A Brief Analysis of Lean Quality Management |
| , Lean quality management is the new trend of quality management Quality management course of 100 years, has gone through the traditional quality inspection, statistical quality control, total quality management stage, as the core of Lean Six Sigma management quality management quality management is becoming a new trend. In the traditional quality inspection phase, inspection and separation from the production, such as Taylor advocated the establishment of a special inspection department to change the way the original workers from the inspection, the establishment of full-time inspectors to carry out product testing. In the 1940s and 1950s, mathematical statistics was introduced to the quality management, quality management into the statistical quality control stage, constantly improve the theories and methods of sampling, statistical process control, and is applied to a large number of quality management. Later stages of the quality of mathematical statistics used in operating the whole process of quality management tools continues to introduce new ISO 9000 standard also began to take shape and continue to be accepted by the enterprise, the concept of quality management as the market competition is also referred to new heights, total quality management to become management Hundred Years of far-reaching development stage. In the 1990s, GE as the representative of the world-class excellent enterprise, began a new stage of exploration of the quality management, the proposed use of its significant contribution to the Six Sigma ideas and methods. Six Sigma management emphasizes Measurement and Improvement used in quality management, including business process. Six Sigma management inherited the idea of total quality management (TQM), and TQM has a new development in the pursuit of excellence goals through continuous improvement metrics and indicators found for TQM implementation method. GE, Motorola and other enterprise applications, the huge success of Six Sigma Six Sigma is being more and more companies are understanding and awareness, as the core of Lean Six Sigma quality management will become the new trend of the development of quality management. Lean quality management is the quantitative analysis on the basis of the key quality data, the integrated use of a variety of knowledge and methods of key quality indicators continuous system improvement, the pursuit for excellence, such as Six Sigma standards to achieve significantly improve the performance and operation of enterprise quality The purpose of the performance. Second, the quality of data analysis is the basis of lean mass Quality data refers to the quality characteristics of quality indicators, the quality of the meaning of the term, both the narrow sense of the quality of products, including generalized quality of work, and thus the quality of a variety of indicators in the enterprise, the quality of data in the enterprise almost everywhere. The narrow sense of the quality of the data related to product quality data, such as the number of defective products, the passing rate of first pass yield, repair rate. The broad quality data reflect the quality of the work of the data, such as loss of quality costs, production volume, inventory, invalid operating time. These will become the object of the study and improvement of lean quality management. Statistical analysis of quality data, pay special attention to the three indicators, the central location of the data, the second is the degree of dispersion of the data, the data distribution law. The central location of the data mean, median, mode three methods, each with advantages and disadvantages, which mean the most commonly used. The degree of dispersion of the data expressed by the standard deviation, represented by the symbol s (sigma), the degree of dispersion of the data quality management is the volatility of the value of the quality characteristics, reflecting the process capability. Distribution of data in quality management, statistical Overall normal distribution, the distribution law of the statistical laws of the theory and practice prove. The quality of statistical data analysis is to focus on the overall normal distribution of the known background research mean and standard deviation of the normal distribution. Quantitative analysis of quality data quality management and business management is important, which is the basis for lean quality management. Through research, we lean quality management task is divided into two levels. Lean quality management basic task is to use quality tools to analyze the actual quality status, to detect anomalies and eliminate abnormalities quality; lean quality management level task is the level of continuous quality improvement, continue to reduce fluctuations in the quality, a reduction of the sample standard deviation. The realization of the second-level task relying on the tasks of the first level. Lean quality management tools Combined with the previously mentioned two-level task of lean quality management, support tools focus on basic tasks, histograms and control charts, related theory is a statistical process control, SPC; second layer of tasks on the basis of the previous tool , focusing on Six Sigma management theory and methods. 1, histogram Profile Histogram quality data sequence is divided into a number of equal intervals group, the group Sandy bottom edge, in order to fall within the frequency data for each group number as the basis for a number of rectangular bars constituted by the proportion Ordering FIG. Histogram typical role include: observation and judgment of product quality characteristics distribution; shape of the histogram to determine the production process is normal, the stability of the judgment process, and to identify the causes of abnormal; calculation process capability estimates from production of non-conforming product rate. In the judgment of whether the normal production process, by their typical shape of the histogram can be judged. Histogram Typical shapes include: normal, biased, bimodal, zigzag, flat and island type. Summed up the different shapes of common quality reasons, provides an important way to quickly identify and solve quality problems. Normal histogram further with the tolerance limit of the combination, intuitive and fast way to determine the process capability and quality, intuitive discovery process exception. Typical graphics: ideal type, eccentric, no surplus, surplus, lack of capacity type. 2, control charts Profile The control chart is to draw a diagram of control limits on the production process, product quality control. Control charts is the histogram a variant of the histogram Shun to turn 90 degrees and then reverse, and then draw the center line of the upper and lower control limits. The center line of the mean of a sample statistic, upper and lower control limits were based on mean plus or minus three times standard deviation. When there is no systematic errors in the production, product quality characteristics (overall) follow a normal distribution, the sample values appear in the mean range of plus or minus 3 sigma probability of 0.9973. According to related statistics theorem, if the production in a controlled state, the sample value must fall within the range of 3 sigma. The control chart is a histogram biggest feature is the introduction of a time series or sample sequence statistics by observing the sample points are within the control limits to determine if the process is in control, arranged by observing the sample point is random so as to detect abnormal. Control charts than the histogram is much improved in terms of quality prevention and process control. The main purpose of the control chart: analysis to determine whether the production process; timely detection of anomalies in the production, prevention of non-conforming product generated; check production equipment and process equipment accuracy meets the production requirements; product quality assessment. 3, Six Sigma Introduction 6 Sigma Sigma level, usually using Z as the measure of the degree of level of quality to meet customer requirements. Sigma level is a combination of the standard deviation of the calculated value of the tolerance limit, the formula Z = (USL-LSL) / 2 sigma, the customer requirements tolerance limit divided by twice the standard deviation. Is increasing due to customer requirements, that is represented by the molecular formula tolerance will continue to reduce the standard deviation should continue to reduce, in order to adapt to customer requirements and improving quality competitiveness. The level of Six Sigma is the Z equals 6. Explained by a normal distribution, is from the mean in the normal distribution unilateral tolerance upper or lower range can accommodate up to 6 standard deviation; traditional control chart theory is one-sided three standard deviation, the failure rate was controlled at 0.27% level. Six Sigma management control charts three times the control limit were thoroughly break, the Sigma level indicators from 3 to 6. We should recognize that FIG 3 Sigma level for the standard control and statistical process control SPC theory and methods, and is still valid in practice. With the improvement of the Sigma level, three times the standard deviation of the control limits range has been continually compressed, found abnormal quality control charts will still be effective. Z, there is another form of expression: parts per million defect rate (ppm). A normal distribution process, the percentage of defects exceeds the specification limit is one-to-one correspondence with the Sigma level. According to this rule, by measuring the ratio of the defect, we can estimate the process sigma level Z, the process of inspection and the ability to meet customer requirements. When distribution center no drift, ie the sample mean and distribution center coincides 3 sigma level corresponding failure rate of 0.27%, ie 2700 ppm; Six Sigma level corresponding to a failure rate of parts per billion, which is 0.0024ppm. Distribution center no drift as the ideal state. When distribution center drift up and down 1.5σ, the failure rate of the 3 sigma level corresponding 66807ppm; 4 sigma level of 6210ppm; 5 sigma level of 233ppm; Six Sigma level of 3.4ppm. GE has taken a 1.5σ drift up or down to set the standard Sigma, Six Sigma level of 3.4ppm. Six Sigma management default standard. In the pursuit of business by 3 Sigma Six Sigma process, each increase of one sigma level, the improvement of the quality level showed a number of times. According to the study, a 3 sigma level of enterprises, a Sigma obtain the following income: profit margin increase of 20%, 12% -18% increase in output capacity, a decrease of 12% of the labor force, capital investment decreased by 10% - 30%. Fourth, to be found in the value of quality data Lean quality management ideas and methods applied to the product quality related activities, can also be applied to the management of the quality of the work. Lean quality management is a continuation and development of total quality management. Lean quality management is concerned with the statistical measure of jobs or functions can have a major impact on the quality of corporate performance, lean quality management metrics volatility or called sigma value to measure the level of quality of work, lean quality management learn from the experience and shortcomings of the QC group activities, the introduction of project management theory and methods, organization and management mode with its own characteristics and project management system, and jointly promote the improvement of the performance of enterprise quality. Lean quality management requires companies to improve existing quality management habits, awareness of indices of good at discovering and using statistical methods to measure and improve. Enterprises should pay attention to the impact of the index volatility on the competitiveness of enterprises, emphasis on the continuous improvement of the quality indicators. Lean quality management to set up a new concept of quality cost, the cost of poor quality loss instead of the traditional four quality costs, improve the quality of performance in order to reduce the cost of quality loss to find the focal point. In the course of business, competition is everywhere. Competition intuitive to see is a comparison. Indicators can be used to compare a variety of business, such as product quality, price, delivery, service level. Operations due to various reasons, different periods of the same indicator is often manifested travel the opposite sex, otherwise known as volatility. This volatility size of the different enterprises customers or stakeholders will bring significantly different feelings. Such as the quality of performance indicators, traditionally, we are concerned about the non-conforming product, often no longer to analyze data on the quality of qualified products. With qualified products, are within the tolerance limit, but quality indicators volatility of small enterprises will be more affected by the trust of customers. Another example of the delivery or service time, within the time allowed by the customers, we achieve the delivery, but ignore the fluctuations of each batch of product delivery time, if competitors can achieve more accurate and more narrow time range each batch of products delivery, its competitiveness in this regard will be stronger than us. In the field of production management, lean manufacturing, JIT respected by the pursuit of production processes to the delivery of the number of accurate, accurate delivery time, the pursuit of the minimization of waste, combined with the concept of quality indicators volatility, is to pursue the relevant indicators within specified limits within the volatility minimum. LEAN production and reduce production costs, save money, improve production efficiency has an important role in the process of production processes arrangements, the beat set, the production plan has a very important significance, is an important aspect of lean quality management applications. In the business, the indicators can be used to measure very extensive, many of the indicators are often overlooked. For example, a pursuit of "quick win" business, reaction time, if you can not measure yourself carefully for critical business processes such as product development cycles, new product trial production cycle, production cycle, delivery period, feedback cycle, companies will do not know their process time bottleneck can not develop more accurate competitive response, corporate pursuit of fast is based, can not really make a difference on the "fast". Lean quality management emphasizes the role of the measure, often referred to in the Six Sigma management: we do not care what we do not measure, we can not do something does not measure. Lean quality management is to promote companies continue to find that the key quality indicators should be measured and improved, systematic approach to achieve continuous improvement, lean quality management is to improve the competitiveness of enterprises and business performance management initiatives, has been proved by the well-known international companies for business success an important strategy. <Source: Construction Engineering Education Network> |
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