Applying a System of Systems Philosophy for Sustainability Success

Author photo: Larry O'Brien
By Larry O'Brien

Executive Overview

The drive to sustainability, decarbonization, and energy transition is creating unique challenges for industrial and critical infrastructure end users. Unlike traditional industrial operations, where the worlds of enterprise and operations were largely separate, the drive to sustainability and energy transition is requiring unprecedented levels of integration of both enterprise and operations data from a wide variety of disparate systems. Sustainability is changing the requirements for end user access to data. Sustainability related initiatives and their related metrics can draw from widely disparate sources of data, including financial, supply chain, operations management, and real-time control data. 

In the past, data flows in organizations were fairly hierarchical, data from operations was shared with the enterprise for various business functions. Conversely, enterprise level data was driven down to operations for things like production planning, scheduling, and other functions. Sustainability and energy transition initiatives cannot be supported with this traditional hierarchical structure. The push to sustainability represents the final step toward a holistic environment that can integrate data from any point or system in the organization, regardless of whether it is IT/enterprise or OT centric. 

Not surprisingly, sustainability initiatives usually go hand in hand with digital transformation programs. New technologies must be implemented to attain goals of open data access and contextualization, and this requires industrial grade data fabrics. It’s also important for end users to adopt a systems engineering mindset for sustainability. Having access to data and an appropriate data fabric is important, but this data must also be systematized, with a single pane of glass for visualization, reporting, analytics, the application of AI, digital twins, and other advanced technologies. With system of systems, the whole is truly greater than the sum of its parts. System of systems also allows existing systems within the organization to remain autonomous and does not require a significant investment in replacement. 

System of Systems Definition

A system of systems (SoS) is formed from a collection of multiple systems that all operate independently. It’s built from components that are systems in their own right. We interact with “system of systems” every day. Familiar examples include the Internet, power grids, and national transportation systems. The defense sector also heavily relies on system of systems to coordinate land, sea, and air operations, or to coordinate military operations with combined forces of different nations. 

Examples of SoS

Bulk power systems that control our power grids are system of systems, as are the Internet, oil and gas pipeline networks, and many other complex systems. All these systems have common characteristics – they are all comprised of independently managed and geographically distributed constituent systems that, when integrated together, have far greater capabilities than the sum of their parts. 

Philosophy for Sustainability Success

System Engineering Thinking and Design

Designing, implementing, and operating a system of systems is part of the systems engineering discipline. Using system engineering thinking and approaches can be very advantageous to solving business issues faced by manufacturing firms, energy companies, power providers, and critical infrastructure operators. Systems engineering thinking is an interdisciplinary approach to designing, developing, and managing complex systems. Systems engineering thinking considers not only the technical aspects of a system, but also the human, social, environmental, organizational, and operational factors that influence its performance and outcomes. It involves defining the problem, identifying the stakeholders and their needs, exploring alternative solutions, selecting the best option, implementing the system, and evaluating its effectiveness. It also entails managing the system throughout its lifecycle, including changes, risks, uncertainties, and trade-offs.

Independence of Constituent Systems

In a true system of systems, the individual component systems retain their autonomy and independence. While the data coming from constituent systems must be placed into a common data model with common format, context, and structure, the SoS does not require fundamental changes to those existing systems. All constituent systems are independently managed and operated just as they were before the implementation of the SoS. 

For industrial applications, preservation of existing systems is important. End users try to avoid “rip and replace” projects for their installed base of systems unless there is absolutely no way those systems can be supported. Retaining independence also benefits people and processes. Workers don’t have to change the fundamental way they interact with existing constituent systems. 


The installed base of systems at any major industrial company or critical infrastructure installation is by nature going to be heterogeneous, comprised of a wide variety of systems from different vendors. Even systems from the same vendor will have different versions, iterations, and even completely different system offerings from the same vendor. In the past, users have struggled to make systems as common as possible to reduce the cost of having to deal with a variety of different systems. Implementing a system of systems approach, however, makes it much easier to manage a diverse and fragmented installed base. 

Emergent Properties of SoS

A system of systems uses its collection of independent systems to create a holistic system that is much greater than the sum of its parts. This is the core value proposition of the SoS – it can do things that none of its independent constituent systems can do. For example, many subsystems are interrelated, and what happens in one system can affect what happens in another. 

In a city, for example, a strong hurricane will have impacts on stormwater management systems, emergency management systems, power systems, and other domains. Having a system of systems can provide the holistic view required to see those interrelationships and the implications they may have. It also gives users the ability to provide better control and resilience for the overall enterprise. 

Evolutionary Development

A system of systems is by nature designed in an evolutionary fashion. It naturally arises from the requirement to integrate the disparate data from multiple systems together. Since the SoS is designed to adapt to an environment of disparate systems, it can also evolve over time as the requirements of the constituent systems and the overall SoS changes. This characteristic also provides the ability to adapt to new technologies and approaches as they come along. An SoS can facilitate the adoption of new technologies available today also, including industrial AI, digital twins, and more. 

Geographic Distribution

Typically, the constituent systems that make up an SoS are geographically distributed. In the oil and gas supply chain, for example, systems could be distributed across a huge area encompassing multiple time zones. Similarly, SoS enables end users to coordinate, manage, and optimize operations on a global scale. 

Data Size and Complexity

Systems of systems can ingest enormous amounts of data. The data that is owned, controlled, and managed by an SoS is typically orders of magnitude greater than the code of the SoS itself, so this requires a unified and common environment to manage all the data. This requires the adoption of a data fabric, which is described later in this report. Some also refer to this data fabric as a common data model. 

How Sustainability Drives SoS Adoption

Most industrial end users are implementing some kind of sustainability related initiative today, whether it’s reducing emissions, increasing energy efficiency, meeting more stringent regulatory requirements, or decarbonizing their supply chain, end users and owner operators are finding that sustainability can have significant business benefits. Although regulatory compliance is a key dimension of sustainability, end user companies have found that investing in sustainability has a considerable, multifaceted business value proposition. According to a recent ARC survey, the sustainability budget is often a major source of funding for many industrial digital transformation programs. 

Sustainability Related Data Sources

What’s different about these sustainability solutions versus previous efforts in the industrial and manufacturing space is the data requirements. It’s an unprecedented mix of different data types from all levels of the organization. Sustainability related initiatives require access to data from myriad sources both at the enterprise and OT level. At the enterprise level, this requires access to financial as well as planning and supply chain data. At the operational level, data can come from any one of hundreds of different subsystems, from emissions monitoring systems to power management systems and more. Adopting a system of systems approach also means adoption of a unified data fabric to place all these different data types into a common framework. 

Digital Transformation

End users almost always discover that these sustainability initiatives simply cannot be realized without the implementation of a digital transformation strategy. The urgent need to improve sustainability is a key driver in accelerating industrial digital transformation initiatives at many companies. The pursuit of innovation and continued investment in digital transformation are critical to ensuring that acceleration. Leading companies understand that the path to effectively addressing sustainability and environmental, sustainability, and governance (ESG) concerns is through the acceleration of digital transformation initiatives, and they are already on that path. 

Accelerating New Technology Adoption: Industrial AI

Adoption of new technologies can be accelerated by adopting a system of systems approach. In engineering, AI can help reduce total cost and reduce risk in capital projects through automated design generation. In production, predictive analytics technologies can take energy efficiency to the next level, with predictive emissions capabilities allowing organizations to minimize their carbon footprint, ensuring that processes are optimized and consistent with operating and regulatory requirements. 

In some circumstances, AI can automate and monitor closed loop processes to support full autonomous operation. Planning and scheduling can be optimized through AI with simulation helping create a self-learning approach for continuous improvement to reduce risk and maximize profitability. In maintenance, several AI techniques can be used to increase the reliability and performance of assets through predictive and prescriptive analytics and predictive maintenance. 

The payoff can be big. In one example, a regulated and non-regulated utility with over 60 plants in 7 states added low-cost sensors and connectivity to their generating fleet for high-fidelity data access. They centrally monitor the power generation assets with predictive asset analytics software. The system saved the company over $34 million in a single early catch event in 2016.

Regulatory Compliance and Reporting

Currently, governments across the globe are increasingly establishing stricter policies on emissions reporting, trade, and energy. Energy regulations are a tool that governments use to address complex challenges, questions, and initiatives, such as achieving net zero emissions within the next three decades. Depending on the policy design, they can either reward or penalize certain actions or outcomes related to energy production, consumption, or emissions.

Examples of energy regulations at different scales and domains include the EU’s Corporate Sustainability Reporting Directive, the Climate Corporate Data Accountability Act (SB 253), and New England’s Regional Greenhouse Gas Initiative (RGGI). These policies aim to improve transparency, accountability, equity, and innovation in the energy sector. Regulations affect different stakeholders in different ways and require them to adapt their practices and strategies. A reliable and verifiable database is required to track and report energy-related data, as well as new methods and tools to measure and reduce carbon footprints across the value chain, such as lifecycle analysis or double materiality assessment.


Table of Contents

  • Executive Overview

  • System of Systems Definition

  • How Sustainability Drives SoS Adoption

  • Key SoS Technologies: Industrial Data Fabrics and IoT Data Platforms

  • SoS Sustainability Use Cases

  • Recommendations

ARC Advisory Group clients can view the complete report at the ARC Client Portal.

Please Contact Us if you would like to speak with the author.

Obtain more ARC In-depth Research at Market Analysis


Engage with ARC Advisory Group

Representative End User Clients
Representative Automation Clients
Representative Software Clients