Evaluation of benchmarking attribute for service quality using multi attitude decision making approach

被引：3

作者：

Singh B. ^{[1
]}

Grover S. ^{[1
]}

Singh V. ^{[1
]}

机构：

[1] Department of Mechanical Engineering, YMCA University of Science and Technology, Faridabad, 121006, Haryana

来源：

International Journal of System Assurance Engineering and Management | 2017年 / 8卷 / Suppl 2期

关键词：

Benchmarking; Graph theory; Linguistic variables; Service quality;

D O I：

10.1007/s13198-016-0485-4

中图分类号：

学科分类号：

摘要：

The essence of benchmarking is the process of identifying the highest standards of superiority for products, services or processes and then make the essential improvements to reach those standards. Today, assessment of service is a main management concern for industries tends to convert benchmark in its respective field. In this study, an endeavor has been made by the authors to evaluate the performance of benchmark model in service industries by using Fuzzy MADM approach where linguistic labels are used to describe attributes of benchmarking which are identified through literature and experts opinion. Afterwards overall numerical index has been computed by using graph theoretic approach, which helps to compare different alternatives of benchmarking for industries’ existence. The results show that responsiveness and reliability is found best alternatives among all the alternatives and Tangibles system is considered as nastiest for the study. This study imposed managers to select significant attributes and alternatives for enhancement of industries at global level. © 2016, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.

引用

页码：617 / 630

页数：13

共 54 条

[1]

Agrawal V.P., Kohli V., Gupta S., Computer aided robot selection: the multiple attribute decision making approach, Int J Prod Research, 29, 8, pp. 1629-1644, (1991)

[2]

Anand G., Kodali R., Selection of lean manufacturing systems using the PROMETHEE, J Model Manag, 3, 1, pp. 44-70, (2008)

[3]

Arlbjorn J.S., Freytag P.V., de Haas H., Service supply chain management: a survey of lean application in the municipal sector, Int J Phys Distrib Logist Manag, 41, 3, pp. 277-295, (2011)

[4]

Attri R., Grover S., A graph theoretic approach to evaluate the intensity of barriers in the implementation of total productive maintenance (TPM), Int J Prod Res, 52, pp. 3032-3051, (2014)

[5]

Benitez J.M., Et al., Using fuzzy number for measuring quality of service in the hotel industry, Tourism Manag, 28, pp. 544-555, (2007)

[6]

Boonitt S., Pongpanarat C., Measuring service supply chain management processes: the application of the Q-sort technique, Int J Innovation, Mgmt Tech, 2, 3, (2011)

[7]

Chen S.J., Hwang C.L., Fuzzy multiple attribute decision making methods, Springer Berlin Heidelberg, pp. 289-486, (1992)

[8]

Chien C.-J., Tsai H., Using fuzzy numbers to evaluate perceived service quality, Fuzzy Sets Syst, 116, pp. 289-300, (2000)

[9]

Dou J., Dai X., Meng Z., Graph theory based approach to optimize single-product flow-line configurations of RMS, Int J Adv Manuf Technol, 41, 9-10, pp. 916-931, (2009)

[10]

Gandhi O.P., Agrawal V.P., Failure cause analysis a structural approach, J Press Vessel Tech, 18, 4, pp. 434-440, (1996)

← 1 2 3 4 5 6 →