چکیده:
Efficiency is regarded as an important factor for both managers in different companies and organizations and customers who are interested in using the services related to these companies and organizations. However, the biggest challenges managers are coping with include an increase in the competition, an increase in the efficiency of production, and finding suitable suppliers. This study aimed to investigate the efficiency of green supply chain by using Data Envelopment Analysis (DEA) based on Malmquist Productivity Index (MPI) according to the input and output indicators of the Balanced Scorecard (BSC) model and accordingly providing some rules using the decision tree. To this aim, the efficiency of 15 manufacturer firms of automotive parts in Iran was evaluated. Finally, the implicit rules in the data were extracted by using the decision tree. The results indicated that the proposed model had a high degree of accuracy and interpretation in evaluating performance compared to previous models and helps managers to make better decisions.
خلاصه ماشینی:
This study aimed to investigate the efficiency of green supply chain by using Data Envelopment Analysis (DEA) based on Malmquist Productivity Index (MPI) according to the input and output indicators of the Balanced Scorecard (BSC) model and accordingly providing some rules using the decision tree.
Further, to introduce a new approach for selection of right indicators based on BSC-DEA model (Danesh Asgari, Haeri, & Jafari, 2018), presented the BSC-DEA model for efficiency measurement in supply chains management (Haghighi, Torabi, & Ghasemi, 2016), presented a system of performance evaluation for companies by integrated DEA and BSC model (Kadarova, Durkacova, Teplicka, & Kadar, 2015), finding a model for DMUs in various stages of BSC by using BSC-DEA model (Kianfar, Ahadzadeh Namin, Alam Tabriz, & Najafi, 2016) and to introduce a new approach for selection of right indicators based on BSC-DEA model (Tan, Zhang, & Khodaverdi, 2017).
The studies on the integrated DEA and data mining method can be applied to present a DEA model combined with bootstrapping to assess performance of one of the data mining Algorithms (Alinezhad, 2016), presented a way to the efficiency evaluation of business projects by using decision tree and DEA (Sohn & Moon, 2014 ), present a new integrated DEA and data mining model which is able to find most efficient association rule by solving only one mixed integer linear programming (MILP) for measuring the efficiency of association rules with multiple criteria (Toloo, Sohrabi, & Nalchigar, 2009), the performance of judicial institutions in order to advance the efficiency and quality of judicial verdict by using the DEA and decision trees (Tsai & Tsai, 2010), present a combination of DEA and requisite data mining techniques same as artificial neural network (ANN) and decision tree are employed in order to enhance the power of predicting the DMUs evaluation performance because of their well-known efficiency, and present precise decision rules for improving their efficiency (Rahimi & Behmanesh, 2012), analyze the business performance and technical efficiency of Taiwan’s ICT industry with the MPI of DEA and decision tree (Chiang, Cheng, & Leu, 2017).