Author(s): Neelkanth Dhone
In the Supply Chain (SC) forum, Construction Management (CM) is a sizzling research sphere due to the high turbulent demand of infrastructure entities such as buildings, malls, homes, etc., by clients. The rush of Colonizers is escalated at the marketplace to perform the said infrastructure entities. The colonizers identified brick, sand, and cement as necessities to build malls, modern houses, etc. The bricks are executed in bulk to create any infrastructure entity. It is extracted by the accurate empirical survey of 500 colonizers of Asian continents, especially in India, that colonizers' high earnings and revenues only depend upon materials procurement at feasible cost with other variables (to build practical construction SCM). During a survey of 500 colonizers, it is sound that 60% of colonizers prioritized the qualitative service variables and residue, and 40% were advised to focus on only cost variables for purchasing any construction stuff from alternative vendors. The brick procurement problem identified by the author experienced the lack of a decision support system that can address the qualitative and quantitative variables at a time to aid in buying bricks from alternative vendors. This research gap is accepted as a research objective. It is also observed by a peer-literature survey that there is still no invention of a soft computing technique to tackle the data in the form of % and crisp score vs. qualitative and quantitative variables, respectively, to lead Brick Vendor (BV) evaluation and benchmarking decision in the field of CSCM. This research gap is also respected as a research objective. The authors proposed a DSS consisting of module’ service-based qualitative as well as production cost-related quantitative variables in conjunctive with MOO-RA (Multi-Objective Optimization via Ratio-Analysis) simulation decision technique’ to lead the Brick Vendor (BV) evaluation and benchmarking decision. The objective of the research is to help the colonizers of Asian continents evaluate the optimum BV among feasible BVs. The results are illustrated in the conclusion section.