line as seen below.
The Pareto Chart gets its name from the use of the Pareto Principle which states “ 80% of the effect comes from 20% of the causes”. Vilfredo Pareto, an Italian economist, originated this principle by determining that 80% of the land inItalyis owned by 20% of the population. Later it was found to hold true in many things and help us focus on the critical few. With a chart like this a team can decide where to place its priority and focus ( the big hitters). This is extremely helpful when time and money is limited as it is in most cases.
Scatter plot are a very simple tool to use to see if there is a correlation between two things (i.e. does one thing lead to another). I always before going into any major analysis of data, plot the data in some way to get a “gut feel” of what is happening. This tool lets you create a simple picture showing how two or more variables change “together”.
As one can see in the chart above the fruit on the tree increase in weight the longer it is on the tree. In scatter charts we see if one thing relates (correlates) with another. Below is a set of chart that shows some of the relationships you might find with this tool.
To me Stratification is a catch-all for summarizing, picturing, or applying some tool to data so you can understand what is happening. Stratification is the process of dividing members of a population into homogeneous subgroups before using it. The data (strata) should be mutually exclusive: every element in the population must be assigned to only one subgroup (stratum). The data should also be collectively exhaustive: no population element (data) can be excluded.
That’s a mouthful, but if you look at above six tools all of them do this stratification of the data. In many texts they list either flow charts or run charts under this seventh tool area. A run chart is just the “Individuals Chart” of the above control chart without control limits. A flow chart takes a group of steps in a process and summaries them into a map of the way the process works. They are sometimes called a Process Map or a Process Flow Map.
They are created to:
Well there you have a short description of the Seven Basic Quality Tools. Stay in touch as I go into each tool with details of how to construct and interpret them. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.
Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090
[1] Montgomery, Douglas (2005). Introduction to Statistical Quality Control. Hoboken, New Jersey: John Wiley & Sons, Inc.. pp. 148. ISBN 9780471656319. OCLC 56729567.
[2] Ishikawa, Kaoru (1990), Introduction to Quality Control (1 ed.), Tokyo: 3A Corp, p. 98, ISBN 9784906224616, OCLC 23372992
In an earlier article, “Creating Customer Value” I explained that to insure you are creating customer value at any given step or your process you need to ask and answer three questions with a yes. They are:
Well in those three questions is one from the customers view point “Does the customer care about this change?” Many time we do not really understand or see this view (customer cares about) clearly. We might say for them to care about something that they must understand what they want. Well let try to set the record straight on this one.
First, I’d like to start with my definition of customer value (Want):
Customer value is a product or service that is received by the customer at the right time, place, cost and functions AS DEFINED BY THE CUSTOMER.
It should be noted that time, place, and cost are all parts of your delivery process (which we try to streamline) and only function addresses the actual product or service once in hand of the customer. In the titles question many times we only focus on this “function” but if we do, we miss three other major parts of customer value and may loose the customer because of that narrow sightedness. So DO NOT FORGET the other parts of customer value.
But now let’s talk about this “function” in terms of what the customer wants. I have had discussions with some colleagues that will hold fast; that the customer DOES NOT always know what they want. And, I can not fault them on it when it come to the exact details of what they want. A good example I was given was my colleague said his wife’s birthday was coming up and he had no idea what to get her. I believe him, I have the same problem but some how he and I both get something they like. How does that happen? I think it because we don’t know the details but we do have some more global thoughts (even if they are what NOT to get her). So in reality we do have some, even though vague, ideas of what to get. And those thought will lead us to some places that we think we can find that present.
For instance, my wife love to help in the remodeling of the house, but it will not be “my friendly hardware store” that I will go to purchase her present. No I’ll, and so might my colleagues, go to stores that my wife goes and buys things for herself. I do know what she likes and dislikes as I see what she purchases at these stores. Plus with a little help, I hope, for the store personnel, I can find something that will be just the right thing. It usually works well.
So does the customer always know what they want? I say a BIG YES!! Maybe not the details. But if I walk into your place a business there was a reason and your sales persons will need to understand that and work with me to fill in the details or I will probably go somewhere where I will find the help.
Well there you have it. Customers do know what they want even if it is some what vague. So I hope you are listening when they show up. There are other articles on Customer value that you can find on my blog http://www.sixsigmatrainingconsulting.com/knowledgebase/ . As always, if you have any questions feel free to contact me.
Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
1.520.829.0090
In Six Sigma, we are very focused on the Voice of the Customer and creating Value for the customer. But getting our arms around this thing value is not real easy. In fact, I believe that it is this constant changing of what is of value that keeps all Quality folks employed. You see, over time, customers change and what they think is of value changes as well.
Thirty years ago, if you wanted to send someone a message most of the time you would mail them a letter. If it was really important, you could fax or telegram them. But today we have Email, Twitter, and Face book. I am not sure anyone really writes letter today. So here, you see a change in what is of value to “customer” (at least customers of the post office). In today’s market, the Post office continues to raise rates to cover costs. There is a think called the Kano Model that explains this very well.
The Kano model is a chart with that has two axis and three levels of quality or characteristics.
The two axis are Customer Satisfaction (this is their perception of satisfaction) and Customer Expectation (this is the reality of how well the expectation was met [usually in a percentage]). Some have labeled Customer Satisfaction as Quality.
Customer Satisfaction – This axis runs vertical with the top end of the axis (scale) being extremely satisfied and the bottom of the axis being extremely dissatisfied.
Customer Expectation – This axis runs horizontal with the left end of the axis (scale) being 0% expectations met and the right end of the axis being 100% of the expectations being met.
Note: the two axis cross dead center of each line.
The three levels of quality or characteristics are Must (Basic Quality), Wants (Expected Quality) and WOW (Exciting Quality)
Must (Basic Quality; Dissatisfiers) – These are characteristics do not sale a product but the customer assumes they are there. These are things like brakes, windows and tires on a car. Customers expect them to be there and will walk if they are not. But they are not on the list of things (specifications) customer walk in looking for in a product. You will note that in the Kano Model (fig. 1 below) the MUST curve lies totally below the Customer Expectation axis line representing dissatisfaction. This means providing must characteristics alone will not satisfy the customer.
Wants (Expected Quality; Satisfiers) – These characteristics are what the customer wants to see. Here the customer has come in specifically looking for these. With items that are more complex the customer has a list, specification, or drawing that includes all of these characteristics. Examples of these are a particular color, and multi-year warranty, or a short wait time. Customer usually will use these to decide to buy or not. In the Kano Model, these characteristics (Wants) are a straight line. Where it shows the customer is dissatisfied if there Wants are not met. But their satisfaction increases as more of these characteristics are met.
WOW (Exciting Quality; Delighters) – These characteristics are sale the product if all the others are met. These are characteristics that are above and beyond the customers expectations. Here the customer receives more than they expected. Examples of these characteristics are: collision avoidance systems, life time warranties, and free upgrades for life. In the Kano Model, the curve for the WOW characteristics is completed above the customer expectation axis.
Figure 1: Kano Model
You will notice another line in this model in the upper right hand corner labeled “Competitive Push”. This is what represents the “ever changing customer voice”. You see things that WOW, delight and are unexpected today will be wanted and expected tomorrow (in the near future) and become must have and basic requirements further into the future. Things never stand still. Having a Desktop Computer instead of a mainframe terminal was a WOW in the eighties. In the nineties Desktops were wanted/expected and the Laptop was a WOW. Now Desktops are Musts with Laptops a Want and the IPAD the WOW. Who no’s what is next, but I can bet someone is coming up with that next WOW that will push the Desktop off the chart just like the wire dialup phone and the pay phone booth.
This model gives us an idea of how customer’s expectation (value) is constantly changing. One they see something they like most likely someone will make it affordable for that customer and soon. Who know some day we will all have a spacecraft in our garages and there will no longer be a need for streets. What a confusing airspace we will have. Oh well expectations will keep changing and those in the quality profession will constantly be watching for those shifts in customer expectations.
Well there you have my thoughts on the ever changing voice of the customer and the Kano Model. I hope this helps you with your projects' focus on customer value and where it might have moved.
Oh, think what would happen if where you worked moved its product focus to a different industry, group or customer set. What happens to the model now?? Most likely, all the characteristics would still exist, but the customers expectation of each may change dramatically. Wow’s, What’s and Must’s could be totally reshuffled.
If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.
Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090
In Six Sigma we are always wanting to be able to show a return on investment to management. Most of the time in Dollars. To do that we need to consider both the cost and the benefits the project will obtain. But costs and benefits do not happen in one lump sum. Usually they fluctuate over time. This is referred to as a cash flow or a cash flow stream. The best tool to look at both the cost and benefits to see what the return would be is calculation what is called “Net Present Value” (NPV).
Definition of Net Present Value (NPV): The difference between the present value of cash inflows and the present value of cash outflows. NPV is used in budgeting to analyze the profitability of an investment or project[1].
In Excel there is a function [NPV(rate,value1,value2, ...)] to calculate NPV, but this formula can be misleading if you do not understand what it is doing. The function help on this function explains it all be rarely do we read these unless we really don’t know what goes where. In this functions case most people need to read it. In the above formula the rate is applied to every value listed. That is OK but in many projects the first value is costs/benefits obtained before the end of the first year (or period of the rate) so the rate should not be applied. That why in many projects we list the first year as “year 0”. These cost/benefits should be added to the above formula thus eliminating the rate from being applied.
Example: Lets say you have a project that will cost you $10,000 this year and $2000 next year to implement, but next year you will see a benefit of $500 and the year after that 5,000, Then in the third year $10,000 benefit and in the last two years $15,000 each year in benefits. Lets also say management want to see a rate of return of 10% over a five year period. You want to know if you can meet that with these figures. Below is the table in Excel I would create to show these figures and calculate the NPV.
You can see that the formula calculating NPV has year 0 being added to the formula so the interest rate is not applied to calculating the present value of it this year (year 0 is the present). But year 1-5 we did have the rate applied as we what to make sure that the expected rate of return is met by year five. This show we have meet managements expectations because it is a positive number.
The reason for doing this is because not all investments have cost/benefits in year 0. Take for instance the purchase of a new piece of equipment that the purchase price is not paid for 12 months. In this case year 0 maybe to delivery and setup of the machine which is included in the cost of $12000. Let say on this example we have the same benefits and expected return. Here is the table and calculations for this one:
You can see that this can be big if you do it wrong, so make sure that you apply this formula correctly. In most, not all, Six Sigma projects you will use the first way. But if you make sure to include always a year 0 and put it in the formula even the lower table will be correct using “=NPV(D2,D4;D8) + D3”.
Well there you have what to look out for in using the NPV formula in Excel. If you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.
Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090
[1] http://www.investopedia.com/terms/n/npv.asp#axzz1PrAMAMip
| Peter L. Bersbach | Philip R. Wahl | |
| Quality Engineering | Quality Engineering | |
| GM Hughes Electronics | GM Hughes Electronics | |
| Tucson, Arizona | El Segundo, California |
I wrote this and presented this at the 1990 ASQ Quality Congress but thought some may find it interesting and informative.
This paper describes a real life application of Quality Function Deployment (QFD) to a factory of the future in the Aerospace and Defense industry. The factory of the future is a proposed high rate low cost microwave hybrid manufacturing facility. It is felt that without the application of QFD the facility will never achieve its goal of high rate and low cost. More a diary than a historical account, this paper describes an application that is still in progress. The completion of the project is planned for 1992.
Besides the goals of high rates and low cost mentioned above, several other reasons for the choice of QFD as a tool for managing this project will be covered. Some examples are: customer requirements, better company communications, and the integration of customer requirements into production requirements.
This paper discusses the resources required by QFD on this project. These resources include personnel needed for cross departmental teams; personnel needed for formal and informal facilitation and training; time spent in training and team meetings; and other costs such as software, tools, and equipment required to implement process controls.
A description of the QFD tools (matrices) employed and how they were employed, as well as, why certain QFD tools were not used and why others were modified to better fit the situation is discussed. This project incorporated a mix of the ASI basic waterfall of four basic, but large, matrices and the GOAL/QPC approach of 32 smaller matrices.
An in-depth look into the obstacles encountered will be presented. Included will be the understanding and overcoming of any resistance to this new approach by management, as well as the removal of any barriers found between departments. The obstacle of ignorance toward QFD is also discussed, with the approaches used to educate all of its advantages explained.
Finally, this paper provides the complete description of the results achieved. This includes the accomplishments, failures,
and the current status of the project. Also it will look at the future plans and goals that the project has and will try to project a completion date when the development of new matrices will essentially stop and the maintenance of the old ones begin.
In October of 1988, a team of engineers from Hughes Aircraft, Delco Electronics, General Electric, AT&T, and Pacific Monolithics, was formed to write a proposal for a factory of the future. The Air Force had requested bids from several companies to assemble a manufacturing process capable of building Microwave Modules (Hybrids) at a rate of at least 1000 Modules a day and at a cost of less than $400 each. The intent was to use this module in a low cost active array radar system that was as good as or better than the current radar systems found in today's aircraft.
Hughes calculated it could meet the Air Force challenge by using the right mix of technology and people. A proposal team of experts from all areas of engineering and manufacturing, including a group of quality engineers was pulled together. From the start, the program management understood the need to design and build quality into the product. In our case management decided to get involved and stay involved supplying momentum to this quality effort throughout the program. They felt that a new management approach was needed to obtain high rates, low cost, and maintain the product quality and reliability. Management believed that by insuring quality in the product, rework and scrap would be reduced or eliminated, which would cause yields to increase and reduce costs to our customers. This new approach required quality involvement at every stage of the program. Involvement at the proposal writing stage, to designing of the module, to high rate production of the final product. The quality team introduced the rest of the staff to Quality Function Deployment (QFD) .
The Hughes team was one of the teams awarded a contract to develop this module production line. The project is now in Phase 1 and 2 of 4, the Module Design for Affordability Manufacturing Phase. Currently there are ten QFD teams developing matrices, with several more teams anticipated before the completion of Phase 4.
There were three reasons for using QFD on the project: customer requirements, improved communications, and better integration of customer requirements into production requirements. The customer did not specifically call out QFD, but his request for quotes placed a heavy interest in the implementation of a Total Quality Management (TQM) System.
The Department of Defense is restructuring its management system toward a TQM system. Project management envisioned that a team of quality experts could pull together the best tools for the TQM system. The TQM system would require process controls with little or no formal inspection of the product. Convincing the Air Force that the process controlled the product quality and that we had control of the process would be critical.
In addition, the product needed to be characterized by a high rate process that could be manufactured at a low cost. Both of these require designing quality into the product. Designers now realized they had another customer, process or manufacturing engineering. To address these diverse customer requirements and not lose sight of the external customer needs, QFD became a major element of the TQM system.
The second reason for QFD -communications -became apparent right at the start of writing the proposal. We, the internal customers, were transmitting complex information (Whats) and ideas (Hows) thousands of miles, from Delco in Indiana, to Hughes in California, to General Electric in New York, to AT&T in Pennsylvania, to Pacific Monolithics in California, to Hughes in Arizona, and then back again. A tool was needed to keep the team focused and on track with the customers needs. Team members familiar with QFD knew that QFD could fill this need. The matrix structure of QFD allowed each team member to bring his or her expertise to the table and have direct input into the design and manufacture of the module. Everyone's contributions were recorded on the matrix and reviewed by the team through this process, lines of communication were clearly established and exploited. Once completed the matrix represented the best of all areas that met both the internal and external customer needs.
The final reason for QFD is better integration of customer requirements into production requirements. QFD requires teamwork and simultaneous engineering since each company, group, and department involved in building the product is another smaller, but just as important; customer. As customers they have wants (or Whats) that need to be filled by their suppliers. Design engineering can always design a product that the external customer wants but if manufacturing cannot produce it, in volume and at a low cost, then the company will fail in its attempt to produce the product. In our case we already had a working product, but it was too costly and volumes were too low to meet our external customer requirements. Some of this excess cost and time was due to old technology, but most was due to a lack of understanding of the internal customers (other companies, groups, and departments) requirements. Through teamwork and simultaneous engineering each area was represented on the QFD team. Each area had an input into how we were to meet our external customers needs (Whats), as well as that external customer (see Fig. 1) . Included were the difficulties associated with each "How" so that the team could decide the optimal approach for solving and meeting our customers needs.
The primary resource needed was our experts and their time to work on the team activities. The first matrix developed was the "Requirements Matrix" which laid the foundation for development of new teams and matrices. Several of the Requirement Matrix team members were on subsequent matrix teams. In fact, at least one member of the Requirements Matrix team is found on every team developed. This aided in keeping the voice of the customer moving into and through each subsequent matrix. Each team is made up of four to thirteen members. As each team became larger, it was harder for that team to reach a consensus. with the size of each team being restricted, it was extremely important that we selected the right team members. Each team member had to bring to the team an expertise that was unique and important in fulfilling the teams goals. On our Requirements Matrix we had thirteen team members from the areas listed in Table 1.
Each member received four to eight hours of training on QFD before any work started on the matrix development. In fact everyone working on the program was given this training. In some cases, a second day of training was included. On the second day, Taguchi methods of Design of Experiments was covered. A select few went through several weeks of Design of Experiments (DOE) training covering Taguchi, Box, and Shainin methods. This extensive training augmented the QFD process. Once trained, the teams met for two hours twice a week, which often requires the use of other Quality Tools such as DOE, until the initial matrix was completed in about two to three months. Thereafter, the frequency and duration were reduced to an as needed basis. Currently once every two weeks the House of Quality team formally meets to update the matrix. This matrix maintenance will continue for the life of the program. QFD is never completed, it is continuously refined and improved.
| TABLE I: House of Quality Team Composition | |
| Design Engineering | System Engineering |
| Process Engineering | Material Office |
| Test Engineering | Reliability Engineering |
| Contract Office | Engineering Program Office (2 Individuals) |
| Quality Engineering | Business Office |
| Manufacturing Engineering | QFD Facilitator (Quality) |
The program TQM (QFD) facilitators were trained in two basic approaches to QFD. The two were the American Standards Institute (ASI) approach, which uses a few large matrices, and the GOAL/QPC approach, which uses several smaller matrices (see Fig. 2) . Adhering to a principle stated by Bob King (President of GOAL/QPC) , we used "the pieces" of both methods that worked best for us. Our method employed a foundation of one small matrix (the Requirements Matrix) .The matrix was developed to gain the "Voice of the Customer" and determine critical design requirements (see Fig. 3) .Then a functional tree diagram was developed to aid in selecting design concepts and technologies to be used for each function. At this stage, about ten specialized materials matrices were developed. These matrices were used in trading off different design concepts or for developing the processes to be used with the above technology. In retrospect, we trained our teams basically in the ASI approach and used more of a GOAL/QPC approach in the- actual designs.
Initially, ignorance of QFD was the biggest obstacle. Very few of the proposal team members knew what QFD was. Our saving grace was the customer interest in three basic elements.
1. TQM system (understand that TQM includes QFD)
2. The requirement for a low cost module (less than $400.00)
3. The requirement for a high rate manufacturing operation (over 1000 modules per day)
As mentioned before, these three elements of the request for bid sent a direct message to management: control of product quality is mandatory. Thus management supported the quality group efforts for training and implementation of the TQM system which included QFD. without managements support and active participation, QFD had no real chance of being effective.
A second obstacle was the resistance to change that was felt from many areas or departments involved. QFD was new to each area and that usually means resistance to change. Most have found ways of doing their job, and doing it fairly effectively. They understood that things could be improved but unless "others" followed suit, their efforts would be wasted. Management led the way, convincing others that all involved should listen and learn the tools needed to complete their jobs in the best possible way. Management also participated in both the training and design of the matrices. This role modeling had high payoffs. Engineers entered the training with open minds and exited with new tools to help them do their job better. The program, in turn, now had a new way of doing business.
Do we still find resistance? Yes, some. Just when one group thinks it is finished and does not need the QFD matrices, it discovers other ways to improve the process. This means reassessing the QFD matrices to see how the new approach will effect the overall system and the customer needs. There is also resistance by the teams to integrate their jobs with other TQM tools such as DOE and SPC. Although many are using QFD in their jobs, other elements remain to be integrated. There are elements in the lower level QFD's that only a DOE will show the true interaction between. It is a logical step that tends to be missed. Our planned solution is to train the staff sufficiently in DOE to make that logical step, and then do the same for SPC.
To date, the QFD effort has been very successful. All the disciplines involved in the program have been trained on QFD, and over 10 teams (matrices) have been developed or are being worked (see Fig. 4). The QFD process has also been very successful at helping us truly hear and understand the voice of the customer. By clearly and effectively communicating with the customer through the QFD process, we are now better equipped to satisfy his needs. QFD is definitely putting us and our customer in the "win-win" situation. QFD is also fostering teamwork and simultaneous engineering, the benefits of which are always desirable.
Even though we have been very successful there have been failures. Some teams staggered because of unclear problem and goal definitions. It is very important that this definition be done when the team is first organized. An overall lack of experience by our team members and facilitators was another problem. This was overcome largely by the robustness of the QFD process and the creativity and cooperation of the team members. We also discovered that the size of the matrices and the number of team members must be manageable. As the matrices get larger, and the participants increase, evaluating all the relationships becomes overwhelming. Although the size of the matrix varies and must not be reduced only to reduce the workload, a 25 by 25 matrix is very manageable and capable of yielding excellent results.
As mentioned earlier another failure was the lack of integration of all the quality tools into the engineer's daily job. Only further training and time will tell if we fail at this task. However, successful and full integration of all the quality tools into the engineer's daily job will assure quality is being built into the' product the first time.
The program is still in the development stage with a lot of paper being scrapped, but no losses of materials and no unusable production equipment purchased. The manufacturing matrices are being developed with the controls and verification matrices roughed out. By the middle of 1990, most of the QFD matrices should be complete, with only continuous maintenance needed to sustain their impact on the program. The first production part should be coming off the new process and an evaluation of the benefits of the whole TQM system can start. We anticipate that a few adjustments to both the process and the QFD matrices will be made to assure our efforts, and product, reflect our customers needs.
BIBLIOGRAPHY
1.) American Supplier Institute, Quality Function DeDloY!l1ent OFD. Dearborn,MI. ASI Press 1988
2.) King, Bob, Better Designs In Half the Time. First Ed. Methuen,MA: GOAL/QPC, 1987
3.) Re Velle, Jack B., The New Quality Technology. Los Angeles,CA: Hughes Aircraft Company, 1988
LCS; 600-10-991
Well there you have my use of Quality Fuction Deployment and the House of Quaoity. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.
Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090
In reality this is only non-numeric in the sense that it is data we collect about the presence or absence (nominal data) of some characteristic or attribute of an item. Usually we take this data and transform it into a count of the characteristic; like the number of “naughty” or “nice” kids on Santa’s list. Or, more practical to some, the number of red cars going through an intersection; the number of order forms with mistakes in them, etc. These counts are all called nominal scale measurements. This scale of measurement gives us the least amount of information of the four types of measurement scales (Nominal, Ordinal, Interval, Ratio).
The question I’d like to address here is once you have the data how can you compare it to a standard or another collection to determine if there is a significant difference between the two. An example of this is with order forms. Say you made, what you think is an improvement in the way you handle orders but you really want to know if there actually is an improvement. How do you do that? You can use what is called the Chi Square Test.
Chi Square Test is used to evaluate count data presented in 2-dimensional tables (rows and columns) to answers the question: “Do the groups differ with regard to the proportion of items in the categories?” In our order form example we might have these three categories: No Errors, Minor Errors, and Major Errors. We would collect data from these three categories, before and after the improvement.
Lets say before the improvement we had 60% error free, 30% minor errors and 10% major errors. After the improvement we looked at 136 orders and found that 93 were error free, 33 had minor errors, and 36 had major errors.
Our two dimensional table would look like this ( In this table Chi Squared is the value marked X2):
For those who want to calculate the Chi Square value the formula is below:
BUT !!! there is an easier way using Excel formulas. To do this we need to use the “CHITEST”formula in Excel.
So in our example I entered the formula: =CHITEST(Actual Range [new process], Expected Range[old process]) OR =CHITEST(B2:B4,D2:D4)
As you can see this gives us a formula result of 0.0000004152 or 0% [.00004152%]. This says the probability that the new and the old process are the same is 0%. The two processes are different! Looking at the counts you can see the new process improved minor errors but increased major errors. Go back to old process!
Well there you have my thoughts on comparing non-numeric data. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.
Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090
Interval Scale is use when we are measure the differences between observations. Difference between any two successive points is equal. Interval scale numbers that are equally different represent differences of equal magnitude. The zero vale of an interval scale is arbitrary. Often data of this scale are treated as a Ratio scale even if the assumption of equal intervals is incorrect. Some examples of Interval scale data is Calendar time, Voltage and Temperature.
Ratio Scales is like Interval Scale except it has a true zero point. In other words you can have nothing less than zero, on negative values. Some examples of ratio scale data is Time, Distance, weight, Speed, ad Frequency.
Of all the measurement scales of data these two give us the most information about the thing we are studying.
So once we have done this data collection how can we look at these data to see better what we found? Well for this scale there several types of statistical tools we can use, but let me tell you about four of the major ones. These are these four are the t-test, F-test, Correlations, and Multiple regressions.
This test is used to compare and determine differences between the averages or means of two groups of normalized data. It is used for small scale experiments. If you have a large sample you can use want is called a Z test which uses the normal distribution instead of the t distribution. Below is the shape of the normal and two t distributions (sample size =2 and 10) You can see that the t distribution is a good approximation of the normal.
What is done here is you have to calculate an actual “t” value and compare it to a Tabled t value. The Table t values can be found in any statistic book. To calculate the actual ”t” you have to calculate the following formula:
If you have excel and have add the Analysis ToolPak (which is a free download) you can do this comparison using the t-Test: Two Sample Assuming unequal Variances in this add-on.
If the actual is more than the table value then the two means are different.
Where the t test compares the averages or means, the F test is used to compare and determine differences between the variation (distribution spread) of two groups of normalized data.
Like the t test we calculate F and compare it to a tabled value. The formula for calcultating F is:
If you have excel and have add the Analysis ToolPak (which is a free download) you can do this comparison using the F-Test: Two-Sample for Variances in this add-on.
If the actual is more than the table value then the two variances are different.
A Scatter plot is one of the most useful correlation tools available. In a scatter plot all you do is plot one factor against another.
In this chart you can see a direct correlation between the time, in days, of fruit on the tree and its weight.
Multiple regression is the term use to describe a study in which we want to learn more about the relationship between several independent or predictor variables and a dependent or criterion variable. An ANOVA is a type of multiple regression. Here is a simple regression on trying to find the key predictors of engine knock.
Regression Analysis: Knock versus Spark, AFR, Intake, Exhaust
The regression equation is
Knock = 23.8 - 0.296 Spark + 3.19 AFR + 0.359 Intake + 0.0134 Exhaust
Predictor Coef SE Coef T P
Constant 23.815 8.137 2.93 0.019
Spark -0.2965 0.3072 -0.97 0.363
AFR 3.1918 0.2398 13.31 0.000 A P value less than .05 means it
Intake 0.35870 0.07848 4.57 0.002 is a predictor
Exhaust 0.013376 0.005421 2.47 0.039
S = 0.510560 R-Sq = 98.8% R-Sq(adj) = 98.2%
Analysis of Variance
Source DF SS MS F P
Regression 4 170.245 42.561 163.28 0.000
Residual Error 8 2.085 0.261
Total 12 172.331
Source DF Seq SS
Spark 1 84.250
AFR 1 80.029
Intake 1 4.380
Exhaust 1 1.587
Well there you have my thoughts on tools to measure Interval and Ratio Scale Data. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.
Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090
Ordinal data is information that you collect on items that you can rank order some characteristic or attribute. Examples of this type of data scale is the count of food items on a table that taste excellent, good or bad. Another would the count of dress that are very attractive, look OK, or are ugly. You can see with this type of count data you can arrange the counts in order of best to worse. This scale of data gives us more information than Nominal scale but not as much as the other types of measurement scales (Interval, Ratio). Scales are ways we collect data.
So once we have done this data collection how can we look at the data to see better what we found? Well for this scale there two types of correlation tools one can use are Pearson correlation, Chi Square which are some what complicated. But one of the simplest is Spearman’s Rank order correlation. In this correlation you are comparing how two people/inspectors/groups correlate with each other. This will let us know if the two saw things the same way or not. This could be anything like rating several wine, movies, cars, TV’s etc. For example if you had two friend (x and y) rate 5 movies (A, B, C, D, E) from best(1) to worst(5). you would create the below table and chart to compare your friend results and tell if they look at these movies the same.
Well there you have my thoughts on tools to measure the Ordinal Scale. Next time I am going to discuss the different statistical tool used for the Interval scales of measurement. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.
Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090
Just to refresh your mind Nominal Data (Count Data) is information that you collect about the presence or absence of an attribute (characteristic). Like the number of “naughty” or “nice” kids on Santa’s List. Or, more practical to some, the number of red cars going through an intersection; the number of Order forms with mistakes in them. These counts are all, what we call, nominal scale measurements. This scale of measurement gives us the least amount of information of the four types of measurement scales (Nominal, Ordinal, Interval, Ratio). Scales are ways we collect data. For instance here we are counting the occurrence of something which is what is called a nominal scale.
So once we have done this counting how can we look at the data to see better what we found. Well for this scale there are a few good tools.
Percentage (%) – This gives you a feel for of all the things you saw, how many were what you were looking for. For example lets say you sat at an intersection and counted red cars going through that intersection in one hour. And during that hour you saw 300 cars go through that intersection and 30 were Red. That would mean that for that hour 10% of the cars that went through it were red. (30 red cars/300 cars through the intersection*100=10%).
Proportion (1/10, 1 in 10) – This, like percentage, gives you a feel for of all the things you saw, how many were what you were looking for. This gives you one other piece of information and that is out of how many you looked at. This, if you are doing the study for yourself, may not be important, but if you are convincing others with a percentage they may want to know how many in the total count. A good example where I like this best is on the internet when looking at customer ratings (those stars showing you that customers really liked the product. I always want to know how many customers actually rated the product at all. When you see 1 to 5 I am not impressed. But if there was 100 now I feel better about the rating. Remember that 100% liked something out of 1 (1/1) customer is different that 100 (100/100).
Chi-square Test (X2) – There are many times where we want to compare the percentages of items in several different categories. For instance, instead of just red cars we want to collect the number of all cars by color (not just red). It might be, instead of cars, operators, materials, TV channels, Hospitals or any other grouping we might have in mind. In any of these groups your could collect data and place it into different categories (Colors, Sizes, Ratings). The results can be put into what is called a Chi Square Table to answer the question ”Do the groups differ with regard to the proportion of items in the categories?” An example that one could use Chi-square test would be: (The following example is from Narrella(1963) and the Six Sigma Handbook [Pyzdek, 2003]).
Rejects of metal castings were classified by cause of rejection for three different weeks. The question that the Chi-squared test would help answer is: “Does the distribution of rejects differ from week to week?
Well there you have my thoughts on tools to measure the Nominal Scale. Next time I am going to discuss the different statistical tool used for the ordinal scales of measurement. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.
Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090