This paper presents two studies with packaging design engineers and quality and risk professionals in the pharmaceutical packaging industry, addressing the critical need for design support. The studies contribute to the development of a framework aimed at balancing risk management and multi-user experience in the context of product support. A review of prior work highlights the gap in tailored support for designers in this field. Using structured interviews and thematic analysis, seven key requirements were identified to guide the framework’s creation. A user persona was also developed, capturing the core responsibilities, challenges, and motivations of quality and risk professionals. These findings provide actionable insights to aid designers address complex regulatory and user-centric challenges, paving the way for innovation and improved outcomes in pharmaceutical packaging design.

This systematic literature review comprehensively assesses the risks associated with implementing Industry 4.0/5.0 technologies. It clusters these risks into six groups (strategic, financial, operational, technological, environmental, and sociocultural). Using a PRISMA-guided approach, the analysis of 83 peer-reviewed papers identified 36 unique risks out of a total of 811. The findings reveal critical challenges, including in cybersecurity threats, financial burdens, technological obsolescence, and workforce adaptation. These results provide a structured risk categorization that can assist enterprises, in effectively mitigating risks and aligning their strategies with Industry 4.0/5.0 transitions. This framework closes knowledge gaps and offers actionable insights for a robust and sustainable implementation.

To meet the upcoming sustainability challenges, aerospace manufacturers need to develop products that both address complex sustainability factors and ensure profitable realization. Furthermore, the sustainability perspective needs to be lifted from focusing on carbon emissions, and broadened to include a system-level socio-ecological view. Manufacturers are thus challenged to balance sustainability, manufacturability, and performance, but lack the methods and tools to make well-informed decisions. We propose a method for conducting multi-domain trade-off studies in the early design phase. A functional architecture modelling approach is utilized to model performance and manufacturing aspects. Together with a relative sustainability fingerprint conducted on design alternatives, design spaces can be explored with respect to performance, manufacturability, and sustainability.

The increasing complexity and connectivity of the mobility system and modern automotive systems, particularly connected autonomous vehicles, demand a paradigm shift toward resilience-by-design to address disruptions in dynamic environments. Unlike established safety and cybersecurity engineering in automotive, resilience engineering has yet to be systematically integrated into development processes. This paper defines resilience using a standard-based definition method, emphasizing disruption tolerance, adaptability, and recoverability. We identify action fields to advance the topic and propose a resilience-by-design framework extending safety and cybersecurity perspectives. Resilience-by-design offers strategies and methods to design robust, adaptive systems, ensuring reliability and availability of automotive systems, functions, and components in operation.

Unintended technical interactions across system interfaces can lead to costly failures and rework, particularly in the early design stages of complex products. This study examines how structured risk assessment tools influence teams’ ability to identify, evaluate and mitigate risks from such indirect interactions. In a controlled experiment, 14 engineering teams (comprising professionals and graduate students) engaged in simulated design decisions across three system configurations. Tool usage – including models of direct and indirect risk propagation and value-based trade-offs – was continuously logged and linked to outcomes. Teams that engaged earlier and more deliberately with the tools identified risks sooner and selected mitigation actions with more favourable cost–benefit profiles. Results show that strategic, not merely frequent, tool use improves risk management performance, particularly when addressing cascading effects from indirect physical interactions. These findings support the use of structured supports to enhance both the efficiency of early-stage risk evaluation and the efficacy of risk treatment.

This paper examines in what way providers of specialized Large Language Models (LLM) pre-trained and/or fine-tuned on medical data, conduct risk management, define, estimate, mitigate and monitor safety risks under the EU Medical Device Regulation (MDR). Using the example of an Artificial Intelligence (AI)-based medical device for lung cancer detection, we review the current risk management process in the MDR entailing a “forward-walking” approach for providers articulating the medical device’s clear intended use, and moving on sequentially along the definition, mitigation, and monitoring of risks. We note that the forward-walking approach clashes with the MDR requirement for articulating an intended use, as well as circumvents providers reasoning around the risks of specialised LLMs. The forward-walking approach inadvertently introduces different intended users, new hazards for risk control and use cases, producing unclear and incomplete risk management for the safety of LLMs. Our contribution is that the MDR risk management framework requires a backward-walking logic. This concept, similar to the notion of “backward-reasoning” in computer science, entails sub-goals for providers to examine a system’s intended user(s), risks of new hazards and different use cases and then reason around the task-specific options, inherent risks at scale and trade-offs for risk management.

The systemic nature of climate risk is well established, but the extent may be more severe than previously understood, particularly with regard to cyber risk and economic security. Cyber security relies on the availability of insurance capital to mitigate economic security sector risks and support the reversibility of attacks. However, the cyber insurance industry is still in its infancy. Pressure on insurance capital from increasing natural disaster activity could consume the resources necessary for economic security in the cyber domain in the near term and create long-term conditions that increase the scarcity of capital to support cyber security risks. This article makes an original contribution by exploring the under-researched connection between the nexus of cyber and economic security and the climate change threat. Although the immediate pressure on economic resources for cyber security is limited, recent natural disaster activity has clearly shown that access to capital for cyber risks could come under significant pressure in the future.

This paper investigates a well-known downside protection strategy called the constant proportion portfolio insurance (CPPI) in defined contribution (DC) pension fund modeling. Under discrete time trading CPPI, an investor faces the risk of portfolio value hitting the floor which denotes the process of guaranteed portfolio values. In this paper, we question how to deal with so-called ‘gap risk’ which may appear due to uncontrollable events resulting in a sudden drop in the market. In the market model considered, the risky asset price and the labor income are assumed to be continuous-time stochastic processes, whereas trading is restricted to discrete-time. In this setting, an exotic option (namely, the ‘cushion option’) is proposed with the aim of reducing the risk that the portfolio value falls below the defined floor. We analyze the effectiveness of the proposed exotic option for a DC plan CPPI strategy through Monte Carlo simulations and sensitivity analyses with respect to the parameters reflecting different setups.

In today’s insurance market, numerous cyber insurance products provide bundled coverage for losses resulting from different cyber events, including data breaches and ransomware attacks. Every category of incident has its own specific coverage limit and deductible. Although this gives prospective cyber insurance buyers more flexibility in customizing the coverage and better manages the risk exposures of sellers, it complicates the decision-making process in determining the optimal amount of risks to retain and transfer for both parties. This article aims to build an economic foundation for these incident-specific cyber insurance products with a focus on how incident-specific indemnities should be designed for achieving Pareto optimality for both the insurance seller and the buyer. Real data on cyber incidents are used to illustrate the feasibility of this approach. Several implementation improvement methods for practicality are also discussed.

Novel methods of data collection and analysis can enhance traditional risk management practices that rely on expert engineering judgment and established safety records, specifically when key conditions are met: Analysis is linked to the decisions it is intended to support, standards and competencies remain up to date, and assurance and verification activities are performed. This article elaborates on these conditions. The reason engineers are required to perform calculations is to support decision-making. Since humans are famously weak natural statisticians, rather than ask stakeholders to implicitly assimilate data, and arrive at a decision, we can instead rely on subject matter experts to explicitly define risk management decision problems. The results of engineering calculation can then also communicate which interventions (if any) are considered to be risk-optimal. It is also proposed that the next generation of engineering standards should learn from the success of open source software development in community building. Interacting with open datasets and code can promote engagement, identification (and resolution) of errors, training and ultimately competence. Finally, the profession’s tradition of independent verification should also be applied to the complex models that will increasingly contribute to the safety of the built environment. Model assurance will be required to keep pace with model development to identify suitable use cases as adequately safe. These are considered to be increasingly important components in ensuring that methods of data-centric engineering can be safely and appropriately adopted in industry.