Journal of Quality Engineering and Management

Abstract In this research, using Markov model, general formula for calculating reliability and MTTF, which are the main engineering factors in quality in systems with redundancy of 1 of n, from two methods of solving differential equations and shortcut method (direct calculation of MTTF without the need for function Reliability is obtained by using transition matrices in the Markov model, which are sometimes briefly mentioned in the article. One of the remarkable results of this research is that the possibility of estimating parameters for any desired number of n has been facilitated. Also, using the obtained functions, the effect of increasing add-on items on improving reliability in these systems (with ready-to-serve and active modes and repairable and non-repairable components), along with factors such as repair rate and reliability of systems. Switching has been evaluated. Mathematica software has been used to perform calculations and data analysis. 

  

Abstract Many industries are not aware of the after-sales service features and their impact on customer satisfaction. Disappointed customers have become competitors in order to better compete after-sales service. Due to the specific conditions of the liquefied petroleum gas (LPG) industry (harvesting, delivery, transportation, loading, standardization, etc.), researchers have different opinions in the field of customer service. In this regard, the present study was written with the aim of designing an appropriate after-sales service model in the Iranian liquefied gas industry. The combination of fuzzy Delphi method and structural-interpretive modeling with content approach has been used in the design of this model. Since this method is based on the opinion of experts, the opinions of managers and experts involved in the field of liquefied gas industry in Iran, which included 10 people, were used. In this study, fuzzy Delphi method was used to screen the indices. Out of 20 identified indices, only 3 indices of de-fuzzy mean were less than 0.7 and 17 main indices were identified in this industry. Then, structural-interpretive modeling (ISM) and MICMAC analysis were used to cluster the identified components. After analyzing the data, the variables were classified into six different levels and plotted according to the ISM graph relationships. After MICMAC analysis, the variables were divided into three groups of independent or key variables, linked and dependent, and no variables were included in the group of autonomous variables. The results of research in determining the relationships between variables and the type of variables can help to better understand the issue and make appropriate decisions in identifying after-sales service indicators. Attention to the indicators of reliability, reactivity, quality of interactions, agencies and visits according to the output of the structural model show the impact of these indicators in the field of services and in the liquefied gas industry. 

Abstract The real world energy sector is one of the most sophisticated systems that must be viewed in a holistic view. In this research, researchers have identified and analyzed the effective interactions of after-sales services in the Iranian LPG industry. Since post-sales services can be assessed on a variety of criteria, the Multi-criteria Decision Approach (MCDM) approach for such an analysis is a good approach. Due to the particular complexity of the liquid gas industry from the refinery to the end user's end, in this research, DEMATEL FUZZY is used to analyze internal interactions, and for the reader to have a deeper understanding of the subject, the causal diagrams (CLD) in the form of Dynamic Systems (DYNAMIC SYSTEM). After thorough review of the theoretical foundations and expertise, 17 industry-leading indicators of the post-sales service have been identified in this industry and 10 respondents have answered questions. In addition, the researchers express specific definitions of service in this industry. The results of the research show that reliability variables, representations, documents have the most impact, and improvement variables, responsiveness, timing of visits have the greatest interaction with other components in the model. Dynamic model also depicts the real world image of internal service interactions.