Albert Sanzari IE-673 Assignment 5

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1 Albert Sanzari IE-673 Assignment 5 1. Quality Function Deployment (QFD) is a method used to make sure customer s needs and wants are a main focus of the product or service s design and production. The customer s wants are required for the method to be carried out, so it becomes the voice of the customer. This helps a company to not produce a product that isn t desired, or provide a service that isn t needed. The structure is in the form of an open house structure with 6 parts all relating to something important that make up the House of Quality (HOQ). 2. The WHAT s in the QFD s HOQ is the matrix that develops the set of customer needs. This involves gather data on the customer s wants and needs. This can be done through focus groups, polling, surveys, questionnaires, and other customer data gathering methods. Once the data is gathered, a good way of sorting the data into categories and importance is to create an affinity diagram (Fig. 1). This is a method of categorizing the data so it is not overwhelming, and to weed out the less important data. A tree diagram is also a good method. Fig The HOW s in QFD s HOQ is the matrix that develops the technical requirements. This is not the design specifications of the product or service. This is where it is decided how the company will meet or surpass the customer s needs found earlier in the WHAT s matrix. Tools that can be used for this are a tree diagram (Fig. 2) or affinity diagram.

2 Fig In the interrelationships between WHAT s and HOW s matrix rankings are used for the strength of the relationships. These rankings, or scales, are usually the numbers 1, 3, and 9. The number 9 is used to show a very strong relationship between a customer s need or want, and how the company will accomplish taking care of it. The number 5 shows a medium shows a medium strength, and the number 1 shows a weak relationship, but it means there still is a relationship between the two. If there were no relationship or connection at all, a blank would be used in the matrix. 5. The technical priorities in the design target matrix are calculated using multiplication of the interrelationship rankings in the Interrelationship Matrix by the overall weighting in the Planning Matrix. These values are added down each column in the Interrelationship Matrix and the total is tallied in the Design Target Matrix. Each calculated total is then divided by the sum of all the calculations to find the percentages of the total priorities. This is used to show greatest need for resources, shown by highest percentage to lowest. 6. Statistical Process Control (SPC) is a tool that helps eliminate variation from special causes in a process. Every process has natural causes variation. SPC helps separate the two so the special causes can be found, and eliminated. Once the variation from special causes can be found and eliminated, the process can maintain consistency. At this point the process can be worked on for improvement. 7. For control charts of variables data, two individual but directly related charts are created. They are the mean and variation of samples over time. The following are steps to create them:

3 a. Determine the sampling size. This requires subgroups from 3 to 10. More for a startup operation. I ll follow the books lead and use 10. b. Collect initial data points of 100 or more, and the process cannot be bothered during this stage. I have created 100 data points below in 10 subgroups. Date x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 Jul Jul Jul Jul Jul Jul Jul Jul Jul Jul c. Calculate the mean (average) of each subgroup. On the following page in yellow. The mathematical step is sum of all data in row/number of data points In the row. Date x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 Sum Mean Jul Jul Jul Jul Jul Jul Jul Jul Jul Jul

4 d. Calculate the data range for each subgroup, below in blue. The range is the highest data point the lowest data point in the rows. Date x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 Sum Mean Range Jul Jul Jul Jul Jul Jul Jul Jul Jul Jul e. Calculate the process average which is the average of the subgroup average. In this case it will /10=501.94, which will be the centerline for the mean chart. f. Calculate average for the subgroup range. In this case it will 45/10=4.5, which will be the centerline for the R chart. g. Use the table of factors, like in the book on page 305, to calculate the Upper and Lower limits for both charts at 3 sigma. These are the control lines for the charts. For the Mean chart the Upper limit is (Average of the subgroup average + A2 (From book chart)* The Range average) = (.31)*4.5= The Lower limit for the mean chart is (.31)*4.5= For the Range chart, the Upper limit is D4 (From book chart)*range mean and Lower limit is D3*Range mean. Upper limit is 1.78*4.5=8.01. The Lower limit is.22*4.5=.99 h. The charts are below.

5 All points are well within, except for one on the range chart that is at the upper limit, but Is still in. 8. For control charts of attributes data, the P-chart is used. This is for anything that can be counted, such as defects of a group, not measurements. It is actually counting the number of anything that pertains to a group, which creates a fraction. An example is how many toy trains of a lot were missing a wheel when finished. The number missing would be a percentage of the entire group, creating the p. The following are steps to create them: a. For this example we will create 10 subgroups of 100 parts each, or 1000 total parts. We will create a chart like the X and R control charts, but this one will just have the subgroup number, number defective, and the percentage of the subgroup that amount defective represents. That formula is p=np/n with p being the percentage, n being the number of parts in the group, and np being the number of defective parts in that group.

6 Date Subgroup np p Jul Jul Jul Jul Jul Jul Jul Jul Jul Jul b. Calculate the process average which is the sum of all defects/total parts. This is the middle line, just like in the mean and range charts. P average=13/1000=.013 c. Now we create the Upper and Lower limits. We are just creating this, so it s treated as trial limits. The formulas are: UCL=p average + (3 Square root (p average*(1- p average))/square root (n) With n being number of parts in the subgroup LCL=p average - (3 Square root (p average*(1- p average))/square root (n) UCL=.013+ (3* Square root (.013*(1-.013))/Square root (100) = LCL=.013-(3* Square root (.013*(1-.013))/Square root (100) = For the lower limit, if negative set as 0. d. Now plot the chart, same as the X bar and R bar charts.

7 9. Continual improvement takes place after the process is in control with SPC. It can be in many areas. The book mentions two in the engineering department. These are concurrent engineering and design-for-manufacture techniques. Concurrent engineering is a method where products are designed and produced at the same time instead of consecutively. It saves development time, and shortens the build time which reduces costs and increases production. Design-for manufacture means what it says. Products are designed to be easier to manufacture. This would lower waste, time and costs, and raise productivity. Another continual improvement mentioned is customer satisfaction. If a company is thinking of its customers first, even in design, it is making the products their customer is looking for. The tools used to go with these ideas are the quality tools mentioned in Chapter 15 which are Pareto charts, flow charts, and cause and effect diagrams, to name few. These are also used to get the processes ready for SPC, so they are important all the way around. 10. The control charts are tools for improvement. They let you know when a process is out of control and to what degree. Once the work is done to eliminate the special cause that was out, the control chart helps guide the improvement. It does this with its guidelines which are the upper and lower control limits and the process average. As improvements are made, these limits will shift. Improvement means they will close in. The more closed in the better. This is less variation, which is the enemy of quality and ultimately good products.