| Title | Measuring performance of sustainable manufacturing with recyclable wastes: A case from China's iron and steel industry |
|---|---|
| ID_Doc | 18492 |
| Authors | Wu, HQ; Lv, K; Liang, L; Hu, HH |
| Title | Measuring performance of sustainable manufacturing with recyclable wastes: A case from China's iron and steel industry |
| Year | 2017 |
| Published | |
| Abstract | In recent years, performance evaluation of sustainable operations has attracted a significant amount of research. The current paper seeks to evaluate sustainable manufacturing performance when wastes are recycled and re-used. For example, a typical sustainable process in iron and steel making involves in the first stage forging iron and steel products and in the second stage disposing the wastes. The sustainable manufacturing process then re-uses the recycled waste water in the first stage production. It is important that performance metrics include the operations related to recycling and re-use of the wastes. In a simplified view, such a sustainable operation is a two-stage process. Due to the ability of incorporating multiple performance metrics, data envelopment analysis (DEA) is used to develop our evaluation approach. While traditional DEA treats such two-stage manufacturing process as a black box by ignoring the internal conflicts and cooperation among multiple manufacturing stages, this paper proposes a modified two-stage network DEA model that deals with good and undesirable outputs from the sustainable manufacturing process. A Nash bargaining game between efficiencies of two-stages is proposed to produce unique and fair efficiency decomposition for the two-stage sustainable manufacturing process. The modified two-stage network DEA model is applied to wastewater recycling and reusing in a set of iron and steel makers in China. The results show that the proposed model is more effective than the black box DEA model in calculating efficiency of both two-stages and in identifying the sources of the inefficiency of overall sustainable manufacturing processes. (C) 2016 Elsevier Ltd. All rights reserved. |
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