Since it first appeared in literature in the early nineties, the Circular Economy (CE) has grown in significance amongst academic, policymaking, and industry groups. The latest developments in the CE field have included the interrogation of CE as a paradigm, and its relationship with sustainability and other concepts, including iterative definitions. Research has also identified a significant opportunity to apply circular approaches to our rapidly changing industrial system, including manufacturing processes and Industry 4.0 (I4.0) which, with data, is enabling the latest advances in digital technologies (DT). Research which fuses these two areas has not been extensively explored. This is the first paper to provide a synergistic and integrative CE-DT framework which offers directions for policymakers and guidance for future research through a review of the integrated fields of CE and I4.0. To achieve this, a Systematic Literature Review (SLR; n = 174) of the empirical literature related to digital technologies, I4.0, and circular approaches is conducted. The SLR is based on peer-reviewed articles published between 2000 and early 2018. This paper also summarizes the current trends in CE research related to manufacturing. The findings confirm that while CE research has been on the increase, research on digital technologies to enable a CE is still relatively untouched. While the “interdisciplinarity” of CE research is well-known, the findings reveal that a substantial percentage is engineering-focused. The paper concludes by proposing a synergistic and integrative CE-DT framework for future research developed from the gaps in the current research landscape.

This paper presents an investigation on how simulation informed by the latest advances in digital technologies such as the 4th Industrial Revolution (I4.0) and the Internet of Things (IoT) can provide digital intelligence to accelerate the implementation of more circular approaches in UK manufacturing. Through this research, a remanufacturing process was mapped and simulated using discrete event simulation (DES) to depict the decision-making process at the shop-floor level of a remanufacturing facility. To understand the challenge of using data in remanufacturing, a series of interviews were conducted finding that there was a significant variability in the condition of the returned product. To address this gap, the concept of certainty of product quality (CPQ) was developed and tested through a system dynamics (SD) and DES model to better understand the effects of CPQ on products awaiting remanufacture, including inspection, cleaning and disassembly times. The wider application of CPQ could be used to forecast remanufacturing and production processes, resulting in reduced costs by using an automatised process for inspection, thus allowing more detailed distinction between “go” or “no go” for remanufacture. Within the context of a circular economy, CPQ could be replicated to assess interventions in the product lifecycle, and therefore the identification of the optimal CE strategy and the time of intervention for the current life of a product—that is, when to upgrade, refurbish, remanufacture or recycle. The novelty of this research lies in investigating the application of simulation through the lens of a restorative circular economic model focusing on product life extension and its suitability at a particular point in a product’s life cycle.

The emergence of new technologies such as the Internet of Things, big data, and advanced robotics, together with risks such as climate change, rising labour costs, and a fluctuating economy, are challenging the current UK manufacturing model. In this paper, business models for re-distributed manufacture (RdM) are developed using anIDEF (Icam DEFinition for Function Modelling) description to serve as a guide for the implementation of the RdM concept in the consumer goods industry. This paper explores the viability of a re-distributed business model for manufacturers employing new manufacturing technologies such as additive manufacturing or three-dimensional (3D) printing, as part of a sustainable and circular production and consumption system. An As-Is value chain model is presented alongside the proposed new business model for a sustainable re-distributed manufacturing system. Both are illustrated via a case study drawn from the shoe manufacturing industry. The case study shows that there is a need for robust facilities in close proximity to the customer. These facilities are store fronts which can also manufacture, remanufacture, and provide services. The reduction in transportation and increase in customer involvement throughout the process are the main benefits that would accrue if a re-distributed model is implemented in the given industry.

The market for electric motors is experiencing a step-growth due to their adoption across a range of industrial sectors. This increased demand also highlights the importance of end-of-life management of electric motors and a requirement for appropriate strategies for the high value materials embedded in them. This paper aims to offer a holistic view on the circular economy research for electric motors and the role of Industry 4.0 technologies by presenting the state-of-the-art available in literature and comparing it with the industrial perspective. The literature review revealed the absence of a methodology for selecting the best end-of-life scenario for industrial electric motors. Recycling, which is an end-of-product-life strategy, was found to be the key focus area of research. Reuse, which is a better strategy in terms of waste hierarchy, was the least researched area due to lack of information about the condition and availability of returned products. In order to capture the current landscape within the UK for the repair, remanufacture and recycling of electrical machines, a structured survey of UK based companies was conducted. The survey revealed that nearly half of the companies do not undertake any repair strategies for electrical machine components; however, there was an aspiration from the respondents to migrate their companies towards more sustainable activities. The industry survey and the review of existing literature led to the identification of research trends, challenges and recommendations for future research.

The UK is home to several major air commercial and transport hubs. As a result, there is a high demand for Maintenance, Repair, and Overhaul (MRO) services to ensure that fleets of aircraft are in airworthy conditions. MRO services currently involve heavy manual labor. This creates bottlenecks, low repeatability, and low productivity. Presented in this paper is an investigation to create an automation cell for the fan-blade reconditioning component of MRO. The design and prototype of the automation cell is presented. Furthermore, a digital twin of the grinding process is developed and used as a tool to explore the required grinding force parameters needed to effectively remove surface material. An integration of a 6-DoF industrial robot with an end-effector grinder and a computer vision system was undertaken. The computer vision system was used for the digitization of the fan-blade surface as well as tracking and guidance of material removal. Our findings reveal that our proposed system can perform material removal, track the state of the fan blade during the reconditioning process and do so within a closed-loop automated robotic work cell.

Monitoring of complex industrial processes can be achieved by obtaining process data by utilising various sensing modalities. The recent emergence of deep learning provides a new routine for processing multi-sensor information. However, the learning ability of shallow neural networks is insufficient, and the data amount required by deep networks is often too large for industrial scenarios. This paper provides a novel deep transfer learning method as a possible solution that offers an advantage of better learning ability of the deep network without the requirement for a large amount of training data. This paper presents how Transformer with self-attention trained from natural language can be transferred to the sensor fusion task. Our proposed method is tested on 3 datasets: condition monitoring of a hydraulic system, bearing, and gearbox dataset. The results show that the Transformer trained from natural language can effectively reduce the required data amount for using deep learning in industrial sensor fusion with high prediction accuracy. The difficult and uncertain artificial feature engineering which requires a large workload can also be eliminated, as the deep networks are able to extract features automatically. In addition, the self-attention mechanism of Transformer aids in the identification of critical sensors, hence the interpretability of deep learning in industrial sensor fusion can be improved.