Industrial Internet of Things Collaborations: A Contingency Framework for Smart Grid Development in Renewable Energy

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Industrial Internet of Things Collaborations -

A Contingency Framework for Smart Grid Development in Renewable Energy

Leo Haglund Emil Jonsson

Industrial and Management Engineering, master's level 2021

Luleå University of Technology

Department of Social Sciences, Technology and Arts

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ABSTRACT

Purpose - As energy demand increases in tandem with an increasing climate crisis, the world runs towards renewable energy generation. Within the area of Industrial Internet of Things (IIoT) there are a multitude of opportunities that should be capitalized on, but this requires an integration of the connected systems of Information Technology (IT) and the governing systems of Operational Technology (OT). In the utility sector, this has proven very complex. Hence, the purpose of this paper is to explore the challenges between utility companies, IT providers, and OT providers in the ecosystem to identify activities to combat these challenges by developing a contingency framework. Thus, contributing to the development of Smart Grids (SG) within renewable energy generation.

Method – To fulfill the purpose of this study, the partnership between the Swedish branch of a global technology company and a sizeable Swedish energy producer has been investigated. A qualitative single case study has been conducted with an inductive, explorative approach.

Empirical data were collected from 22 interviews and 4 workshops from six different companies across five countries. The interviews and workshops were conducted in three different waves:

1) Explorative, 2) Investigatory, and 3) Validatory. The data were analyzed using thematic analysis.

Findings – Findings from our data analysis have identified challenges and key activities in four main categories: 1) IT/OT Collaborative Challenges, 2) IT/OT Technical Challenges, 3) IT/OT Collaborative Activities, and 4) IT/OT Technical Activities. These findings are combined to form a contingency framework that emphasizes the activities to overcome industry challenges.

Theoretical and Practical Implications – Our findings and framework expand on current literature in IIoT, SGs, and Innovation Ecosystems development by investigating the collaborative challenges and activities within IT/OT collaboration rather than specific technologies or ecosystem structures. It also expands the literature on IT/OT convergence by taking a broader ecosystem perspective than only IT and OT companies. Our framework provides practical contributions for managers by identifying key challenges and activities and how these relate to each other.

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Limitations and Future Research – Our study is limited to a single case study on wind power generation in northern Europe. Therefore, future studies are recommended to investigate if our findings apply to other companies, industry sectors, and geographical areas.

Keywords: Smart Grids, IT, OT, Integration, Industrial IoT, Innovation Ecosystems, Collaboration, Challenges, Contingency Framework, Utility

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ACKNOWLEDGEMENTS

This master thesis is the final assignment of our master’s degree in Industrial Engineering and Management with Innovation and Strategic Business Development as our specialization at Luleå University of Technology.

Firstly, we would like to thank Luleå University of Technology and our supervisor, Wiebke Reim, for guiding us and providing us with valuable insights throughout this thesis work.

Secondly, we would like to thank the case company and its partners for making this study possible. A special thanks are directed towards our two supervisors at the case company, Niklas Bengtsson & Frida Hugne, who inspired us to deliver the best product possible, provided support, and offered guidance throughout the project. Thirdly, we would like to direct a thank you to our respondents, taking time out of their schedules to offer their knowledge and insights.

Lastly, we want to thank our fellow students for providing continuous feedback that contributed to the report.

_________________________ _________________________

Leo Haglund Emil Jonsson

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1. INTRODUCTION

Internet of Things (IoT) has changed how analog products are used and transformed them into a global network of machines and devices with the capability of interacting with each other (Lee

& Lee, 2015). No matter the application area, the main advantages derived from IoT technology come from device-to-device or human-to-device interactions that unlock the potential value in capabilities such as self-monitoring, optimization, control, and autonomy (Lee & Lee, 2015;

Heppelman & Porter, 2014). The area of IoT covers multiple technologies and industry sectors.

IoT in an industrial setting such as manufacturing or utilities, i.e., Industrial Internet of Things (IIoT), can be described as a convergence between Information Technology (IT) and Operational Technology (OT) (Boyes et al., 2018). Traditionally, these technologies have been separated in industrial settings where IT handled the office environment, enterprise network, and Internet Protocol (IP) based networks, systems, and devices. OT, on the other side, handled the industrial automation and control systems as well as devices, networks, and controls to operate, automate and maintain industrial processes in systems isolated from IP connectivity (Boyes et al., 2018). The applications of IIoT solutions in historically analog industries serve as enablers for innovation and can provide solutions to complex problems. Tabaa et al. (2020) investigate how IIoT can actively solve sustainability challenges. The electricity demand is increasing, and the climate crisis pushes us away from non-renewable energy sources (Broeer et al., 2014). The current power grids need modernization to solve this problem (Tuballa &

Abundo, 2016).

According to Broeer et al. (2014), the complexity of the system producing, transmitting, and delivering electrical power has to be developed and evolved to overcome the obstacles we are facing within sustainability. One promising solution is the concept of smart grids (SG) (Tuballa

& Abundo, 2016; Wang et al., 2011). With the help of IIoT-technologies implemented in our power grids, renewable energy sources with high potential but less stable production than fossil fuels, e.g., wind- and solar power, can be optimized and distributed more efficiently than currently possible (Broeer et al., 2014; Tuballa & Abundo, 2016; Cecati et al., 2011). This can reduce our dependency on non-renewable energy sources such as fossil fuels, reduce CO2 emissions from the power grid, and lower operational costs for energy production (Wang et al., 2011; Holttinen et al., 2007). Therefore, for consumers, SGs can lower utility costs and lower the environmental impact of energy consumption. This development of our power grids also goes in line with the UN’s sustainable development goals, especially SDG 7: Affordable and

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clean energy and SDG 13: Climate action (UN, 2020). Despite this, most of today’s research in SG development focuses on electricity distribution (Moleshi & Kumar, 2010; Brown, 2008;

Fang et al., 2012; Broeer, 2014; Cecati et al., 2011). Therefore, more research regarding electricity generation concerning to SGs is needed.

The basic concept of an SG is to provide an electrical grid that accommodates a variety of generation options, e.g., central, distribution, intermittent, and mobile solutions (Farhangi, 2010). Fang et al. (2012) describes SGs as a grid with a two-way flow of electricity and information to create an automated and distributed advanced energy delivery network. Since energy systems today already have many stakeholders involved, adding a connectivity aspect to the system will add complexity and expand the current ecosystem of stakeholders (Hall & Foxon, 2014). As a result, it becomes increasingly important to ensure successful collaborative partnerships between the different stakeholders involved in making SGs a reality, e.g., between IT providers, OT providers, and utility companies. While these stakeholders have historically been separated (Agarwal & Brem, 2015), the IIoT implantation in our power grids required for SG development now forces these stakeholders to work towards integrated solutions. Due to the historical separation of these different fields, challenges mainly arise due to low goal congruence, especially regarding differences in life cycles and managerial domains in IT and OT (Hahn, 2016).

Research in the field focuses on describing, defining, and understanding SGs (Hassan & Radman, 2010; Wang et al., 2011; Tuballa & Abundo, 2016; Güngör et al., 2011; Farhangi, 2010), detailing different security aspects to consider (Khurana et al., 2010; Metke & Ekl, 2010;

McDaniel & McLaughlin, 2009), and societal impact as well as the impact on electrical distribution systems (Moleshi & Kumar, 2010; Brown, 2008; Fang et al., 2012). However, there is a lack of research on ecosystem collaboration within SG development. As explained by Farhangi (2010), the development of SGs will follow a trajectory of incremental innovation rather than a complete overhaul of the current energy grids. This provides opportunities to investigate the generation side of SGs because, without a complete value chain, full-scale implementation will prove difficult. There is also a lack of research on IT/OT convergence in terms of security, the convergence of networks, differences in life cycles, and managerial challenges when collaborating between these previously separated categories of technology (Boyes et al., 2018; Petty, 2017; Hahn, 2016; Agarwal & Brem, 2015). IT/OT convergence is

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a crucial part of SG development since IIoT is a key enabler for SG development (Tabaa et al., 2020).

Therefore, it is important to investigate how stakeholders need to collaborate within the ecosystem to develop a functioning smart grid. Specifically, to see how connectivity solutions can be implemented at the generation stage in the value chain of smart grids. Because wind power is tied with hydropower as the largest renewable energy source in Europe (Eurostat, 2021), this paper will focus on renewable energy, with wind power as a focal point. To do this effectively, investigations into the current state of connectivity in energy production are essential.

Since this is a collaboration between several actors, it is equally important to investigate how the different stakeholders should interact and transfer knowledge within the ecosystem. Therefore, the purpose of this paper is to explore the challenges between utility companies, IT providers, and OT providers in the ecosystem to identify activities to combat these challenges by developing a contingency framework. Thus, contributing to the development of SGs within renewable energy generation.

To achieve this, we conducted a single case study at a global connectivity company delivering solutions to a Swedish energy producer and distributor. Our study focuses on identifying the main challenges for ecosystem stakeholders in renewable energy generation and what activities can be performed to overcome these challenges. The contingency framework focus on how these activities fit to specific challenges, providing opportunities for incumbent connectivity companies and utility companies when transitioning towards SGs. Thus, expanding on previous research, providing a new perspective on the transition towards SGs. This study will also lay the groundwork for future research, specifically, research on managing collaboration and convergence between IT and OT in sustainable energy generation ecosystems, the utility sector, and the industry.

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2. THEORETICAL FRAMEWORK 2.1 Smart grids

The current structure of electrical grids follows the value chain of central generation, transmission system, a network of substations, and customer loads, see Figure 1 (Farhangi, 2010).

There is an issue of communication between the customer loads and the distribution network in the value chain, resulting in that generation of electricity is overengineered to withstand maximum anticipated peak demand across its aggregated load. This creates an efficiency challenge as the current system is poorly engineered to meet today’s standards. Butt et al. (2020) states that this becomes an issue as the electricity demand increases in tandem with our technological development. It is unclear how long these outdated grids will be able to meet this increased demand.

Figure 1 - Value Chain for electricity grid (Farhangi, 2010)

The new concept of SG has emerged to address this challenge (Güngör et al., 2011; Farhangi, 2010). According to Fang et al. (2012) SGs differentiate from traditional power grids, SGs have a two-way flow of energy and information to create an automated and advanced energy network.

Farhangi (2010) expand on this notion by concluding that SGs are built from three distinct parts:

IT, Communication Technology (CT), and power system engineering, the latter two forming OT. From this standpoint, OT provides the energy flow in the grid while IT provide the information flow. According to Fang et al. (2012), the concept of a SG differs between different researchers, however, Farhangi (2010) map the critical factors that differentiate SGs from traditional grids, see Table 1. Literature on the topic concur that SG would solve sustainable electricity generation and distribution problems (Moleshi & Kumar, 2010; Butt et al., 2020;

Tuballa & Abundo, 2016). Farhangi (2010) concludes that the development of SG will follow an incremental path rather than a complete shift. Ipakchi and Albuyeh (2009) concluded that there would be a shift from the traditional system of producing electricity with a large remote power station towards smaller intermittent sources from, e.g., wind- and solar power. With this shift, it becomes increasingly important to manage the flow of information between these small- scale generation sources and the power distributors.

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Table 1: Key differences between SGs and traditional grids (Farhangi, 2010)

Traditional Grid Smart Grid Electromechanical Digital One-Way

Communication

Two-Way Communication Centralized Generation Distributed Generation

Hierarchical Network

Few Sensors Sensors Throughout

Blind Self-Monitoring

Manual Restoration Self-Healing

Failures and Blackouts Adaptive and Islanding Manual Check/Test Remote Check/Test Limited Control Pervasive Control

Few Customer Choices Many Customer Choices

2.1.1 Wind Power in Smart Grids

There are several problems in optimizing the generation and distribution of wind power in the current electricity grids. According to a report by the International Energy Agency (IEA), wind power brings benefits such as lower emissions and operating costs when replacing fossil fuels.

However, there are also significant challenges, such as high variability in generation and a high risk of forecast errors (Holttinen et al., 2007). The result of these challenges is that it is hard to produce wind power to meet the energy demand of end consumers (Broeer, 2014; Holttinen et al., 2007; Cecati et al., 2011). The report also notes that the current costs of grid reinforcements to combat these challenges are dependent on the location of wind farms relative to the load and grid infrastructure (Holttinen et al., 2007).

Part of the solution also lies in optimizing the on-site energy generation of wind farms. Since many wind farms are located either offshore or in remote areas (Ipakchi & Albuyeh, 2009; Kwon et al., 2016), there is much value found in remote monitoring, optimization, and automation solutions. Siemens “Wind Service Solutions” and GE’s “Digital Wind Farms” are examples of companies that provide capabilities such as Remaining Life Cycle (RUL) estimations, optimization of turbine performance, and predictive maintenance requirements (Kwon et al., 2016; Heppelman & Porter, 2014). Kwon et al. (2016) also propose using prognostics and system health management (PHM) to better monitor maintenance requirements and allow for condition-based maintenance cycles (CBM), also known as predictive maintenance. CBM uses

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sensor data rather than fixed cycles or waiting for assets to fail, resulting in better reliability, a more stable power supply, and fewer power interruptions for wind power generation (Kwon et al., 2016). While these systems are widely discussed by research, few examples of these technologies are being used in industry. Furthermore, while the systems are discussed in the research, there is a lack of research into what prerequisites are needed and how renewable energy producers can best utilize these new technologies.

2.1.2 IT & OT Technology in Wind Power

With the current goals within sustainable development, it is advantageous to start to digitalize our current electrical grids to achieve efficiency, automation, and shifting towards more renewable sources of energy production. In practice, this means adding digital solutions to existing infrastructure (Wu et al., 2021). The authors discuss which type of technologies shape the journey from analog to digital, e.g., IoT solutions. With an increase in smart devices, there is an increase in communication between the devices and control mechanisms. Historically within wind power, most of the communication regarding the operational aspects of production has been managed via the OT system Supervisory Control and Data Acquisition (SCADA) (Daneels & Salter, 1999). SCADA was developed in the 1960s to supervise hardware and manage control signals sent to operational devices via programmable logic controllers (PLC).

According to Boyes et al. (2018), there are conflicting views in research regarding whether SCADA is a part of the IIoT ecosystem or simply a predecessor since SCADA-systems have evolved and become more connected but still do not have the analytic or connectivity capacity otherwise found in IIoT. In an article by Miller and Rowe (2012), current risks with SCADA are brought to light. The article describes 15 incidents over 30 years where SCADA systems have been subject to digital warfare and hacks, one of the more famous incidents being the Stuxnet attack in Iran. A computer worm accessed control over a nuclear power plant to disrupt operations and distort data. In the Stuxnet case, the worm was spreading for four days before being discovered (Miller & Rowe, 2012); with IIoT technology, superior self-monitoring, and visibility over the network, the breach could have discovered and mitigated earlier. Anton et al.

(2017) goes further, explaining how, in the last two decades, SCADA has been exploited because of the systems being connected to more open networks as opposed to the physically isolated networks. These risks directly correlate to the functionality desired when designing the system, a time where cybersecurity was mostly nonexistent (Igure et al., 2006). Despite this, SCADA is still viewed as the industry standard within the energy industry.

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The physical components for which the IT technologies rely on in the current landscape are optical fiber cables at the base of the towers, connected via an ethernet switch to a control center (Ahmed & Kim, 2014). However, the authors point out shortcomings with this method and suggest switching to a wireless solution with three separate networks; a turbine network, a farm network, and a control network. Advantages with this model mainly focus on how the wind turbines can communicate amongst themselves, e.g., knowing the amount of power generated relative to the other turbines (Ahmed & Kim, 2014). However, Bera et al. (2015) raise challenges with a wireless approach, stating that there are security risks, i.e., data leakage, data management, and privacy issues, when related to adding complexity to the architecture of the grid. Because the energy grid is a critical part of any nation’s infrastructure, it is a high-profile target for potential cyber-terrorism attacks (Cisco, 2016; Bera et al., 2015).

2.2 Collaborative Partnerships

In a study on SG investments in the UK, Hall and Foxon (2014) describe modern energy systems as having “traits of large technical systems.” This, due to a large number of physical assets and non-physical assets. These non-physical assets, according to the authors, span, e.g., companies, regulations, investors, and politics (Hall & Foxon, 2014). Due to the large number of stakeholders, which increases as these energy systems become more connected in future SGs, collaborative partnerships between the different actors will be vital. While more actors might make innovation more complex, collaborative partnerships between different actors positively impact innovation development in knowledge-intensive business services, i.e., R&D-intensive industries (Bustinza et al., 2017).

A Study by Cao and Zhang (2010) which researched collaborative advantage by studying supply chain collaborations, further developed seven dimensions that contribute to success in collaboration between supply chain partners. The found dimensions were information sharing, goal congruence, decision synchronization, incentive alignment, resource sharing, collaborative communication, and joint knowledge creation (Cao & Zhang, 2010). The dimensions focus mainly on collaboration and do not consider differences in the products delivered within the ecosystem. For instance, even if the goal congruence is high, it can be negatively affected by parameters such as differences in product life cycles.

2.2.1 Innovation Ecosystems

Due to the complex relationship of collaborating actors working together in innovation and technology development, SGs show many traits connected to the concept of Innovation

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Ecosystems (Jackson, 2011; Ginsberg et al., 2010). Innovation ecosystems are a concept that builds on business ecosystems but differentiates in that it mainly focuses on value creation while business ecosystems mainly focus on value capture (Gomes et al., 2018). Innovation ecosystems as defined by Ginsberg et al. (2010, p.1) as “a network of actors (e.g., firms or individuals) whose differing interests are bound together in a collective whole for purposes of promoting business development through innovation.” Since SG development is still at an R&D level with a focus on value creation for society, innovation ecosystems best suit this study’s purpose. The study by Ginsberg et al. (2010) looked at innovation ecosystems specifically in SGs, since SGs show a significant need for diverse innovation at many levels. Examples of innovation areas for the utility sector in SG development include rapid adoption of breakthrough development, a higher degree of agility, greater market sensitivity, more diversity in terms of actors with different backgrounds and capabilities as wells as a higher degree of innovation at multiple parts of the overall system (Ginsberg et al., 2010). The study also looks at the structure of three different SG innovation ecosystems by General Electrics (GE), IBM, and Cisco Solutions from three parameters;

diversity, i.e., representation of different industry categories in the ecosystem, centrality, i.e., degree to which an ecosystem owner occupies a central node in the network, and network density, i.e., number of connections between actors. It was found that GE and Cisco both show similarities in that they display a high degree of diversity and centrality and a low degree of network density, with IBM having a similar approach apart from being more decentralized (Ginsberg et al., 2010). While a high degree of diversity might give way for a large variety of competencies in an innovation ecosystem (Ginsberg et al., 2010), an article by Adner (2006) reveals potential challenges with this type of structure within an ecosystem. According to the article, a higher number of intermediaries in an innovation ecosystem directly relates to uncertainty in market success (Adner, 2006). Furthermore, centrality can also cause challenges due to a higher dependency on external actors leading to external risk, which is harder to manage and mitigate than internal risks (Adner, 2006). However, the mismatch between GE, IBM, and Cisco (Ginsberg et al., 2010), and the acknowledgments by Adner (2006) might lie in the studied application areas. In the article by Adner (2006), the studied application area for innovation ecosystems was in product development for the consumer market, considerably different in complexity from SG development. Thus, it would be very interesting to see more qualitative research on innovation ecosystems in the industrial sector. Specifically, the collaboration aspects of the stakeholders in the ecosystem are lacking in the research.

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2.2.2 IT/OT Convergence

While collaboration in a supply chain setting is typical, industrial applications of IoT innovation like smart grids are converging two traditionally separated types of technology (Agarwal & Brem, 2015). Namely IT and OT, a marriage not without its challenges (Petty, 2017; Hahn, 2016;

Agarwal & Brem, 2015). According to Hahn (2016), the managerial challenges facing IT/OT convergence in industrial control systems (ICS) are long lifecycles, financial investments, vendors

& procurement, and managerial domains. Of these, long lifecycles, financial investments, and vendor & procurement directly relate to cybersecurity. The long lifecycles of 10-20 years, lack knowledge of cybersecurity, and the absence of protocols for software patches in OT does not correlate with the short lifecycles of IT, i.e., 3-5 years, the need for security investments, and patching requirements in IT technology to keep up with cybersecurity threats (Hahn, 2016;

Agarwal & Brem, 2015). Managerial domains relate to the challenge in managing IT and OT staff when IT is incorporated into operations and who has the managerial responsibility for these converging systems, software, and people (Hahn, 2016). According to an article by Agarwal and Brem (2015) exploring GE’s approach to IT/OT convergence in their Industrial Internet Initiative, GE overcame these difficulties by redesigning their business structure by centralizing IT functions in the company. The article also states that this redesigning process might be even more challenging for industrial companies without a clear project management process for software development (Agarwal & Brem, 2015). As a solution, partnering with IT firms that can share knowledge in software development throughout this process can help manage the transition (Agarwal & Brem, 2015). Due to the close partnerships needed for this process, alignment of vision, strategies, and goals must be jointly formulated to assure a joint value proposition from all connected actors (Humbeck et al., 2020). On top of this, interconnectedness through continuous networking between actors and transparency through sharing knowledge, data, and skills must be maintained throughout the partnership (Humbeck et al., 2020). While Humbeck et al. (2020) discuss the importance of partnering with IT firms, the importance of other ecosystem stakeholders is overlooked. Since many of the industry’s challenges are complex, it is essential to include as many perspectives as possible in the solution to reduce complexity.

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3. METHOD

3.1 Research Approach

To fulfill the purpose of exploring the challenges between energy producers and IIoT integrators to identify solutions for SG development in sustainable energy generation, a qualitative research approach was chosen. With limited previous research around the connectivity within energy generation in SGs, this study adopted an inductive, exploratory approach (Saunders et al., 2009).

This method allowed for an understanding of the research area to gradually emerge as the purpose and scope of the study were reviewed continually. Initial exploratory interviews provided us with the necessary information to decide which areas were relevant to explore in the theoretical background (Saunders et al., 2009).

In exploratory qualitative studies, the theoretical background needed and the specific research purpose are not always clear from the start, as with the case of this study. According to David and Sutton (2011), analysis and collection of data often happen simultaneously, which can often affect the direction of the study based on the data analysis. For an overview of the applied method, see Figure 2.

Figure 2 - Method for research

3.1.1 Case Selection

This single explorative case study has been carried out at the Swedish branch of a global technology company, Alpha, together with their collaborative actors and customers within the utility sector. This technology company provides network hardware, software, telecommunication equipment, and other high-tech products and services for their customers.

At the time of the study, Alpha had become increasingly invested in offering digital connectivity solutions for the industrial market, making them interesting for this study as an IT company working in traditional OT-centric markets. For this case study, the customer company, Beta, is a sizeable publicly owned Swedish energy producer and distributor for the northern European market focusing on sustainable energy such as hydro-, solar- and wind power. Beta, therefore, provided to this study extensive experience in both the utility sector and OT environments in

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sustainable energy generation. At the time of the study, Alpha had started providing connectivity solutions for the utility sector aiming to become a key player in future SG developments in Europe. To do so, they had an ongoing growing partnership with Beta in connectivity solutions for energy distribution. To expand this partnership and extend knowledge about the utility sector, Alpha was interested in extending its value offering to energy generation, especially in wind power. Alpha was therefore chosen for the study based on their current commitment to SG development and desire to extend their value delivery into energy generation. The close relationship between Alpha and Beta is also of benefit to this study since Beta could provide crucial insights into utility companies’ digital requirements and their view and commitment to SG development. Since Alpha and Beta are just two parts of the complex ecosystem of stakeholders in energy generation, representing IT providers and utility companies, third-party respondents were also used in the study. These are the respondents from companies: Gamma, Delta, Epsilon, and Zeta, presented in Table 2.

Table 2 – Summary of third-party companies Company Description

Gamma Swedish technical university

Delta Independent contractor for Wind Power projects managing Operations and Maintenance (O&M) for private investors in Wind Power

Epsilon IT-firm and reseller for Alpha that manages the account for a large wind power Original Equipment Manager (OEM)

Zeta Start-up company developing predictive maintenance software for Wind Power

3.2 Data Collection

Data was collected and analyzed in three waves during this study, with some overlap during the transition between waves. Each wave had a distinct purpose; 1) Exploratory purpose, 2) Investigatory purpose 3) Validatory purpose. All interviews and workshops were conducted digitally via video link and recorded. In total, we conducted 22 interviews and 4 workshops presented in Table 3 and Table 4. In addition to this, we had weekly update meetings with our supervisors at Alpha. The collected data was analyzed with thematic analysis.

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Table 3 - Summary of respondents

ID Position Company Date Duration

Wave 1: Exploratory interviews

R1 Account Manager Alpha 2021-01-20 60 min

R2 Technical Solutions Architect Alpha 2021-01-21 45 min

R3 Sales Specialist IoT Alpha 2021-01-29 60 min

R4 Customer Solutions Architect Alpha 2021-01-29 60 min

R5 Systems Engineer Alpha 2021-02-01 60 min

R6 R&D Manager Distribution Beta 2021-02-04 30 min

R7 Senior R&D Engineer Beta 2021-02-10 25 min

R8 System Engineer Alpha 2021-02-05 60 min

R9 O&M Roadmap Manager Beta 2021-02-05 45 min

Wave 2: Investigatory interviews

R10 PhD Researcher, Cybersecurity Gamma 2021-02-05 35 min

R11 Chief Technical Officer Delta 2021-03-04 60 min

R12 Senior Asset Manager Onshore Beta 2021-03-05 60 min R13 Senior Technical Lead, Industrial IoT Alpha 2021-03-08 60 min

R9 O&M Roadmap Manager Beta 2021-03-08 60 min

R14 Account Manager Epsilon 2021-03-12 30 min

R4 Customer Solutions Architect Alpha 2021-03-12 30 min

R3 Sales Specialist IoT Alpha 2021-03-15 60 min

R15 Project Development Manager Beta 2021-03-16 60 min

R16 CEO Zeta 2012-03-23 60 min

R17 Founder & chairman of the board Zeta 2021-03-23 60 min R18 Senior Technical Specialist Beta 2021-04-08 60 min R19 Business Development Manager Beta 2021-04-08 30 min

3.2.1 Wave 1 – Exploratory

During the first wave of data collection and analysis, the purpose was to gain as much knowledge as possible before starting the second wave of data collection. The collection was divided so that initial interviews were conducted at Alpha, to give us the necessary information about the product portfolio and what services could potentially be offered. Following this, exploratory interviews at Beta were conducted to understand the customer pains and the gap between the two different companies. All the interviews were transcribed in the language they were held in, ensuring that we were unbiased and allowing us to go back in time and assess what had been said during the interview. The structure for all interviews was open, and no predetermined questions were prepared. In total, we conducted 9 interviews during wave one; see Table 3. The high number of interviews during wave one is due to the complexity of the investigated area.

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To get an adequate understanding of the area it was required to get perspectives of technical experts, security experts, and sales personal. The differences between IT and OT further reinforce the need to interview people from both sides, which increased the overall number of interviews. However, this also allowed us to gain more than only exploratory knowledge and preliminary challenges were indicated already in wave one.

3.2.2 Wave 2 – Investigatory

The second wave of data collection aimed to use previously gained knowledge from wave one to dig deeper into the problem and potential solutions. The respondents for the second wave of interviews were selected via recommendation from the first wave or by re-interviewing previous respondents. The respondents from Alpha and Beta were chosen based on their expertise. In addition to Alpha and Beta, third-party respondents from Gamma, Delta, Epsilon, and Zeta were interviewed. These companies and respondents were chosen based on respondents’

recommendation from Alpha and Beta based on their specific capabilities. A summary of these third-party companies is presented in Table 2. The structure of these interviews followed a semi- structured style enabling a standard for analysis and room for follow-up questions to not miss out on any vital information. During these interviews, pre-prepared questions were used as a question battery, meaning that questions for each respondent were chosen based on their area of expertise and responses from previous questions during the interviews. The questions were based on the data from wave one and the theoretical background, see Appendix A. All interviews were recorded and transcribed in their original language. In total, we conducted 13 interviews during wave two; see Table 3.

3.2.3 Wave 3 – Validatory

The last wave had the purpose of validating findings from previous waves. In total we had four validatory workshops, three at Alpha and one at Beta. Workshops were chosen to validate our findings since it offers many different viewpoints at once and allow for open discussion. The first two workshops were conducted after the first wave of interviews. The first workshop was done with four account management team members with previous experience working with Beta.

The second workshop was done with three technical experts in IoT and IT architecture. These two workshops were conducted to validate the initial direction and what to further explore in wave two from a management and technical perspective. From these workshops, certain themes, e.g., data storage, data selection, and predictive maintenance, were recommended to further

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explore by the participants. The third workshop was conducted after wave two to validate our findings. This workshop involved nine participants, excluding us, with sales, IoT, IT architecture, and cyber-security. The last workshop was conducted at Beta to get an OT perspective on our findings. The workshop involved three participants: asset management, business development, and operations. The validations from the last two workshops yielded positive feedback, and only minor changes were made. For example, the visual structure of the framework was changed to make it easier to follow. For an overview of conducted workshops during wave three, see Table 4.

Table 4 – Summary of validatory activities ID Company Number of participants

excl. researchers Areas of expertise from participants Duration Wave 3: Validatory workshops

WS1 Alpha 4 Sales 60 min

WS2 Alpha 3 IoT & IT architecture 60 min

WS3 Alpha 9 Sales, IoT, IT architecture & Cyber

security 90 min

WS4 Beta 3 Asset Management, Business

Development & Operations 60 min

3.3 Data Analysis

The data from the interviews were analyzed using thematic analysis (TA). The method for the TA follows the pattern presented by Braun and Clarke (2012) to analyze and identify patterns in the collected qualitative data. Thematic analysis was chosen as the main analytic method due to its flexibility and accessibility and its ability to make large sets of qualitative data easily accessible and manageable when linked and compared to previous theories and concepts (Braun & Clarke, 2012). During the analysis, we used the five-step process proposed by Braun and Clarke (2012):

1) Familiarizing yourself with the data, 2) Generating initial codes, 3) Searching for themes, 4) Reviewing potential themes, 5) Defining and naming themes. We used an iterative approach to move back and forth between steps to review and change previous decisions when needed. The analysis was also reviewed as time passed to ensure details of importance were not overlooked.

Most of the data analyzed were collected during the second wave of data collection.

3.3.1 Step 1: Familiarizing yourself with the data

The dataset from the interviews was read thoroughly by each researcher to get familiar with the content. Since both researchers were partly familiar with the data from conducting the

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interviews, this step also had a validatory purpose so that no important details were missed or forgotten. Each researcher took individual notes from the dataset regarded as interesting for further analysis during this step. Notes were later compared and discussed to create a mutual understanding of the collected data and its content.

3.3.2 Step 2: Generating initial codes

Data related to our purpose were further analyzed and coded based on their content during this step. Codes were named by summarizing the main point of the respondents’ quotes. Each quote was color coordinated depending on whether it was considered a challenge or an activity; blue represented challenges and green represented activities. For example, the quote: “We have captured data for 15-20 years, but to be honest we have not had the full benefit of all this data”

was first changed to the color blue since it represented a challenge and then put under the initial code “Not benefitting from captured data.”When the first code had been created, subsequent data of interest were applied under the same code if the data shared the same central point, or a new code was created and named under the same criteria as described above. Codes were constantly modified based on new material to incorporate similar data throughout this step.

Codes were also combined if they shared similar main points or separated if the content of the corresponding data was deemed to be conflicting or too broad in their points. This step aimed to separate relevant data to make it more accessible for further analysis and sort out irrelevant data collected during the interviews.

3.3.3 Step 3: Searching for themes

Once our initial codes were created and reviewed, the next step was to create themes. During this step, similarities between codes were analyzed to create overarching sub-themes with groups of codes sharing similar intention areas. Codes not corresponding to any created sub-themes were grouped individually, and either formed the basis of new sub-themes or discarded if they did not contribute to an overall purpose of the study. The created sub-themes were later analyzed again to make groups of sub-themes and create more significant themes based on similarities in their content. We also used color coordination in this step to group codes and themes based on what RQ they correlated to. For example, the codes “security hinders usability” and “security hinders innovation” were grouped in the sub-theme “security hinders digital development” and later grouped in the theme “IT/OT technical challenges.” The more significant themes were based on two criteria: 1) is this part of a challenge or an activity? And 2) is this part of a technical or collaborative challenge/activity? Four overarching themes were formed: 1) IT/OT

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Collaborative Challenges, 2) IT/OT Technical Challenges, 3) IT/OT Collaborative Activities, and 4) IT/OT Technical Activities. At the end of this step, a thematic map was formed to get a clear overview of codes, sub-themes, themes, and their relation to each other, see Figure 3.

3.3.4 Step 4: Reviewing potential themes

Once codes, themes, and sub-themes were created, the next step was to review the process in its entirety to quality check the previous steps. Hence, themes were compared and analyzed based on their incorporated codes and data to ensure the themes correlated with the data. If the initial data did not correlate with the themes, it was moved to other themes that could provide a better fit, or boundaries of the initial theme were redrawn to incorporate the codes better, or codes were discarded. An example of this process was the preliminary sub-theme “Lock-in effect by OEMs,” which was changed into the code “OEMs desire lock-in effects.” This was done because the challenge was built up of two codes sharing similar points and that the new code fits better under the sub-theme “Complex Ecosystem.”

When a coherent set of themes correlated with the coded data was created, the themes were compared with the entire data set. In practice, this meant rereading the entire data set to make sure that themes corresponded to essential elements from the entire data collection. Throughout this process, the thematic map was used and updated to create an overview of themes, sub- themes, codes, and their relation to each other, see Figure 3.

3.3.5 Step 5: Defining and naming themes

Once the final themes, sub-themes, and codes were reviewed and refined, defining and naming the themes was done. This was done to make sure that themes were based on empirical data and relevant towards fulfilling our purpose. The final names of themes are presented in the thematic map, see Figure 3.

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Figure 3 - Thematic Map

3.4 Quality Improvement Measures

The following four criteria were evaluated to ensure high trustworthiness and high quality of the study; credibility, transferability, dependability, and confirmability (Lincoln & Guba, 1985).

The credibility criteria refer to whether the findings are true and accurate (Murphy et al., 2017).

To ensure that our study had high credibility, we both attended all 22 interviews and 4 workshops; to mitigate knowledge loss, we transcribed all interviews. Furthermore, all the interview questions are presented in Appendix A. Due to the COVID-19 pandemic, we could

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not conduct any of the interviews face-to-face (F2F); however, we made sure that all the interviews were conducted with a working camera setup that allowed for us to see body language and mitigate the risk of misinterpretations.

Transferability covers how well the results of the study can be transferred to other cases. Since most theories are dependent on time and context, it is difficult to confirm the criteria of transferability in a single study (Lincoln & Guba, 1985). For this study, we tried to achieve transferability by describing the context around the case study so that future researchers can compare it to their context.

Dependability describes the consistency of the findings, i.e., how accurate the findings are when considered from multiple perspectives (Murphy et al., 2017). To achieve this, we have interviewed respondents from 5 different countries, all relevant to the case study in some capacity. These countries were Sweden, Norway, Denmark, Germany, Netherlands, and Pakistan. By examining the different viewpoints, it was possible to verify that the result was consistent with the context of multiple markets. To further increase dependability, we have received both written and verbal feedback from 3 other students and our supervisor at Luleå University of Technology. These seminars took place at three different times during the period for conducting the study, further aiding the consistency of the study.

The final criterion, confirmability, is to ensure that we were unbiased in drawing our conclusions. To achieve this, we analyzed the findings from the interviews using a thematic approach that is well documented in the study and conducted validating interviews with relevant respondents to confirm the themes and findings extrapolated from the primary data. The findings are based on the respondents’ answers instead of our preconceptions, which further increase the confirmability of the study (Shenton, 2004).

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4. RESULT

The study’s findings are divided into two main categories: IT/OT challenges and IT/OT activities. These categories are then further segmented into collaboration challenges/activities and technical challenges/activities. These four themes consist of sub-themes built up of codes from the thematic analysis, see Figure 3. IT/OT challenges relate to the challenges facing utility companies producing renewable energy, OT providers, and IT companies aiding the transition towards the development of SGs. IT/OT activities relate to the managerial steps these stakeholders can take and the technical solutions they can implement to overcome challenges they are currently facing successfully. Representative quotes will be presented for the different themes, sub-themes, and codes to exemplify the challenges and activities; the quotes are summarized and presented in Appendix B, C, D, and E. The challenges and activities are also integrated into the proposed framework, focusing on which activities and technical solutions should be utilized to overcome the industry’s challenges. The study’s general findings are that the industry is facing an inevitable convergence of the two previously separated areas of IT and OT, two areas with different mentalities, ways of working, and concerns. The main differences are that IT is an industry with rapid development, short life cycles, and a high focus on security as the OT industry’s fundamental characteristics are functionality, long-lasting solutions, and simplicity. Successful collaboration and goal congruence emphasize the requirement for good communication and understanding of the opposing side.

4.1 IT/OT Collaboration Challenges

The respondents from the primary data collection expressed several challenges when trying to integrate IT and OT. These challenges can be divided into five sub-themes: 1) Decentralized Decision-Making, 2) Diverging Life Cycles, 3) Communication Paradox, 4) Complex Ecosystem, and 5) Cultural Differences Between IT & OT. The section below explains further and exemplifies through representative quotes from the respondents; for a complete list of quotes see Appendix B.

4.1.1 Decentralized Decision-Making

Challenges related to decentralized decision-making cause difficulties in translating value from technical solutions to management in IT and OT higher up in the organizational hierarchy.

When presenting new ideas aiming to connect the IT side with the OT side, these projects need to function at a technical level, fulfilling the requirements from both sides. However, issues present themselves when, e.g., an IT solution is viable at a lower level in the organization, but

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the solution’s value is not understood correctly at the managerial level. A respondent at Delta expressed it as follows:

“These solutions are costing money and it is not always easy to get decision-takers that lack the insight to understand what this means. It is a little what we talked about earlier, about backup. Yes, but why should we have backup? Nothing has happened? Exactly, not yet...” (R11).

The quote suggests a lack of understanding of the different decisions taken at the managerial level; these mainly present themselves in tandem with innovation and further development of the business. This example showcases a lack of understanding of the security requirements in a solution. However, the challenge can also present itself the other way around, when security demands from the managerial side cause friction for the different teams involved in the solution.

A respondent at Alpha said the following: “The issue comes in through security, usually. Because security is a CXO level discussion. Because it's so high priority, not to lose intellectual property, not to stop production. So, then you can have conflict between IT and OT” (R3).

Decentralized decision-making challenges can also relate to the company strategy. Solutions can seem reasonable but lack alignment with the company strategy, which employees positioned lower in the company’s hierarchy easily miss.

4.1.2 Diverging Life Cycles

The challenges connected to diverging life cycles in IT and OT mainly result in increased costs and incompatible functionality. Many of the costs generated by the integration are due to IT solutions having to be turned around more quickly than OT solutions. If the hardware in place accommodates both IT and OT functionality, it must be turned around at the pace of the product with the shortest life cycle. The challenges also stretch further out into the ecosystem of stakeholders; before deals finalize, there is still room to discuss who will shoulder some of the costs. For IT providers, this brings additional costs if contracts are formed after OT life cycles, as stated by a respondent at Alpha:

“I mean, they are expecting us as a vendor to guarantee the solution and repair the solution for the next 25 years. And from the IT Company that is a very hard contract to sign up to because half of your product is going to be gone within the next five years” (R4).

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One of the key challenges is that the OT equipment still functions properly when hardware turnaround needs to occur.

4.1.3 Communication Paradox

The identified communication paradox presents itself as an interesting catch 22 challenge.

Communication is required to attain the best solution as it brings multiple perspectives and allows for an open discussion in what the best solution is. Paradoxically, an open discussion causes internal competition between the different ecosystem partners as the providers’ product portfolio often overlaps. As stated by a respondent at Alpha:

“But it also stops a lot of the innovative talk in the beginning. If you want to start a project, the 1^st thing you want to do is have an open discussion, but if you are having an open discussion, then you are competing” (R3).

The perspective from the utility company is that this communication is vital to ensure the best solution is implemented. Therefore, the main challenge related to communication can be formulated as the need for communication to ensure the best solution. In contrast, the same communication provides a forum for competition that draws out processes and lowers innovation.

4.1.4 Complex Ecosystem

Due to the complexity of the industry, a large ecosystem of partners is required to produce renewable energy. A plethora of interactions within the ecosystem causes specific challenges.

These challenges are partly due to safeguarding individual goals for each stakeholder, and the added complexity of more actors, each with their expertise. The key challenge within this area is a lack of goal congruence, especially from the OEMs seeking to create lock-in effects, something that hampers innovation. As stated by a respondent at Alpha:

“Because they want the data, they want to sell you the full stack solution and the more of their equipment is in the full package that easier list and then sell their management solution, their AI solution in the future, their everything else. So, um, yeah. But that's not a windmill specific challenge, that's an industry challenge, that's just how it works” (R3).

This phenomenon is mainly because of monetary reasons, as they often offer full-service contracts over wind farms. The complexity adds to this, requiring many of employees to be present at all meetings. Since each person has a cost, there is a risk of projects shutting down due

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to lengthy discussions with an excess of opinions. A respondent from Beta formulates it: “We have way too many meetings with too many people. And I could say that if every person costs 1000 SEK per hour and everyone attends every meeting, it will not take long before this kills any project” (R15). This is further emphasized as many utility companies working with several energy sources are often fragmented and regarded as different companies. A respondent from Alpha exemplifies this:

“Beta looks at Hydro as one company, windmills as one company and... It is definitely possible to do this, but then my experience working with Beta is that you have 20 people joining a meeting, and you have 20 different expectations and it is, uh, it requires much much more from their end…” (R3).

4.1.5 Cultural Differences Between IT & OT

As there are significant differences between IT and OT, a myriad of challenges arises when trying to converge the functionalities of the areas. Firstly, there is a lack of technical knowledge of the opposing side that influences the collaborations. However, also cultural differences come into play when IT experts communicate with OT experts. Furthermore, specific security threats need to be addressed when IT knowledge is to be applied in an OT environment. This is mainly because OT technology is responsible for the operational aspects of energy production, which is considered a critical societal function, increasing security demands. A respondent from Beta states this: “What you see with societally critical functions is that you have higher demands on security so that no one can come in and shut down a large part of the electrical production” (R12). The technical knowledge gap between IT and OT is further enforced by the cultural differences between the two sides where trust is lacking from the OT side, and IT has a view of OT employees as old-fashioned and slow.

Finally, there is a lack of knowledge regarding what will be required for the future of wind farms.

This lacking standardization causes a challenge for the IT side to know what is needed. A respondent from Alpha says:

“So, everyone is just jumping into this like saying yeah, we were creating windmills where we're gonna do it on the sea, we're gonna do it in the desert, we're gonna do it on top of a mountain. It's so new that they don't know

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exactly how, when will it stabilize like, what a wind farm is, and what are the requirements from a connectivity perspective?” (R3).

4.2 IT/OT Technical Challenges

The technical challenges presented by the respondents focus on aspects related to assets such as raw data, requirements in security architectures, data storage, and data selection. The theme is built up of four sub-themes: 1) Need for IT/OT Convergence, 2) Security Concerns, 3) Security Hinders Digital Development, and 4) Insufficient Storage and Use of Available Data. A complete list of the representative quotes can be found in Appendix C.

4.2.1 Converging IT & OT is Complex

The technical needs for IT/OT convergence, as stated by the respondents’, revolve around adding functionality to the OT equipment through IT solutions and using the abundance of data to gain actionable insights to develop the business. Challenges relate to the latter as most of the data is generated on the OT side while enriching the data happens within the IT domain. A respondent at Alpha said the following: “So, the data is generated on the OT, but you are creating the additional value on the IT side, meaning that that's why I also need a secure integration between IT and OT” (R4). The critical issues in this lie in deciding whether to securely extract the data from the OT networks before enriching it or directly analyzing the OT network data. As this poses different security risks, it is more complicated for incumbent firms to implement solutions without precedent. This creates a dilemma where competitors that try new solutions to increase functionality could gain competitive advantages over incumbent firms.

As stated by a respondent at Alpha:

“On the other hand, if they don't do it, then competitors will do it and start building more or producing more efficient power. Using machine learning, using all of the tools from the IT side, so they have to do it” (R3).

4.2.2 Inefficient Storage and Use of Available data

Since the renewable energy industry is relatively new, there is a lack of knowledge about what data is required to utilize new technologies, e.g., predictive maintenance. As stated by a respondent from Beta: “We have captured data for 15-20 years, but to be honest we have not had the full benefit of all this data” (R9). This is increasingly relevant as OEMs control and owns large portions of data; utility company’s need to know what data is relevant to them and how to extract insights from the data. A respondent at Beta says: “One thing is that you collaborate with

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different suppliers, turbine suppliers, who basically own a lot of data and then you need to know what data we want to own, what data is relevant for our O&M activities” (R19). Data selection also brings another dimension regarding the cost of storing the data as this drives high costs for the utility companies; they need to be conservative with the data they select to analyze. Unique to this industry is that many of cheaper options are not available due to a lack of security requirements, further driving up the cost of storage. As stated by a respondent at Beta:

“I know there are two parts. Either you have it in the cloud somehow and that becomes cheaper and the other method is to have dedicated servers. That is where we are today where we have a quite expensive solution that to the best of my knowledge is required due to security” (R12).

4.2.3 Security Hinders Digital Development

The higher security requirements bring increased costs in data storage, as mentioned above, and increase complexity when trying to implement new solutions. Since the security is high but not standardized to a great degree, adding new software solutions becomes complicated as many stakeholders need to be involved. Ultimately, the usability and innovation are halted through rigorous security protocols. This further reinforces the idea that this challenge primarily manifests for incumbent utility companies. A respondent at Beta says: “Yeah, it can be difficult to just to get approval to integrate a third-party software solution to a wind farm, which is normally done in the market they as plug and play“ (R9).

4.2.4 Security Threats

According to the responders, the main goal of security is to protect against malware and cyber- attacks. However, with good security, this should not negatively affect usability. Other industries achieve this through clear standards, widely implemented across all stakeholders from an IT perspective. Within wind power, there are some standards, but they are not implemented across all stakeholders in the ecosystem to the same extent. A respondent from Alpha states: “There are sector-specific things and for a vendor, it's a challenge to build, say, switches that are secure for five or six industries, and if within a given industry, like in the utility industry, you would have again diverging standards” (R13). As an IIoT provider, this makes it increasingly challenging to deliver the best solutions to the utility company. All solutions need to be customized to fit the specific use case. This means a less secure network with less visibility from a security standpoint.

From Alpha and Delta, respondents categorized visibility, seeing what devices are talking to each other as the most crucial security aspect.

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4.3 IT/OT Collaboration Activities

IT/OT collaboration activities aim to overcome the challenges facing the industry and vary from activities managing hardware to activities regarding communication and stakeholder management. The theme consists of the sub-themes: 1) Take Steps for Successful Collaboration, 2) Capitalize on Industry Opportunities, and 3) Emphasize Shared Value in IT/OT Collaboration. A complete list of representative quotes can be found in Appendix D.

4.3.1 Take Steps for Successful collaboration

According to the respondents, three criteria need to be fulfilled to ensure a successful collaboration. 1) Hardware needs to be managed in a way that functionality is not compromised while keeping costs low; 2) The goals for all stakeholders need to be holistic and focus on the end-customers value while keeping trust high amongst the stakeholders; 3) Frequent and clear communication must be allowed to take place, especially initially to foster a long-term relationship. There is a clear need for a maintenance plan to manage the hardware that considers the overarching systems to allow for synergistic effects between IT and OT functionality in hardware. It is essential to ensure all stakeholders realize the end-customers need and unite under a common goal. As a respondent from Alpha puts it: “One solution could be to unite on common goals. That it is not about getting the biggest piece of the pie, but that we instead focus on making the pie bigger” (R19). With this mindset, the stakeholders can focus on long-term collaborations instead of maximizing their short-term profits. To create incentives for this, the stakeholders should implement KPIs that measure them on end-customer value and not short- term sales numbers. As a representative at Alpha states:

“I mean, if you have like, KPIs within the collaboration saying that we would like to measure this, this, and this, then other than all participants in the ecosystem have to go through a common goal and that is actually to make the best KPI coming out of it. So, then you remove that from your internal bubble and then you actually start to think about the overall good for the utility company. So, that might be the success criteria is going in” (R4).

For this to become a reality, the new perspective needs to be communicated to the utility company and the other stakeholders. According to several stakeholders, this is best done by creating a one-stop-shop solution where clear roles are outlined before approaching the customer. This should then be complemented by regular follow-up meetings that allow for dynamic and continuous relationship development.

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4.3.2 Capitalize on Industry Opportunities

The opportunities identified in the case are specific opportunities tied to the industry that can allow for unique advantages in implementing the IT/OT collaboration activities. The industry is young, with less legacy and proof of concepts than other more mature electricity generation options. The possibility of building an industry standard that solves many of the challenges plaguing the utility industry presents a novel opportunity. Since the industry is young, there is much of innovation taking place, resulting in many third-party software companies with solutions to increase the efficiency and efficacy of the power generation. Lastly, companies are learning from past mistakes in the industry and emphasizing security, recognizing the risks in the market. A respondent at Beta expresses the want for an open standard in the following way:

“At this moment we have not a proper network standard, so that’s the way we will go for the next couple of years. I would like to use more standardized equipment with this, like standardized in the world and not like “Beta-standards”

(R18).

A respondent from Alpha agrees, suggesting that the goals align across different stakeholders in the ecosystem:

“So, now we're in, like, a golden age where OT, and IT kind of can work together because it's so new and there's no legacy that they need to be, that is dragging behind them. I would say, that's the greatest advantage they have now, is that they're actually restarting from a technology perspective where you have IT and OT, in the same meetings working together” (R3).

This alignment exists within security as well, as stated by respondents from both Alpha and Delta.

4.3.3 Emphasize Shared Value in IT/OT Collaboration

The increased shared value in IT/OT collaboration, according to the respondents’ is an increase in; efficiency, as specific competencies can be utilized in their respective areas; security, as the best aspects from both fields, can be combined with more transparent communication;

knowledge transfer in other areas than efficiency and security. By having this knowledge transfer occur, the trust for the respective sides will increase and allow for the collective competencies to solve the industry’s challenges. With this increase in shared value, utility companies will become better customers who are more aware of what requirements and products are available,

Industrial Internet of Things Collaborations: A Contingency Framework for Smart Grid Development in Renewable Energy