# Hybrid Methods for Selecting Precast Concrete Suppliers Based on Factory Capacity
## 7 Conclusions  
This research proposes an integrated methodology for selecting the best precast concrete supplier in the context of KSA's construction boom. The methodology employs three well-established techniques: SWARA, WASPAS, and VE. SWARA helps determine the weights and rank the identified criteria. A new method was established to estimate the CQWs of alternatives and create the CQW matrix. This study developed and validated a hybrid MCDM framework for selecting precast concrete suppliers, yielding several key conclusions with significant implications for the construction industry, particularly in high-demand markets like the KSA. Instead of reiterating the methodology, this section outlines the principal findings of the research:  
1. The framework's ability to rank suppliers in a manner perfectly consistent with expert judgment (Spearman's coefficient = 1.0) validates its core premise: that a supplier's production capacity is a robust indicator of its overall quality and reliability. The top-ranked supplier (A3) demonstrated a superior capacity, which translated into a higher Value Engineering (VE) score, confirming that in capital-intensive projects, capacity is a critical differentiator. 2. The SWARA revealed that \"Method of payment\" was the most heavily weighted criterion by the expert panel. This indicates that in the Saudi Arabian construction context, a supplier's financial terms and their impact on a contractor's cash flow are considered paramount, often outweighing purely technical specifications in the final decision-making calculus. 3. The novel use of PCA to systematically link an objective metric (factory capacity) to the subjective quality criteria (the CQW matrix) proved highly effective. This automated approach reduces the subjective burden on decision-makers, enhances consistency, and provides a transparent, data-driven foundation for the evaluation, addressing a common limitation in many traditional MCDM models. 4. The framework demonstrated high stability in the sensitivity analysis, with rankings remaining consistent despite significant variations in criteria weights and aggregation parameters. This robustness, combined with the strong validation against expert opinion, establishes the proposed hybrid model as an unprecedentedly effective and reliable tool for strategic supplier selection in the precast concrete industry. A significant concern is its continued multi-stage dependency on expert judgment for tasks such as initial criteria weighting (SWARA), defining the impact of capacity (PCA), and the final validation. Using different and, at times, small expert panels (e.g., only three experts for validation) across these stages introduces potential inconsistencies and raises questions about the generalizability and robustness of the findings. Additionally, greater transparency regarding the specific selection and differential treatment of criteria (e.g., using only 9 out of 15 initially identified criteria, with cost and capacity handled separately) would strengthen the model's methodological clarity. Finally, as acknowledged by the authors, the model's specific application within the KSA construction sector limits its immediate applicability, as different precast elements may dictate varying priorities in selection criteria (e.g., quality vs. design), and selection outcomes could differ significantly across countries. Therefore, future research should expand the framework to encompass broader industries and diverse geographical locations, providing a more comprehensive and widely applicable analysis that would greatly assist stakeholders in enhancing supplier selection efficiency. Conceptualization, M.I.A. and K.S.A.-G.; Data curation, M.I.A. ; Formal analysis, M.I.A. and N.M.A. ; Funding acquisition, K.S.A.-G. and S.I.A. ; Investigation, M.I.A., K.S.A.-G. and N.M.A. ; Methodology, M.I.A. and K.S.A.-G.; Project administration, K.S.A.-G. and S.I.A. ; Resources, M.I.A. and K.S.A.-G.; Software, N.M.A. ; Supervision, K.S.A.-G. and S.I.A. ; Validation, M.I.A.