WCM精华之QC Workshop.ppt

1、1,QC Workshop,Dr. H. Yamashina Professor Emeritus, Kyoto University Fellow of RCA (The U.K.) Member of Royal Swedish Academy of Engineering Sciences,19th July, 2007,2,Contents 1. Japans QC History 2. What Is the QC Problem Solving Approach? 3. The QC Viewpoint Vital for QC-Type Problem Solving 4. Qu

2、ality Specification and Process Capability 5. How to Improve Process Capability 6. Quality Assurance of Every Quality Item 7. The QC Seven Step Formula Solving Problems the QC Way 8. The Four Major Factors Influencing Process Capability 9. Processing Point Analysis 10.Diagnostic Table to Check Quali

3、ty Control Key Questions,3,CHAPTER 1,Japans QC History,4,The Development of Japans Quality Control : a Long Journey,Vision, strategy Satisfactions of stakeholders Management R standardize the best working methods, teach the standards, and see that these are observed. Quality is built in via the proc

4、ess, not through inspection. This is what makes it so important to control processes properly. Look beyond the results, reflect on the process that produced them, improve working methods, and raise the quality of work. Analyze the reasons for any shortfalls between targets and results and control th

5、e cause-and-effect system.,84,3.9 Dispersion Control Pay attention to dispersion and identify its causes 3.9.1 What Is Dispersion Control? Data will always be dispersed around a certain value. We must take note of the mean and the dispersion, search out the cause of the dispersion, reduce the disper

6、sion and keep it within acceptable limits, and maintain the process in a stable state. This is dispersion control.,85,The Key Points of Dispersion Control Eliminate deviation from standards. Since data are dispersed about a certain central value, we must identify the following factors:,If there is a

7、ny deviation from the standard, we investigate the causes and take countermeasures. One tool we can use for comparing results with standards is the histogram.,There are two key points in dispersion control, as described below:,86,2. Keep the process in a stable state. Every process contains a large

8、number of factors that create dispersion in the quality of the product. These factors or causes can be classified into the following two types:,Chance causes: dispersion due to chance causes is unavoidable. It arises even when the materials, working methods, and other conditions all conform to stand

9、ards. Assignable causes: dispersion due to assignable causes must not be ignored. It arises for various reasons, such as because the work standards are not being observed or because the standards are inadequate.,To maintain a process in the stable state, we can ignore the chance causes but we must e

10、liminate any assignable causes and take appropriate action to ensure that the same causes do not arise again in the future. Control charts are used to analyze and control processes by classifying dispersion into the kind due to chance causes and the kind due to assignable causes.,87,3.10 Recurrence

11、Prevention Institute radical countermeasures to ensure that the same mistake is not repeated,3.10.1 What is recurrence prevention? Recurrence prevention means identifying the causes of trouble and taking countermeasures against those causes to ensure that they never recur. To achieve this, the follo

12、wing three types of countermeasure must be taken (see Table 3.3).,88,Table 3.3 Countermeasure types,89,Fig. 3.17 Recurrence-Prevention System,Primary countermeasure,Secondary countermeasure,Tertiary countermeasure,Anything which adversely affects subsequent processes Anything which causes actual har

13、m Anything which appears likely to cause harm if it happens again,Why did it occur? Why was it not detected in advance in upstream processes? Delve down to three successive levels to find the root cause,Emergency improvement proposal for problematic product, process or work,Permanent improvement pla

14、ns for process or work containing trouble Plans for improving detection methods in process or phase where problem should really have been detected,Plans for improving working methods or systems which led to trouble,Stopgap countermeasure,Individual recurrence-prevention countermeasure,Clarification

15、of causes,Systematic recurrence-prevention countermeasure,Identification of trouble(problem),90,3.11 Standardization Formulate, observe, and utilize standards 3.11.1 What is standardization? Standardization means setting standards for materials and working methods and putting them into effect. When

16、preparing work standards, it is important to ensure:,That the work procedures are appropriate. That the standards are expressed in specific, concrete terms. That the priorities are clear. That the standards are easily understood and make plentiful use of diagrams and charts.,We should strive to avoi

17、d the “I set standards, you obey them” dichotomy and try to get everybody working together to formulate and observe standards.,91,Operational standards,If operational standards are not well managed,Will not match the existing situations,Will not match the improvement,Yields will decrease, defectives

18、 will increase,There will be a problem for the delivery time,Will lose customers credibility,There will be problems of safety,If operational standards do not exist,Operators carry out their jobs in their own ways,Operational conditions will fluctuate,There will be changes in quality,Variation will i

19、ncrease,Yields will decrease, defectives will increase,There will be change in quality characteristics,Dispersion will become bigger,If standards are not kept,If standards are ignored,Based on the operational standards,Operational management,Training and education,Improve the yields. Reduce the numb

20、er of defectives,Improve skill level,Operational standards are ignored and become merely an existence,Regulations become merely a name,Problems on cost, process, quality,There arise more operational management problems,Fig. 3.9 Influences of operational standards,92,In a workplace, activities for fo

21、rmulating, revising, and observing standards can be developed in accordance with the scheme shown in Fig. 3.10.,93,Fig. 3.10 Standardization flowchart showing key points,Teach standards,Give workers technical training or reallocate to different jobs,Instruct and guide workers to follow standards,Con

22、sider stationing of workers in improved working environment,Improve working methods by introducing error-proofing devices,Workers do not understand or misunderstand standard,Workers lack ability to follow standard,Workers do not appreciate necessity for following standard,Working conditions are inad

23、equate,Easily-mistaken procedure or complicated equipment,Was the standard being followed?,Trouble (nonconforming products, defects, etc.) occurs,Plan and implement emergency countermeasures,Investigate the cause of the trouble,Is there a work standard?,Is the work standard appropriate?,Prepare a wo

24、rk standard and ensure that all workers follow.,Decide where to keep it and rewrite it to suit present work methods,Rewrite it in the form of a specific procedure. Use diagrams to make it easy to understand,Review it from the technical viewpoint,Not in usable form,Hard for workers to understand,Impr

25、actical or does not lead to good results,:Recurrence-prevention countermeasure,No,No,No,Yes,Yes,Yes,1,2,3,4,5,6,7,8,9,94,Work Standards Specifying Key Points,The models to which they apply are clearly specified. They use sketches to facilitate understanding. They itemize the key work points concisel

26、y. They record the values of important characteristics clearly. They specify causes, not results.,95,96,97,98,CHAPTER 4,Quality Specifications and Process Capability,4.1 Quality Specifications and Tolerances 4.2 Process Capability,99,4.1 Quality Specifications and Tolerances The quality we want to i

27、mprove and control is concretely represented by figures showing length, hardness, percentage of defectives, etc. They can be called quality characteristics. There are various factors such as chemical composition, diameters, workers, etc. which can cause the dispersion of the figures. Thus, the requi

28、rement of a quality characteristic is given, by a specific value with its tolerance. There are two categories of values : * Indiscrete (or continuous) values based on measurements eg. 1 yields of a chemical process 2 board weight * Discrete (or enumerated) values based on counting eg. 1 defective ar

29、ticles or the number of defects 2 blisters 3 cocklings,100,4.2 Process Capability,If the quality characteristic is assumed to follow a normal distribution, where 3 includes 99.73 percent of the population, process capability is defined in the following way : Process capability = 3 or 6,Fig. 4.1 Norm

30、al distribution,Process capability,101,Fig. 4.2 Relationship between characteristic distributions and tolerances,102,103,In case where x deviates from the target value, the following Cpk,Fig. 4.3 Cpk and Cmk,104,Fig. 4.4 The rule of design engineers and operators,6m,105,Remember that If Cp 1, the pr

31、ocess is not capable to produce products properly. If Cp = 1, 27 items out of 10,000items are out of tolerances. If Cp About 64 items out of 1,000,000 items are out of tole- rances. With this low level of defective production, the process can be managed.,4,106,Fig. 4.5 Judgment on the process based

32、on the value of Cp,107,CHAPTER 5,How to Improve Process Capability 5.1 Detection of Problems and the Seven QC Tools 5.2 How to Calculate Process Capability : Histogram 5.3 4M Analysis of Process Capability 5.4 How to Improve Process Capability : Cause and Effect Diagram 5.5 Causes of Defective Produ

33、ction and Countermeasures 5.6 How to Detect Changes of a Condition : Control Chart,108,Tab. 5.1 Detection of Problems and 7 QC Tools,Pareto diagram, check sheet, graph Histogram, control chart, check sheet cause and effect diagram, stratification, check sheet Scatter diagram Stratification,1. Find o

34、ut important points(problems) 2. Grasp the current situations 3. Analyze current situations 4. Check the correlation 5. Narrow the problems,7 QC tools,Steps of problem detection,5.1 Detection of Problems and the Seven QC Tools,109,5.2.1 Data have dispersion We live in a world of dispersion. To know

35、the quality of a given amount of products, we must use averages and dispersion. Lets assume that we take four samples of a certain part from a production line daily for one month and take measurements. There are two ways of looking at the data for the 100 samples: 1) Overall appearance of the parts

36、as a group. 2) Changes in the daily measurements over one month. For (1), we can construct a frequency table showing the number of parts for each dimension. Then, if we make a histogram, it will be easy to find the shape, the central value, and the manner of dispersion of the size measurement. For (

37、2), in order to see the changes in the data chronologically, control charts or graphs giving the date vertically and the dimensions horizontally are often used.,5.2 How to Calculate Process Capability : Histogram,110,5.2.2 How to prepare a histogram The data in Table 5.2 represent the thickness (in

38、millimeters) of 100 metal blocks that are parts of optical instruments. When there is as much data as the 100 samples here, it is difficult to determine the distribution of data just by looking at the figures. In a situation such as this, if we arrange the data in sequence orders and show how many f

39、igures are alike (see Tab. 5.2 and then draw up a graph, we can perceive the overall tendency. There are many kinds of graphs, but one of the most common is the histogram (Fig.5.1). Lets examine the method for making a histogram.,111,(1) Count the data. N=100 (2) As shown in Tab.5.2, divide the data

40、 roughly into ten groups. Record the largest values in each group as XL and the smallest values as XS ( this is comparable to a local election). Next, record the largest XL and the smallest XS on the whole (comparable to a national election). XL =3.68, XS = 3.30.,112,Table 5.2 Metal block thickness

41、(in mm),113,Fig. 5. 1 Metal block thickness,114,(3) The range (R) of all the data is: R= XL -XS =0.38. Thisrange can be divided into classes and the number of data belonging to each class can be investigated. The number of classes (the number of histogram bars) can be determined on the basis of Tabl

42、e 5.3. However, to get the rough number of classes, take K=10, and divide it into the range (R),(4) This class interval, h, which will be used as the horizontal graduation unit for the histogram, should be expressed as a multiple of an integer (the data have values of, for example, 3.56, so the unit

43、s of measurement are 0.01). Here h could be considered equal to 0.04, but to make class division easier we will put it at 0.05.,h= = = 0.038,K,XL -XS,10,0.38,115,Table 5.3 Number of Data and number of classes,116,(5) Class boundary, which we must determine in order to make a bar graph, is demarcated

44、 starting at one end of the range. It is troublesome when actuals fall on the class boundary. To avoid this, the boundary unit is taken as half the actual measurement unit. In this case it is 0.005. In other words, the boundaries the width of bars will be 3.2753.325, 3.3253.375, etc. With check mark

45、s such as /, /, /, /, /， /, etc, the data which belong to each class are enumerated as shown in Table 5.4 and a frequency table is made. The total should correspond to N as outlined in step (1) above. (Mistakes often occur here, so be careful.),117,Table 5.4 Frequency table,118,(6) After examining t

46、he frequency table, you can get an idea of the overall picture, but if it is indicated on a graph it becomes much clearer. On graph paper, mark the class boundaries horizontally and the frequency vertically like the histogram in Fig. 5.1. In the blank areas write the background of the data N, averag

47、e values, standard deviation, etc. If there is a company or industrial standard it is good to show this also. In this example, the specification limits on the metal blocks are 3.283.60 mm, so this has been recorded on the graph.,119,5.2.3 How to use a histogram,(1) What is the shape of the distribut

48、ion? Example 1.,Fig. 5.2 Comparison of histograms,120,Example 2. This histogram has a cliff-like appearance on the left edge and therefore seems unusual.,Fig. 5.3 Cliff-like histogram,121,Example 3.This histogram looked abnormal.,Fig. 5.4 Comb-like histogram,122,Example 4. All of the examples so far

49、 have been for histograms showing continuous data values. However, figures for numbers of defective parts, absentees, defects, etc (what we call discrete values) can be used as data for histograms in the same way that continuous data are. Fig. 5.5 shows the number of daily machine failures in a hist

50、ogram made to assist in preventive maintenance. The distribution is skewed to the right. With this kind of discrete value number of defective parts, percentage of defective parts, number of accidents, and number of defects the distribution of these data will often be found to assume an asymmetrical

51、form.,Fig. 5.5 Failure occurrence distribution,123,(2) What is the relationship with specifications?,What is the percentage of out-of-specification products? Do products fully meet the specifications? Is the average value at the exact centre of the specification limits? Lets compare a histogram with

52、 the specifications. In Fig. 5.1, where the thickness of metal blocks is shown, we see that the average value is roughly in the centre of the specification limits, but the dispersion is greater than the width of the specification limits. So this dispersion must either be reduced or the specification

53、 must be re-examined.,124,Example 5. A histogram showing the load characteristics of a microswitch is given in Fig. 5.6. There are many defective microswitches, and on the chart over half of the defects are due to load characteristics. For this reason, the data on load characteristics taken during t

54、he manufacturing process were studied by using a histogram. As can be seen clearly, the average value inclines toward the upper specification limit and the dispersion is broad. These problems were analyzed through control charts and various statistical methods; the result was a reduction in the numb

55、er of defectives. This is a good example for showing how a histogram can be used to perceive the state of the manufacturing process, to help people learn what the problems are, and thus to improve process capability and reduce defects. A process capability index is used to determine whether the disp

56、ersion is sufficiently small in comparison with the specification limits.,125,Fig. 5.6 Histogram of load characteristics,126,(3) Is there a need to change the histogram?,When the data are stratified in accordance with the materials, machines, shifts, workers, months, etc, the distribution is probabl

57、y different for each. In extreme situations, the histogram distribution may take the shape of two peaks (bi-modal distribution). In the case of bi-modal distribution or broad dispersion, this distribution often includes two or more distributions which have different averages. Example 6. A subcontrac

58、ting company processed sheet metal panels for an electric machine maker, with sheet metal supplied by the parent company. However, the pressed products had many wrinkles and cracks, and they were often not the right size. Therefore, hardness tests were carried out on the sheets, and the results were

59、 shown in a histogram.,127,Since the dispersion was broad, investigations were made. It was discovered that the parent company had ordered sheets from two suppliers, A and B. The sheets from these suppliers were tested separately, resulting in the stratified histograms in Fig. 5.7. It is clear that there is a difference in the hardness of the sheets of the two suppliers. When two separate graphs are drawn like this, such differences tend to become clear.,Fig. 5.7 Hardness histograms