In this paper we describe the results of a study on the design of open and co-opetitive systems of systems for mobility. A system of systems (SoS) is a set of independent systems (CS – Constituent Systems) that interact to create capabilities that none of the constituent systems can accomplish on their own. A CS can be simultaneously part of several SoS. The independence of the constituent systems is an important and central ingredient in an SoS, and is often divided into managerial and operational independence. We argue that the mobility SoS of the future can benefit from being open and co-opetitive (i.e., simultaneously collaborating and competing) to enable combined mobility (MaaS) for real. If competition between CS is not allowed, new entrants will choose to compete with the SoS instead of becoming part of it. If a new entrant develops much better vehicles or better ways to find transport solutions for users, that entrant must be able to become part of the SoS and take market share from other actors

In many areas, independent and heterogeneous systems collaborate towards a common goal, and their assembly is referred to as a System of Systems (SoS). The mediating actors that orchestrate the SoS are frequently used to enhance collaboration. They support onboarding new constituent systems, monitoring and capability identification, goal transformation, workflow composition and execution, world modelling, data curation, and interoperability translation, as well as ensuring security and data privacy. With recent advances in artificial intelligence (AI) and machine learning (ML), there is potential to improve orchestration, management, and overall performance. Thus, SoS is transitioning towards an enhanced, intelligent System of Systems (iSoS). This paper explores the opportunities and challenges of enhancing intelligent governance. Industrial and academic experts on SoS and AI have systematically analysed the potential and challenges for AI-based enhancement in each orchestration role. These contributions indicate the most promising areas for improvement, primarily in terms of more efficient and effective functionality and adaptability to changing circumstances

Systems-of-systems are often characterized as systems where the constituent parts have some independence from the whole. Recent research has aimed at clarifying in more detail what this independence means. It has shown that independence requires the constituent systems to be agents that observe the system-of-systems from within and construct internal models of it as a basis for decisions. This view on observers as parts of the system-of-systems parallels development in the field of second-order cybernetics several decades ago, yet the connection between that field and systems-of-systems has not been explored previously. This paper, therefore, summarizes key concepts from second-order cybernetics, including the subtopic autopoiesis. It then discusses what the implications are on systems-of-systems engineering through the identification of 17 concerns. These concerns relate to the physical topology, behavior, and control of the system-of-systems. This paper shows how these concerns directly relate to the theoretical concepts of second-order cybernetics and autopoiesis, and thereby, opens the door to further exploitation of this theoretical foundation. 

This article describes the architecture life cycle effect analysis (ALCEA) method, a structured method for evaluating proposed new architectures for software-intensive systems. The method evaluates a proposed architecture by quantifying its effect on the performance of system life-cycle phases. The method is instantiated by identifying the relevant life-cycle phases of the system under investigation and a set of evaluation functions that capture, in terms of basic factors, the effect of different architectural decisions on key life-cycle PAs, such as revenue, operating resources, and investments. The method results in a transparent cost and revenue structure, documented in a tabular form, based on quantifiable factors from the developing organization. The results of the method can be used directly as part of a business case, and their robustness can be estimated by sensitivity analysis. The ALCEA method is designed for system-level architectural analysis, covering both software and hardware aspects. In this article, we introduce the ALCEA method and provide a detailed example of how to apply it in the evolution of embedded systems. Moreover, we share early experiences of using the method in large-scale industrial settings.

Systems-of-systems are characterized by the independence of their constituent elements. Such an element is usually socio-technical, comprising technology, humans, or organizations. To capture its independence, it needs to be viewed as an intelligent agent that relies on an internal model of the world for its decision-making. Hence, a system-of-systems model will include multiple agents that inside themselves contain different models of the same system-of-systems. Describing these overlapping subjective models and their usage by the agents is essential to properly understand the resulting behavior of the overall system-of-systems. Current modeling practices are not well suited for dealing with this, and the paper therefore outlines an ontology that makes the agents and their internal models more explicit. The paper also discusses the implications such models have on systems engineering practices and how they address known system-of-systems engineering pain points.

There is a large potential for automation and optimisation of transports within quarrying and mining, but operational models and characteristics for this purpose are lacking. This paper aims to provide insight into cyclic transports and the parameters that affect energy consumption and productivity. Detailed operational data from machines has been collected and analysed through automatic logging of the machine’s internal communication network. The paper presents and discusses the characteristics of the operation identified, develops models for energy consumption and productivity, and discusses their relation for optimisation and automation purposes. A conclusion is that stochastic fluctuations in activity times need continuous real-time control for an optimisation system to be effective. The method used in the paper resulted in regression models for cycle energy cost and hauler fuel rate, which provide both correlation and significance, which is promising for future validation and use in energy optimisation control systems. 

Managing a system can be critical for its successful functioning. This is especially crucial for the socio-technical systems of systems (SoS) that characterize many of modern society’s critical operations. However, the management of SoS becomes complex as systems are increasingly interconnected and the dependence among connected systems intensifies. This paper explores the current state of the art on SoS management and governance from a risk management perspective. Our findings show a higher focus on SoS management; however, many studies do not holistically deal with the SoS. Moreover, our findings indicate fewer studies in SoS governance. Hence there is still a research gap. The study contributes to the body of knowledge by adding insight into how risk management fits in these domains and provides direction on the possible areas of mitigating risks.

In a system-of-systems, independent constituent systems collaborate to achieve broader capabilities they cannot provide on their own. This paper investigates the nature of the constituent system capabilities beyond basic operational actions, to achieve a deeper understanding of what is required to participate in a system-of-systems. Through a case study of industrial ecosystems, the need is shown for planning how to use basic operational capabilities, for dynamic capabilities to achieve long-term evolution, and for resilience capabilities to deal with perturbations. This also affects the governance of the system. The findings are used to extend an existing conceptual model of constituent systems and to characterize collaboration in a system-of-systems that implements a value network. 

Cybersecurity is an important concern in systems-of-systems (SoS), where the effects of cyber incidents, whether deliberate attacks or unintentional mistakes, can propagate from an individual constituent system (CS) throughout the entire SoS. Unfortunately, the security of an SoS cannot be guaranteed by separately addressing the security of each CS. Security must also be addressed at the SoS level. This paper reviews some of the most prominent cybersecurity risks within the SoS research field and combines this with the cyber and information security economics perspective. This sets the scene for a structured assessment of how various cyber risks can be addressed in different SoS architectures. More precisely, the paper discusses the effectiveness and appropriateness of five cybersecurity policy options in each of the four assessed SoS archetypes and concludes that cybersecurity risks should be addressed using both traditional design-focused and more novel policy-oriented tools. 

Understanding the needs and constraints of systems in general and a system-of-systems in particular can be challenging, yet crucial. Relying only on upfront activities will not be sufficient. Important information can be gathered around the performance and behavior of the system as well as stakeholder needs in operation. A digital twin is a way to model and understand the operation of a system. To understand the challenges and enablers related to digital twins in a system-of-systems context, we performed a literature study. In total, only 10 papers were identified that explicitly address this topic, all from the last five years, indicating that this is an active field of research. The papers revealed that definitions and terminology are unclear and that similar challenges as for systems-of-systems also exist for systems-of-digital twins. The complexity and dynamic nature of systems-of-systems motivate further study of digital twins to understand needs and constraints. However, key challenges such as concepts and principles of digital twins for systems-of-systems, cost and benefits, and evolution needs to be better understood.