آموزش تجزیه و تحلیل منحنی در نگهداری و تعمیرات بهره ور

The continuous improvement concepts such as total quality management, just-in-time and total productive maintenance have been widely recognized as a strategic weapon and successfully implemented in many organizations. In this paper, we focus on the application of total productive maintenance (TPM). A random effect non-linear regression model called the Time Constant Model was used to formulate a prediction model for the learning rate in terms of company size, sales, ISO 9000 certification and TPM award year. A two-stage analysis was employed to estimate the parameters. Using the approach of this study, one can determine the appropriate time for checking the performance of implementing total productive maintenance. By comparing the expected overall equipment effectiveness (OEE), one can improve the maintenance policy and monitor the progress of OEE.

Many systems in practice today do not perform as intended, nor are they cost effective in terms of their operation and support. Manufacturing systems, in particular, often operate at less than full capacity. Consequently, productivity is low and the cost of producing products is high. In dealing with the aspect of cost, experience has indicated that a large percentage of the total cost of doing business is due to maintenance-related activities in the factory (i.e., the costs associated with maintenance, labor and materials and the cost due to production losses). Further, these costs are likely to increase even more in the future with the added complexities of factory equipment through the introduction of new technologies, automation, the use of robots, and so on. In response to maintenance and support problems in the typical factory environment the Japanese in 1971, introduced the concept of total productive maintenance (TPM), an integrated life cycle approach to factory maintenance and support. Since then, TPM methods and techniques have been successfully implemented in Japan, and later on in some other advanced and advancing countries in the world. Inherent within the TPM concept are the aspects of enhancing the overall effectiveness of factory equipment, and providing an optimal group organizational approach in the accomplishment of system maintenance activities. Both the equipment and the organizational sides of the spectrum need to be addressed in fulfilling the objectives of TPM. It is believed that while many successes have been realized in structuring organizations to respond better to the maintenance challenge, very little progress has been made in relation to the prediction of total equipment utilization while implementing TPM. In this paper, we focus on the application of TPM. A random effect non-linear regression model called the Time Constant Model [1] was used to formulate a prediction model for the learning rate in terms of company size, sales, ISO 9000 certification and TPM award year. A two-stage analysis was employed to estimate the parameters. Using the approach of this study, one can determine the appropriate time for checking the performance of implementing total productive maintenance. By comparing the expected overall equipment effectiveness (OEE) one can improve the maintenance policy and monitor the progress of OEE. The literature review on learning curves and TPM studies is presented in the following section. The learning curve analysis in TPM is discussed in Section 3, followed by some examples to demonstrate the application of the proposed methodology. The conclusions are made in the final section.

A random e3ect non-linear regression model called the Time Constant Model was used to formulate a prediction model for learning rate in terms of the size of company, sales, certi6ed as ISO 9000 or not, and number of years from the starting of the TPM program to the award TPM. A two-stage analysis was employed to estimate the parameters. Using the approach of this study, one can determinethe appropriate time for checking the performance of implementing TPM. Further, comparing the expected OEE, one can improve the maintenance policy. Our research results show that TQM and TPM programs are closely related. In addition, there is no strong evidence indicating that the mean of estimated learning index from the companies in Taiwan is di3erent from that in Japan. Also, small plants as well as large plants can implement TPM and have the same maintenance performance. The approach of this research can help a company when it starts implementing the TPM program. The company can use this multiple linear equation to obtain the estimated learning index where the award year can be treated as the expected TPM award year. Then the expected OEE can be easily obtained and used to monitor the maintenance progress.