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1 – 10 of 133Devrim Murat Yazan, Guido van Capelleveen and Luca Fraccascia
The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the…
Abstract
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.
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Abderisak Adam and Göran Lindahl
The purpose of this paper is to examine the Company Dynamic Response Map (CDRM) risk management model that uses the dynamic capabilities concept. The study examines risks…
Abstract
Purpose
The purpose of this paper is to examine the Company Dynamic Response Map (CDRM) risk management model that uses the dynamic capabilities concept. The study examines risks associated with strategic decision-making in construction projects and evaluates proposed methods that connect the dynamic capabilities of project-based organisations with risk management.
Design/Methodology/Approach
This preliminary study examines risks associated with strategic decision-making in construction projects and evaluates a proposed model that connects the dynamic capabilities of project-based organisations with risk management. Specifically, the CDRM model is evaluated, a risk management model developed by Arena et al. (2013) to better respond to risks and opportunities based on the concept of dynamic capabilities.
Findings
We argue that although the CDRM presents a promising development in that it uses dynamic capabilities prospectively in a risk management model to produce tangible results, there are, nonetheless, impediments to the CDRM being used by construction clients. The primary impediment relates to the issue of categorisation, the difficulty in assigning a specific identified risk to a particular category of dynamic capabilities.
Research Limitations/Implications
A conceptual argument is made and not an empirical one.
Practical Implications
The CDRM model was developed to be used in practice and this paper evaluates that model.
Originality/Value
Contributes to both the dynamic capabilities literature as well as risk management literature. The paper ends with a discussion on the possible merits of the CDRM, and an evaluation on potential impediments to its use by construction clients.
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