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The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the sustainability targets for 2030–2050 increasingly become a tougher challenge, society, company managers and policymakers require more support from AI and IT in general. How can the AI-based and IT-based smart decision-support tools help implementation of circular economy principles from micro to macro scales?

This chapter provides a conceptual framework about the current status and future development of smart decision-support tools for facilitating the circular transition of smart industry, focussing on the implementation of the industrial symbiosis (IS) practice. IS, which is aimed at replacing production inputs of one company with wastes generated by a different company, is considered as a promising strategy towards closing the material, energy and waste loops. Based on the principles of a circular economy, the utility of such practices to close resource loops is analyzed from a functional and operational perspective. For each life cycle phase of IS businesses – e.g., opportunity identification for symbiotic business, assessment of the symbiotic business and sustainable operations of the business – the role played by decision-support tools is described and embedding smartness in these tools is discussed.

Based on the review of available tools and theoretical contributions in the field of IS, the characteristics, functionalities and utilities of smart decision-support tools are discussed within a circular economy transition framework. Tools based on recommender algorithms, machine learning techniques, multi-agent systems and life cycle analysis are critically assessed. Potential improvements are suggested for the resilience and sustainability of a smart circular transition.

This paper aims to understand the implementation of a circular economic business where animal manure is used to produce biogas and alternative fertilizer in a regional network of manure suppliers and biogas producers and to reveal the impacts of five variables (manure quantity, transportation distance, manure dry content, manure price and manure discharge price) on the economic sustainability of manure-based biogas supply chains.

An enterprise input-output approach is used to model physical and monetary flows of the manure-based biogas supply chain. Computational experiments are performed on all variables to identify under which conditions the cooperation is beneficial for all actors.

The cooperation is profitable for a large-scale farm (>20,000 t/year) if biogas producer (b) pays farmer (f) to receive its manure (5 €/t) or if f sells manure for free and manure disposal costs are >10 €/t. Cooperation is always profitable for b if f pays b to supply its manure (5€/t). If b receives manure for free, benefits are always positive if b is a medium-large-scale plant (>20,000 t/year). For a small-scale plant, benefits are positive if manure dry content (MDC) is ≥12 per cent and transportation distance is ≤10 km.

The paper adds value to the biogas production research, as it makes holistic analysis of five variables which might change under different policy and geographical conditions. The investors in biogas production, suppliers and transportation companies can find correspondence to empirical findings for their own site-specific cases.