Industrial Symbiosis at Scale: Using Real-Time Data Sharing and Predictive Analytics to Match Waste Streams Across Co-Located Manufacturing Firms

Abstract

Industrial symbiosis, the practice of routing one firm's waste output as another firm's material input, has demonstrated substantial resource efficiency and waste reduction potential in eco-industrial parks worldwide. However, the realization of symbiotic exchanges has historically depended on opportunistic identification by manual brokers operating with incomplete information, limiting both the scale and the speed at which symbiosis networks can expand and adapt. This paper proposes a data-driven architecture for industrial symbiosis at scale, integrating real-time IoT waste stream characterization, machine learning quality prediction, constrained optimization matching, and automated governance interfaces within a platform connecting co-located manufacturing firms. We review the evidence base for each platform component, characterize five representative waste stream exchange types with estimated value creation, and analyze the governance requirements for translating matched opportunities into executed transactions. The evidence from digital twin-enabled waste stream monitoring demonstrates that the data infrastructure for symbiosis matching already exists and produces measurable recovery value at the facility level; the contribution of this paper is to show how that infrastructure can be extended to multi-firm, real-time, dynamic matching at industrial park scale. The proposed architecture directly addresses the five primary barriers to symbiosis network formation identified in the industrial ecology literature: information asymmetry, temporal mismatch, quality uncertainty, brokerage cost, and regulatory ambiguity.