A Socio-Technical Fuzzy Decision-Support Framework for Sustainable Smart Waste Management

Abstract

The adoption of smart waste management systems (SWMSs) is increasingly important for textile organizations seeking to improve sustainability while addressing interconnected technological, managerial, organizational, and environmental constraints. A persistent gap is that prior SWMS studies often examine digital technologies or individual barriers in isolation, offering limited support for jointly structuring socio-technical constraints and selecting implementation strategies under uncertainty in expert judgment. This study develops an integrated socio-technical fuzzy multi-criteria decision-support framework in which technical infrastructure and data capabilities are evaluated alongside managerial decisions, organizational capabilities, workforce factors, and the broader environmental and policy context. Barriers and implementation alternatives were identified from the literature and refined through purposive consultation with three experienced textile-industry professionals in Pakistan, with 7–14 years of experience across environment, health and safety, technical, and procurement functions. Spherical fuzzy Analytic Hierarchy Process (SF-AHP) converts linguistic pairwise judgements into criteria and sub-criteria weights while accounting for varying degrees of agreement, disagreement, and uncertainty in expert assessments. These weights are then transferred to triangular fuzzy VIKOR (TF-VIKOR) to evaluate four implementation strategies against the identified barriers. Organizational constraints receive the highest weight (0.284), followed by managerial (0.254), environmental (0.233), and technological (0.230) constraints. Employee empowerment and training (A3) emerges as the preferred strategy (Q = 0.0000), followed by investment in R&D and innovation (A1; Q = 0.7702), policy advocacy and government support (A4; Q = 0.8996), and cross-sector collaboration and partnerships (A2; Q = 0.9795). The first-place ranking of A3 remains unchanged when the VIKOR decision parameter varies from 0.1 to 0.9, although the ordering of A2 and A4 changes at higher values of the parameter. The findings are specific to this expert-based case in Pakistan’s textile industry and should not be interpreted as statistically representative of the national industry. The framework supports managers and policymakers in prioritizing capability building, collaboration, technology investment, and policy support while considering environmental, economic, and social sustainability outcomes. In practical terms, the ranking can guide the sequencing of SWMS investments by emphasizing workforce readiness before sequencing technology investment, policy support, and cross-sector collaboration for capabilities such as waste tracking, automation, recycling technologies, and data analytics.