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Consensus has built on the need for a sustainable and digital transition of industries to happen in parallel. Sustainability may come from electrification of currently carbon-reliant activities, while digitalisation promises to augment and enhance the abilities of humans. Yet literature suggests a complex relationship between the interconnected social and technical systems that must co-evolve to deliver these transitions, as well as the likely impossibility of their top-down control. Insights may be derived from the first wave of electrification that began over a century ago. In particular, we develop the wave metaphor by focusing on the less visible undercurrents that feed waves and transitions. Based on a systematic review of historical transitions, the paper identifies international cases of electrification and examines factors that could explain past and inform future practice. In particular, it suggests that the transition from ownership to access may be an important but underdeveloped factor.

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

The transition to sustainability coincides with an industrial digitalization. While this latest industrial revolution creates new challenges, it also revives historical ones encountered in previous transitions. Through two parallel systematic reviews, challenges are identified for the current digitalization transition and historical transitions: mechanization, electrification and computerization. The aim of this research is to identify lessons from history that may help overcome the challenges of industrial digitalization. The paper provides illustrative examples of social factors that are either internal to a technology adopting organization or external, related to wider societal change. These factors suggest actionable insights that may support the adoption of Industrial Digital Technologies.

Implementing an Advanced Services strategy may bring economic, social and environmental sustainability, but requires orchestration of a complex system of interdependent actors. Customers, suppliers, contractors and other intermediaries must be co-ordinated effectively for mutual benefit. Doing so requires appropriate data not only related to products, but also service delivery and (customers’) use environment. This research proposes a Digital Twin approach to capturing and processing real-time data from each of these three levels, in order to orchestrate successful value creation.

This Virtual Special Issue (VSI) was proposed on par with the fascinating and exponentially growing development of smart enabling technologies, such as Internet of Things (IoT), Cyber-Physical System (CPS), Cloud Computing (CC), Artificial Intelligence (AI), Big Data Analytics (BDA), Digital Twin (DT), etc, which have greatly advanced the development of sustainable smart manufacturing throughout the lifecycle. The VSI addressed issues that were not properly or even incorrectly addressed in the existing literature. The authors of this VSI sought to introduce new knowledge and debates to lead the research directions to new paths. The editorial team invited well-established researchers in this area and received about 40 highly qualified submissions, out of which 12 were accepted after standard peer-review procedure of the Journal of Cleaner Production, which covered the three main themes defined in the "Call-for-Papers". The contributing authors were from Brazil, China, Finland, Pakistan, Sweden, USA (in alphabetical order). The coordinators of this VSI are confident that the contents of this VSI will advance the science of digitalisation and will help society to make real progress towards sustainable societies. (C) 2019 Elsevier Ltd. All rights reserved., The accepted version of this article is available on the author's personal web page