The recent circular economy movement has raised awareness and interest about untapped environmental and economic potential in the manufacturing industry. One of the crucial aspects in the implementation of circular or closed-loop manufacturing approach is the design of circular products. While it is obvious that three post-use strategies, i.e., reuse, remanufacturing, and recycling, are highly relevant to achieve loop closure, it is enormously challenging to choose “the right” strategy (if at all) during the early design stage and especially at the single component level. One reason is that economic and environmental impacts of adapting these strategies are not explicit as they vary depending on the chosen business model and associated supply chains. In this scenario, decision support is essential to motivate adaptation of regenerative design strategies. The main purpose of this paper is to provide reliable decision support at the intersection of multiple lifecycle design and business models in the circular economy context to identify effects on cost and CO₂ emissions. The development of this work consists of a systematic method to quantify design effort for different circular design options through a multi-method simulation approach. The simulation model combines an agent-based product architecture and a discrete event closed-loop supply chain model. Feasibility of the model is tested using a case of a washing machine provided by Gorenje d.d. Firstly, design efforts for reuse, remanufacturing, and recycling are quantified. Secondly, cost and emissions of different design options are explored with different business model configurations. Finally, an optimization experiment is run to identify the most cost-effective combination of reused, remanufactured, and recycled components for a business model chosen on the basis of the explorative study results.

This paper introduces an agent-based approach to study customer behavior in terms of their acceptance of new business models in Circular Economy (CE) context. In a CE customers are perceived as integral part of the business and therefore customer acceptance of new business models becomes crucial as it determines the successful implementation of CE. However, tools or methods are missing to capture customer behavior to assess how customers will react if an organization introduces a new business model such as leasing or functional sales. The purpose of this research is to bring forward a quantitative analysis tool for identifying proper marketing and pricing strategies to obtain best fit demand behavior for the chosen new business model. This tool will support decision makers in determining the impact of introducing new (circular) business models. The model has been developed using an agent-based modeling approach which delivers results based on socio-demographic factors of a population and customers’ relative preferences of product attributes price, environmental friendliness and service-orientation. The implementation of the model has been tested using the practical business example of a washing machine. This research presents the first agent-based tool that can assess customer behavior and determine whether introduction of new business models will be accepted or not and how customer acceptance can be influenced to accelerate CE implementation. The tool integrates socio-demographic factors, product utility functions, social network structures and inter-agent communication in order to comprehensively describe behavior on individual customer level. In addition to the tool itself the results of this research indicates the need for systematic marketing strategies which emphasize CE value propositions in order to accelerate customer acceptance and shorten the transition time from linear to circular. Agent-based models are emphasized as highly capable to fill the gap between diffusion-based penetration of information and resulting behavior in the form of purchase decisions.

Stretchable electronics is a new innovation and becoming popular in various fields, especially in the healthcare sector. Since stretchable electronics use less printed circuit boards (PCBs), it is expected that the environmental performance of a stretchable electronics-based device is better than a rigid electronics-based device that provides the same functionalities. Yet, such a study is rarely available. Thus, the main purpose of this research is to perform a comparative life cycle analysis of stretchable and rigid electronics-based devices. This research combines both the case study approach and the research review approach. For the case study, a cardiac monitoring device with both stretchable and rigid electronics is used. The ISO 14044:2006 standard's prescribed LCA approach and ReCiPe 2016 Midpoint (Hierarchist) are followed for the impact assessment using the SimaPro 9.1 software. The LCA results show that the stretchable cardiac monitoring device has better environmental performance in all eighteen impact categories. This research also shows that the manufacturing process of stretchable electronics has lower environmental impacts than those for rigid electronics. The main reasons for the improved environmental performance of stretchable electronics are lower consumption of raw material as well as decreased energy consumption during manufacturing. Based on the LCA results of a cardiac monitoring device, the study concludes that stretchable electronics and their manufacturing process have better environmental performance in comparison with the rigid electronics and their manufacturing process.

Life cycle assessment (LCA) is used frequently as a decision support tool for evaluating different design choices for products based on their environmental impacts. A life cycle usually comprises several phases of varying timespans. The amount of emissions generated from different life cycle phases of a product could be significantly different from one another. In conventional LCA, the emissions generated from the life cycle phases of a product are aggregated at the inventory analysis stage, which is then used as an input for life cycle impact assessment. However, when the emissions are aggregated, the temporal variability of inventory data is ignored, which may result in inaccurate environmental impact assessment. Besides, the conventional LCA does not consider the environmental impact of circular products with multiple use cycles. It poses difficulties in identifying the hotspots of emission-intensive activities with the potential to mislead conclusions and implications for both practice and policy. To address this issue and to analyze the embedded temporal variations in inventory data in a CE context, the paper proposes calculating the emission intensity for each life cycle phase. It is argued that calculating and comparing emission intensity, based on the timespan and amount of emissions for individual life cycle phases, at the inventory analysis stage of LCA offers a complementary approach to the traditional aggregate emission-based LCA approach. In a circular scenario, it helps to identify significant issues during different life cycle phases and the relevant environmental performance improvement opportunities through product, business model, and supply chain design.