The valorization of plastic wastes in the Circular Economy poses the challenge of developing inter-organizational systems. Concerning the implementation, the inter-organizational level consists of a set of companies from the same segment acting in different roles in the circular system. This research aims to demonstrate a structured method designed to develop a demand-driven inter-organizational closed-loop circular system for the valorization of packaging waste.

The five-step method includes (i) Contextualization/problematization, (ii) Closed-loop system mapping, (iii) Closed-loop system consolidation, (iv) Building Commitment,(v) Pilot system implementation and operation. A real case illustrates the method. As the unit of analysis we have chosen the supply chain and reverse channels of expanded and extruded polystyrene packaging in Brazil. The project was developed from December 2021 to March 2022.

The contribution to the literature is to validate the premises and to provide insight into the challenges of developing real circular economy closed loop systems. The practical contribution is to support the development of a recycling system of packaging in a developing country.

Our society is built on engineered systems. Engineers are becoming increasingly concerned with the sustainability of systems, particularly their ability to adapt to a changing world. Recently, there has been increased interest in exploring how design margins provide opportunities for a system change. There have been great developments in determining how design margins can absorb change at a system level, but it is still not clear how design margins might provide change opportunities at a decision variable level. In this paper, we show how system-level margins could be deconstructed to explore what change opportunities they may provide at a decision variable level. We also investigate how the coupling of functional requirements limits how system-level margins can be operationalized. Our analysis suggests that design margins can provide meaningful change opportunities at the decision variable level, but the mechanisms that produce these opportunities are complex. These insights lay the groundwork for future research on mapping and representing design margins in the context of system adaptability.

This research elaborates the engineering design of high value low volume (HVLV) artefacts (aka Capital goods, investment goods). Our goal is to describe what information needs the practitioners have when doing sales engineering and engineering in HVLV projects. The research approach uses Design Research Methodology with four company cases.

Our findings are that engineering design of HVLV artefacts reuses several module systems, module libraries, technology catalogues, engineering-to-order and variety of design support systems, configurators, design guidelines, parametric models and lean-based design reasoning patterns etc. This poses major challenges for the engineers; how to use all relevant information and how to find it from different IT-systems.

This study indicates that in HVLV context such engineering strategy is required, which guides and drives tactical and operational engineering decisions not only within a project delivery but across project deliveries. Operative and tactical engineering is done during the delivery project and value capture is not achieved in full potential if the engineering strategy is neglected or overruled. This is challenge for current modularisation and ETO-methods and tools.

Maintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.

This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.

Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

Flexibility analysis helps improve the expected value of engineering systems under uncertainty (economic and/or social). Designing for flexibility, however, can be challenging as a large number of design variables, parameters, uncertainty drivers, decision making possibilities and metrics must be considered. Many available techniques either rely on assumptions that are not suitable for an engineering setting, or may be limited due to computational intractability. This paper makes the case for an increased integration of Machine Learning into flexibility and real options analysis in engineering systems design to complement existing design methods. Several synergies are found and discussed critically between the fields in order to explore better solutions that may exist by analyzing the data, which may not be intuitive to domain experts. Reinforcement Learning is particularly promising as a result of the theoretical common grounds with latest methodological developments e.g. decision-rule based real options analysis. Relevance to the field of computational creativity is examined, and potential avenues for further research are identified. The proposed concepts are illustrated through the design of an example infrastructure system.

The design of ITER, a large-scale nuclear fusion reactor, is intertwined with profound research and development efforts. Tough problems call for novel solutions, but the low maturity of those solutions can lead to unexpected problems. If designers keep solving such emergent problems in iterative design cycles, the complexity of the resulting design is bound to increase. Instead, we want to show designers the sources of emergent design problems, so they may be dealt with more effectively. We propose to model the interplay between multiple problems and solutions in a problem network. Each problem and solution is then connected to a dynamically changing engineering model, a graph of physical components. By analysing the problem network and the engineering model, we can (1) derive which problem has emerged from which solution and (2) compute the contribution of each design effort to the complexity of the evolving engineering model. The method is demonstrated for a sequence of problems and solutions that characterized the early design stage of an optical subsystem of ITER.

This paper proposes an optimization framework based on the OpenMDAO software library intended for engineer-to-order products and applies it to the conceptual design of a Mobile Miner. A Mobile Miner is a complex machine and a flexible alternative to Tunnel Boring Machines for small-scale tunneling and mining applications. The proposed framework is intended for use in early design and quotation stages with the objective to get fast estimates of important product characteristics, such as excavation rate and cutter lifetime. The ability to respond fast to customer requests is vital when offering customized products for specific applications and thereby to stay competitive on the global market. This is true for most engineer-to-order products and especially for mining equipment where each construction project is unique with different tunnel geometries and rock properties. The presented framework is applied to a specific use-case where the design of the miner's cutter wheel is in focus and a set of Pareto optimal designs are obtained. Furthermore, the framework extends the capabilities of OpenMDAO by including support for mixed-variable formulations and it supports an exploratory approach to design optimization.

Industrial automotive development differs significantly from ideal agile conditions. Complex development structures, interlinkages between teams and non-functional physical dependencies between components result in agile constraints of scale and physicality. This qualitative study researches the influence of the product integration process on these constraints. The results show, that automotive integration characteristics such as duration, frequency, scope and transparency fail agile requirements and therefore cause constraints. Alternatives regarding IT and process design are discussed.

Visual representation of product architecture models is crucial in complex engineering systems design. However, when the number of entities in a model is large and when multiple levels of hierarchies are included, visual representations currently in use need to be more intuitive. As such, improved visual representations that enable better system overview and better communication of essential product- related information among design participants are needed. This paper uses interactive information visualisation techniques – collapsible hierarchical tree, edge bundling and alluvial diagram – and provides the foundations of a computerised tool that improves the traceability of connections between design domains, including stakeholders, requirements, functions, behaviours and structure. The case of a cleaning robot is used as an illustrative example. The approach supports designers by providing an enhanced overview during the development of complex product architecture models, in particular in the communication with external stakeholders, in the identification of change propagation paths across several design domains, and in capturing the design rationale of previous design decisions.

Underpinned by a contemporary view of automotive systems as cyber-physical systems, characterised by progressively open architectures increasingly defined by their interaction with the users and the smart environment, this paper provides a critical and up-to-date review of automotive Integrated Vehicle Health Management (IVHM) systems. The paper discusses the challenges with prognostics and intelligent health management of automotive systems, and proposes a high-level framework, referred to as the Automotive Healthcare Analytic Factory, to systematically collect and process heterogeneous data from across the product lifecycle, towards actionable insight for personalised healthcare of systems.

Coordination in system design requires an interplay between different roles. In this work, we identify five design team roles that pertain to the partitioning and coordination of distributed design team tasks. The proposed characterization is based on self-reported responsibilities and communication behaviors from 109 student designers in 22 teams at the conclusion of a semester-long design project. The self-reports capture both how team members viewed their own work as well as communication patterns between team members. We leverage two representations of this data. Through text analysis, we identify keywords describing team member roles and responsibilities. Social network analysis can further distinguish roles based on team communication behaviors. Cluster analysis on both types of data identifies groups of individuals with similar characteristics. The resulting five clusters capture common roles in system design teams that simultaneously capture the diverse responsibilities and communication patterns.

An ageing population leading to more chronic disease is straining healthcare systems. This paper makes two core contributions to healthcare systems design research: Firstly, a systemic techno-behavioural approach is presented to support intervention design with value-effective health outcomes. The systemic techno-behavioural perspective takes into consideration the interaction between three angles: The current healthcare system in place, the technological opportunities for addressing an issue and a broader and deeper understanding of the behaviour of those involved. The purpose of considering these three angels is to create interventions that are more robust. This will help inform healthcare systems design researchers and other stakeholders. Secondly, it is proposed that interventions should be grounded in behavioural theory, a collection of theories are presented to be incorporated in the design process of interventions. The systemic techno-behavioural approach is applied to dementia care highlighting the need to understand the dynamic relationship between the context of the current healthcare delivery system, technology, and behaviour to improve quality of care during the progression of the disease.