Simulation-Based Digital Twins Enabling Smart Services for Machine Operations: An Industry 5.0 Approach

Abstract

The Industry 5.0 initiative seeks the sustainability and resilience of production systems through digital technologies. Derived from such an initiative, the Operator 5.0 concept surged to place the operator as the main value-creation contributor of sustainable and resilient human-machine systems. Smart service systems are data-driven services that provide intelligent capabilities to support decision-making in business processes. Despite previous research on smart services driven by multiple technologies in different contexts, further studies approaching Digital Twins as enablers of smart services in machine use phases are yet to be explored. Digital Twin, an Industry 5.0 enabler, is a technology that virtually represents physical assets that collect and analyze data from actual operations to make predictions for decision-making. Simulation-based Digital Twins generate simulation models that are continuously upgraded with asset data in real time, improving the accuracy of predictions. This paper aims to investigate how the Simulation-based Digital Twins (SDT) can enable the development of smart services in the Operator 5.0 context. We build on a multiple case study of 13 interviews from nine heavy mobile machinery manufacturers. We capture our results in categorizing SDT-enabled smart services for the use phases of the machine lifecycle. The categorization identifies the capabilities of each smart service for decision-making support and value co-creation.

Keywords:

• Digital Twins
• simulation
• Industry 5.0
• smart service systems
• value co-creation

1. Introduction

The European Commission recently announced the Industry 5.0 initiative as an extension of the well-known Fourth Industrial Revolution, i.e., Industry 4.0, mainly focused on the industry’s technological development and digital transformation (Breque et al., 2021). Industry 5.0 focuses on the social impact the Industry 4.0 on workers, with a focus on human-centricity, sustainability, and resilience (Xu et al., 2021). Romero and Stahre (2021) introduced the concept “Operator 5.0,” which aims to empower operators to collaborate with intelligent machines to reach unseen levels of productivity, safety, and resilience in production systems (Romero & Stahre, 2021).

The human-centricity considers the collaborative interaction between workers and technology towards a common goal (Carayon, 2006; Rankin et al., 2014; Walker et al., 2008). The sustainability perspective aims to use digital technologies to improve operators’ personal and professional development (Hutchins & Sutherland, 2008; Rajak & Vinodh, 2015). The resilience aspect aims to create flexible and resilient human-machine systems by monitoring current circumstances, anticipating events and responding accordingly (Hollnagel, 2009; Madni & Jackson, 2009).

The Industry 4.0 is a technology-driven initiative to improve manufacturing systems by connecting them through the internet (Nahavandi, 2019). This includes a range of industries, such as manufacturing, agriculture, construction, and logistics (Dhanabalan & Sathish, 2018). The new Industry 5.0 builds on Industry 4.0’s technologies to create sustainable and resilient systems (Müller, 2020).

Digital Twin (DT) is a tool that creates a virtual version of a physical object or system (Xu et al., 2021). It collects real-time data from the physical object to predict and forecast how it will perform (Barricelli et al., 2019; VanDerHorn & Mahadevan, 2021). Traditional DT technology analyzes historical data to identify asset failures, making it difficult for decision-makers to predict future events accurately (Fuller et al., 2020; Martinez et al., 2018). Simulation-based Digital Twins (SDT) (Aivaliotis et al., 2019) address this challenge by communicating bi-directionally with the physical object to run and upgrade simulations that model diverse predictive scenarios (Sepasgozar, 2021). SDT can contribute to improving operator resilience and social sustainability by providing decision-making support during machine use (Boschert et al., 2018).

Due to their data-driven and intelligent characteristics (Lu et al., 2020), the DT are considered enablers of smart service systems (C. Lim & Maglio, 2018; West et al., 2021). Smart service systems are services in which value is co-created through intelligent (smart) technologies that collect, analyze and communicate data for decision-making support (Maglio & Lim, 2016; Spohrer & Banavar, 2015). Previous studies have shown how smart service systems create value in several industries (C. Lim et al., 2018; C. Lim et al., 2019). However, limited research approach the operationalization of value co-creation through smart services (C. Lim & Maglio, 2019). Particularly, further research is needed to illustrate how DT-enabled service systems create value for decision-making support (West et al., 2021).

To bridge such gaps, we introduce a categorization of SDT-based smart services within the Operator 5.0 context. We conducted a multiple case study with nine mobile machinery manufacturers in Finland and interviewed experts in simulation engineering and product development to strengthen our results’ reliability. Our findings sorted SDT-enabled smart services into two categories: operator-centric services and resilience-based services. Operator-centric services show how the SDT can support machine operations processes to improve the social sustainability of operators. Resilience-based services harness the SDT capabilities to anticipate needs and events, and therefore, enhance the resilience of machine-operator systems.

This paper contributes by proposing value-creation mechanisms of SDT-based smart services, which can be considered for further developments and service design in different phases of the machine lifecycle (Kuo et al., 2021). It also contributes to the field by introducing smart services that promote the human-centricity, social sustainability, and resilience of machine operations. In practice, this study presents to the manufacturers the potential applications and business opportunities to develop after-sales services based on the SDT. Moreover, this study gives the manufacturers a better understanding of the value created through the interactions between the operator, the machine and the digital twin, allowing them to develop different smart service configurations.

The objectives of this paper are: (1) examine the attributes of Digitial Twins and Simulation-based Digital Twins and how they can enable smart services in machine operations, (2) explore the contributions of the SDT to support decision-making in machine operations processes, and (3) investigate how SDT-driven smart services enable value co-creation in the context of the Operator 5.0. To achieve these objectives, a literature review is carried out to understand how Digital Twins contribute to value creation in the context of the Operator 5.0. Consequently, a qualitative inductive study grounded on multiple Finnish heavy machinery manufacturers was conducted to explore how the SDT can be utilized in supporting decision-making in machine operations. Finally, the results of the literature review and the inductive study were combined to determine the mechanisms of the SDT to co-create value in machine operations within the context of the Operator 5.0.

The paper is organized in four sections. Section 2 presents the theoretical background of the Operator 5.0, Digital Twins, and their interaction with smart service systems. Section 3 introduces the research methodology and describes its approach and architecture. Section 4 presents the results organized around smart service characteristics, which are then integrated into a categorization of SDT-enabled smart services. Finally, section 5 discusses the study’s theoretical contributions and managerial implications.

2. Theoretical background

2.1. The notion of the Operator 5.0

The fourth industrial revolution, known as Industry 4.0, has given intelligent capabilities to production systems by incorporating digital technologies into physical objects (Lasi et al., 2014). For instance, the Industry 4.0 contributed that agricultural tractors became intelligent by automating previously mechanized tasks (Liu et al., 2021). Despite its technological advantages, the Industry 4.0 has left aside the human and sustainability aspects. Therefore, the new paradigm of the Industry 5.0 surged to add three new elements, human-centricity, sustainability, and resilience of production systems (Breque et al. 2021). The new paradigm aims to create intelligent production systems that contribute to social stability, resource conservation, and environmental protection (Adel, 2022).

Derived from the Industry 5.0 paradigm, a recent concept places the operators at the core of truly resilient and human-centered production systems, called the Operator 5.0 (Romero & Stahre, 2021). To address the three aspects of the Industry 5.0, The Operator 5.0 concept suggests that three research domains should be considered, sociotechnical systems, social sustainability, and resilience engineering.

Sociotechnical systems consider the collaborative and augmented operations between workers and technology towards a common goal (Carayon, 2006; Rankin et al., 2014; Walker et al., 2008). To be prepared to interact with such technologies, operators must have a certain level of trust, openness, and knowledge (Blayone & VanOostveen, 2021). For instance, to achieve effective collaboration between robots and workers, robots might perform repetitive and monotonous tasks and assist operators in making decisions in more complex tasks (Gjeldum et al., 2022).

Social sustainability seeks the development of worker competencies, learning, satisfaction, wellbeing, health, and safety (Hutchins & Sutherland, 2008; Rajak & Vinodh, 2015). It aims to harness digital technologies to enhance operator skills and general wellbeing with applications that include real-time and automatic training systems, workload measurement systems, virtual simulators, and collaborative robots (Pinzone et al., 2020).

Resilience engineering refers to the ability of productive systems to anticipate events, identify and monitor current circumstances, respond to unexpected situations, and learn from previous encounters (Hollnagel, 2009; Madni & Jackson, 2009). In the context of the Operator 5.0, the anticipation ability can be manifested in the prediction and monitoring capabilities. With intelligent technology, operators can predict equipment failures, maintenance needs (Romero et al., 2016), operation safety risks, and health issues such as stress and workloads (Sun et al., 2020).

2.2. Digital Twins enabling the operator 5.0

Digital Twins (DT), an Industry 5.0 technology enablers (Xu et al., 2021), create a digital counterpart of a physical product or system (e.g., machine, production line) and utilize data to obtain intelligent capabilities, such as asset monitoring, prediction, optimization, control and decision support (Lattanzi et al., 2021; Rasheed et al., 2020). Originally, DT technology contributed to product development and engineering teams to design more efficient equipment and machines (Donoghue et al., 2018). However, next-generation DT create value through services in all phases of the product lifecycle, especially during machine use phases (Boschert et al., 2018).

The next generation of DT upgrade the unidirectional sensor-fed DT by virtually representing a system that communicates bi-directionally with its physical counterpart to create simulated models, which provide more extensive and accurate information of the system (Sepasgozar, 2021). The next-generation DT interact with its physical counterpart by integrating real-time data into the physical system (Leng, Yan, et al., 2021).

Simulation-based Digital Twins (SDT) are next-generation DT that enhance the traditional computer simulation modeling of machines and production systems by constantly upgrading its model with data from sensors (Guerra-Zubiaga et al., 2021). SDT run reality-driven simulations in parallel with the machine operation, enabling the so-called “virtual sensors” to estimate measurements that physical sensors do not provide or provide with lower accuracy (Kurvinen et al., 2022), as illustrated in Figure 1. Traditional simulation cannot be entirely accurate, over time the inaccuracies accumulate causing the simulation model to drift away from and substantially differ from reality. However, SDT continuously correct the simulation model to bring it closer to the actual machine behavior (see Figure 2) (Jaiswal et al., 2022). The enhanced simulation models are thus used to support decision-making in operations, design, and management (Savolainen & Knudsen, 2022).

Figure 1. Simulation-based Digital Twins process.

Figure 2. SDT-enabled correction of traditional virtual simulation models.

Previous literature has presented the industrial applications of DT throughout the product lifecycle. Liu et al. (2021) introduced an in-depth literature review of the applications of DT in the design, manufacturing and service phases of the product lifecycle. In product use phases, some examples include predictive maintenance, fault detection, and performance prediction (Liu et al., 2021). Moreover, West et al. (2021) introduced a multiple case study of DT-enabled services to improve value co-creation and decision-making support in production environments. The authors introduced the DT’s capabilities for the whole product lifecycle, including asset monitoring, control, and operation optimization (West et al., 2021).

In the design phase of the product lifecycle, DT can be used to validate and verify the functionality of prototypes in machine development. The DT allow designers to simulate several machine scenarios to identify early potential errors and malfunctions and, therefore, reduce reconfiguration costs in design corrections (Leng, Wang, et al., 2021). The DT fed with IoT-generated data from actual machine environments, i.e., SDT, enable machine designers to simulate semi-physical machine configurations and reconfigurations with a high level of accuracy, reducing prototype validation costs (Leng, Zhou, et al., 2021). During machine use phases, the DT can enable interoperability between the digital and physical worlds, providing operators feedback instructions from real-time virtualized machine analysis, improving the performance of the human-machine system (Leng et al., 2019).

Despite recent literature approaching the DT and simulation as means for value co-creation, further studies related to the value co-creation of SDT in the context of the Industry 5.0 paradigm are yet to be explored. Nevertheless, previous research has presented practical cases of the DT technology that match the human-centricity, social sustainability, and resilience of the Operator 5.0 (see Table 1).

Table 1. DT and simulation applications enabling the Operator 5.0.

Industry 5.0 perspective	Operator 5.0 aspect	Digital Twins applications
Human-centricity	Sociotechnical Systems: Collaborative and augmented operations between workers and technology toward a common goal.	DT-based assistance through Virtual Reality (Havard et al., 2019) DT visualization and interaction through Augmented Reality (Vidal-Balea et al., 2021) DT-assisted safety management system (Agnusdei et al. Agnusdei et al., 2021) Conversational assistants (softbots) intermediating DT and workers (Rabelo et al., 2021)
Sustainability	Social Sustainability: Development of worker competencies, learning, satisfaction, wellbeing, health and safety.	Safety training (Kaarlela et al., 2020) Operations training (Pérez et al., 2020) Individual stress levels prediction (Scheuermann et al., 2020) Gamified operations experience for worker engagement and satisfaction (Loizou et al., 2019) Physical and cognitive worker workload monitoring (Peruzzini et al., 2020)
Resilience	Resilience Engineering: Ability to anticipate events, identify and monitor current circumstances, respond to unexpected events and learn from previous encounters.	Operator risk prevention and prediction (Bevilacqua et al., 2020) Predictive maintenance (van Dinter et al., 2022) Equipment status monitoring (Botkina et al., 2018) Failure identification and root cause analysis (Boschert et al., 2018)

To meet the human-centricity, DT utilize complementary technology to interface the DT, the machine, and the operator (Qi et al., 2021). For instance, Virtual Reality (VR) (Havard et al., 2019) and Augmented Reality (AR) (Vidal-Balea et al., 2021) are used to train operators in controlled environments and assist operators in actual machine setups, respectively. Safety management systems (Agnusdei et al., 2021) have interfaced with the DT to improve the operator’s occupational health and safety. Artificial Intelligence (AI) has been integrated into the DT to provide cognitive capabilities, enabling the semi-autonomy of machines (Groshev et al., 2021). For instance, conversational assistants (i.e., softbots) have been used to intermediate between the DT and the operator in a conversational interface (Rabelo et al., 2021).

The Operator 5.0 new strategic vision will affect one of the most important resources of production systems, the human operators (Pinzone et al., 2020). In the Operator 5.0, social sustainability aims to harness the professional and personal development of operators (Ajmal et al., 2018). DT applications can train operators in diverse topics. For instance, safety (Kaarlela et al., 2020) and operations (Pérez et al., 2020) training applications utilize measurements from real environments to simulate different possible scenarios that may challenge operators. From the health perspective, DT monitor and predict stress levels (Scheuermann et al., 2020), physical and cognitive workload (Peruzzini et al., 2020). From the wellbeing perspective, the DT can offer interfaces to improve the operator’s satisfaction. For example, gamified interfaces embedded in the machine controls can serve as an incentive to keep the operators engaged and motivated (Loizou et al., 2019).

The DT can monitor current circumstances, identify, anticipate, and respond to unexpected events, i.e., resilience engineering (Hollnagel, 2009; Madni & Jackson, 2009). Considering the interaction between the operator, the machine, and its digital twin as a symbiotic human-machine system (Romero et al., 2020), the capabilities given to any of the three elements of the system affect its whole. When multiple human-machine systems operate in a production environment, effective communication and decision-making between them can give process control systems the capacity to make coordinated adjustments in case of disruptions, improving the resilience of the whole production (Leng, Sha, et al., 2023). To achieve resilience of multiple human-machine systems, modularized architectures that consider the integration and coordination of multiple agents are needed (Leng, Sha, et al., 2023). Moreover, technologies such as blockchain can ensure that multiple control systems achieve secure and robust data exchanges among them (Leng, Zhu, et al., 2023).

The DT utilizes predictive decision techniques to anticipate unexpected events, and therefore enhance the system’s resilience. However, different industries might require specific prediction capabilities depending on the nature of their operational processes (Leng et al., 2022). For instance, DT can predict maintenance needs, and optimize scheduling and spare parts requirements before a breakdown occurs (van Dinter et al., 2022). However, the operator also benefits from such a prediction due to the improved understanding of machine maintenance needs and safer operations (Jasiulewicz-Kaczmarek et al., 2020). Similarly, the DT for machine failure identification and root cause analysis (Boschert et al., 2018) helps operators to prevent failures and respond if they unexpectedly occur. Lastly, some of the DT-based applications cover the three aspects of the Operator 5.0. For instance, Bevilacqua et al. (2020) introduced a DT-based safety management system to predict and prevent operational safety risks through an instructional interface using VR and AR.

2.3. DT-enabled smart services for the Operator 5.0

The servitization of manufacturing consists in the transition from traditional product-centric offerings to a combination of product and service bundles, which utilice resources such as people, technology, and information, embedded in networks of value co-creation, known as service systems (Kohtamäki et al., 2018; Maglio et al., 2009). These networks consider the interactions between various customers, suppliers, and business partners to co-create value in complex systems (Patrício et al., 2018).

Smart services are systems that use human, digital, and physical resources to create value (C. Lim et al., 2016). Smart service systems are “service systems in which value co-creation between stakeholders are automated or facilitated on a connected network, data collection (sensing), context-aware computation, and wireless communication” (C. Lim & Maglio, 2019, p. 361). The data collection (sensing), computation, and analysis is made by an intelligent object called smart product, which mediates the interactions between the customer and provider, enabling the creation of smart services (Beverungen et al., 2019). Examples of smart services include condition monitoring, predictive maintenance, remote diagnosis, remote operation, asset performance optimization, and autonomous and semi-autonomous operation (Kohtamäki et al., 2021, 2022; Thomson et al., 2022).

Smart services can be enabled by different technologies embedded into the smart product. Computer simulation and modeling within the context of the Industry 5.0 evolves into the so-called Simulation-based Digital Twin (Xu et al., 2021). The DT constitutes one of the smart technologies that act as a resource that supports value co-creation as smart service systems (West et al., 2021).

According to the socio-technical systems view of Operator 5.0, the DT (technology) and humans (operators) interact in a cyclical way. The DT provides knowledge to help operators make decisions, while operators give real-time data input to the DT for physical operations (Kuo et al., 2021). This interaction creates DT-driven smart services that help the operator make decisions. These services fall under the category of human-centered smart service systems, which support decision-making by improving human expertise (Spohrer & Banavar, 2015).

In smart service systems, value co-creation between customers, the smart object (product) and service providers is achieved by a continuous cycle of data interactions and mutual feedback, enabling the constant improvement of decision-making (C. Lim & Maglio, 2019). Based on the literature review, we introduce the value co-creation cycle for the particular case of SDT (see Figure 3). The value co-creation cycle occurs when the machine sends data to the SDT provider, and the machine gains smart capabilities from the SDT. The operator provides input from the machine operation, and the smart machine supports decision-making. Finally, the operator and customer perceive value from decision-making support, and the service provider gets feedback on the smart service’s performance.

Figure 3. Simulation-based Digital Twins value co-creation cycle.

3. Methodology

3.1. Research approach and case selection

We followed an inductive research method based on qualitative exploratory research design (Frankel & Devers, 2000). The exploratory research was grounded on a multiple case study (Yin, 2009) of heavy mobile machinery manufacturers operating in Finland. The selection of such a research approach contributed to appropriately examining that how the SDT can enable smart service innovations, and how they enhance resilience capabilities and operator abilities. Inductive research methodology based on multiple case study aims to understand complex phenomena and develop new theory from the understanding of the multiple factors derived from the differences and similarities between cases (Hunziker & Blankenagel, 2021; Stake, 2013). Contrary to the single case study, multiple case research contributes to achieving comprehensive results by answering “how to” questions using various sources of information (Creswell & Poth, 2016; Eisenhardt & Graebner, 2007).

The cases included in this study were selected by theoretical sampling (Eisenhardt & Graebner, 2007; Glaser et al., 1968), where manufacturers were considered to explore the potential of SDT to enable smart services for this particular segment. Nine case companies of forestry, agriculture, material handling, and construction machinery were selected to improve the ubiquity of our results. Case companies from diverse backgrounds and characteristics were selected to understand better the contrasts and similarities between participants, i.e., family, public, multinational and multi-brand companies. The aspects considered for this study are: (1) the context of the current offer of digital (smart) services, (2) the perspective of machinery manufacturers on SDT, and (3) the understanding of the suitability of such technology for further developments.

The factors that influenced the selection of the case companies were the following. First, we selected the machinery manufacturers with whom we had established contact due to their participation in the research project and have expressed interest in extending their service offering. Second, we selected companies that currently use simulation technologies for product development and training purposes and are exploring the applications of SDT for future developments in after-sales services. For example, one of the material handling manufacturers is interested in harnessing its simulation engineering in-house expertise to develop services that increase the added value of its machines. Third, we selected companies currently offering smart services in their solutions catalog and that are seeking improvements and further developments. For instance, the agriculture machinery manufacturer is interested in improving the prediction accuracy of their remote diagnosis and failure prediction service.

We addressed ethical considerations to ensure the participants’ privacy and the scientific integrity of our study (Hasan et al., 2021). We maintained the confidentiality of manufacturers by omitting brand and product names, and personal names of participants in the presentation of this paper. Moreover, we obtained informed consent from the manufacturers concerning their current status of digitalization and the opportunities that SDT bring to their development goals, which also contributed to ensuring our research findings’ validity. By conducting the inductive research that involved interviews with experts in the field of technology in heavy machinery, specifically in the areas of simulation, digitalization, and services, we enhanced the reliability of our results.

Our inductive study might present limitations in its scope by approaching a specific industry, i.e., Finnish heavy machinery manufacturers, and could affect the generality of our findings (Yin, 2003). However, the exploratory nature of our research approach allowed us to explain a complex and novel topic that might not be achieved through experimental and quantitative methods (Zainal, 2007). In addition, the interviewed manufacturers operate in different international markets, meaning that the responses from participants were given with a global-oriented perspective of the industry, which helped to increase the generality of our results.

3.2. Data collection

The data collected in this study were gathered through semi-structured interviews with members of the nine case companies. The respondents were selected and contacted based on their degree of experience and familiarity with digital twins and simulation technologies. In total, 13 in-depth interviews were conducted, particularly focused on topics related to the scope of this study, i.e., physics-based simulation, real-time simulation, digital twins, machine operations, and predictive services, among other themes. The interviews were conducted simultaneously to more than two interviewees, including product development managers, product managers, engineering managers, product specialists, product designers, simulation engineers, and control systems engineers. Table 2 shows an overview of the company profiles and positions of respondents within each organization.

Table 2. Overview of the case companies and respondents.

Company	Employees	Industry	Respondents
A	24,000	Agriculture machinery manufacturer	Research and Engineering Manager Product Performance Specialist
B	14,000	Construction machinery manufacturer	Product Manager Product Specialist I Product Specialist II
C	11,000	Material handling machinery manufacturer	Head of Product Product Sales
D	2,000	Forestry machinery manufacturer	Simulation Engineering Manager Simulation Engineer
E	700	Forestry machinery manufacturer	Automation Specialist
F	450	Material handling machinery manufacturer	Development Manager Simulation Engineer
G	330	Material handling machinery manufacturer	Product Development Director Simulation Engineer Control Systems Engineer
H	250	Forestry machinery and tractor equipment manufacturer	Product Development Manager Product Designer I Product Designer II
I	250	Forestry and agriculture machinery manufacturer	Product Development Manager

We asked open-ended questions to company respondents following an interview guide. Three different guides were considered depending on the company profile (size, resources), level of technology maturity, current digital offer, and level of technical expertise of the respondents in question. The questions were focused on the current digital offer, the use of simulation, and its future potential for smart services, such as: “What is your current offer of digital/smart services?,” “How are simulation models utilized in your company?,” “What advantages could the DT and simulation bring for customers?,” “What kind of digital aids (assistive/semi-automatic systems) do operators use in different types of lifting situations?,” “What kind of potential has been identified in the development of the usability, productivity, and safety of the work machine?” The interviews were conducted mainly via online conference meetings and lasted approximately 45–90 minutes each. Interviews were transcribed, which contributed to obtaining the basis for the data analysis.

We collected data regarding companies’ digital offer from multiple sources, i.e., data triangulation (Carter et al., 2014). The respondents shared documents and sources containing product brochures, white papers, company reports and other product-related content to provide a broader understanding of the current technologies and services (see Table 3). In addition, the data triangulation allowed us to contrast the differences in development needs and product strategies among case companies. For example, large multinational companies focused on improving specific aspects of certain services, whereas small family companies concentrated on finding a cost-effective solution to compete in the market.

Table 3. Current digital offers by machine manufacturers.

Company	Digital offering	Description
A	Online condition monitoring Predictive maintenance and problem solving Intelligent machine control	Remote telemetry service to access to data about machines, such as real-time usage, remote diagnostics and maintenance data. Transmitted machine data is processed and analyzed to predict a wide range of service needs in advance, such as future problem and failure detection to prescribe specific maintenance plans. Smart control system integrated by a touchscreen interface, an intelligent multi-use drive lever and a hydraulic joystick to control the tractor’s smart farming functions.
B	Remote condition monitoring Payload optimization Operator assistance	The telematics system captures machine data remotely to monitor and predict potential failures, sending alerts for intervention required. Optimization of payload in mass excavation by maximizing the load count of each truckload based on type of material and weight. Real-time feedback on operator behavior and performance, recommendations on improvement areas and machine guidance on how to use controls and functions.
C	Real-time condition monitoring Proactive maintenance Operator assistance	Real-time monitoring of machine utilization, condition and operation for remote identification and diagnosis of operational issues. Optimized maintenance scheduling based on machine needs in inspections and spare parts. Virtual Reality and camera system that analyze and assess the working conditions to assist operators in moving the machine with more precision.
D	Machine condition monitoring Predictive maintenance and problem solving Operator assistance	The interface in machine control tells the operator the condition status of machine components. Data from forest machine movements, operations and performance is collected and analyzed to help maintenance services to evaluate maintenance needs and solve operation problems. Operator guidance system that monitors productivity, instructs the user and gives feedback based on actual measurements.
E	Remote monitoring Preventive maintenance and problem solving Intelligent machine control	Remote tracking of machine health diagnostic of trouble codes and record of machine performance readings. Telematics and alert management tools filter and analyze the data generated by machines to identify critical issues and preventative maintenance. Smart boom control keeps the machine boom at a steady pace to anticipate machine movements, improving machine controllability.
F	Remote monitoring Automation	Remote tracking of vehicle locations and movement to monitor transport operations, facilitating remote support and diagnosis. Process optimization and operator assistance with autonomous and semi-autonomous robotics.
G	Machine condition monitoring Intelligent machine control	Machine data is analyzed for improvement of operational performance by providing instant feedback on the health of the machine. The system displays to the operator information on optimal machine functions.
H	–   N/A	–   The offer of digital services is currently under development phase.
I	–   N/A	–   Maintenance and services are offered by the company’s dealer network.

3.3. Data analysis

The method for analyzing the collected data was thematic analysis, an inductive approach used to identify themes and patterns from relevant codes contained in data sets (Braun & Clarke, 2006). We followed a systematic process for new concept development (Corley & Gioia, 2004; Gioia et al., 2013) to introduce a categorization of smart services enabled by SDT from Operator 5.0 perspective. The process consists of following a series of iterations and comparisons that link themes from extracted data until developing an empirically grounded model.

The first step of the process consists in identifying the first-order themes, which are codes (words, themes, and topics) extracted from the interview transcripts and mentioned in respondents’ own words. In the context of our study, the identified codes were the specific needs that case companies have regarding machine operations and service needs, challenges faced by operators in the workplace, and improvement opportunities.

The second step consisted in associating first-order themes with shared patterns through an iterative process. Such iterations emerged in second-order themes, which are conceptualizations representing the resulting first-order theme associations. From the context of our study, the second-order themes relate to service classifications linked to the smart services, SDT applications in accordance with the insights from the literature review, consulted documents and sources, and interview transcripts. The resulting second-order themes are control assistance, operator augmentation, failure prediction and diagnosis, need-based and predictive maintenance, reality-driven simulation, and optimization.

The final step involved the generation of aggregate dimensions, which represent the highest level of the abstracted codes. The aggregate dimensions link the empirical first and second-order themes to a theoretically grounded categorization formed from the literature review of this paper. From the perspective of this study, the aggregate dimensions relate to the three aspects of the Operator 5.0. The operator-centric services dimension covers the services that focus on the human-centricity and social sustainability of operators. This sort of services co-creates value through human-technology collaborations that support operators’ decision making, and therefore, improve their skills, safety, and wellbeing. The resilience-based services dimension comprises the services that anticipate, identify and respond to unexpected circumstances. Such services co-create value by harnessing the decision-making capabilities needed to achieve higher levels of machine-operator system productivity and efficiency. Figure 4 illustrates the categorization resulting from the data analysis. In the next section, a thorough explanation of how each service contributes to decision-making and value co-creation is presented.

Figure 4. Categorization of SDT-enabled smart services.

4. Results

In this section, we describe the categorization of smart services enabled by SDT in the Operator 5.0 context, resulting from the inductive analysis of the multiple case study. The results are presented in two parts. The first part describes the operator-centric services, which include the smart services focused on the social sustainability through human-technology collaborations (sociotechnical systems). The second part describes resilience-based services, consisting of smart services that can monitor current circumstances and anticipate and respond to unexpected events (resilience engineering).

Related to the purpose of this study and fundamental part of our results, we illustrate how the presented smart services contribute to support decision-making in machine operations, and therefore, how this support can enable value co-creation for machine operators, manufacturers, and customers. Table 4 summarizes the two classes of smart services, the machine operation processes where the decision-making is supported, and the resulting co-created value.

Table 4. SDT-driven value creation categories.

DT-driven smart service	Decision-making process support	Value co-creation
Operator-centric services
Control assistance	–   Signal operators of potential damage in machine and attachments. –   Instruct operators the optimal parameters for different working conditions and materials.	–   Risk reduction of impacts and accidents: Reduction in machine degradation and safety improvement. –   Operator upskilling: Increases in productivity and work satisfaction, and training cost reduction.
Operator augmentation	–   Guidance on payload swinging and target points. –   Guidance when operators surpass work process limits.	–   Reduction of machine movements and errors: Safety improvement, stress reduction and efficiency increases. –   Risk reduction of accidents and errors: Safety improvement, stress reduction and cost savings.
Resilience-based services
Failure prediction and diagnosis	–   Inform decision-makers on occurred failures and their causes. –   Inform decision-makers on upcoming failures and malfunctions.	–   Reduction of diagnosis times and action measures: Downtime reduction, improvement of machine-related knowledge and cost reduction. –   Increase in machine productive time: Downtime reduction, productivity increases and extension of machine lifetime.
Need-based and predictive maintenance	–   Inform decision-makers on optimal maintenance scheduling and spare parts needs.	–   Increase of machine availability: Downtime reduction, cost reductions and extension of machine lifetime.
Reality-driven simulations	–   Monitor machine parameters that would be challenging to estimate from sensor measurements.	–   Awareness of working settings: Safety and productivity improvement, improvement of operator knowledge and skills, stress reduction and downtime reduction.
Optimization	–   Recommend operators on efficient energy consumption operation. –   Recommend operators on optimal bearable payload according to material.	–   Reduction of machine energy consumption: Cost reduction, and efficient use of resources. –   Optimized machine performance: Productivity increases and safety improvement.

4.1. Operator-centric services

The operator-centric services are referred to as smart services that contribute to the human-centricity and social sustainability of the Operator 5.0. These services focus on the human-technology interactions that use SDT to assist machine operators, and therefore, meet the sociotechnical systems area of the Operator 5.0. As described in the literature review, the SDT utilize complementary technologies such as VR, AR and conversational agents to assist the operators in performing better in their processes. In addition, the SDT contribute to the social sustainability of operators. In this regard, SDT can be used to train the operators in real-time, monitor and predict the operator’s workload and stress levels, and provide assistive guidance while operating the machine.

4.1.1. Control assistance

Control equipment located in heavy machine cabins includes handling instrumentation, and in most cases, control software systems embedded in hardware. The interface between such systems and the operators is regularly displayed on monitors. The operators can visualize information regarding the machine and the working process. One of the manufacturers affirmed that the most sophisticated control systems are those with integrated intelligent software, which assists operators to avoid mistakes and improve their abilities.

The SDT can enable intelligent control assistance functions by coordinating the control system of the machine with its digital counterpart. Thus, the operator receives decision-making support from diverse working situations already simulated in the SDT. A common concern among manufacturers is that operators might not be familiar to run the machine in different weather conditions. For example, operating in low temperatures with snow presence can be challenging, or in extremely humid environments with low visibility. Furthermore, the material composition plays an important role when handling machines. A manufacturer commented on the extensive variability of operating with different materials in different environments:

In earthmoving equipment, you have a million ways to use the machine and a million different environments where they are used, it can be underground, in the mountains, hot weather, or cold weather. There are also a lot of variables in the composition of the soil and material, where the degradation of the bucket varies considerably.

We identified two processes where control assistance can support operator decision-making. The first is by signaling operators when significant damage in the machine or attachment is about to occur. According to the manufacturers, damage in attachments, such as buckets, occur especially operating on rock surfaces, causing impacts that can cause accidents and accelerate machine deterioration. By preventing operators from damaging the machine, they can improve their safety, and customers can reduce machine degradation, and therefore, extending the machine’s lifetime. The second is by instructing operators the optimal parameters to operate in different working conditions and materials, and the effects of such conditions on the machine. It can improve operators’ competencies through the real-time training they receive before and during machine operations. By doing so, operators could reduce health risks involved with high levels of stress and increase worker levels of satisfaction. From the customer side, they could increase productivity levels and reduce training costs due to operator upskilling.

4.1.2. Operator augmentation

Operator augmentation services aim to extend human capabilities by guiding operators to execute previously manual tasks. Unlike control assistance services, operator augmentation services assist with mechanical semi-automated movements with a predefined target, helping operators complete their workloads faster and safer. One of the advantages of such skills augmentation is that the less experienced operators can run the machines effectively, with minimal risk of committing a mistake that eventually can cause a breakdown.

The SDT do not provide the mechanical guidance to achieve the targeted tasks. However, it provides the necessary calculations and process limits to the control system, bringing semi-automatic capabilities to machines. According to a manufacturer, one of the challenges faced by operators is to keep the payload safe while the machine is in motion. Another manufacturer commented on the need to improve the machine’s controllability by automating routine movements, such as rotary and swinging movements.

Two processes can be improved by augmenting operator abilities. First, by enabling guidance systems to automate the repetitive movements with shorter variability. As described above, the DT can establish the necessary parameters to automate routine tasks and achieve the work target more efficiently and effectively. Thus, operators can reduce their stress levels and workloads, while improving safety by avoiding collisions. The customer can reduce the risks of accidents, which would lead to considerable monetary losses and safety hazards. Second, by enabling the functionality to guide operators when they surpass work process limits. By delimitating work areas in the Digital Twin, operators can reduce mistakes and accidents, and therefore, reduce their stress levels and workloads. Customers can improve workplace safety and reduce costs associated with reworks.

4.2. Resilience-based services

Resilience-based services relate to the smart services that meet the resilience engineering aspects of Operator 5.0. Such services have the capabilities to monitor operations in real-time, anticipate failures before they occur, predict maintenance needs, identify and diagnose sources of failures and disruptions, and learn from previous events. As illustrated in the literature review, the DT can provide resilience capabilities to the machines and operators, i.e., sociotechnical systems. The SDT can enable accurate failure and maintenance need predictions, causes of machine faults, simulations of multiple scenarios, and optimization of resources and machine capacities.

Disruptions and disturbances are usually understood as similar concepts, however, there are significant differences between them. Disruptions are the unanticipated events that may affect operations, which can be overcome with resilience capacity (Essuman et al., 2020), as mentioned in previous sections. On the other hand, disturbances are variations in the operational processes that could affect the production performance, which can be addressed with robustness capacity (Stricker & Lanza, 2014). Moreover, flexibility differs from resilience and robustness in its capacity to adapt to changing conditions and requirements, and it is considered a robustness enhancer (Stricker & Lanza, 2014).

4.2.1. Failure prediction and diagnosis

Remote monitoring, failure prediction and diagnosis are smart services that have been offered by machine manufacturers in recent years. However, as manufacturers remarked, the most important challenge for them lies in the accuracy of prediction and cause identification. Manufacturers already collect vast amounts of data from machine sensors, yet not enough to provide accurate estimations. The most advanced manufacturers utilize sophisticated data analytics techniques for failure prediction and diagnosis, whereas small manufacturers still do not offer such services. For the most advanced manufacturers, the SDT can contribute to improving the accuracy of the already existing services. In contrast, for small manufacturers, it can enable highly accurate capabilities for new service development. An engineering manager of one of the manufacturers commented:

The challenges are that uses of the machine can be done in many different ways and taking into account all the factors and variables is important. One of the main challenges is to make reliable predictions with the available data you have.

The SDT can improve the accuracy of failure prediction and diagnoses through the so-called “virtual sensors.” According to the simulation engineer of one of the manufacturing companies, the DT can produce “synthetic data” with virtual sensors installed in machine positions that physical machines rarely have or would be expensive to have. The DT then produces the virtual data in predefined scenarios for long periods of time. In such a way, the DT can inform the operator and involved parties when and where potential failures are about to occur. Moreover, failure scenarios can be simulated to find out the causes of machine faults and malfunctions.

From the value co-creation side, the operator can predict failures in different working conditions, and predict risks that can affect the productivity and safety of the operator-machine system. The machine’s lifetime can also be extended by minimizing the frequency of failures. Root cause identification and diagnosis of machine failures might allow the customers to act accordingly and reduce machine downtimes, which are eventually translated into monetary losses.

4.2.2. Need-based and predictive maintenance

Similarly to failure prediction services, digitally mature manufacturers already offer predictive and need-based maintenance based on analyzed data from multiple sources, including sensor measurements. The service consists of informing customers on optimal maintenance scheduling, and specific needs in spare parts and inspections. The prediction model determines a time range when the maintenance service should be carried out before the machine fails. Such as the case of failure prediction, the main concern of this sort of solution is to improve the accuracy of prediction. For manufacturers who do not offer this service yet, predictive maintenance represents a “big key factor that everybody wants to achieve,” according to a product specialist of one of the manufacturers.

Besides its capabilities to improve prediction accuracy through “virtual sensors,” the DT can enable effective predictive maintenance by integrating historical machine data into the SDT. Thus, multiple simulations can be run to find the optimal scheduling point. These simulations can also predict which components of the machine suffered the greatest degradation, and therefore, determine spare parts needs. The SDT integrated into the machine control system would inform in advance to operators and decision-makers about the optimal schedule dates and spare parts needs. In need-based and predictive maintenance services, operators ensure that machines will be available for more time. For customers, these services might represent extended machine lifetime, reduced downtimes, and reduced costs in spare parts and maintenance services.

4.2.3. Reality-driven simulation

As previously illustrated, SDT is the upgraded simulation modeling that improves its accuracy from sensor data. Contrary to conventional physics-based simulation, the SDT constantly improves its accuracy with measurements from working environments in real-time. Another characteristic of SDT is its capability to communicate with the machine control system to support operator decision-making. Despite being reality-driven simulation as the core technology to enable the rest of the smart services presented in this study, the difference in simulation services lies in the decision-making support offered to different stakeholders, i.e., operators, customers, and manufacturers. To the manufacturers, the SDT can support engineers in improving product designs. As the simulation engineer from one of the manufacturers commented on the importance of SDT to improve product development:

The utilization of physical models based on reality can improve the agility of product development by testing machine functionality in our own development environment using simulation. In this way, we can understand failure situations, obtain dimensioning information for the design phase, a deeper level of understanding of boom dynamics, eliminate vibrations and optimize machine weight.

From the operator’s side, reality-driven simulation services can help operators to monitor machine parameters and work settings that would be challenging to estimate from sensor measurements exclusively. Operators can be aware of previously unknown conditions by setting a virtual representation that presents comprehensive and real-time information from machine’s health. It might reduce operator stress levels, which would eventually lead to safer operations. In addition, the operator might increase his knowledge about the machine and improve his skills to operate the machine in diverse working conditions. For the customer, less stress and skilled operators represent fewer risks associated with machine collisions and accidents, and therefore, less machine downtime and more productive time.

4.2.4. Optimization

As pointed out in previous sections, the SDT is capable of making calculations that match sensor data and “synthetic data.” The DT accurately estimates and predicts machine measurements that can be optimized for future improvements in resource consumption, machine performance, and component durability. One of the main concerns of customers is the high consumption of fuel and power in heavy machinery, which represents higher costs in already resource-intensive industries, such as the construction, forestry, and material handling industries. An engineering manager of one of the forestry machinery manufacturers commented:

Optimizing fuel consumption is a hot topic at the company. Fuel consumption is the guiding factor in the customer’s purchase decision. We are at risk of losing customers to competitors if we fall behind in this issue. For this reason, the development investment is directed to this area, because fuel costs account for about 30% of the forest machinery contractor’s business.

In addition to fuel reduction, the SDT can estimate the maximum payload, forces, and stresses bearable by the machine. The construction machinery manufacturer offers a payload optimization service where the machine control system tells the operator the optimal material payload needed to fill a hauler. However, one of the product specialists commented that they need to improve the service with more accurate estimations:

At the moment, we have a 4% error in the payload estimator application using the raw data from machine sensors. Sensor data is not always reliable because there may be noises like vibrations.

Optimization services can indicate to operators the optimal handling practices for reducing energy consumption and increasing optimal loads. The operators can benefit from such services by increasing their productivity, and therefore, lower workloads. From the customer side, they can reduce costs associated with energy consumption, benefit from productivity increments, and reduce machine breakdowns due to overloads and excessive demand to the machines.

5. Discussion and conclusion

Based on the qualitative data obtained from a multiple case study analysis of nine Finnish heavy machinery manufacturers, the results indicate that the Simulation-based Digital Twins represent significant opportunities for manufacturers to expand their smart service offerings. The cases show that manufacturers’ current expertise in simulation, digitalization, and services can be a unique advantage to upgrade to the SDT to enhance decision-making in machine operations.

Our results reveal that how the SDT contribute to improving decision-making in specific lifecycle processes. For instance, our findings show that the SDT can provide instructions on optimal parameters for different working conditions, give alerts of potential machine damage, and guide machine movements on payload swinging and target points. Furthermore, our findings show that the SDT can inform on upcoming failures and maintenance needs, simulate diverse scenarios before operations, recommend optimal bearable weights in lifting material, among other capabilities in operational processes. In essence, the experimental results demonstrate that the “virtual sensors” of the SDT can solve one of the main challenges in the industry’s digitalization, which is the limited accuracy and utility of machine data.

5.1. Theoretical contributions

This study contributes to the existing literature by presenting an analysis and conceptualization of SDT-enabled smart services and applications and how it might help machinery manufacturers to design services in the use phases of the product lifecycle (Kuo et al., 2021). The study suggests three main contributions by illustrating the key decision making-processes where machinery manufacturers might improve machine operations with the SDT.

First, by presenting the development of a categorization of smart services and application areas enabled by the emerging technology of SDT. Conceptualizations on smart and digital services for machine manufacturers have been proposed (Kohtamäki et al., 2021, 2022). Additionally, prior research has presented conceptualizations of DT-based services (Aheleroff et al., 2021; Liu et al., 2021; Qi et al., 2018). However, we have found none for smart services enabled by the SDT from the point of view of value co-creation for operators, customers, and manufacturers. In addition, it contributes by illustrating the value co-creation mechanisms (Leone et al., 2021) that can be achieved through the interactions between the SDT, the smart machine and the operator.

Second, by providing the foundations to operationalize value co-creation through decision-making support. According to C. Lim and Maglio (2019), most of the smart service system studies do not focus on managing and improving service systems in the context of value co-creation in operations. Regarding the DT for value co-creation, West et al. (2021) call for future research focused on the decision-making processes supported by the DT and how it contributes to value co-creation. We fill this gap by defining the machine operations processes where the SDT can support decision-making, and therefore, enabling value co-creation through smart services in the use phases of the product lifecycle (Kuo et al., 2021).

Third, it contributes to the smart manufacturing systems literature by addressing a human-centered approach to improve the Operator 5.0 (Romero & Stahre, 2021) aspects through DT-enabled services by improving the social sustainability of operators and resilience capabilities of machines in the human-automation symbiosys (Romero et al., 2020).

5.2. Managerial implications

This study entails several practical implications for heavy machinery actors involved in the development and use of smart services enabled by digital twins and simulation technologies. Four implications for value co-creation from the introduced smart service categorization are described, considering the point of view of machine operators, customers, manufacturers, and society in general.

First, it illustrates to operators how the SDT can harness their decision-making in different phases of machine operations, which would lead to perceiving personal value in diverse aspects. From the user point of view, it can show operators how the SDT can improve their skills and knowledge, reduce their levels of stress and workload, reduce risks of accidents, improve their performance with the machine, and increase their work satisfaction and engagement, among other benefits. Moreover, operators might reduce possible concerns of being replaced by automation by realizing this technology augments their abilities, and therefore, increase awareness that in their industries only specific tasks can be fully automated.

Second, it clarifies to machine customers how the SDT can contribute to perceiving higher value from the after-sales (smart) services offered by manufacturers. Specifically, it shows how the improved decision-making capabilities can bring more resilient and sustainable operations. Customers can perceive quantifiable results that are translated into economic, social, and environmental value. From the economic value perspective, customers might increase machine productivity and efficiency and reduce costs in maintenance, spare parts, training operators, staff turnover, repairs, and associated costs of accidents and downtime. From the social value perspective, customers might improve occupational health and safety by reducing risks associated with work accidents, high levels of worker stress, and hazards associated with extreme weather conditions. From the environmental perspective, significant energy savings can be achieved, which leads to lower carbon emissions.

Third, it might orientate machine manufacturers on how to develop value propositions based on SDT-enabled smart services. According to the availability of resources, degree of technological maturity, and know-how in simulation and data-oriented techniques, manufacturers may plan development strategies in such regard. For example, whilst large and mature enterprises might use the SDT as a means for smart service improvement, smaller companies can place such technology as the core resource for their complete smart service offer. Moreover, this study guide manufacturers in determining how reality-driven simulations contribute to improving product development and designing more effective prototypes.

Fourth, it might contribute to creating sustainable production systems from the social and environmental points of view. From the social standpoint, SDT respond to the shortage of highly-skilled workforce by easing machine operations so that less experienced workers operate with comprehensive information and semi-autonomous assistance. The SDT technology represents a breakthrough towards the acceleration of machine autonomy, which will help workers to reduce repetitive tasks that eventually will enhance their health and safety rather than replace them. From the environmental viewpoint, SDT can respond to the environmental challenges faced by several industries to adopt sustainable practices in operations and efficient use of resources. The SDT will assist operators to follow the optimal work sequences and cycles that lower energy consumption and reduce emissions.

5.3. Limitations and further research

This research offers a comprehensive study of SDT-enabled smart services in the Operator 5.0 context and their mechanisms for decision-support and value co-creation in machine operations. The results obtained in this study are formulated from a multiple case study of nine heavy machinery manufacturers in Finland. Thus, this study presents limitations in its research approach and scope.

From the research approach, this study presents limitations in its methodology. This study lays its foundations of the case study methodology, which presents limitations since it might be affected by subjective evidence, researcher biases, limited results generality, and extensive data collection (Zainal, 2007). In the context of this study, we identified three limitations of the multiple case study. First, its reliance on data collection from engineers and managers who might have biases and limited knowledge related to the needs and requirements in machine operations. Second, the inductive thematic analysis utilized to identify and extract themes in our data analysis might have had limitations in its objectivity and approach. Third, limitations in its scope, as discussed in Section 3.1, by selecting specific industry members from a particular country that might affect the generality of our findings.

To address the aforementioned limitations, we established the following research directions. First, further research might extend the sample of respondents by including a wider group of profiles, especially those involved in the decision-making processes, such as maintenance supervisors, service technicians, and machine operators. Second, to increase the objectivity of responses, future studies might consider alternative data analysis methods such as grounded theory, content analysis, or framework analysis (Thorne, 2000) to mention a few. Third, to broaden the generality of results, future studies might include an extensive group of manufacturers in different industries and regions.

From the scope perspective, this study presents limitations in its theoretical contributions and managerial implications. In the theoretical contributions, the study addresses research gaps in the contexts of the Operator 5.0 (Romero & Stahre, 2021), operationalization of DT-based value co-creation (West et al., 2021) and conceptualizations in smart services (Kohtamäki et al., 2021, 2022). However, the study contributions are limited to particular human-machine interactions in machine operations, meaning that a macro perspective of the production system was not considered. In the practical implications, the study focuses on the application of the SDT for use phases of heavy machinery lifecycle. However, implications on machine design and development were briefly presented. In addition, the study acknowledges the potential benefits of the SDT technology, yet it does not discuss the potential limitations in machine lifecycle processes. For instance, since the SDT rely on creating models that estimate measures from virtual sensors, thus manufacturers and other interested parties might distrust the technology’s capacity to provide reliable information (Kurvinen et al., 2022).

To address the limitations in scope, we present future research areas. To address the limitations on the theoretical contributions, further investigations might focus on studying SDT-based smart services of multiple human-machine entities, analyzing the factors that determine the value co-creation for several system members. From the practical perspective, future research might consider studying decision-making processes in machine design, and investigate how the SDT can support such processes throughout the lifecycle. Moreover, future research might address the trustworthiness issues of the technology by conducting empirical studies that measure the actual accuracy of the SDT-estimated measurements of machines to validate its effectiveness. Besides, studies might further investigate the factors that affect the adoption of the SDT.

Furthermore, this research lays the foundation for extensive studies that might include a broader cluster of value co-creation participants in heavy machinery operations. Some examples of participants include technology providers, project contractors, machinery dealers, and other relevant members who might have influence in the SDT-driven value co-creation cycle. Thus, a more comprehensive understanding of decision-making needs in machinery processes can enable the further development of smart services from an ecosystem perspective.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Business Finland [project: Next Generation Digital Twin Solutions-XGen-DigiTwin].

Notes on contributors

Mario Verdugo-Cedeño

Mario Verdugo-Cedeño is a Doctoral Researcher in the department of Industrial Engineering and Management at LUT University, Finland. He received a MSc degree in the field of Technology and Innovation Management in 2016. His research interests include smart service development, operator-centric innovations, and decision support for value creation.

Suraj Jaiswal

Suraj Jaiswal is a Postdoctoral Researcher in the Laboratory of Machine Design at LUT University, Finland. He received a PhD degree in Mechanical Engineering in 2021. His research interests include multibody dynamics, hydraulic actuators, Kalman filters, friction modeling, and real-time simulation.

Ville Ojanen

Ville Ojanen is a Professor of Industrial Innovations in the department of Industrial Engineering and Management at LUT University, Finland. He leads a research team of innovation and technology management, and his current research interests focus on the intersections between industrial renewal, digital service business development, and sustainability-oriented innovations.

Lea Hannola

Lea Hannola is an Associate Professor and Head of department of Industrial Engineering and Management at LUT University, Finland. She received a PhD degree in Industrial Engineering and Management in 2009. Her research focuses on innovation and technology management, particularly customer needs assessment, digital tools, and product-service systems.

Aki Mikkola

Aki Mikkola is a Professor in the Department of Mechanical Engineering at LUT University, Finland. He received a PhD degree in machine design in 1997. He leads the Research Team of the Laboratory of Machine Design, and his research interests include machine dynamics and vibration, multibody system dynamics, and bio-mechanics.