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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]

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]

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]

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]

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]

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]

Industrial Product-Service Systems (IPS2) have been increasingly researched and practiced in a variety of fields and sectors. Nevertheless, such solutions are not as significantly implemented as expected by the CIRP keynote in 2010: it is not a dominant business of manufacturing companies today despite their economic and environmental advantages. One reason for this delay is assumed to be that the initial efforts were mainly on business and service when design was an afterthought. The promising digital technologies that have become easily implementable in practice will in the next years facilitate design and implementation of IPS(2 )smartly to satisfy users and contribute to sustainability. This keynote analyses literature and practice in the last decade, aiming to propose the main characteristics of IPS2 of the future and design processes adequate for IPS2. The design process of both production systems for sustainability and high-value systems for sustainable development goals may need to be supported differently than today. The list of recommendations for future research on IPS2 design is provided at the end of the paper. (C) 2022 CIRP. Published by Elsevier Ltd. All rights reserved., Accepted version uploaded on author´s home page with CC-BY-licens: https://tomohikosakao.wixsite.com/home/work-as-a-professor-in-a-university

Industrial Product-Service Systems (IPS2) have been increasingly researched and practiced in a variety of fields and sectors. Nevertheless, such solutions are not as significantly implemented as expected by the CIRP keynote in 2010: it is not a dominant business of manufacturing companies today despite their economic and environmental advantages. One reason for this delay is assumed to be that the initial efforts were mainly on business and service when design was an afterthought. The promising digital technologies that have become easily implementable in practice will in the next years facilitate design and implementation of IPS(2 )smartly to satisfy users and contribute to sustainability. This keynote analyses literature and practice in the last decade, aiming to propose the main characteristics of IPS2 of the future and design processes adequate for IPS2. The design process of both production systems for sustainability and high-value systems for sustainable development goals may need to be supported differently than today. The list of recommendations for future research on IPS2 design is provided at the end of the paper. (C) 2022 CIRP. Published by Elsevier Ltd. All rights reserved., Accepted version uploaded on author´s home page with CC-BY-licens: https://tomohikosakao.wixsite.com/home/work-as-a-professor-in-a-university

Industrial Product-Service Systems (IPS2) have been increasingly researched and practiced in a variety of fields and sectors. Nevertheless, such solutions are not as significantly implemented as expected by the CIRP keynote in 2010: it is not a dominant business of manufacturing companies today despite their economic and environmental advantages. One reason for this delay is assumed to be that the initial efforts were mainly on business and service when design was an afterthought. The promising digital technologies that have become easily implementable in practice will in the next years facilitate design and implementation of IPS(2 )smartly to satisfy users and contribute to sustainability. This keynote analyses literature and practice in the last decade, aiming to propose the main characteristics of IPS2 of the future and design processes adequate for IPS2. The design process of both production systems for sustainability and high-value systems for sustainable development goals may need to be supported differently than today. The list of recommendations for future research on IPS2 design is provided at the end of the paper. (C) 2022 CIRP. Published by Elsevier Ltd. All rights reserved., Accepted version uploaded on author´s home page with CC-BY-licens: https://tomohikosakao.wixsite.com/home/work-as-a-professor-in-a-university

Industrial Product-Service Systems (IPS2) have been increasingly researched and practiced in a variety of fields and sectors. Nevertheless, such solutions are not as significantly implemented as expected by the CIRP keynote in 2010: it is not a dominant business of manufacturing companies today despite their economic and environmental advantages. One reason for this delay is assumed to be that the initial efforts were mainly on business and service when design was an afterthought. The promising digital technologies that have become easily implementable in practice will in the next years facilitate design and implementation of IPS(2 )smartly to satisfy users and contribute to sustainability. This keynote analyses literature and practice in the last decade, aiming to propose the main characteristics of IPS2 of the future and design processes adequate for IPS2. The design process of both production systems for sustainability and high-value systems for sustainable development goals may need to be supported differently than today. The list of recommendations for future research on IPS2 design is provided at the end of the paper. (C) 2022 CIRP. Published by Elsevier Ltd. All rights reserved., Accepted version uploaded on author´s home page with CC-BY-licens: https://tomohikosakao.wixsite.com/home/work-as-a-professor-in-a-university

Industrial Product-Service Systems (IPS2) have been increasingly researched and practiced in a variety of fields and sectors. Nevertheless, such solutions are not as significantly implemented as expected by the CIRP keynote in 2010: it is not a dominant business of manufacturing companies today despite their economic and environmental advantages. One reason for this delay is assumed to be that the initial efforts were mainly on business and service when design was an afterthought. The promising digital technologies that have become easily implementable in practice will in the next years facilitate design and implementation of IPS(2 )smartly to satisfy users and contribute to sustainability. This keynote analyses literature and practice in the last decade, aiming to propose the main characteristics of IPS2 of the future and design processes adequate for IPS2. The design process of both production systems for sustainability and high-value systems for sustainable development goals may need to be supported differently than today. The list of recommendations for future research on IPS2 design is provided at the end of the paper. (C) 2022 CIRP. Published by Elsevier Ltd. All rights reserved., Accepted version uploaded on author´s home page with CC-BY-licens: https://tomohikosakao.wixsite.com/home/work-as-a-professor-in-a-university