With the growth of the world's population and depletion of limited resources, economic development is shifting from ‘take-make-waste’ linear model to a sustainable Circular Economic (CE) model. It is becoming essential to extend the useful life of products to avoid throwaway. In the manufacturing industry, this is especially relevant for long-life expensive capital items. Literature has presented many approaches to extend product life, such as refurbishment, upgrade, repair, and recycling. Depending on a capital item's condition, manufacturers perform a series of activities to extend its useful life, such as refurbishment activities followed by upgrade activities. These life extension activities may involve various stakeholders and systems and significantly impact the capital item's life cycle. It is a challenge to enable cross-collaboration between these different stakeholders and systems, and provide manufacturers a holistic view of the activities performed along the entire life cycle of a capital item. The Digital Thread (DT) concept, which has been applied in product lifecycle management and manufacturing, provides an opportunity to tackle these challenges. However, there is little research about developing and applying DT for extending the useful life of capital items in manufacturing companies. This paper presents ongoing research to develop a DT design tool for product life extension. We take the development of a DT sample for the Refurbishment protocol deployment as an example to illustrate the approach. This tool will pave the way to support manufacturers to extend their large capital items' useful life.

Industry 4.0 enables the modernisation of machines and opens up the digitalisation of processes in the manufacturing industry. As a result, these machines are ready for predictive maintenance as part of Industry 4.0 services. The benefit of predictive maintenance is that it can significantly extend the life of machines. The integration of predictive maintenance into existing production environments faces challenges in terms of data understanding and data preparation for machines and legacy systems. Current AI frameworks lack adequate support for the ongoing task of data integration. In this context, adequate support means that the data analyst does not need to know the technical background of the pilot’s data sources in terms of data formats and schemas. It should be possible to perform data analyses without knowing the characteristics of the pilot’s specific data sources. The aim is to achieve a seamless integration of data as information for predictive maintenance. For this purpose, the developed data-sharing infrastructure enables automatic data acquisition and data integration for AI frameworks using interoperability methods. The evaluation, based on two pilot projects, shows that the step of data understanding and data preparation for predictive maintenance is simplified and that the solution is applicable for new pilot projects.

Product design is crucial for product success. Many approaches can improve product design quality, such as concurrent engineering and design for X. This study focuses on applying product usage information (PUI) during product development. As emerging technologies become widespread, an enormous amount of product-related information is available in the middle of a product’s life, such as customer reviews, condition monitoring, and maintenance data. In recent years, the literature describes the application of data analytics technologies such as machine learning to promote the integration of PUI during product development. However, as of today, PUI is not efficiently exploited in product development. One of the critical issues to achieve this is identifying and integrating task-relevant PUI fit for purposes of different product development tasks. Nevertheless, preparing task-relevant PUI that fits different product development tasks is often ignored. This study addresses this research gap in preparing task-relevant PUI and rectifies the related shortcomings and challenges. By considering the context in which PUI is utilized, this paper presents a systematic procedure to help identify and specify developers’ information needs and propose relevant PUI fitting the actual information needs of their current product development task. We capitalize on an application scenario to demonstrate the applicability of the proposed approach.

The ‘take-make-dispose’ consumption model that remains shackled to the global economy, is also affecting production assets. Industry is promoting the transition to a circular economy (CE). Deployment of the evolving digital technologies and realisation of Industry 4.0 (I4.0) enhance machines automation, connectivity and provide large amounts of data that can be interpreted in a way that were not possible before. It offers to companies the opportunity to retrofit their legacy equipment that cannot meet the constantly increasing market demands. This helps to extend the useful lifetime of this equipment and accelerate the transition to a CE. However, Industry is still lacking an efficient systemic multi-level approach that enables a cost-effective, holistic, and integrated application of CE principles to the digital uplifting of factory 4.0 capital investments. Companies and particularly SMEs face diverse challenges for implementing I4.0 systems in their businesses due to lack of knowledge, expertise and investment. The translation of CE strategies into concrete actions will help professionals to consider circularity measures in a broad sense. This paper introduces the 10 Circularity Protocols for extending the useful life of large industrial equipment, addressing issues at product, machine, and process level. With guidance on the application of I4.0 technologies, the Protocols help to digitally re-engineer outdated machines to be more productive and energy-efficient, to decrease maintenance downtime, spare-parts storage and off-spec pieces. The Protocols also support on the End-of-Life (EoL) management of the different machine components that are not technically, economically and environmentally feasible to be reused or repaired. As a final step, the future research line and methodology focusing on the digital implementation of the Circularity Protocols guidelines for their deployment and validation in real industrial cases is introduced.