Background and challenges
Swedish manufacturing companies must implement green practices to demonstrate contribution to sustainable development and attract investments to prosper in the long term. Approaches to do so include eco-efficiency and circular economy, i.e. creating social and economic value through goods and services while minimising the environmental impact of production through efficient, closed-loop circulation of resources. In addition, industrial digitalisation presents the opportunity to unlock new ways to measure manufacturing systems’ performance and support continuous improvements towards climate-friendly and circular production systems.
Sustainability indicators are still rarely integrated in the definition of production performance (not used as drivers of improvement). Although digitalization presents new opportunities to handle more complex factors of performance, environmental impacts are not systematically considered in systems design, production planning, and process optimization. Several studies have proposed ways in which industrial digitalization can support manufacturing sustainability functions and sustainable manufacturing supply chains. However, these approaches are not systematically implemented due to challenges and drawbacks requiring further research. Developing the data infrastructure and accompanying methodologies is critical to support people in conducting sustainability assessments, communicating the assessment results and integrating them in decision making.
To address these challenges, the Factory Energy and Resource Efficiency through Digitalization (FREED) project focuses on digital solutions for environmental sustainability at factory level. The feasibility study will focus on the systems in place at the partners’ manufacturing sites. Core activities will include process mapping, data inventory, maturity assessments, and gap analysis to identify existing strengths and define areas of improvements to boost the environmental performance of production systems.
Aim and impact
The overall vision of the FREED project is to boost the “green & digital transition” using the concept of eco-efficiency applied at factory level (i.e. focusing on actions and processes within the control of manufacturing companies) to increase the environmental performance of industrial systems and progress towards absolute sustainability. Within the FREED framework, the environmental implications of production improvements on the product and service life cycles are defined as an integral part of industrial systems’ performance.
To deliver the expected impacts of the Produktion2030 strategic innovation programme, the FREED project will anchor its operating principles on eco-efficiency to include reduced material and energy intensity (increase efficiency and eliminate or minimize waste), reduced toxicity (eliminate or minimize environmental impacts), increased sustainable use of renewable sources (for both energy and materials), value retention (circular strategies for product life extension and material waste valorisation) and increased service intensity (product-service systems and industrial services).
The main objective of FREED is to develop data-supported approaches for the integration of systemic environmental impacts into production performance management and to avoid problem-shifting between product life cycle stages. Environmental tools, methods, and indicators, including circularity indicators and science-based assessment mechanisms, will be considered (for Step 2) to identify tradeoffs between upstream and downstream impacts when making changes within production systems.
The FREED project contributes to the global goals of sustainable production (SDG12), climate action (SDG13), and sustainable industrialization (SDG9) by promoting efficient use of natural resources and minimised environmental impacts through digitalization. The FREED toolkit aims to support workers and managers in manufacturing companies in integrating seamlessly environmental information in their role and actions by reducing the cognitive loading for processing this information. In addition, the toolkit aims to be generic and adaptable to ensure broad applicability in the manufacturing industry and to meet specific company’s needs building on existing strengths and supporting improvement in critical areas.
Mélanie Despeisse, Chalmers University of Technology