Supply chain management
Abbas Nasiri; Ali Mansory; Nabiollah Mohammadi
Abstract
Major purpose of this research is identification and evaluating of effective factors on implementation of Green Supply Chain Management (GSCM) at Fanavaran Petrochemical Company by using statistical methods of Kolmogorov-Smirnov, mean and decision making method by topic SWARA (Stepwise Weight Assessment ...
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Major purpose of this research is identification and evaluating of effective factors on implementation of Green Supply Chain Management (GSCM) at Fanavaran Petrochemical Company by using statistical methods of Kolmogorov-Smirnov, mean and decision making method by topic SWARA (Stepwise Weight Assessment Ratio Analysis). Research methodology of present research base on purpose is practical and based on data gathering method is descriptive-measurement. In order to extracting the effective factors on GSCM at the company, in first, by literature review, 22 factors were identified. Then data were gathered by using of opinions of population members containing 55 persons of experts and senior managers in the first class of company. Finally, after analyzing the questionnaires and statistical tests above, 11 factors were confirmed and selected. In continues, in order to evaluating the final factors and ranking them base on importance in success implementation of GSCM system, the SWARA technique is used. Final outcome of this technique showed that second factor as “Designing products to reduce energy and material consumption, reuse and recycling of materials, prevent the use of hazardous materials in the production process” by most weight is extracted as the most important factor. Such, the factors of “Materials and compliance with the standards required for the purchase of raw materials” and “Procurement, distribution and reverse logistics” placed in next ranks base on importance. The factor of “total environmental quality management” by least weight is identified as last factor in implementation of GSCM at the company. In the end of research, it is proposed that organizations focus on environmental problem to acquire skill green environment advantage through green supply chain activities.
Data Envelopment Analysis, DEA
F. Z. Montazeri
Abstract
One of the best techniques for evaluating the performance of organizations is data envelopment analysis. Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the performance of decision-making units (DMUs) that recognizes the relative performance of DMUs based on mathematical programming. ...
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One of the best techniques for evaluating the performance of organizations is data envelopment analysis. Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the performance of decision-making units (DMUs) that recognizes the relative performance of DMUs based on mathematical programming. The classic DEA model was initially formulated for optimal inputs and outputs, But in real-world problems, the values observed from input and output data are often ambiguous and random. In fact, decision-makers may be faced with a specific hybrid environment where there are fuzziness and randomness in the problem. To overcome this problem, data envelopment analysis models in the random fuzzy environment have been proposed. Although the DEA has many advantages, one of the disadvantages of this method is that the classic DEA does not actually give us a definitive conclusion and does not allow random changes in input and output. In this research data envelopment analysis models in fuzzy random environments is reviewed.
