جستجوی مقالات مرتبط با کلیدواژه "soft modeling" در نشریات گروه "مدیریت"

Effective management of engineering changes in the Automotive industry is an essential ability in new product development, and products evolve in an environment with an iterative nature and increasing changes from the idea stage to the final product and shortening the life cycle of products and also the duration of product Launch from The idea-to-market stage is a severe requirement of a competitive environment.First, the complexity of the product development environment, the challenges of engineering changes and the need to control and governance its effects in a competitive environment in the vehicle development process are described. Then by literature review, an approach to modeling engineering change management using two hard and soft models has been introduced.in our modeling approach, the assumptions of cybernetics and system dynamics approaches have been practiced in building causal loop diagrams that integrated SOFT effects with HARD dimension parameters. Using this approach to develop simulation scenarios, strategic managers have better insight into effective management of engineering changes to find appropriate policies configuration to control its effects in a competitive environment.

Abstract As one of the approaches developed in the framework of soft modeling, fuzzy cognitive mapping has the ability to model the problems with complex and ambiguous nature in the form of causal relationships and provide a quantitative analysis of the problem for decision makers. The purpose of this study is to analyze the effective elements in adopting sustainable supply chain management in the petrochemical industry using fuzzy cognitive map method. The petrochemical industry has a major role in developing value chain in the oil and gas fields. Considering that sustainable adaptation in supply chain management has become a social demand, the development of petrochemical industry in the country and penetrating global markets requires adaptation of sustainable development approaches in this industry. In order to achieve the goal of the research, the effective elements in the stabilization adaptation in the supply chain were extracted using the Meta Synthesis method and then by using the fuzzy cognitive map methodology, the structure of sustainable supply chain was developed. Finally, each component was analyzed in the framework of this structure. Therefore, resource sharing, commitment to sustainability, commitment to sustainable development and awareness of supply chain members respectively have the greatest impact on sustainability in the petrochemical industry supply chain.   Introduction Exacerbating environmental issues, increasing social concerns, as well as regulations by governments, forced companies to adapt to the components of sustainable development in their supply chain (Garg et al, 2017). Lack of sustainability at the lower levels of the supply chain leads to negative advertisements against company products (Wilhelm et al., 2016), Because of this , the extension of sustainability concept in supply chain management has become a strategy to improve company performance and enhance its competition (Chardine-Baumann & Botta-Genoulaz, 2014). For achieving sustainability through supply chain management, companies need to adopt appropriate strategies (Tseng et al., 2015) and to implement these strategies, they need a framework to match their plans and activities. In today’s highly competitive environment, managers faced a lack of a decision framework to address sustainability issues (Zhang et al., 2018). Due to having the largest hydrocarbon reserves in the world (ranked first in gas reserves and third in oil reserves), Iran is in an exceptional position in the field of oil and gas (BP Annual Report, 2013). The petrochemical industry is one of the downstream industries in the field of oil and gas which Iran has a competitive advantage in this scope, due to access to raw, educated labor and regional markets. Studies show that Iran has 2.4 percent of global production in the petrochemical industry (Ilias, 2011). Nevertheless, studies show that in 2016, the country's neglected capacity to produce petrochemicals was over 45 million tons (Ghasemi & Nadiri, 2016). Since the successful conduct of oil and gas projects have a significant impact on the economy, increase competitiveness, protect the environment, increase production, improve quality, increase productivity and reduce costs have become as the mission and objectives of the company’s that active in Iran's oil and gas field (Ilias, 2011). Therefore, extending the sustainable development concept in the petrochemical industry is one of the competition requirements. Sustainability adaptation in the supply chain requires attention to the various factors that are effective in driving the supply chain towards sustainability and play a role in determining which organization is required to achieve sustainability. Identifying and analyzing these elements is necessary to achieve sustainable supply chain management and there is a fundamental need for their identification in various industries (Diabat et al, 2014). Based on this, the main purpose of the present research is to use the Fuzzy Cognitive Maps approach to achieve the structure of the relationships between the factors that effective on adopting the sustainable development concept in petrochemical supply chain management and analysis them. Case study The statistical population of the research is senior and middle managers of the National Petrochemical Company. Sampling is based on Delphi method. Since the purpose of the research is not to generalize the results, a targeted sampling method has been used to select the sample. The criteria for selecting experts are theoretical knowledge, practical experience, willingness and ability to participate in research and access. Materials and Methods In order to achieve the research objectives, based on Meta Synthesis method, the past studies were systematically studied and the components of the sustainable supply chain management enabling were identified, extracted and classified. Then, based on the Fuzzy Cognitive Maps methodology, the enabler components were structured and analyzed. Discussion and Results In order to identify and extration sustainable supply chain management enablers, firstly, past studies over the period 2010-2018 have been investigated based on Meta Synthesis methodology and effective components on adopting sustainability to supply chain management have been identified. Then, by collaboration with one of the university's experts, the identified components were categorized as enablers. As a result, 23 effective components have been identified for sustainability adaptation in the supply chain, which were eventually classified as 18 enabler’s factors. In this research, based on the Fuzzy Cognitive Maps capabilities to problem modeling and decision making, the effective components in achieving sustainable supply chain management have been analyzed. Based on the analysis of causal structure of sustainable supply chain management enablers, the components of “resource sharing between partners in the supply chain”, “the commitment of partners to sustainability”, “commitment to sustainable development”, and “the awareness of supply chain members on sustainability” has the most impact on the sustainability adopting in the petrochemical supply chain. Conclusion According to research results in order to achieve a sustainable supply chain, it is suggested that petrochemical industry managers focus on policies that enhance the level of cooperation between companies in the supply chain. Adopting policies such as strategic partnerships with supply chain members, joint ventures, and partnerships in profit and loss can help to develop cooperation in the supply chain and its continuity. Also, in order to develop Fuzzy Cognitive Maps application, it is suggested that the dynamic properties of this method be used in problem analysis by using the fit function. In addition, it is suggested to use a Multilayer Fuzzy Cognitive Maps for analysis of bulky issues.