The purpose of this study is to design a DSS for construction sectors, which can determine the status of the related supplier alternatives, accompanying G-T, SC measures and their interrelated metrics. In today’s era, a supplier is observed as significant among entire agents of green supply chain (SC) management. Presently, it is determined that appraising worth of the supplier under green-traditional (G-T), SCs concerns still require the support of novel algorithmic/decision support systems (DSSs), which could embrace potential decision-making.
The authors have proposed a DSS (consisting of the implementation of multi-level multi-criterion decision-making [ML-MCDM], reference point approach [RPA] and multi-objective optimization on the basis of simple ratio analysis [MOOSRA] methods on constructed MCDM supplier evaluation appraisement module) for measuring the performance score of clay-brick suppliers coming under G-T SCs corresponding to fuzzy and non-fuzzy information. A comparative analysis is conducted among the performance scores against alternatives, obtained by the three methods, i.e. ML-MCDM, RPA and MOOSRA, for robustly making a potential decision.
The presented research offers a DSS toward managers of construction sectors for benchmarking the performance scores against supplier alternatives under G-T SC measures and their interrelated metrics, modeled by fuzzy cum non-fuzzy information.
Presented research work exhibited a DSS that can be used by construction sectors for benchmarking the supplier alternatives in accordance with their performance scores under G-T SCs. The MCDM G-T supplier evaluation appraisement module is constructed pertaining to small-scale clay-brick production units, located in the northern part of India to check the effectiveness of the proposed DSS.
Sahu, A.K., Sahu, N.K. and Sahu, A.K. (2018), "Knowledge based decision support system for appraisement of sustainable partner under fuzzy cum non-fuzzy information", Kybernetes, Vol. 47 No. 6, pp. 1090-1121. https://doi.org/10.1108/K-01-2017-0020
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