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

2、. Quality 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 Q

3、uality 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

4、the process, 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 co

5、ntrol the 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

6、the dispersion 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:,I

7、f there is any 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 con

8、tains a large 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 co

9、nform to standards. 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

10、 but we must eliminate 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,

11、3.10 Recurrence 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

12、 this, the following 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 whi

13、ch causes actual harm 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,Perma

14、nent improvement plans 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 counterme

15、asure,Clarification 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 t

16、hem into effect. When 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

17、 should strive to avoid 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 w

18、ill decrease, defectives 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

19、quality,Variation will increase,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

20、 yields. Reduce the number 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 wo

21、rkplace, activities for formulating, 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 wor

22、kers to follow standards,Consider 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

23、,Working conditions are inadequate,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 standa

24、rd appropriate?,Prepare a work 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 fo

25、r workers to understand,Impractical 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 itemiz

26、e the key work points concisely. 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

27、 and Tolerances The quality we want to improve 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 di

28、spersion of the figures. Thus, the requirement 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) val

29、ues based on counting eg. 1 defective articles 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 :

30、 Process capability = 3 or 6,Fig. 4.1 Normal 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

31、and operators,6m,.,105,Remember that If Cp 1, the process 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 ma

32、naged.,4,.,106,Fig. 4.5 Judgment on the process based 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

33、: Cause and Effect Diagram 5.5 Causes of Defective Production 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, stratifica

34、tion, check sheet Scatter diagram Stratification,1. Find out 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 Dat

35、a have dispersion We live in a world of dispersion. To know 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 d

36、ata for the 100 samples: 1) Overall appearance of the parts 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,

37、 and the manner of dispersion of the size measurement. For (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

38、a histogram The data in Table 5.2 represent the thickness (in 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,

39、 if we arrange the data in sequence orders and show how many figures 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

40、) Count the data. N=100 (2) As shown in Tab.5.2, divide the data 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 elec

41、tion). XL =3.68, XS = 3.30.,.,112,Table 5.2 Metal block thickness (in mm),.,113,Fig. 5. 1 Metal block thickness,.,114,(3) The range (R) of all the data is: R= XL -XS =0.38. This range can be divided into classes and the number of data belonging to each class can be investigated. The number of classe

42、s (the number of histogram bars) can be determined on the basis of Table 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

43、 of an integer (the data have values of, for example, 3.56, so the units 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 bo

44、undary, which we must determine in order to make a bar graph, is demarcated 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 bounda

45、ries the width of bars will be 3.2753.325, 3.3253.375, etc. With check marks 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 he

46、re, so be careful.),.,117,Table 5.4 Frequency table,.,118,(6) After examining the 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 histo

47、gram in Fig. 5.1. In the blank areas write the background of the data N, average 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

48、 graph.,.,119,5.2.3 How to use a histogram,(1) What is the shape of the distribution? 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 l

49、ooked abnormal.,Fig. 5.4 Comb-like histogram,.,122,Example 4. All of the examples so far 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

50、 that continuous data are. Fig. 5.5 shows the number of daily machine failures in a histogram 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

51、of defects the distribution of these data will often be found to assume an asymmetrical 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 av

52、erage value at the exact centre of the specification limits? Lets compare a histogram with 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 o

53、f the specification limits. So this dispersion must either be reduced or the specification 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 d

54、ue to load characteristics. For this reason, the data on load characteristics taken during the 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 th

55、rough control charts and various statistical methods; the result was a reduction in the number 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 cap

56、ability and reduce defects. A process capability index is used to determine whether the dispersion 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 ac

57、cordance with the materials, machines, shifts, workers, months, etc, the distribution is probably 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

58、 often includes two or more distributions which have different averages. Example 6. A subcontracting 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 no

59、t the right size. Therefore, hardness tests were carried out on the sheets, and the results were 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 hardn