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All systems are likely to be affected by obsolescence. A component is considered obsolete when it is no longer manufactured or requested. Obsolescence must be taken into account, analysed and resolved effectively as it can lead to temporary or permanent stoppage of a system or make it impossible to repair it. The consequences of an obsolete entity must be minimised as they may have impacts, among others, on system characteristics such as availability or maintainability. These consequences can cascade through the system architecture. Understanding the propagation mechanisms can greatly contribute to obsolescence resilient design, but also to obsolescence management enabling a more effective determination of the scope of impact of actual or predicted obsolescence. The objective of this research work is to propose models to describe such propagation and to understand its principles. In fact, even if the obsolescence or rarefaction propagation is real and commonly accepted, there are no models describing its mechanisms. This paper focuses on two main points. First, it contributes to the concepts of obsolescence and rarefaction. Second, we propose descriptive and mathematical models of obsolescence and rarefaction propagation which allow to describe the likelihood of this propagation through the architecture of the system via two types of identified links, namely dependency and jump-up/jump-downs. These proposals are then illustrated through an example of a monitoring unit for a perfume bottle packaging line. After a discussion of the results obtained, the paper ends with the definition of future research pathways to ease the use of probabilistic inference tools for the exploitation of the proposed models. [ABSTRACT FROM AUTHOR]

The objective of this document is to contribute to the modelling of engineering changes and their propagation. Usable in preliminary redesign activities, a new approach is suggested that allows greater efficiency. Given a product model, the idea is to use the experiments to calculate in advance the consequences of potential changes in continuous variables. These consequences are collected, analysed and structured in a dependency model, noted ⟨ Γ , Φ ⟩ , composed of a dependency graph Γ and its associated set of influence functions Φ . Bilateral influence functions, associated with arcs, quantify the dependencies between node pairs. Multilateral influence functions, identified by the application of the total differential theorem, define the dependencies between a node and all influential nodes on which it depends. Finally, the relative error is calculated by following the infinitesimal assumption of the total differential for each variables. Our findings show that such a dependency model is informative and allows effective prediction and evaluation of changes. The approach is illustrated by a geometric bicycle model. The results are discussed and future areas of research are finally presented. [ABSTRACT FROM AUTHOR]

Optimization metaheuristics have become necessary due to the growing demand for better and more realistic designs. This paper proposes a metaheuristic-based approach for solving design problems in a reasonable time while browsing large spaces of solutions. The objective of this article is to compare the performance of two methods Genetic Algorithm GA and Particle swarm optimization PSO, combined with A* algorithm, in solving a constrained facility layout problem. The two chosen metaheuristics have been successfully applied in many search problems. We consider their speed and performance. The performance of the obtained solutions is measured in terms of the total distance traveled by products in the workshop. In order to determine the shortest path in a realistic way between workstations in a given irregular area (with aisle structure, or material storage areas, lunchrooms and offices), the A* algorithm was integrated with them. The comparison therefore concerns and . GA and PSO algorithms generate configurations for which the shortest path for any couple of machines is identified through the A* search algorithm taking into account of obstacles. The mathematical model used and the parameters of the genetic algorithm are those developed in (Besbes et al. 2019). The numerical results show the feasibility and effectiveness of both approaches. Our results demonstrate that GA yields a better solution than Particle Swarm Optimization in total distance travelled while PSO is faster. [ABSTRACT FROM AUTHOR]

Obsolescence is the fact that an entity (physical or logical) is becoming outdated or no longer possesses the required level of performance. The objectives of this article are twofold. First, it is intended to contribute to the understanding of obsolescence propagation. Secondly, two supporting approaches for the Identification and Assessment phases are proposed: the House of Obsolescence and the System Obsolescence Criticality Analysis. The former allows the mapping of obsolescence propagation via dependencies, whether imposed changes are desired or imposed, by external actors to the system architecture. Whereas, the objective of the latter is to assign an obsolescence criticality index to the identified risks in order to prioritize them for solution or mitigation determination during the analysis phase. The tools make extensive use of the modeled system knowledge through the application of Systems Engineering. The application of these approaches is presented through an illustrative study. [ABSTRACT FROM AUTHOR]

One-off or Batch upgrading is the term given to the process of improving one or several operational instances of complex products or systems (e.g., trains, aircrafts, etc.) during their life span. Other products such as cars or personal computers are regularly improved through redesign, generation after generation; however, redesign never concerns only one or several instance(s). The main challenge presented in this paper is to provide a suitable framework to analyse one-off and batch upgrading processes. This framework targets at containing required concepts and drivers for upgrade problem posing. The axiom beneath this research is that the goal of upgradings is to improve the performances of products or systems. Upgrade-to-performance, UtP in short, represents this paradigm that will be defined and illustrated through examples. To reach this goal, necessary concepts are defined and linked together through a generic data model. The UtP data model can be used in a conceptual way clarifying the scope of the upgrading process. It can also be specialized to specific industrial domain and used as the main driver of the design of a database for a given upgrading projects. The use case reported concerns the conversion of a passenger train into an express freight where the focus is put on enlarging a coach’ door. The results are then discussed to figure out the links between the UtP problem-posing and problem-solving methods and the gaps to fill. Finally, further research niches are described. [ABSTRACT FROM AUTHOR]

Firms look for procuring a sustainable advantage over their competitors. Therefore, they should be both operationally and strategically efficient. However, as firms work with other companies, they are under threats of misunderstanding, inefficiency, difficult synchronisations, etc. These threats will be transformed into real business traps. In this paper, authors propose a global framework which focuses on collaborative new product development (NPD) projects. The ultimate goal is to minimise harmful potential consequences of collaborations. This framework emphasises critical characteristics of any collaboration among partners. It helps users to have a clearer understanding of the overall environment of such projects. This understanding leads not only managers towards a better definition of their strategy but also guides them to acquire a more resourceful operational efficiency. [ABSTRACT FROM AUTHOR]

Technologies, evolving at an ever faster pace, particularly in the electronics and IT sectors, lead inevitably systems to face the problems generated by obsolescence. Clearly, the manufacturer must be able to design systems so that an obsolescence problem has the least possible damaging or undesired consequences. The fact that a system remains as long as possible unaffected by changes imposed by obsolescence is called resilience. It, therefore, determines the ability of the system to continue providing its services while having at least one obsolete component, function or technology. The objective of this research work is to propose a modeling methodology and tools to ensure resilience to obsolescence; this resilience being directly defined by design choices. The methodology is based on the exploitation of modeled dependencies in the architecture of a system. The mapping of dependencies is done using different models obtained by applying a system engineering methodology, ARCADIA. The dependency model is then transformed into Bayesian networks to predict quantitatively not only the consequences of possible obsolescence issues in a given architecture but also to qualify the alternatives. This methodology, called Obsolescence Resilience By Construct, ORByC, is applied to the study of the rail signaling system. [ABSTRACT FROM AUTHOR]