The society today lives on the philosophy of ‘take-make-use-dispose.’ In the long run, this is not sustainable as the natural resources and the waste carrying capacity of the earth are limited. Therefore, it is essential to reduce dependency on the natural resources by decoupling the growth from the consumption. In this venture, both the society and the manufacturing industry have a vital role to play. The society needs to shift towards Circular Economy that rests upon the philosophy of ‘take-make-use-reuse’ and the manufacturing industry has to be a major stakeholder in this shift. Despite being proven to be both economically and environmentally beneficial, successful examples of circular systems are few today. This is primarily due to two reasons; firstly, there is a lack of systemic and systematic approach to guide industries and secondly, there is a lack of analysis methods and tools that are capable of assessing different aspects of circular manufacturing systems. Taking on to these challenges, the objective of this research is to bring forward a framework with methods and decision support tools that are essential to implement circular manufacturing systems. The initial conceptual framework with the systemic approach is developed based on extensive review and analysis of research, which is further adapted for industrial implementation. Systematic analysis methods, decision support and implementation tools are developed to facilitate this adaptation. This development has been supported by four cases from diverse manufacturing sectors. Behind each decision support tool, there are analysis methods built upon mainly system dynamics principles. These tools are based on simulation platforms called Stella and Anylogic. Among other things, these tools are capable of assessing the performance of closed-loop supply chains, consequences of resource scarcity, potential gains from resource conservation and overall economic and environmental performance of circular manufacturing systems.

Manufacturing industry is under permanent pressure to maintain its economic growth and profitability as strong societal backbone. At the same time pressures of waste generation and resource consumption are increasing as result of manufacturing operations. Since manufacturing industry is one of the major consumers of natural resources it is therefore essential to reduce dependency on natural resources by decoupling economic growth from consumption. Resource efficiency approaches can improve the performance of production systems by reducing resource losses. However, the fundamental assumption at the basis of resource efficiency approaches is that resources are available infinitely. As a consequence, challenges of sustainability and resource scarcity remain inadequately addressed. The objective of this research is to develop analysis methods and decision support tools for manufacturing industry to facilitate its transition from linear production systems to circular manufacturing systems, which are economically viable and environmentally sustainable. The initial scope of study focuses on industrial resource efficiency assessment in production systems. Expanding the view to a manufacturing system perspective, the current research is explored with regard to circular manufacturing systems in the context of economic benefits, resource scarcity and waste generation. Systematic analysis methods and decision support tools are developed for industrial companies to facilitate the adaption of circular manufacturing systems. These developments are supported by industrial case studies. The analysis methods are to the largest extent based on agent-based simulation approaches. The tools are capable of assessing the economic and environmental impact of different business models, design strategies as well as supply chains settings. Moreover, the tools are able to determine whether introductions of new (circular) business models will be adopted by customers. One empirical market study is performed to investigate value propositions of a circular business approaches based on customer decisions.