At the end of the 20th century, the rapid development of high-tech technology as the main characteristics, promoted economic globalization, accelerating the innovation of technology and management. At the same time, Six Sigma as a new era product came into being. It is a set of management methods based on mathematical statistics, emphasizing eliminating errors, reducing consumption, avoiding duplication of labor, its core is data definition, measurement, analysis, improvement optimization and control effect, so that companies are in production, design management To achieve the best realm.
1, the contents of the data and 6σ overview
Data is a record of quantitative or qualitative recording of nature, social phenomena and scientific test; it is the most important foundation for scientific research; research data is collectively referred to as a series of activities such as data collection, classification, entry, storage, statistical analysis, statistical inspection. . Statistical analysis, statistical inspection requires some logical reasoning to analyze the key factors affecting the output. It is used in many cases due to objectivity of data. Six Sigma is a model that successfully applies data in management.
Six Sigma is a data-based, pursuing almost perfect quality management method. Sigma is a Chinese translation of a Greek alphabet σ, statistically used to represent standard deviation, that is, the degree of dispersion of data. The quality characteristics of continuously measurable: the degree of deviation of the target value is generally used with the "σ" metric quality characteristics. Several Sigma is a statistical scale that represents quality. It is different from other quality management methods. It is based on strict data acquisition and statistical analysis, finds the root cause of the error, and seeks to eliminate these errors, and determine the management activities according to customer requirements.
Six Sigma implements teams consisting of black belt masters, black belts, and green belts. The black belt is responsible for the direction of the project improvement and the planning of the project resources; the black belt is the backbone of implementing management, responsible for the training of green belt, which is coordinated in it; the green belt focuses on the specific implementation of Six Sigma.
During the implementation of Six Sigma, small to a single product and service, one project, a department, and a large to one enterprise should be measured. So, in management, whether it is a black belt master, black belt or green belt, there is absolutely unable to have some fuzzy concepts such as "maybe", "almost", "possibly", even when collecting data from the customer, even when collecting data from customers It is also not allowed to use similar words, which should be the most basic requirements in Six Sigma management. In other words, 6 Sigma cannot be successfully applied to the process of not using data.
Now, (d), measure (M), analyze (a), improve (i), control (c), to see the data flow in Six Sigma management.
2,
6σ data flow
Definition (D): That is. The black belt master is required to be market-oriented, based on enterprise existing resources, using customer feedback data and information obtained by the information obtained by the employees directly dealt with the machine, and perform data comparison, thus determining the improved goals such as high return on investment Or market share, planning project resources.
Measurement (M): The purpose of measurement is to identify and record process parameters that have impact on customer critical process performance and product (ie output variable), quantify customer needs, and obtain corresponding collection data from customers to carry out these data. Classify, groups in order to analyze. Understand the existing quality level, confirm the customer, users evaluate the improved expected benefits, this phase is the data collection phase. Once the measurement is determined, its composition must formulate the corresponding "Data Collection Plan". And quantify the various events in the actual business. The data is organized by process flow charts, causal pictures, scattered maps, arrangement, and other methods. Analysis (a): That is, the main cause of the problem, the key factor and the gap between the competitors are identified. At this stage, team members should analyze the past, current performance data and clear future, the performance direction should be obtained, and determine the confidence interval of key issues to determine the confidence interval of the key issue, and conduct variance analysis, and pass the assumption test. Methods to obtain their needs. It is also possible to analyze the collected data by brainstorming, histogram, arrangement, and other methods to find accurate causal relationships. At this stage, the team must be cautious, analyze potential problems by piloting in a small scale, to determine what results will occur, and prevent it. To this end, data must be accurately analyzed, establish a mathematical model of input and output data, and track and verify the effectiveness of the solution.
Improvement (i): Improvement is based on analysis, and establishing best improvement programs for key factors. At this stage, key issues can be adjusted and improved through quality functions, and the test design, and orthogonal testing can be adjusted, improved. At this stage, attention should be paid attention to, and the key issue should be solved by small problems. Affect the development direction of the entire design or management. All of this is also based on the mathematical model of process performance to determine the operating range of the input and set the process parameters, and optimize the improved input.
Control (C): Mainly controlling key factors for long-term control and take measures to maintain improved results. Regular monitoring may affect the variables and factors that may affect the data, and develop things that have never been expected when planning. At this stage, to apply the appropriate quality principles and technical methods, focus on improving object data, controlling key variables, formulating process control plans, revising standard operating procedures, and job guidance, establishing measurement systems, monitoring workflows, and developing some Coping measures for emergencies.
These processes are not single, independent, but interconnected (as shown in Figure 1). It is easy to see by these processes that Six Sigma is a data-based decision-making method, emphasizing data, not with intuition, and experience. Its basis is the quantification of demand, role and processes, which can objectively reflect our current situation and attract people's attention. Data Defining Sampling Data Collection Statistical Analysis Test Design Control Data Defining Measurement Analysis Improvement Control
3. Data processing precautions
In the early days of 6 Sigma Administration implementation, many problems can be fully received through simple statistics, such as spending greatness to collect a lot of manpower, material, financial resources. These require black strips to implement 6 Sigma when they are implemented, and some are targeted.
Of course, if you serve a small company, you have to perform Six Sigma management in our usual, but your company only completed several transactions every month, or your customer group is very concentrated, it is difficult to get a lot of Data, on this basis, comprehensively analyzed, statistics, more powerful, but this does not mean that you cannot manage your own business on this data and facts, but you should change your thinking to collect these data. For example, if you collect the customer data of the competitors, or discover some of the causes of potential customer groups do not buy and they manage the product needs. When optimizing the Six Sigma method, you should first pay attention to avoiding the sensitive peak data points (SensitivePeaks), that is, this small change may cause changes to changes, reduce risks, ensure the reliability of their methods, and avoid avoidance ActiveConstraints, including some non-determinant factors, and variations in these factors may result in failure of design. So in implementing Six Sigma, don't guess, be sure to discover the crux of the problem by scientific analysis of real data, try to eliminate some possible and interference factors.
4 Conclusion
In summary, 6 Sigma management is mainly based on data, to find key factors, main issues, and make statistical analytics and solutions for problem data. Long-term control of the improvement method to ensure the feasibility and effectiveness of the solution. Therefore, in Six Sigma learning and implementation, it is no longer to ignore the learning and application of data collection, calculation, statistical analysis, etc., which is the key to Six Sigma success.
references:
Peter S. Pand, Liu Heguang and other translations, 6σ management law pursues excellent ladder, Beijing: Machinery Industry Press, 2001
Peter Pand, Le Rui Husipe, Wang Jinde, etc., Six Sigma, Beijing: China Finance Economic Press, 2002
Chowdhury, S.), Guo Ren Song Translated Liu Xigma's Power, Beijing: Electronic Industry Press, 2002
Yin Xuezhu, Zhang Xiaohua, Hu Zhiki, Six Sigma Management Training, Beijing: Jinghua Publishing House, 2002
