Exploring the role of Digital Twin for Asset Lifecycle Management

Proceedings,16th IFAC Symposium on Proceedings,16th IFAC Symposium on Information Control Problems in Manufacturing Proceedings,16th IFAC Symposium on Available online at www.sciencedirect.com Information Control Problems in Manufacturing Bergamo, Italy, June 11-13, 2018 Proceedings,16th IFAC Symposium on Information Control in Manufacturing Bergamo, Italy, JuneProblems 11-13, 2018 Information Control in Manufacturing Bergamo, Italy, JuneProblems 11-13, 2018 Bergamo, Italy, June 11-13, 2018

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Exploring the role of Digital Twin for Asset Lifecycle Management Exploring the role of Digital Twin for Asset Lifecycle Management Exploring the role of Digital Twin for Asset Lifecycle Management Exploring the role of Digital Twin for Asset Lifecycle Marco Macchi*, Irene Roda*, Elisa Negri*, Luca Fumagalli*Management

Marco Macchi*, Irene Roda*, Elisa Negri*, Luca Fumagalli*  Elisa Marco Macchi*, Irene Roda*, Luca Fumagalli*  Elisa Negri*, Marco Macchi*, Irene Roda*, Negri*, Luca Fumagalli*  *Department of Management, Economics and Industrial Engineering, Politecnico di Milano, P.za Leonardo da Vinci 32,  *Department of Management, Economics and Industrial Engineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133 Milano, Italy Contact author's email: [email protected] *Department of Management, Economics and Industrial Engineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133 Milano, Italy Contact author's email: [email protected] *Department of Management, Economics and Industrial Politecnico di Milano, P.za Leonardo da Vinci 32, 20133 Milano, Italy - Contact Engineering, author's email: [email protected] 20133 Milano, Italy - Contact author's email: [email protected] Abstract: This work is an explorative study to reflect on the role of digital twins to support decisionAbstract: is anmanagement. explorative study to reflect on the the role of convergence digital twins to needs support making inThis assetwork lifecycle The study remarks fordecisiondecision Abstract: This work is an explorative study to reflect on the current role of digital twins of to support decisionmaking in asset lifecycle management. The study remarks the current convergence of needs formodeling. decision Abstract: This work is an explorative study to reflect on the role of digital twins to support decisionsupport from Asset Management and of potentials for decision support offered by Digital Twin making in asset lifecycle management. The study remarks the current convergence of needs for decision support from Asset Management and of potentials for decision support offered by Digital Twin modeling. making in asset lifecycle management. The study remarks the current convergence of needs for decision The importance of digital twins isand evident through for statedecision of the art as welloffered as practical use case support from Asset Management of potentials support by Digital Twinanalysis. modeling. The importance of digital twins isand evident through for statedecision of the art as welloffered as practical use case analysis. support from Asset Management of potentials support by Digital Twin modeling. The importance of digital twins is evident through state of theHosting art as well as practical userights case analysis. © 2018, IFAC (International Federation of Automatic Control) by Elsevier Ltd. All Keywords: Asset Lifecycle management, Digital twin, Decision support. The importance ofmanagement, digital twins is evident through state of the art as well as practical use case reserved. analysis. Keywords: Asset management, Lifecycle management, Digital twin, Decision support. Keywords: Asset management, Lifecycle management, Digital twin, Decision support.  Keywords: Asset management, Lifecycle management, Digital twin, Decision support.  Big Data analytics, that access the data and promise to  1. INTRODUCTION Big Data that access dataofand promise to  provide fastanalytics, decision-making with the the use smart analytics 1. INTRODUCTION Big Data analytics, that access the dataofand promise to provide fast decision-making with the use smart analytics 1. INTRODUCTION Big Data analytics, that access the data and promise to (Davis et al. 2012; Lee, Kao, and Yanganalytics 2014), Nowadays, Asset Management (AM) has gained considerable tools provide fast decision-making with the use of smart 1. INTRODUCTION tools (Davis et al. 2012; Lee, Kao, and Yang 2014), Nowadays, Asset Management (AM) has gained considerable provide fast decision-making with the use of smart analytics momentum Asset and interest in both(AM) academia and industry as a tools complement IoT further for decision(Davisthe al. with 2012; Lee,features Kao, useful and Yang 2014), Nowadays, Management has gained considerable momentum and interest both(AM) academia and et industry as a complement theet with further features for discussed decisiontools etIoT al. 2012; Lee, Kao, useful and Yang 2014), Nowadays, Asset Management has gained considerable business process and as ain discipline (El-Akruti al., 2013). making(Davis support. Advanced simulation is also momentum and interest in both academia and industry as a complement the IoT with further features useful for decisionbusiness process and as ainof discipline (El-Akruti et al., 2013). making support. Advanced simulation isofalso discussed momentum and interest both academia and industry as a complement the IoT with further features useful for decisionThe ISO 5500X body standards (ISO 55000, 2014) amongst the tools relevant for prediction the stochastic business and as a of discipline (El-Akruti et al., 2013). making support. Advanced isofalso discussed The ISOprocess 5500X body standards (ISO 55000, amongst the relevant forsimulation prediction the Fumagalli, stochastic business process and as it. a of discipline (El-Akruti et al., 2013). contributed reinforcing The established consensus is2014) that making support. simulation also discussed processes thattools occurAdvanced in the physical world is (Negri, The ISO 5500X body standards (ISO 55000, 2014) amongst the tools relevant for prediction of the stochastic contributed reinforcing it.ofThe established consensus that processes that occurrelevant in the physical world (Negri, The ISOcontribute 5500X body standards (ISO 55000, 2014) AM can to ensure proper management of theis assets amongst the tools for prediction of the Fumagalli, stochastic and Macchi 2017). contributed reinforcing it. The established consensus is that processes that occur in the physical world (Negri, Fumagalli, AM can to(Schuman ensure proper management of theisassets Macchi 2017). contributed reinforcing it. The established consensus that along thecontribute lifecycle and Brent 2005; El-Akruti et and processes that occur in the physical world (Negri, Fumagalli, In such a technology landscape, an important concept is often AM can contribute to(Schuman ensure proper management of the assets and Macchi 2017). along the lifecycle and Brent 2005; El-Akruti et In such a technology landscape, anmeant important concept isdigital often AM can contribute to(Schuman ensure management of theofassets al., 2013): the assets are proper brought at the center the and Macchi 2017). remarked: the Digital Twin (DT), as a system’s along the lifecycle and Brent 2005; El-Akruti et In such a technology landscape, anmeant important concept isdigital often al., 2013): the assets are brought atalong the center of the remarked: the Digital Twin (DT), as a system’s along the lifecycle (Schuman and Brent 2005; El-Akruti et management process to create value their lifecycle. In such a technology landscape, an important concept is often counterpartthe along its lifecycle. Themeant DT can considered as a al., 2013): the assetsto are brought atalong the their centerlifecycle. of the remarked: Digital Twin (DT), as be a system’s digital management process create value counterpart along its lifecycle. The DT can be considered asofa al., 2013): the assets are brought at the center of the Other processes, as maintenance management, contribute to virtual remarked: the Digital Twin (DT), meant as a system’s digital entity, relying on the sensed and transmitted data management process to create value along their lifecycle. counterpart along its lifecycle. The DT can be considered asofa Other processes, as maintenance management, contribute to virtual entity, relying on the sensed and transmitted data management process to create value along their lifecycle. AM, as they are seen as key activities part of the larger AM counterpart along its lifecycle. The cantransmitted be considered asofa IoT entity, infrastructure as well as on DT theand capability to elaborate Otherasprocesses, as maintenance management, contribute to the virtual relying on the sensed data AM, they are seen as key activities part of the larger AM IoT infrastructure as well as Otherasprocesses, as 2010; maintenance management, contribute to the on theand capability topurpose elaborate process (Hastings CSN EN 16646 2014; Amadivirtual entity, relying on the sensed transmitted data of data by means of Big Data technologies, with the to AM, they are seen as key activities part of the larger AM IoT infrastructure as welltechnologies, as on the capability elaborate process (Hastings 2010; CSN EN 16646 AmadiAM, as they seen as key activities part of 2014; the larger AM the data byoptimizations means of Bigand Data with theto to Echendu and are Brown 2010). the IoT infrastructure as well as on the capability topurpose elaborate allow decision-making. In this scope, the process (Hastings 2010; CSN EN 16646 2014; Amadidata by means of Big Data technologies, with the purpose to Echendu and asset-related Brown2010; 2010).decision-making process (Hastings CSN EN 16646process 2014; isAmadioptimizations and decision-making. In this scope, the In AM, the a key allow data by means of Big Data technologies, with the purpose to DT is often overlapped with advanced simulation. Overall, Echendu and Brown 2010). allow optimizations and decision-making. In this scope, the In AM, the asset-related decision-making process is a key Echendu and asset-related Brownet2010). is optimizations often overlapped with advanced simulation. Overall, matter (El-Akruti al., decision-making 2013; Komonen process et al., 2006): it DT allow and decision-making. In this scope, the DT, as a virtual entity, can regard everything of the In AM, the is a key is often with can advanced simulation. Overall, matter et for al.,supporting 2013; Komonen et al., 2006): it DT In AM,(El-Akruti the asset-related decision-making process is a key as aoverlapped virtual entity, regard everything oftaken the requires proper tools the decision-making, and DT DT, is often overlapped withassets advanced simulation. Overall, physical world, thus physical can also be directly matter (El-Akruti et for al.,supporting 2013; Komonen et al., 2006): it the the DT, as a virtual entity, can regard everything oftaken the requires proper tools the decision-making, and matter (El-Akruti et al., 2013; Komonen et al., 2006): it physical world, thus physical assets can also be directly should be built based on principles that address the holistic the DT, as a virtual entity, can regard everything of the as a targetworld, scopethus of DT modeling. requires proper tools for supporting the decision-making, and physical physical assets can also be directly taken should be built based on principles that address the holistic requires proper tools for supporting the decision-making, and as a target scope of DT modeling. vision associated to AM (Schumanthatand Brentthe 2005). A physical world, thus physical assets can also be directly taken Some questions arise: what does really mean having a DT for should be built based on principles address holistic a target scopearise: of DTwhat modeling. vision associated to (Schumanthat Brentthe 2005). A as should be built based onisprinciples address holistic Some questions does really mean having a DT for challenge to this endAM enabling anand informed decisionas a target scope of DT modeling. aSome physical asset? What is the role of DTs in AM? Motivated vision associated to AM (Schuman and Brent 2005). A questions what does mean havingMotivated a DT for challenge to this to end is enabling anand informed decisionvision associated AM (Schuman Brent 2005). A aSome asset?arise: What isintended the rolereally of DTs in making, which requires a proper data/information questions arise: does really mean having a DTisfor byphysical these questions, thewhat purpose of AM? this paper to challenge to this end is enabling an informed decisiona physical asset? What is the role of DTs in AM? Motivated making, which requires a proper data/information challenge to this end is enabling an informed decision- by thesean questions, the to of this paper is to management (Ouertani, Parlikad, and McFarlane 2008). aby physical asset? What isintended the role purpose of DTs in AM? Motivated provide exploration discuss the role of DTs in AM, in making, which requires a proper data/information these questions, the intended purpose of this paper is to management (Ouertani, Parlikad, and McFarlane 2008). making, which requires proper provide exploration discuss the role of of this DTs in AM, in Information has always been aarelevant issuedata/information well discussed by thesean questions, the to intended purpose paper is to particular in the asset-related decision-making process. Thus, management (Ouertani, Parlikad, and McFarlane 2008). provide an exploration to discuss the role of DTs in AM, in Information has always been a relevant issue well discussed management (Ouertani, and(Ouertani, McFarlane 2008). particular inexploration the asset-related decision-making process. Thus, within the AM body ofParlikad, knowledge Parlikad, and this provide to discuss theatrole of DTs AM, in is aanconceptual paper aiming putting the inbasis for Information has always a relevant issue well discussed particular in the asset-related decision-making process. Thus, within the AM body of been knowledge (Ouertani, Parlikad, and this is a conceptual paper aiming at putting the basis for Information has always been a relevant issue well discussed McFarlane 2008; Borek et al. 2014; Kans and Ingwald 2008; particular in the asset-related decision-making process. Thus, research to contribute to a wider understanding of the benefits within the AM body of knowledge (Ouertani, Parlikad, and this is a toconceptual paper aiming at putting thethebasis for McFarlane 2008; Borek et al. 2014; Kans and Ingwald 2008; research contribute to alifecycle wider understanding benefits within the Lin, AM body of2006). knowledge (Ouertani, Parlikad, and this Koronios, and Gao Today, the interest in debating is a toconceptual paper aiming at putting of thethebasis for of applying DTs in asset management. Thanks to the McFarlane 2008; Borek et al. 2014; Kans and Ingwald 2008; research contribute to alifecycle wider understanding of benefits Koronios, Lin, and Gao 2006). Today, the interest in debating of applying DTs in asset management. Thanks to the McFarlane 2008; et al. has 2014; Kans and Ingwald 2008; research to contribute to the alifecycle wider understanding of the benefits information relevant for2006). AM been renewed asinthe recent knowledge gathered by authors in the scope of different Koronios, Lin, andBorek Gao Today, interest debating of applying DTs in asset management. Thanks to the information relevant for2006). AM has been the renewed asainthe recent of knowledge gathered by the authors in the scope ofindifferent Koronios, Lin, and Gao Today, the interest debating applying DTs in asset lifecycle management. Thanks the projects where they experienced the concept of DT itstouse, breath of technological advancements is having profound information relevant for AM has been renewed as the recent knowledge gathered by the authors in the scope of different breath ofSensor-related technological advancements is havingas a the profound projects where they enabling experienced the in concept oftechnologies DT indifferent its use, information relevant for AM has been renewed recent knowledge gathered by the authors the scope of a focus is adopted to link the new to impact. technologies are enabling an extended breath ofSensor-related technologicaltechnologies advancements having an a profound projects where they enabling experienced the concept oftechnologies DT in its use, impact. are is enabling extended aprojects focus is adopted to link the new to where they experienced the concept of DT in its use, breath of technological advancements is having a profound their applications in industrial settings where asset-related data gathering, while advanced controls are promising the a focus is adopted enabling to link the new technologies to impact. Sensor-related technologies are enabling an extended applications industrial settings where asset-related data gathering, while advanced controls area promising the atheir focus is adopted enabling to link theexperience, new technologies to decision-making is in required. This field developed impact. Sensor-related technologies are enabling an extended “zero/unplanned downtime” of assets, with cost-effective their applications in industrial settings where asset-related data gathering, while advanced controls are promising the decision-making is in required. Thissettings field experience, developed “zero/unplanned downtime” of assets, with cost-effective applications industrial where asset-related and presented through five explorative application use cases, data gathering,These while advanced controls areaaofpromising the their management. opportunities are worth a study that decision-making is required. This field experience, developed “zero/unplanned downtime” of assets, with cost-effective and presented through five explorative application use cases, management. These opportunities are worth of a study that decision-making is required. This field experience, developed is complemented by a state of the art analysis providing the “zero/unplanned downtime” of assets, with a cost-effective looks at potentials for AM arising from digitization. andcomplemented presented through five explorative application use cases, management. These are worth of a study that and is by a five stateexplorative of the art analysis providing the looks at potentials foropportunities AM arising from digitization. presented through application use cases, foundation of the study. management. These opportunities are worth of a study that is complemented by a state of the art analysis providing the Digitization is related to the Internet of Things (IoT) concept looks at potentials for AM arising from of the study. is complemented by athe state of of thetheartartanalysis the Digitization is related to the Internet of digitization. Things (IoT) concept foundation Section 2 synthesizes state on AMproviding and its needs looks at potentials for AM arising from digitization. and the convergent development of many other technologies foundation of the study. Digitization is related to the Internet of Things concept foundation Section thedecision-making, state of the art onand AMonand needs of the study. and the convergent development ofRevolution many other(IoT) technologies in terms22ofsynthesizes asset-related DTits concept Digitization related to the Internet of Things concept Section synthesizes thedecision-making, state of the art onand AMonand its needs discussed inisthe Fourth Industrial (aka Industry and the convergent development of many other(IoT) technologies in terms of asset-related DT concept Section 2 synthesizes state of the art on AM and its needs discussed infounding the Fourth Industrial Revolution (aka Industry and its potentials for the decision support. Section 3DT clears out and the convergent development of many other technologies in terms of asset-related decision-making, and on concept 4.0). As a concept, IoT sees the all devices, with discussed infounding the Fourth Industrial Revolution (aka Industry and its potentials for the decision support. Section 3DT clears out in terms of asset-related decision-making, andononDTs concept 4.0). As a concept, IoT sees the all devices, with the rationale behind explorative study in AM. discussed in the Fourth Industrial Revolution (aka Industry and rationale its potentials for decision support. Section 3 clears out embedded and sees capabilities to connect to the 4.0). As a sensors, foundingelectronics concept, IoT the all devices, with behind explorative study on DTs in AM. its potentials fora the decision support. Section 3inclears out embedded sensors, electronics and capabilities to connect to and Section 4 provides collection of experiences different 4.0). foundingSuch concept, sees all and devices, with the rationale behind the explorative study on DTs in AM. others,Asasa“things”. thingsIoT allow to the collect exchange embedded sensors, electronics and capabilities to connect to Section 4 projects, provides a the collection experiences in modeling different rationale behindto explorative study on DT DTs in AM. others, as “things”. Such things and allow to collect exchange industrial analyze theof of the embedded sensors, electronics capabilities toBrock, connect to the Section 4 projects, provides a collection ofuse experiences in modeling different data internet 2009; Sarma,and and others,through as “things”. Such(Ashton things allow to collect and exchange industrial to analyze the use of the DT Section 4 provides a collection of experiences in different data through internet (Ashton 2009; Sarma, Brock, and concept. Conclusions sectionthe5 use aimsoftothestimulate future others,through as “things”. Such things through allow to networks collect industrial projects, to in analyze DT modeling Ashton 2000) and, in general, ofexchange devices, data internet (Ashton 2009; Sarma,and Brock, and industrial concept. Conclusions sectionthe5 use aimsoftothestimulate future projects, to in analyze DT modeling Ashton 2000) and, in general, through networks of devices, researches. data through internet (Ashton 2009; Sarma, Brock, and concept. Conclusions in section 5 aims to stimulate future named as smart objects (Atzori, Iera, and Morabito 2010). Ashton 2000) and, in general, through networks of devices, researches. Conclusions in section 5 aims to stimulate future named smart objects (Atzori, Iera, and Morabito 2010). concept. Ashton as 2000) and, in general, through networks of devices, researches. named as smart objects (Atzori, Iera, and Morabito 2010). researches. named as smart objects (Atzori, Iera, and Morabito 2010).

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of decision-making process, there is, then, the need, at least, to reduce the time wastes that may occur when collecting the required information. Moreover, there is a clear need to avoid some inconsistency in the decisions with regard to the AM principles (which may emerge due to a lack of information). Think, for example, that, as some information is missed, there is an omission concerning the risk orientation: it may lead to potentially having a cascade effect on other AM principles, e.g. lifecycle orientation, as the achievement of performances / long-term objectives may be partially achievable, or not achievable at all. All these needs require proper data/information management. Nowadays, to the best knowledge of the authors, there is not such a full support, as desired: data/information management is not fully linked to the lifecycle management of the assets. As a final effect, informed decisions are partially achievable. Therefore, the future years will require to develop a better decision support. For practical reasons, it can be assumed that the development occurs in the scope of the asset information systems actually existent, while extending the relationships to the lifecycle management of the assets. The DT modelling can be considered as a relevant concept to this end. The next subsection discusses its potentials.

2. STATE OF THE ART 2.1 Asset Management and the decision support needs The main principles that are the foundations for proper assetrelated decision-making within an AM system are the following (Roda and Macchi 2016):  life cycle orientation, leading to incorporate long-term objectives and performances to drive decision-making;  system orientation, motivating the relevance of a holistic consideration of asset systems in their entirety, and not merely of the individual components;  risk orientation, relating to the relevance to consider risk as assets normally suffer from uncertainties in achieving stated objectives, thus, there is a subsequent need for risk management approaches;  asset-centric orientation, leading to focus on asset data and information of the assets, to make sound business decisions. In accordance to these principles, every time an asset-related decision has to be made, the company should consider two main aspects (El-Akruti et al. 2013; Roda and Macchi 2016)  the asset lifecycle, hence the effect of that decision on the long term over the asset life cycle;  the asset hierarchical control level, hence ensuring that whether the decision is taken at strategic, tactical or operative level within the company, proper actions are taken to ensure that the other levels are also aligned. Both principles and aspects of decisions are, as a whole, the reasons determining specific decision support needs. Some examples may be helpful to deduce such needs. As a first example, it is considered the case of an investment in a new industrial plant as a decision. In this case, a strategic view is certainly required, i.e. a strategic control level, to align, at the beginning of the asset lifecycle, to the long-term objectives and performances needed to generate value from the asset. Nevertheless, this is not enough. A tactical and/or operational view, capable to lead to a clear sensitivity to what will actually be happening as effect of a strategic decision, is also needed. Using these specific views would then lead to an informed decision-making. Eventually, this would enable to properly base the decision on the AM principles, e.g. a risk orientation will mean having it through different asset control levels, i.e. considering the risk at strategic, tactical, operative level. As a second example, it is considered a decision at the end of life of an asset. It may have, as options, two outcomes: to continue with the same asset based on a strategy for its life cycle extension (e.g. a revamping, a retrofitting etc.), or to proceed with the asset decommissioning (and, eventually, with the purchase of a new asset). As with the first example, also this decision requires a strategic view, while not lacking other tactical/operational views, thus not missing, e.g., the risk orientation at different asset control levels. A question now arises after the examples: what is it needed to support such decisions? There is the need, firstly, to integrate a variety of information types (e.g. asset condition due to past decisions, functional role of the asset within the system, etc. …), usually dispersed in many information sources. In terms

2.2 Digital twin modeling to support lifecycle management From literature, the DT did not receive a unique definition, probably because it was not always linked to a specific sector: it was first proposed in the aerospace field, then it was used in robotics and only recently it has been adopted also in the manufacturing domain (Negri, Fumagalli, and Macchi 2017). Its first formal definition was provided by the NASA as “an integrated multi-physics, multi-scale, probabilistic simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin. It is ultra-realistic and may consider one or more important and interdependent vehicle systems” (Shafto et al. 2010). Two necessary aspects emerge, as basis of the DT modeling paradigm: the Data Modelling and the Data Analytics. On one hand, it is important to define a proper model of data / information of the system to be virtually represented. This deals with standards for data / systems representation, while is also connected with the possibility to simulate the system. To properly structure data models, literature cites semantic data models, such as ontologies as good candidates. They are explicit, shared, formal representations of concepts understandable by software systems and usable to include intelligence for the integration and sharing of big amount of data, such as the generated data from sensors in a system (Garetti, Fumagalli, and Negri 2015; Negri et al. 2016; Gruber 1995; Borgo 2014; Heymans et al. 2008). On the other hand, it is impossible to have a consistent virtual representation of a real system without the use of real-time sensed data from the system itself. It is a cumbersome task, without a proper data analytics supporting this. Relying on such prerequisites, the concept and functionalities of the DT can be then understood. Indeed, the contribution by (Negri, Fumagalli, and Macchi 2017) helps to frame them by proposing a thorough literature review on this topic.

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The rationale behind the investigation can be motivated in terms of a technology transfer purpose. On one hand, there is the research that develops the Key Enabling Technologies (KETs such as the cited IoT, Big Data, advanced simulation and others), making them available for the industrial needs. On the other hand, there are the applications to solve practical problems within the industrial scope, in business processes and disciplines, in the case of this research the scope is AM. To enable the technology transfer. the research then aims at investigating how the DT functionalities and characteristics – available through various KETs – may be interconnected with the AM decision-making process. The final objective is to link the research areas of DT modelling and AM, in order to facilitate the use of new technologies and methodologies developed at KET level in the industrial AM setting. The selected projects can be considered as a manifestation of such a link.

The DT was originally created in the aerospace field as the stochastics-based mirror of the aircraft life that replicated, through simulations, the continuous time history of the vehicle, using historical data, sensor data, maintenance information and simulating also interactions with the external world. It was intended to deeply understand what the vehicle experienced and to predict breakdowns and needs (Wang et al. 2015; Bazilevs et al. 2015; Bajaj, Zwemer, and Cole 2016; Ríos et al. 2016). The translation of the DT concept for the robotics environment meant another objective for the DT, which was seen as the “tool” to optimize control algorithms of robots during the development phase (Schluse and Rossmann 2016). When adopted in manufacturing, the DT has taken a new objective: to simulate the complex behaviour of production systems, also including external factors, as human presence and technical constraints (Rosen et al. 2015; Gabor et al. 2016; Arisoy et al. 2017). Overall, a number of functionalities for the use DTs is reported in literature, amongst them: 1. improved maintenance decision making (damage / cracks prediction; material geometric / plastic deformation, and reliability modelling of physical systems) (Cerrone et al. 2014; Schroeder et al. 2016; Fourgeau et al. 2016; Yang, Zhang, and Liu 2013; Bazilevs et al. 2015); 2. system lifecycle mirroring, supporting decision-making in different ways: i) predicting the system’s performances / behaviour in the long term (Bielefeldt, Hochhalter, and Hartl 2015; Grieves and Vickers 2016; Fourgeau et al. 2016), ii) granting data digital continuity along the lifecycle phases of the system (Abramovici, Gobel, and Dang 2016; Rosen et al. 2015), iii) checking the feasibility and optimizing the control software of the system (Schluse and Rossmann 2016), iv) simulating the orchestration of IoT devices (Canedo 2016). 3. statistically-based decision making and optimization (Kraft 2016), such as optimizing the system’s behaviour / performances, by simulating it during the design phase (Gabor et al. 2016; Ríos et al. 2016; Arisoy et al. 2017; Abramovici, Gobel, and Dang 2016; Bajaj, Zwemer, and Cole 2016) or during other lifecycle phases, knowing its past and present states (Ríos et al. 2015; Smarslok, Culler, and Mahadevan 2012). It is evident that DT modelling is full of promises in regard to the lifecycle management of assets. Hence, it can be assumed as a relevant mean to explicitly answer the related decision support needs. 3.

4. INDUSTRIAL USE CASES ANALYSIS

ASSET CONTROL LEVELS

Explorative application use cases analysis is provided to facilitate understanding the functionalities and characteristics of DTs to support AM. The use cases, related to different research projects in which the authors participated, are mapped in the AM framework represented in Fig. 2. The framework (Roda and Macchi 2016) is, in fact, used to organize different kinds of decision according to the asset lifecycle stages (Beginning of Life (BOL), Middle of Life (MOL) and End of Life (EOL)) and the asset control levels (Strategic, Tactical and Operational levels). Strategic level

1

2

Tactical level

3

Operational level

4 Beginning Beginning ofofLife Life(BOL) (BOL)

5

Middle Middle of of Life Life (MOL) (MOL)

End of Life (EOL)

ASSET LIFE CYCLE PHASES

Figure 1 - Mapping the use cases in the AM framework (the numbers on the map refer to the different use cases) Five cases have been chosen and few details are given hereafter for each of them, highlighting how the asset-related decision-making process can be supported by DT modeling. The cases are identified with numbers in accordance with the map provided by figure 2. 1. Asset configuration – The DT is used for modelling and simulating a production line, to evaluate its systemic Reliability, Availability, Maintainability performance (RAM performance), to finally predict its Total Cost of Ownership (TCO). It allows to assess the choice of the best design solution for the production line. 2. Asset reconfiguration – Similarly to the first use case, the DT is used for modelling and simulating a complex process production plant, to evaluate its systemic RAM performance, to finally predict its TCO. It allows to assess the choice of the best reconfiguration alternative to increase the availability of the plant. 3. Asset reconfiguration and planning – The DT is used as semantic data model within a web service-based control system for manufacturing systems. It forms the ground for an open, knowledge-driven Manufacturing Execution

RATIONALE AND OBJECTIVE OF THE STUDY

This research intends to contribute to a better understanding

of the benefits that DTs can bring when they are applied in asset lifecycle management. Therefore, the state of the art analysis is just an initial step to provide the foundation of the study in regard to the needs generated by AM, as well as to the benefits to satisfy such needs, based on the DT concept. Field experience is also considered to complement the state of the art with hands on experience: some selected projects, experienced by the authors, are discussed to point out some use cases of DTs. It serves to explore the use of DT concept for asset-related decision-making.

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orientation will be also enhanced, by incorporating additional long-term performances in order to drive decision-making during the MOL. Case 5. The case study was developed within a process production plant of a company operating in the steel-making sector (Fumagalli et al., 2016). The primary challenge was the need for a decision support to manage maintenance activities in the MOL phase of a critical asset. In front of such challenge, the realization and use of the DT of the asset was required, especially ground on the principles of riskorientation given the safety problem in steel-making industry and asset-centric orientation. To this end, sensor data were modelled and analyzed through advanced analytics in order to extract the features required for the diagnosis of the health status of the asset. In particular, a condition monitoring tool was deployed by seeking the opportunities provided by plant automation. The functionalities of the DT of the plant were essential to support the asset-related decision-making, coping with the primary challenge, by supporting improved statistically-based maintenance decision making at operational level, enabling to detect incipient failures through monitoring of the asset; thus, enhancing the operations by avoiding downtimes that may lead to risky consequences on people safety.

System architecture, which allows quick reconfigurations of the system. 4. Asset commissioning – The DT is used as semantic data model, data analytics and advanced simulation, to make the virtual commissioning of the manufacturing system. Simulation of the system is based on a semantic data model and of a software structure that is able to analyse data in runtime. It allows a quick time to commission the system. 5. Asset condition monitoring and health assessment – The DT is used for the asset diagnosis, helping to assess its health status based on the monitored condition. The DT provides the data analytics in order to extract the features required for the diagnosis. It allows to limit unreliability situations. The use cases put in evidence the wide variety of decisions supported by DTs in different asset lifecycle phases and at different asset control levels in a company. As better detailed hereafter, the use of DTs resulted to be consistent with the benefits generally discussed by literature. In this regard, two use cases are presented with further information, to discuss the functionalities of DTs used to support the asset-related decision-making. Case 1. This case was developed within a process production plant of a company operating in the food sector. The primary challenge identified with the company was a decision support for design activities at the BOL of a Greenfield plant. A secondary challenge was also identified considering the potentiality to further use such a support for the activities in the MOL phase of the asset. In front of such challenges, the realization and use of the DT of the industrial plant was required, especially ground on the principles of risk orientation and system orientation. To this end, historical data, collected from similar existent plants working in similar operating conditions, as well as maintenance information provided by the OEMs were used to create a data model. Stochastic simulation was then adopted, integrating risk orientation, and run based on the reliability model of the plant (established through the Reliability Block Diagram technique) integrating system orientation, i.e. to enable to evaluate the impact of the interactions among the components of the system. The model enabled, firstly, the assessment of the design alternative solutions considered for the plant and, secondly, a proper evaluation of the TCO, through a model built on the performances achieved by the whole plant as an asset system (Roda, Garetti 2015). The functionalities of the DT of the plant were essential to support the asset-related decision-making, coping with the primary challenge through a statistically-based decision making that uses the simulation of the system’s behaviour / performances. The secondary challenge is addressed by the possibility to use/re-use the system model of the plant, inherited from design to operation in order to obtain a system lifecycle mirroring, to finally allow predicting RAM performances and long-term behavior of the system based on the progressive state reached by the system along the MOL phase. Specifically to this end, not only historical data, but also actually recorded data and sensor data would be integrated to achieve the full functionality. During such extension, the life cycle

6. CONCLUSIONS Digitization is a transformation process that leads to plenty of opportunities. It is enabling to introduce new KETs in various engineering and management applications. Considering AM as specific application, through use cases derived by research projects, it was possible to show that there is a convergence between the decision support needs of AM and the benefits provided by the functionalities of DTs along a system / asset lifecycle. In particular, the use cases put in evidence the wide variety of decisions supported by DTs in different asset lifecycle phases and at different asset control levels in a company. Therein, the use of DTs is consistent with the benefits generally discussed by literature (section 2): i) system lifecycle mirror, by predicting performances and long term behaviour of the system (use case 1, 2) and by granting data digital continuity along the different lifecycle phases of the system (use case 3, 4); ii) improved maintenance decision making (use case 5). The main evidence that emerges is that DTs can be considered a concept with high potential for asset lifecycle management. Next research will be relevant to classify KETs needed for DT modelling, with specific emphasis to AM as application domain. Besides, new methodological approaches to engineer DTs for asset-related decision-making will be required for the development of new tools / systems for decision support. Last but not least, DTs are expected to enrich the existent asset information system, thus enhancing the informed decision-making in AM. ACKNOWLEDGMENT This work was supported by the European Union’s Horizon 2020 research and innovation programme under Grant No 678556, project “MAYA”, “MultidisciplinArY integrated simulAtion and forecasting tools, empowered by digital 806

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continuity and continuous real world synchronization, towards reduced time to production and optimization”; and the ARTEMIS/ECSEL, Grant No. 332946, project eScop, “Embedded systems Service-based Control for Open manufacturing and Process automation”. Also, relevant contributions are from industrial projects funded by private companies.

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