نشریه مدیریت راهبردی در سیستم های صنعتی
پیاپی 41 (پاییز 1396)

Taking attention to reliability and maintenance management conception has substantially significantly increased over the recent decades. This paper proposes and integration of DATA MINING, Fuzzy DEMATEL, and FMEA technique in order to improve the reliability and effectiveness of maintenance management in Shazand Petrochemical Company. Firstly, the most critical cluster is selected among final clusters obtained from software by utilizing DATA MINING technique. Secondly, Fuzzy DEMATEL technique is used to identify a collection of most critical and most effective equipment of critical cluster under fuzzy condition. Finally, the FMEA and FTA techniques applied to identify the risks numbers and the main causes of the failure and then the solution will be proposed to solve the problems and improve the system.

The growing importance of technology, especially after the World Wars I and II and its role as an important factor for obtaining military, political, and economic power and wealth creation, attracted much attention to different aspects of technology. Few studies have been conducted concerning technology assessment in firms. This article aimed to propose a fuzzy multi-criteria decision-making model in order to assess Iran's automotive industry performance using the fuzzy VIKOR-ANP technique. The statistical population consisted of 25 IT and technology senior managers working in Iran Khodro, SAIPA Co., and Pars Khodro Co. First, we reviewed the literature. Then, the dimensions and indicators of technology assessment were extracted in Iran's automotive industry using Delphi. The results of weighting dimensions using ANP showed that Creativity Capability scored the top followed by Engineering Design Capability, Manufacturing Capability, Production Capability, Marketing and Sales Capability, Service Capability, Acquisition and Support Capability, respectively. The results can provide a holistic view of decision makers especially those working in the automotive industry. It can also help the technology assessment.

Efficiency is an important issue for the managers of different companies and organizations, as well as customers who are interested in services of these companies and organizations. The aim of this research is to study the efficiency of pharmaceutical companies accepted in stock exchange organization using Data Envelopment Analysis (DEA) and then, providing some rules using the decision tree. The Malmquist index partly resolves the problem of inadequacy of the observations by enabling the combination of time-series and cross-sectional observations. This method acts on the basis of moving average and it is of use for finding performance trends of a unit during the time. In this research, regarding the inputs and outputs and using the Malmquist index, efficiency evaluation of 22 pharmaceutical companies accepted in stock exchange organization has been done in the state of constant returns to scale during 2012-2016, and the results obtained were used as the class label of Decision-Making Units (DMUs) which are in fact inputs of decision tree method. Finally, using the decision tree, rules implicit in the data, are extracted.

In this research, with the improvement of previous existed models and surveying of structural similarities, more efficient model will be offered which is able to an evaluation of DMU performance of a two-stage system simultaneously and also the consequent assessment of the system’s operation. Improved procedures and solutions are offered in such a way that following them the ideal efficient situation can be figured out. The proposed model can solve the previous models’ issues which can lead to impractical and invalid responses. The practical result of this research is the conceptual model for evaluating the performance of projects and inspection of electronic protection in Sorena System Company, where a method is created for optimizing the impact of each component of the primary inputs over the final output and consensus performance of projects effectively and efficiently. The cost of advertising, personnel and equipment’s programs in Rials (as the three factors of input), the monetary value of provided services of (private and public) inspection and electronic protection projects (as two intermediate output component) and the quantity and index‘s monetary value of deviant work in inspection and electronic protection projects (as two components of the final output targets) will be analyzed.

Data envelopment analysis (DEA) is a methodology for assessing the performances of a group of decision-making units (DMUs) that utilize multiple inputs to produce multiple outputs. This paper introduces two virtual DMUs called ideal DMU and anti-ideal DMU into the interval DEA. The resultant interval DEA models are, respectively, referred to as the interval DEA with ideal and anti-ideal DMUs. One evaluates DMUs from the viewpoint of the optimistic efficiency, while the other evaluates them from the perspective of the pessimistic efficiency. The two distinctive interval efficiencies are combined to form a comprehensive index called the relative closeness to the ideal DMU just like the well-known technique for order preference by similarity to ideal solution approach in multiple attribute decision making. The relative closeness index is then used as the evidence of overall assessment of each DMU, based on which an overall ranking for all the DMUs can be obtained. We also present an example on evaluating the performance of twenty bank branches which shows that the proposed interval DEA approach is a simple, efficient, and practical method for performance measurement in real-life situations.

Inventory management is an important part of production planning process for enterprises which involve decision-making process. They can determine when and how many things to buy or decisions can be made by ranking the inventory items. The aim of this paper is providing a model based on fuzzy hybrid MCDM methods by using FDEMATEL and FVIKOR in order to Inventory Management. In this study, a fuzzy DEMATEL approach was proposed to determine the weights of the decision criteria and Fuzzy VIKOR method to evaluate and rank the inventory. Concurrently applying fuzzy DEMATEL and FVIKOR to a multi-criteria inventory classification problem is the novelty of this study in the related literature. A real case study at MAPNA Electric & Control, Engineering & Manufacturing Company used to illustrate the efficiency of the proposed approach. Results showed that the proposed framework for inventory management is effective. Finally, some conclusions and suggestions are provided at the end of this study.

Information technology development in taxation process is of the most basic transformations. In another word, the electronic filling has had wide effects on tax planning and management. The Iranian government has set steps for converting to a full body electronic government recently. One of the super prominent sections in e-Government is electronic tax filing system. Therefore a fully online taxation system has set up lately which is called Integrated Tax System (ITS). The succession of the new system mostly depends on different factors such as adaption by users. Hence in this paper influencing factors affecting adoption of the electronic tax system in Fars Province has been studied. Information technology acceptance model developed by Davis has been accepted and a questionnaire containing 17 professional questions has been distributed among 317 taxpayers and 279 of them have been collected. The statistical method of structural equation modeling has been used to analyze data. Results reveal that information technology knowledge and ease of use along with trust in systems are the most influencing items in accepting electronic tax filing systems in Iran.

The industrial sector is a complex and the most vital pillar of the economy in the world. In fact, in this century, economic activity is formed whereby the continuity of the subprime industry is hardly possible. On the other hand, human resource is considered a major asset and unmatched success and survival of organizations is really depends on it. So, the productivity of human resources is the most important factor which is the ultimate goal of most organizations. Productivity and efficiency require an understanding of the components and analyze the changes in the process of development with regard to the third paragraph of the general policies of Resistive Economy indicating the supply a significant share of economic growth from improving productivity, as well as the undeniable productivity targets of the sixth program of the development and the comprehensive program of delivering productivity. The purpose of this article is to assess of labor productivity at the industrial sector and its relationship with economic component of resistance. Research methodology is a descriptive survey in terms of the purpose and terms of the nature of data collection. The statistical method used in this study is Friedman test and Spearman’s correlation coefficient. The population included 563 employees of the industrial towns of Sanandaj counting a sample of 170 people. Data analysis was performed with the help of software. The findings suggest that there is no significant relationship at the industrial sector among the components of consumption pattern reform, innovation and development, and supporting Jihadi activities with labor productivity.

Proper management and better control of inventory of food items are one of the most important and important goals of food store managers. In this study, we try to provide knowledge of customer segmentation based on various characteristics as inputs in predicting retail demand. The purpose of this paper is to provide a prediction model for retailers based on customer clustering to improve inventory performance. Customer clustering with the genetic algorithm is performed in MATLAB R2016a software. The proposed model is used to predict the demand for five items of supermarket goods in Gorgan. In this paper, the predictions of ARIMA, ARIMA, MLP, and GMDH neural networks are used to predict. Modeling of these models has been done in MATLAB software. The results showed that the GMDH neural network with the clustering of customers had the least predictive error. The model predicted by the inventory control policy reduces the number of days that the shortage faces and increases the level of service to the customer. Retailers can use the proposed model to predict the demand for various items to improve inventory performance and profitability of operations.