فصلنامه مطالعات مدیریت صنعتی
پیاپی 13 (تابستان 1385)

The concept of this research is, assessing the level of readiness for the implementation of Total Productive Maintenance in the SHASI SAZ IRAN Co. In order to satisfy the main goal of this research, “assessing the level of readiness for the implementation of Total Productive Maintenance in the SHASI SAZ IRAN Co” assessment conducted in four areas. 1. The senior management committed to the implementation and execution of TPM. 2. The employee participation regarding the implementation and execution of TPM. 3. The usage level of statistical process control tools. 4. The usage level of maintenance procedures. The statistical target group is SHASI SAZ IRAN Company’s managers, experts and production line supervisors. This research based on the method of data collection, is of the descriptive kind. Collection of data was based on library findings and field studies. In order to analyze the data, the comparison of target group average with assumed average test was employed. After analysis, the readiness information of the SHASI SAZ IRAN Company based on the four questions of the research and as a result, the main question, were all placed below average. Overall, considering the individual answers, employee participation achieved first place in terms of readiness and following were maintenance procedures, management commitment and SPC tools.

This research is done in order to evaluate and manage IT/IS outsourcing risks. Awareness about outsourcing, as a supportive source of organization strategies, is increasing and it is not only seen as a factor to reduce organization costs anymore. For this reason, organizations inclination to use IT Outsourcing, in order to pass useful feedback to the environment, is tremendously increasing. In this research we used expected loss perspective for evaluating risks of IT activities in Ab-Niru Company. Research's results determined that some systems, which are closed to the core competency activities in the company, have highest amount of outsourcing risk and some systems which are not so close to the core competency activities in the company, have lowest amount of outsourcing risk.

This paper uses neural network to predict corrosion rate. Corrosion can not modeled easily, because of wide range of causes either known or unknown. In mechanistic approach, physical, chemical and electrochemical reactions and processes are considered to model and predict. But as stated before this models are not practically successful in prediction because of unknown parameters. This paper uses genetic optimized neural network to predict corrosion rate. Among different neural networks, multi layer neural network with gradient descent learning algorithm has been selected. After developing the network, learning process has been done, using an oil refinery'S data. Then evaluation and test have been performed. After preparing the network Garson's algorithm and sensitivity analysis have been used for knowledge extraction. According to results, neural network approach can predict corrosion rate with acceptable correlation coefficient (R) and mean squared error (MSE). Sensitivity analysis depicts the strength of each oil parameter influence on corrosion rate. Among these results, salt and sulphur are the most affecting parameters in corrosion rate.

Modem organizations face unprecedented challenges in today’s fast- paced, high-tech, information-based competitive environments. Employees empowerment is a powerful tool for managers and leaders that lead to competitive advantages. The concepts and theories of empowerment developed but The meaning of empowerment as used in the literature has, however, been quite vague because the existence of integrated approach to these theories have neglected. In this paper, after present historical context, key concepts, and critical analysis about empowerment, we developed a new model (typology) based on Burrell and Morgan's paradigm included functionalist empowerment, structuralist empowerment, interpretive empowerment and humanist empowerment.

The purpose of this article is about soft computing and its different methods for modeling phenomena. Soft Computing refers to the evolving collection of methodologies used to build intelligent systems exhibiting human-like reasoning and capable of tackling uncertainty. In this paper, we describe the neural networks approach in soft computing at first. Then, other approaches such as genetic algorithm and machine learning will be described. Since the main goal of building the model is knowledge extraction, finally, we will describe the various methods of knowledge and rule extraction from neural networks.

This study analyzes the factors affecting on creating a leaming organization based on Peter Senge's model. He introduces these factors in his book named fifth discipline: leadership, thinking space, localization, balance between family and work, time management and experiencing. There are commonly two basic patterns for creating a learning organization in academic studies: American pattern introduced by Peter Senge and European pattern written by several famous authors and theorists. Other scientists and theorists follow these two patterns. A study with this subject has not been carried in our country. By identifying the factors effecting on learning organization we can prepare to create this kind of organization as a new approach for organization's and human resource's development. Results showed that mentioned factors affect on creating a learning organization. So we can prepare for creating a learning organization by using designer, teacher and steward leaders, create thinking space, learning from case experiences, personal and organizational effort to create balance between family and work, creating learning and thinking opportunity and delegating decision making to employees and supervisors.