Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA)
Measuring productivity is the systematic process for both inter- and intra-organizational comparisons. The productivity measurement can be used to control and facilitate decision-making in manufacturing as well as service organizations. This study’s objective was to develop a decision supp...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-09-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | http://www.mdpi.com/2504-2289/2/3/27 |
| _version_ | 1818178420500070400 |
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| author | Shanta Mazumder Golam Kabir M. Ahsan Akhtar Hasin Syed Mithun Ali |
| author_facet | Shanta Mazumder Golam Kabir M. Ahsan Akhtar Hasin Syed Mithun Ali |
| author_sort | Shanta Mazumder |
| collection | DOAJ |
| description | Measuring productivity is the systematic process for both inter- and intra-organizational comparisons. The productivity measurement can be used to control and facilitate decision-making in manufacturing as well as service organizations. This study’s objective was to develop a decision support framework by integrating an analytic network process (ANP) and data envelopment analysis (DEA) approach to tackling productivity measurement and benchmarking problems in a manufacturing environment. The ANP was used to capture the interdependency between the criteria taking into consideration the ambiguity and vagueness. The nonparametric DEA approach was utilized to determine the input-oriented constant returns to scale (CRS) efficiency of different value-adding production units and to benchmark them. The proposed framework was implemented to benchmark the productivity of an apparel manufacturing company. By applying the model, industrial managers can gain benefits by identifying the possible contributing factors that play an important role in increasing the productivity of manufacturing organizations. |
| first_indexed | 2024-12-11T20:47:42Z |
| format | Article |
| id | doaj.art-2daca4a9b058466c8f26e9c4450c0cc9 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-12-11T20:47:42Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-2daca4a9b058466c8f26e9c4450c0cc92022-12-22T00:51:18ZengMDPI AGBig Data and Cognitive Computing2504-22892018-09-01232710.3390/bdcc2030027bdcc2030027Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA)Shanta Mazumder0Golam Kabir1M. Ahsan Akhtar Hasin2Syed Mithun Ali3Department of Mechanical Engineering, Ohio University, Athens, OH 45701, USADepartment of Mechanical, Automotive & Materials Engineering, University of Windsor, Windsor, ON N9B 3P4, CanadaDepartment of Industrial and Production Engineering (IPE), Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, BangladeshDepartment of Industrial and Production Engineering (IPE), Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, BangladeshMeasuring productivity is the systematic process for both inter- and intra-organizational comparisons. The productivity measurement can be used to control and facilitate decision-making in manufacturing as well as service organizations. This study’s objective was to develop a decision support framework by integrating an analytic network process (ANP) and data envelopment analysis (DEA) approach to tackling productivity measurement and benchmarking problems in a manufacturing environment. The ANP was used to capture the interdependency between the criteria taking into consideration the ambiguity and vagueness. The nonparametric DEA approach was utilized to determine the input-oriented constant returns to scale (CRS) efficiency of different value-adding production units and to benchmark them. The proposed framework was implemented to benchmark the productivity of an apparel manufacturing company. By applying the model, industrial managers can gain benefits by identifying the possible contributing factors that play an important role in increasing the productivity of manufacturing organizations.http://www.mdpi.com/2504-2289/2/3/27productivitybenchmarkinganalytic network processdata envelopment analysis |
| spellingShingle | Shanta Mazumder Golam Kabir M. Ahsan Akhtar Hasin Syed Mithun Ali Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA) Big Data and Cognitive Computing productivity benchmarking analytic network process data envelopment analysis |
| title | Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA) |
| title_full | Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA) |
| title_fullStr | Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA) |
| title_full_unstemmed | Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA) |
| title_short | Productivity Benchmarking Using Analytic Network Process (ANP) and Data Envelopment Analysis (DEA) |
| title_sort | productivity benchmarking using analytic network process anp and data envelopment analysis dea |
| topic | productivity benchmarking analytic network process data envelopment analysis |
| url | http://www.mdpi.com/2504-2289/2/3/27 |
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