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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.

The earth’s carrying capacity cannot withstand the pace of consumption resulting from current economic models, mainly the linear economy (LE) built on a throwaway culture. In the last few decades, the concept of a circular economy (CE), aiming to design waste out of the economy and mimic ecosystems, emerged as a strong alternative to LE. Being at the heart of the economic landscape, supply chains (SCs) need to respond to the necessary shift to CE. In so doing, the planning and execution of circular supply chains (CSCs) require a broader comprehension of CE and more sophisticated and large-scale analytical decision models. This chapter surveys extant literature on available best practices and quantitative models for sustainable supply chains (SSCs) and offers a new definition of CSC. Mapping on the knowledge extracted from this classification, potential gaps and strengths in the literature are identified. Key research papers on the “closed-loop” and “open-loop” ends of CSCs are highlighted. Challenges in developing CSC performance indicators and prescriptive models are emphasized.

The production of waste creates both direct and indirect environmental impacts. A range of strategies are available to reduce the generation of waste by industry and households, and to select waste treatment approaches that minimise environmental harm. However, evaluating these strategies requires reliable and detailed data on waste production and treatment. Unfortunately, published Australian waste data are typically highly aggregated, published by a variety of entities in different formats and do not form a complete time-series. We demonstrate a technique for constructing a multi-regional waste supply-use (MRWSU) framework for Australia using information from numerous waste data sources. This is the first subnational waste input–output framework to be constructed for Australia. We construct the framework using the Industrial Ecology Virtual Laboratory (IELab), a cloud-hosted computational platform for building Australian multiregional input–output tables. The structure of the framework complies with the System of Environmental-Economic Accounting (SEEA). We demonstrate the use of the MRWSU framework by calculating waste ‘footprints’ that enumerate the full domestic supply chain waste production for Australian consumers.

Oversupplied emission basically will create a global economic downturn, which will lead to the implications for the climate action more broadly. Though the pandemic has test the resilience of carbon initiatives, there is urgency in identifying the carbon tax to strengthen as jurisdiction around the world ambitious in adopting and mitigating the targets as an introductory of the associated policy tools. Based on different situations and disciplines, the carbon tax model is simulated in different ways. The purpose of this study is to compare the available approaches that have been utilised by researchers and to determine the methods that suitable the most. The carbon tax and its influence on the construction sector are being benchmark and discussed as the whole of this document. A bibliometric approach is the method in this study in between the keyword of a carbon tax and the construction industry based on the data available in database of Scopus and Web of Science to foresee the interconnection between the knowledge of understanding and definition. The definition of carbon tax is the Pigovian tax that is designed to reduce the greenhouse gases (GHGs) emitted with aim to act as a green tax and been paid by the industries that emitted GHGs as for the carbon emission reduction agenda. The implementation is parallel to the other government policies and in sync to the sustainable development goals.

Climate change and digitisation are unquestionably the two defining features of this era. Both present immense challenges with unimaginable consequences for humankind while promising enormous rewards for those who can adequately address their adverse effects. These two critical factors must be considered while establishing strategies for the businesses' future operations. Hence, post-pandemic, especially with the rise of online commerce, packages and documents are delivered around the globe nearly every day, propelling the logistics industry's growth. This is not the critical challenge in logistics, the issue of sustainability, particularly as the returns are increasing exponentially, leading to a significant impact on transportation is and its reliance on fossil fuel has made it a prime target for society's growing environmental concerns. Thus, real-time visibility, collaboration and integration in reverse logistics (RL) are imperative for business sustainability. The most applicable Industry 4.0 technologies in RL are the Internet of Things (IoT), cloud computing, blockchain and digital twin that enable the defragmentation of the RL market.

This chapter analyses the technological impact of Industry 4.0 on RL. This research investigates the challenges faced by the logistics industry in the context of sustainability and how digital transformation can bring many potential benefits across the entire value chain. This chapter also presents a guidance for a framework based on the literature review that tends to favour the development of elastic logistics, implying improved company responsiveness to market conditions. The study contributes to the body of literature and the establishment of the framework for planning on the application of various Industry 4.0 technologies in developing eco-friendly and sustainable reverse logistics framework.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.