فصلنامه چشم انداز مدیریت صنعتی
سال چهارم شماره 3 (پیاپی 15، پاییز 1393)

The modern world causes increased pollution and environmental problems and governments and organization‘s concerns about the environment. One of the major steps to improve environment is adoption of environmental management standard (ISO 14001) in the organization. Evidence shows in spite of the adoption of environmental management system in most organizations, the successful implementation of laws and regulations of ISO 14001 is different. Thus this study aimed to investigate the factors effecting on the success of the implementation of environmental management systems in ceramic and tile industry in Yazd province. These factors have been classified in three groups such as management elements, organizational culture and financial and nonfinancial resources. Then we compared the accuracy of the SEM and ANFIS in forecasting the success of the implementation of environmental management systems. The result showed that managerial elements, organizational culture and the resources have significant effect on success of the implementation of environmental management systems (respectively 0.50, 0.25 and 0.22). Finally all of the factors that effect on EMS were ranked based on impact factors.

Seeing that a lean cell performance is directly related to assigned operators, this article incorporates boredom in term of continually repeating working cycles as a function of the way in which operators assigned to and rotated in the cell during a specified short-term horizon and develops an integer nonlinear model to two problems, namely balancing and assignment in the case of arranging a new cell. The model is to satisfy some goals in term of lean performance measures and classic ones. None of the optimization packages is able to solve even the small size samples of the model due to formulating great number of nonlinear inequalities and non-pre-identified number of work cycles for each assigned operator per each rotation interval, as well. So, by applying the benefits of genetic algorithm the model is solved.

Determining the optimal policy of inventory have been always one of the main challenges in inventory management area in which several research have been conducted to address this issue. A vast majority of proposed approaches are very simple models to the extent that they simplify the real-world conditions and fail to consider the real uncertainties. To determine the optimal inventory policy of supply chains, on the other hand, there exist many influential uncertainties. In this thesis, an integrative probabilistic model is developed to model the uncertainty of the optimal inventory policy of multi-echolon supply chains using Bayesian networks (BNs) a state-of-the-art technology in modeling uncertainty. BNs provide a framework for presenting cause and effect relationships and enable probabilistic inference among a set of variables. The new approach explicitly quantifies uncertainty in qualitative and quantitative uncertain variables in customer, retailer, manufacturure, and supplier levels and provides an appropriate method for modeling complex relationships for process capability analysis, such as common causal factors, formal use of experts' judgments, and learning from data to update previous beliefs and probabilities. The capabilities of the proposed approach are emplemented in Agenarisk software by a real case study.

Enterprise risk management (ERM) has emerged as a new paradigm that emphasizes a strategic, enterprise-wide focus on risks. Conceptually, ERM is a systematic approach to identifying, assessing, prioritizing and controlling risk exposures and their integral and cumulative effects throughout an organization in a coordinated, consistent manner.Given the potential impact of failure to manage risk on the performance of an organization, it might be somewhat surprising that risk and its management have become of concern to the wider business community only relatively recently. The central aim of this research is to extract and classify the components and dimensions of enterprise risk managementthrough the qualitative method of meta-synthesis.The research tools, documents and proofs are used for analysis which counts to 31 researches totally. The method of coding is based on the open coding approach. The results have shown that the identified 58 Components (codes) can be categorized in six major dimensions namely establishing the context and objective setting, identification and definition of risks, measurement and analysis of risks, risk response, information, and monitoring and review. 

The importance of agility in today’s world which is the era of knowledge and technology is undeniable. In this regard, agility means to respond effectively to a changing and unpredictable environment and using those changes as opportunities for organizational development. The purpose of this study is primarily to identify factors affecting the agility of Phase 2 of Uremia Industrial Estate and then to prioritize agility of the existing industries in this Industrial Estate. By reviewing the literature, the factors affecting the agility identified including agile processes, agility market, agile strategy, Agile Linkages, information technology, and Agile People then is perky and criteria for the following 21 criteria were identified., and then their weights and ranking achieved by using Fuzzy Analytic Hierarchy Process (FAHP) technique And fuzzy TOPSIS technique was used to ranking  industries. Based on the results of the prioritization research, is agility market, agile strategy, Agile Linkages, information technology, Agile People, and agile processes. In ranking the various industries in the Industrial Estathe In order of preference are the metal industry, chemical, cellulose, food and service.

Cellular manufacturing systems (CMSs) are as one of the most important manufacturing methods.In this paper, is presented a multiple objective model to constitute a CMS under dynamic conditions subject to flexibility of operation processing, assigning machine to each operation and defining cost parameters as fuzzy numbers. In the proposed model, are considered objectives such as minimization of manufacturing system costs and fuzzy costs variance. Also in this paper, in order to attainment of effective solutions, is proposed a hybrid algorithm by NSGA-II and progressive Simulated Annealing (SA) and is applied experimental design of Taguchi for tuning the parameters of this algorithm. For surveying efficiency of the proposed algorithm, results obtained from solving several different sample problems, based on criteria of CPU time, generational distance, spacing and quality metric, are compared and analyzed with results of NSGA-II original algorithm.

Data Envelopment Analysis is a management method which applied to performance analysis for decision making units. This paper presents a new hybrid approach based on fuzzy stochastic DEA (FSDEA) and principal component analysis (PCA) to predict efficiency of DMUs. Since the proposed model contains the credibility of fuzzy events in the constraints and the expected value of fuzzy variable in objective function, the solution process is complex. Thus, with the assumption that inputs and outputs are mutually independent triangular fuzzy variables, we then transform the FSDEA model to its equivalent deterministic programming. Then we do PCA on the initial predicted efficiency scores which are obtained from the equivalent deterministic programming under various confidence levels. In order to deal with undesirable initial predicted efficiencies, the required principal components have been selected from the generated ones according to the given choice principal. Then, the principal components are treated as outputs into DEA model to obtain the final predicted efficiencies of DMUs. Finally, the proposed hybrid     PCA-FSDEA approach is applied on real exampleand the computational results will be compared with Fuzzy-SBM model which was proposed by Chen et al. (2013).