Expert Elicitation for Reliable System Design

This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Integrated assessment and valuation of ecosystem services - Guidelines and experiences

EU FP7 OpenNESS project deliverable D33 & D4

Sustainable Assessment in Supply Chain and Infrastructure Management

In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management

Using Bayesian belief networks for reliability management : construction and evaluation: a step by step approach

In the capital goods industry, there is a growing need to manage reliability throughout the product development process. A number of trends can be identified that have a strong effect on the way in which reliability prediction and management is approached, i.e.: - The lifecycle costs approach that is becoming increasingly important for original equipment manufacturers - The increasing product complexity - The growth in customer demands - The pressure of shortening times to market - The increasing globalization of markets and production Reliability management is typically based on the insights, views, and perceptions of the real world of the people that are involved in the process of decision making. These views are unique and specific for each involved individual that looks at the management process and can be represented using soft systems methodology. Since soft systems methodology is based on insights, view and perceptions, it is especially suitable in the context of reliability prediction and management early in the product development process as studied in this thesis (where there is no objective data available (yet)). Two research objectives are identified through examining market trends and applying soft systems methodology. The first research objective focuses on the identification or development of a method for reliability prediction and management that meets the following criteria: - It should support decision making for reliability management - It should be able to also take non-technical factors into account - It has to be usable throughout the product development process and especially in the early phases of the process. - It should be able to capture and handle uncertainty This first research objective is addressed through a literature study of traditional approaches (failure mode and effects analysis, fault tree analysis and database methods), and more recent approaches to reliability prediction and reliability management (REMM, PREDICT and TRACS). The conclusion of the literature study is that traditional methods, although able to support decision making to some extent, take a technical point of view, and are usable only in a limited part of the product development process. The traditional methods are capable of taking uncertainty into account, but only uncertainty about the occurrence of single faults or failure modes. The recent approaches are able to meet the criteria to a greater extent: REMM is able to provide decision support, but mainly on a technical level, by prioritizing the elimination of design concerns. The reliability estimate provided by REMM can be updated over time and is clearly usable throughout the product development process. Uncertainty is incorporated in the reliability estimate as well as in the occurrence of concerns. PREDICT provides decision support for processes as well as components, but it focuses on the technical contribution of the component or process to reliability. As in REMM, PREDICT provides an updateable estimate, and incorporates uncertainty as a probability. TRACS uses Bayesian belief networks and provides decision support both in technical and non-technical terms. In the TRACS tool, estimates can be updated and uncertainty is incorporated using probabilities. Since TRACS is developed for one specific case, and an extensive discussion on the implementation process is missing, it is not readily applicable for reliability management in general. The discussion of literature leads to the choice of Bayesian belief networks as an effective modelling technique for reliability prediction and management. It also indicates that Bayesian belief networks are particularly well suited in the early stages of the product development process, because of their ability to make the influences of the product development process on reliability already explicit from the early stages of the product development process onwards. The second research objective is the development of a clear, systematic approach to build and use Bayesian belief networks in the context of reliability prediction and management. Although Bayesian belief network construction is widely described in the literature as having three generic steps (problem structuring, instantiation and inference), how the steps are to be made in practice is described only summarily. No systematic, coherent and structured approach for the construction of a Bayesian belief network can be found in literature. The second objective therefore concerns the identification and definition of model boundaries, model variables, and model structure. The methodology developed to meet this second objective is an adaptation of Grounded Theory, a method widely used in the social sciences. Grounded Theory is an inductive rather than deductive method (focusing on building rather than testing theory). Grounded Theory is adapted by adopting Bayesian network idioms (Neil, Fenton & Nielson, 2000) into the approach. Furthermore, the canons of the Grounded Theory methodology (Corbin & Strauss, 1990) were not strictly followed because of their limited suitability for the subject, and for practical reasons. Grounded Theory has been adapted as a methodology for structuring problems modelled with Bayesian belief networks. The adapted Grounded Theory approach is applied in a case study in a business unit of a company that develops and produces medical scanning equipment. Once the Bayesian belief net model variables, structure and boundaries have been determined the network must be instantiated. For instantiation, a probability elicitation protocol has been developed. This protocol includes a training, preparation for the elicitation, a direct elicitation process, and feedback on the elicitation. The instantiation is illustrated as part of the case study. The combination of the adapted Grounded Theory method for problem structuring, and the probability elicitation protocol for instantiation together form an algorithm for Bayesian belief network construction (consisting of data gathering, problem structuring, instantiation, and feedback) that consists of the following 9 steps (see Table 1). Table 1: Bayesian belief network construction algorithm 1. Gather information regarding the way in which the topic under discussion is influenced by conducting interviews 2. Identify the factors (i.e. nodes) that influence the topic, by analyzing and coding the interviews 3. Define the variables by identifying the different possible states (state-space) of the variables through coding and direct conversation with experts 4. Characterize the relationships between the different nodes using the idioms through analysis and coding of the interviews 5. Control the number of conditional probabilities that has to be elicited using the definitional/synthesis idiom (Neil, Fenton & Nielson, 2000) 6. Evaluate the Bayesian belief network, possibly leading to a repetition of (a number of) the first 5 steps 7. Identify and define the conditional probability tables that define the relationships in the Bayesian belief network 8. Fill in the conditional probability tables, in order to define the relationships in the Bayesian belief network 9. Evaluate the Bayesian belief network, possibly leading to a repetition of (a number of) earlier steps A Bayesian belief network for reliability prediction and management was constructed using the algorithm. The model’s problem structure and the model behaviour are validated during and at the end of the construction process. A survey was used to validate the problem structure and the model behaviour was validated through a focus group meeting. Unfortunately, the results of the survey were limited, because of the low response rate (35%). The results of the focus group meeting indicated that the model behaviour was realistic, implying that application of the adapted Grounded Theory approach results in a realistic model for reliability management. The adapted Grounded Theory approach developed in this thesis provides a scientific and practical contribution to model building and use in the face of limited availability of information. The scientific contribution lies in the provision of the systematic and coherent approach to Bayesian belief network construction described above. The practical contribution lies in the application of this approach in the context of reliability prediction and management and in the structured and algorithmic approach to model building. The case study in this thesis shows the construction and use of an effective model that enables reliability prediction, and provides decision support for reliability management throughout the product development process from the earliest stages of the process. Bayesian belief networks provide a strong basis for reliability management, giving qualitative and quantitative insights in relationships between influential variables and reliabilit

Ecosystem services for communities in forest frontiers: An assessment of nature’s benefits to local stakeholders under different land use and tenure systems in a tropical frontier landscape in Myanmar

Tropical forest frontier landscapes are subject to land use changes and different claims on land by various actors, often leading to trade-offs and a general decline in ecosystem services (ES) for local communities. However, few comprehensive ES studies have been done in such data-scarce frontier regions and they are limited in terms of area, land uses, and number and types of ES assessed. This doctoral thesis aimed to analyse how local stakeholders in Myanmar’s Tanintharyi Region can benefit from various ES and how these are associated to different factors such as land use, land tenure, market access, or population structures. Applying advanced modelling techniques, I used Bayesian networks to model the supply, demand, flow and final outcomes of nine ES: subsistence foods, commercial products, fuelwood, medicinal plants, biodiversity, climate regulation, water regulation, environmental education, and cultural identity. The models were developed in an iterative process and integrated existing spatial datasets, census data and qualitative data from focus group discussions and interviews with local communities, government representatives, researchers, civil society organizations and non-governmental institutions working on land issues in Tanintharyi. I linked the nine models to spatial data to map ES supply, demand, and flow for local stakeholders across the region. Finally, I combined these maps to identify supply/demand (mis)matches as well as accessibility that particularly affects local communities. The thesis highlights the value of mosaic landscapes and the crucial role of equitable land tenure in tropical forest frontiers for providing multiple ES and enhancing human well-being

Addressing uncertainty and normativity in agricultural sustainability assessment: the example of agricultural digitalization

Agriculture's role in meeting global food needs has historically relied on increased productivity and land expansion. However, conventional agriculture, despite its productivity, poses daunting environmental challenges, including biodiversity loss, climate change, and water pollution. Socio-economic issues such as price instability and rural decline further complicate agricultural sustainability. Agricultural systems face repercussions from challenges they contribute to, such as climate change impacts and soil degradation, raising concerns about resource depletion and public perception of farming practices. While technological advancements such as digitalization offer promise for efficiency improvements, they also introduce potential risks. The United Nations Sustainable Development Goals and the European Union’s Green Deal's Farm to Fork Strategy underscore the necessity of adopting innovative and sustainable agricultural practices. However, achieving agricultural sustainability requires collaborative efforts beyond policy initiatives, involving stakeholders such as farmers, researchers, and civil society organizations. In this regard, context-specific approaches and comprehensive sustainability assessment are crucial for advancing agricultural sustainability and aligning with policy objectives. The primary objective of this thesis is to explore how integrative methodologies can enhance the state-of-the-art of agricultural sustainability assessments. To fulfill this objective, in the first study, a review of agricultural sustainability tools and models was conducted, assessing their thematic coverage of integrative sustainability concepts such as ecosystem services and the UN Sustainable Development Goals (SDGs). In the subsequent study, an interdisciplinary approach integrating policy, law, and foresight analysis was utilized to examine agriculturally related policies and laws, discerning their sustainability implications in the realm of digital agriculture under probable future scenarios. In the last study, stakeholder knowledge was integrated through a participatory modeling approach to construct a Bayesian belief network, which assessed the effects of digital agriculture on agricultural sustainability. The findings of this thesis demonstrate that existing tools and methodologies for assessing agricultural sustainability often lack sufficient integration with the ecosystem service framework and the UN SDGs. Additionally, the thesis emphasizes the advantages of an interdisciplinary approach integrating policy, law, and scenario analysis to evaluate the sustainability impacts of digital agriculture, showing that without clear policy and law to guide and regulate agricultural digitalization, that it will most likely not be leveraged toward achieving sustainability. Finally, the thesis showed that engaging stakeholders in participatory modeling can improve the contextual specificity of agricultural sustainability assessments by capturing both implicit and explicit stakeholder knowledge of local conditions. The thesis demonstrates different analytical tools for managing uncertainty in sustainability assessment. It further highlights that enhancing the comprehensiveness of indicators within sustainability assessment methods will enable better capture of site-specific characteristics of ecosystem service supply and use, while standardization of indicators will help operationalize outcomes for higher levels of sustainability assessment necessary for achieving sustainability goals

Integrated models, frameworks and decision support tools to guide management and planning in Northern Australia. Final report

[Extract] There is a lot of interest in developing northern Australia while also caring for the unique Australian landscape (Commonwealth of Australia 2015). However, trying to decide how to develop and protect at the same time can be a challenge. There are many modelling tools available to inform these decisions, including integrated models, frameworks, and decision support tools, but there are so many different kinds that it’s difficult to determine which might be best suited to inform different decisions. To support planning and development decisions across northern Australia, this project aimed to create resources to help end-users (practitioners) to assess: 1. the availability and suitability of particular modelling tools; and 2. the feasibility of using, developing, and maintaining different types of modelling tools

Cross-layer reliability evaluation, moving from the hardware architecture to the system level: A CLERECO EU project overview

Advanced computing systems realized in forthcoming technologies hold the promise of a significant increase of computational capabilities. However, the same path that is leading technologies toward these remarkable achievements is also making electronic devices increasingly unreliable. Developing new methods to evaluate the reliability of these systems in an early design stage has the potential to save costs, produce optimized designs and have a positive impact on the product time-to-market. CLERECO European FP7 research project addresses early reliability evaluation with a cross-layer approach across different computing disciplines, across computing system layers and across computing market segments. The fundamental objective of the project is to investigate in depth a methodology to assess system reliability early in the design cycle of the future systems of the emerging computing continuum. This paper presents a general overview of the CLERECO project focusing on the main tools and models that are being developed that could be of interest for the research community and engineering practice

A contribution to supply chain design under uncertainty

Dans le contexte actuel des chaînes logistiques, des processus d'affaires complexes et des partenaires étendus, plusieurs facteurs peuvent augmenter les chances de perturbations dans les chaînes logistiques, telles que les pertes de clients en raison de l'intensification de la concurrence, la pénurie de l'offre en raison de l'incertitude des approvisionnements, la gestion d'un grand nombre de partenaires, les défaillances et les pannes imprévisibles, etc. Prévoir et répondre aux changements qui touchent les chaînes logistiques exigent parfois de composer avec des incertitudes et des informations incomplètes. Chaque entité de la chaîne doit être choisie de façon efficace afin de réduire autant que possible les facteurs de perturbations. Configurer des chaînes logistiques efficientes peut garantir la continuité des activités de la chaîne en dépit de la présence d'événements perturbateurs. L'objectif principal de cette thèse est la conception de chaînes logistiques qui résistent aux perturbations par le biais de modèles de sélection d'acteurs fiables. Les modèles proposés permettent de réduire la vulnérabilité aux perturbations qui peuvent aV, oir un impact sur la continuité des opérations des entités de la chaîne, soient les fournisseurs, les sites de production et les sites de distribution. Le manuscrit de cette thèse s'articule autour de trois principaux chapitres: 1 - Construction d'un modèle multi-objectifs de sélection d'acteurs fiables pour la conception de chaînes logistiques en mesure de résister aux perturbations. 2 - Examen des différents concepts et des types de risques liés aux chaînes logistiques ainsi qu'une présentation d'une approche pour quantifier le risque. 3 - Développement d'un modèle d'optimisation de la fiabilité afin de réduire la vulnérabilité aux perturbations des chaînes logistiques sous l'incertitude de la sollicitation et de l'offre

Supply chain integration model: practices and customer values

Dissertation to obtain PhD in Industrial EngineeringIn order to increase partnership efficiency and truly meet the customers' demands, in today's business environment companies are operating in supply chains. Integration of supply chains facilitates minimizing diferent types of wastes and satisfying needs of the end customer. The first step toward supply chain integration is to understandand the customer values, and to reconfigure supply chain to support those values. The current research addresses supply chain integration through quantifying relations between supply chain practice and customer values. It employs Bayesian network and analytic network process as tools to quantify comparative relations among entities. The proposed approach starts with identifying trade-offs along customer values using Bayesian network. In parallel supply chain practices are comparatively analyzed through interviews with experts which is technically quantified using analytic network process. Thereafter, these two parallel phases join together to form a network of customer values and supply chain practices. The network is able to quantitatively identify relations among nodes; in addition, it can be used to plan scenarios and handle senstitivity analyses. This model is expected to be used by supply chain decision makers to have a quantitative measure for monitoring the influence of practices on preferences of the end customer. A survey and two case studies are discussed which go through aforementioned phases. The survey identifies and analyzes six customer values namely quality, cost, customization, time, know-how and respect for the environment. It makes input for the two cases which develop supply chain integration model for fashion and food industry. Supply chain practices are categorized into two groups of manufacturing and logistics practices. The two case studies include five manufacturing practices as cross functional operations, decrease work in process, implement standards, mixed production planning, and use recyclable materials as well as four logistics practices namely visibility to upstream /downstream inventories, information sharing with customer, implement logistics standards, and just in time.Fundação para a Ciência e Tecnologia - (MIT Project: MIT-Pt/EDAM-IASC/0022/2008