Agent-oriented constructivist knowledge management

In Ancient Times, when written language was introduced, books and manuscripts were often considered sacred. During these times, only a few persons were able to read and interpret them, while most people were limited in accepting these interpretations. Then, along with the industrial revolution of the XVIII and XIX centuries and especially boosted by the development of the press, knowledge slowly became available to all people. Simultaneously, people were starting to apply machines in the development of their work, usually characterized by repetitive processes, and especially focused in the production of consuming goods, such as furniture, clocks, clothes and so on. Following the needs of this new society, it was finally through science that new processes emerged to enable the transmission of knowledge from books and instructors to learners. Still today, people gain knowledge based on these processes, created to fulfill the needs of a society in its early stages of industrialization, thus not being compatible with the needs of the information society. In the information society, people must deal with an overloading amount of information, by the means of the media, books, besides different telecommunication and information systems technology. Furthermore, people’s relation to work has been influenced by profound changes, for instance, knowledge itself is now regarded as a valuable work product and, thus, the workplace has become an environment of knowledge creation and learning. Modifications in the world economical, political and social scenarios led to the conclusion that knowledge is the differential that can lead to innovation and, consequently, save organizations, societies, and even countries from failing in achieving their main goals. Focusing on these matters is the Knowledge Management (KM) research area, which deals with the creation, integration and use of knowledge, aiming at improving the performance of individuals and organizations. Advances in this field are mainly motivated by the assumption that organizations should focus on knowledge assets (generally maintained by the members of an organization) to remain competitive in the information society’s market. This thesis argues that KM initiatives should be targeted based on a constructivist perspective. In general, a constructivist view on KM focuses on how knowledge emerges, giving great importance to the knowledge holders and their natural practices. With the paragraph above, the reader may already have an intuition of how this work faces and targets Knowledge Management, however, let us be more precise. Research in Knowledge Management has evolved substantially in the past 30 years, coming from a centralized view of KM processes to a distributed view, grounded in organizational and cognitive sciences studies that point out the social, distributed, and subjective nature of knowledge. The first Knowledge Management Systems (KMSs) were centrally based and followed a top-down design approach. The organization managers, supported by knowledge engineers, collected and structured the contents of an organizational memory as a finished product at design time (before the organizational memory was deployed) and then disseminated the product, expecting employees to use it and update it. However, employees often claimed that the knowledge stored in the repository was detached from their real working practices. This led to the development of evolutionary methods, which prescribe that the basic KM system is initially developed and evolves proactively in an on-going fashion. However, most of the initiatives are still based on building central repositories and portals, which assume standardized vocabularies, languages, and classification schemes. Consequently, employees’ lack of trust and motivation often lead to dissatisfaction. In other words, workers resist on sharing knowledge, since they do not know who is going to access it and what is going to be done with it. Moreover, the importance attributed to knowledge may give an impression that these central systems take away a valuable asset from his or her owner, without giving appreciable benefits in return. The problems highlighted in the previous paragraph may be attenuated or even solved if a top-down/bottom-up strategy is applied when proposing a KM solution. This means that the solution should be sought with aim at organizational goals (top-down) but at the same time, more attention should be given to the knowledge holders and on the natural processes they already use to share knowledge (bottom-up). Being active agency such an important principle of Constructivism, this work recognizes that the Agent Paradigm (first defined by Artificial Intelligence and more recently adopted by Software Engineering) is the best approach to target Knowledge Management, taking a technological and social perspective. Capable of modeling and supporting social environments, agents is here recognized as a suitable solution for Knowledge Management especially by providing a suitable metaphor used for modeling KM domains (i.e. representing humans and organizations) and systems. Applying agents as metaphors on KM is mainly motivated by the definition of agents as cognitive beings having characteristics that resemble human cognition, such as autonomy, reactivity, goals, beliefs, desires, and social-ability. Using agents as human abstractions is motivated by the fact that, for specific problems, such as software engineering and knowledge management process modeling, agents may aid the analyst to abstract away from some of the problems related to human complexity, and focus on the important issues that impact the specific goals, beliefs and tasks of agents of the domain. This often leads to a clear understanding of the current situation, which is essential for the proposal of an appropriate solution. The current situation may be understood by modeling at the same time the overall goals of the organization, and the needs and wants of knowledge holders. Towards facilitating the analysis of KM scenarios and the development of adequate solutions, this work proposes ARKnowD (Agent-oriented Recipe for Knowledge Management Systems Development). Systems here have a broad definition, comprehending both technology-based systems (e.g. information system, groupware, repositories) and/or human systems, i.e. human processes supporting KM using non-computational artifacts (e.g. brain stormings, creativity workshops). The basic philosophical assumptions behind ARKnowD are: a) the interactions between human and system should be understood according to the constructivist principle of self-construction, claiming that humans and communities are self-organizing entities that constantly construct their identities and evolve throughout endless interaction cycles. As a result of such interactions, humans shape systems and, at the same time, systems constrain the ways humans act and change; b) KM enabling systems should be built in a bottom-up approach, aiming at the organizational goals, but understanding that in order to fulfill these goals, some personal needs and wants of the knowledge holders (i.e. the organizational members) need to be targeted; and c) there is no “silver bullet��? when pursuing a KM tailoring methodology and the best approach is combining existing agent-oriented approaches according to the given domain or situation. This work shows how the principles above may be achieved by the integration of two existing work on agent-oriented software engineering, which are combined to guide KM analysts and system developers when conceiving KM solutions. Innovation in our work is achieved by supporting topdown/bottom-up approaches to KM as mentioned above. The proposed methodology does that by strongly emphasizing the earlier phases of software development, the so-called requirement analysis activity. In this way, we consider all stakeholders (organizations and humans) as agents in our analysis model, and start by understanding their relations before actually thinking of developing a system. Perhaps the problem may be more effectively solved by proposing changes in the business processes, rather than by making use of new technology. And besides, in addition to humans and organizations, existing systems are also included in the model from start, helping the analyst and designer to understand which functionalities are delegated to these so-called artificial agents. In addition to that, benefits as a result of the application of ARKnowD may be also attributed to our choice of using the proper agent cognitive characteristics in the different phases of the development cycle. With the main purpose of exemplifying the use of the proposed methodology, this work presents a socially-aware recommender agent named KARe (Knowledgeable Agent for Recommendations). Recommender Systems may be defined by those that support users in selecting items of their need from a big set of items, helping users to overcome the overwhelming feeling when facing a vast information source, such as the web, an organizational repository or the like. Besides serving as a case for our methodology, this work also aims at exploring the suitability of the KARe system to support KM processes. Our choice for supporting knowledge sharing through questioning and answering processes is again supported by Constructivism proponents, who understand that social interaction is vital for active knowledge building. This assumption is also defended by some KM theories, claiming that knowledge is created through cycles of transformation between two types of knowledge: tacit and explicit knowledge. Up to now, research on KM has paid much attention to the formalization and exchange of explicit knowledge, in the form of documents or other physical artifacts, often annotated with metadata, and classified by taxonomies or ontologies. Investigations surrounding tacit knowledge have been so far scarce, perhaps by the complexity of the tasks of capturing and integrating such kind of knowledge, defined as knowledge about personal experience and values, usually confined on people’s mind. Taking a flexible approach on supporting this kind of knowledge conversion, KARe relies on the potential of social interaction underlying organizational practices to support knowledge creation and sharing. The global objective of this work is to support knowledge creation and sharing within an organization, according to its own natural processes and social behaviors. In other words, this work is based on the assumption that KM is better supported if knowledge is looked at from a constructivist perspective. To sum up, this thesis aims at: 1) Providing an agent-oriented approach to guide the creation and evolvement of KM initiatives, by analyzing the organizational potentials, behaviors and processes concerning knowledge sharing; 2) Developing the KARe recommender system, based on a semantically enriched Information Retrieval technique for recommending knowledge artifacts, supporting users to ask and answer to each others’ questions. These objectives are achieved as follows: - Defining the principles that characterize a Constructivist KM supporting environment and understanding how they may be used to support the creation of more effective KM solutions; - Providing an agent-oriented approach to develop KM systems. This approach is based on the integration of two different agent-oriented software engineering works, profiting from their strengths in providing a comprehensive methodology that targets both analysis and design activities; - Proposing and designing a socially aware agent-oriented recommender system both to exemplify the application of the proposed approach and to explore its potential on supporting knowledge creation and sharing. - Implementing an Information Retrieval algorithm to support the previously mentioned system in generating recommendations. Besides describing the algorithm, this thesis brings experimental results to prove its effectiveness

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Towards ontological foundations of research information systems

Despite continuous advancements in information system technologies it is still not simple to receive relevant answers to Science-related queries. Getting answers requires a gathering of information from heterogeneous systems, and the volume of responses that semantically do not match with the queried intensions overwhelms users. W3C initiatives with extensions such as the Semantic Web and the Linked Open Data Web introduced important technologies to overcome the issues of semantics and access by promoting standard representation formats – formal ontologies – for information integration. These are inherent in architectural system styles, where increased openness challenges the traditional closed-world and often adhocly designed systems. However, technology on its own is not meaningful and the information systems community is increasingly becoming aware of foundations and their importance with guiding system analyses and conceptual design processes towards sustainable and more integrative information systems. As a contribution, this work develops a formal ontology FERON – Field-extensible Research Ontology – following the foundations as introduced by Mario Bunge and applied to information systems design by Wand and Weber, i.e. Bunge- Wand-Weber (BWW). Nevertheless, FERON is not aimed at the modelling of an information system as such, but at the description of a perceived world – the substantial things – that an information system ought to be able to model. FERON is a formal description of the Research domain – a formal ontology according to latest technological standards. Language Technology was chosen as a subdomain to demonstrate its field extensibility. The formal FERON ontology results from a hybrid modelling approach; it was first described top-down based on a many years activity of the author and then fine-tuned bottom-up through a comprehensive analysis and re-use of openly available descriptions and standards. The entire FERON design process was accompanied by an awareness of architectural system levels and system implementation styles, but was at first aimed at a human domain understanding, which according to the General Definition of Information (GDI) is achievable through well-formed meaningful data.Trotz kontinuierlich verbesserter Informationssystemtechnologien ist es nicht einfach möglich, relevante Antworten auf forschungsverwandte Suchanfragen zu erhalten. Dies liegt unter anderem daran, dass Informationen in verschiedenen Systemen bereitgestellt werden, und dass die Beschreibung der bereitgestellten Informationen nicht mit den Beschreibungen der gestellten Fragen übereinstimmen. Neuere Technologien wie das Semantische Web oder Linked Open Data ermöglichen zwar verbesserte Beschreibungen und Zugriffe – jedoch sind die Technologien an sich auch nicht bedeutungsvoll. Weitergehende, fundierende Ansätze zur Beschreibung von Informationenen finden daher zunehmend Anerkennung und Zuspruch in der wissenschaftlichen Gemeinde, diese beinflussen konsequenterweise die Systemanalyse sowie das Systemdesign. Die vorliegende Arbeit entwickelt eine formale Ontologie einer Forschungswelt die disziplinenübergreifend skaliert, namentlich FERON – Field-extensible Research Ontology, basierend auf den Ansätzen der Bunge-Wand-Weber (BWW) Ontologie. Der Titel der Arbeit “Towards Ontological Foundations of Research Information Systems” übersetzt: „Zur ontologischen Fundierung von Forschungsinformationssystemen“. Im Titel ist ontologisch zuallererst im philosophischen Sinne zu verstehen, und nicht zu verwechseln mit der dann resultierenden Ontologie im technologischen Sinne einer formalen Beschreibung der wahrgenommenen Forschungswelt – namentlich FERON. Eine Klärung der Begriffe Ontologie, Konzept, Entität, Daten und Information zum Verständnis der vorliegenden Arbeit wird in Kapitel 2.5 versucht, ein Verständnis wurde als kritisch für die Qualität der resultierenden formalen Ontologie FERON, aber auch als hilfreich für den Leser vorweggenommen, insbesondere weil die genannten Begriffe über Disziplinen hinweg oftmals sehr unterschiedlich wahrgenommen werden. Die Analyse und Modellierung von FERON basiert auf der Bedeutung dieser grundlegenden Begriffe wie die philosophische und wissenschaftliche Literatur verschiedener Disziplinen sie belegt. Die vorliegende Arbeit entwickelt FERON, und modelliert eine Welt der Forschung in disziplinenübergreifender Weise mittels neuester technologischer Standards – formal in RDF/OWL. Die fachspezifische Erweiterbarkeit ist durch Eingliederung von Beschreibungen des Gebietes Sprachtechnologie demonstriert. Die Modellierung wurde durchgehend von der Theorie Mario Bunges begleitet, welche Wand und Weber für eine Anwendung während der Systemanalyse und Systemgestaltung interpretierten und welche im Kapitel 3.1.1 vorgestellt wird. Die Idee ist als Bunge-Wand-Weber Ontologie (BWW) zunehmend bekannt und demgemäße ontologische Ansichten sind teilweise in formalen Beschreibungssprachen und Werkzeugen eingebunden, und damit bei der Modellierung explizit nutzbar. Neben BWW werden kurz die Fundierungsansätze von DOLCE, SUMO und Cyc vorgestellt und deren Relevanz für FERON verdeutlicht. Eine fehlende Fundierung in der Disziplin Informationssysteme wurde lange Zeit als wesentliche Ursache für die vermisste wissenschaftliche Akzeptanz der Disziplin betrachtet; größtenteils wurden Informationssysteme pragmatisch und adhoc entwickelt und skalierten daher nicht konsistent. Zunehmend wird jedoch eine theoretische und insbesondere die ontologische Fundierung von Informationssystemen als wertvoll anerkannt – von der Idee bis hin zur Implementierung aber auch während der Umgestaltungsphasen. Konzepte fundierter Informationssysteme im funktional-technischen Sinne sind als modellgetriebene Architektur bekannt und werden hier durch die Ansätze von Zachmann und Scheer verdeutlicht. In der kurzen Geschichte IT-basierter Informationssysteme wurden phasenweise immer wieder strukturell unterschiedliche Modelle angewandt. Diese werden daher im Kapitel 3.2 Modellierungsgrammatiken untersucht und deren Unterschiede dargestellt – namentlich das Entity-Relationship-Modell, semantische Netzwerke, das relationale Modell, hierarchische Modelle und objekt-orientierte Modelle. Darüberhinaus sind insbesondere formale Ontologien durch die Web Standardisierungsaktivitäten und W3C Empfehlungen ein rasant wachsendes Segment, verstärkt durch politische Entscheidungen für offene Daten und implizierend offene Systeme. Im Vergleich zu traditionellen und weitestgehend geschlossenen sogenannten closed-world Systemen sind hinsichtlich der Modellierung bestimmte Aspekte zu beachten. Diese unterliegen im Gegensatz zu offenen Systemen dem Paradigma des kompletten Wissens und sind sozusagen vorschreibend; im System aktuell nicht vorhandene Information wird als nicht existent interpretiert. Dahingegen gehen offene open-world Systeme davon aus, dass nicht vorhandene Information aktuell unbekannt ist – und die bekannte Information nicht vorschreibt sondern beschreibt. Weitere Unterschiede die es bezüglich der Modellierung zu beachten gilt, befassen sich mit zeitlich geprägten Verknüpfungen – über sogenannte Links oder Relationships – aber auch mit Entitäten und deren Identitäten. Da FERON keine Ontologie eines Informationssystems selbst modelliert, sondern eine Welt für eine mögliche Umsetzung in einem Informationssystem bechreibt sind weitergehende Modellierungsaspekte in Kapitel 3.3 lediglich erklärt und es wird auf Beispiele verwiesen. In der vorliegenden Arbeit wird keine explizite Anwendung empfohlen, weil ein Informationssystem immer derjenigen Form entsprechen sollte, welche einer bestimmten Funktion folgt, und weil die Vorwegnahme von Funktionen eine Dimension darstellt die weit über das Maß der vorliegenden Arbeit hinaus geht. FERON beschreibt eine Welt der Forschung; vorhandene Modellierungsansätze von Forschungsinformationssystemem werden mit Kapitel 4.1 den Ansätzen verwandter Arten gegenübergestellt – nämlich, wissenschaftlichen Repositorien, Datenrepositorien, Digitalen Bibliotheken, Digitalen Archiven und Lehre Systemen. Die untersuchten Modelle offenbaren neben inhaltlichen Unterschieden auch die Verschiedenheit der Modellierungsansätze von z.B. Referenzmodellen gegenüber formalen Datenmodellen oder offenen Weltbeschreibungen, und damit auch die einhergehende Schwierigkeit von Integration. Insbesondere formale Ontologien erlauben über die traditionellen Ansätze hinweg, automatische Schlußfolgerungen und Beweisführungen, welche jedoch hier nicht weitergehend erörtert werden. FERON war von Anfang an für den menschlichen Leser konzipiert, wenn auch formal beschrieben. Der Modellierungsansatz in FERON ist hybrid und wird in Kapitel 7 erläutert. Eine hybride Modellierung war möglich durch eine mehr als zehn-jährige Erfahrung und Tätigkeit der Autorin in diesem Bereich, auch belegt durch zahlreiche Peer-Review Publikationen. Der erste Entwurf von FERON erfolgte demgemäß zuallererst im Top-Down Verfahren (Figure 29), bevor mittels umfassender Analyse (dokumentiert in den Kapiteln 5 und 6) von verfügbaren Domänenbeschreibungen sukszessive eine Bottom-Up Anpassung von FERON vorgenommen wurde (Figure 68), welche bereits standardisierte und bereits definierte Beschreibungen und Eigenschaften wenn möglich integrierte (Figure 67). FERON ist eine ontologisch fundierte, formale Beschreibung – eine formale Ontologie – einer Forschungswelt zur vereinfachten, konsistenten Umsetzung von standardisierten, integrativen Forschungsinformationssystemen oder Fachinformationssystemen. Substantielle Entitäten wurden grundsätzlich erkannt, und deren Eigenschaften sowie Verknüpfungen formal beschrieben (Kapitel 7): Ressource unterschieden nach Nicht-Informations-Ressource und Informations-Ressource. Erstere unterscheidet nach Agent (Person, Organisationseinheit), Aktivität (Methode, Projekt, Bildung, Ereignis), Förderung (Programm, Einkommen), Messung und Infrastruktur (Werkzeug, Dienst, Einrichtung), zweitere nach Publikation, Literatur, Produkt (Daten), Wissensorganisationssystem, auch bekannt als KOS (Knowledge Organisation System), wie in der im Dokument integrierten Graphik (Figure 1) demonstriert. Kapitel 7 präsentiert FERON und dessen formale Einbindung von übergreifenden Eigenschaften wie Sprache, Zeit, Geographie, zeitlich geprägte Verknüpfung, ontologische Verpflichtung, Namensraum, Klasse, Eigenschaft, funktionales Schema, Entität und Identität. Seine inherente Struktur erlaubt eine einfache Disziplinen- oder Domänenerweiterung. Die Sprachtechnologie (englisch: Language Technology – abgekürzt LT) wird als Gebiet zur Demonstration der Erweiterung von FERON formal eingebunden, und mit Kapitel 6 insbesondere seine substantiell fach-spezifischen Entitäten wie Methode, Projekt, Daten, Service, Infrastruktur, Messung, aber auch KOS untersucht. Eine Erweiterung der Ontologie FERON für explizit-funktionale Anforderungen an ein Informationssystem, oder für weitergehende disziplinen-spezifische Eigenschaften, z.B. einer linguistisch verbesserten Anwendung für sprachtechnologische Weiterverarbeitung, ist möglich, erfordert jedoch tiefergehendes Fachwissen. Ziel der Arbeit war es zuallererst, das Verständnis für die Domäne Forschung zu verbessern – mit weiterreichendem Blick auf eine allgemeine integrative system-technische Entwicklung zur Verbesserung von Informationszugriff und Informationsqualität. Daneben wurden historische, gesellschaftliche aber auch politische Faktoren beobachtet, welche helfen, die wachsenden Anforderungen jenseits der Technologie zu bewältigen. FERON ist als formales Model FERON.owl valide und wird mit der vorliegenden Arbeit sozusagen als Template zur weiteren Befüllung bereitgestellt. Darauf basierend sind formale Restriktionen sowie disziplinen-spezifische und terminologische Erweiterungen direkt möglich. Daten-Instanzen wie in den präsentierten Beispielen sind mittels FERON.pprj verfügbar

Integrated Manufacturing Process and System Analysis to Assist Sustainable Product Design

Product development in the 21st century requires integrating sustainability performance evaluation with product design and manufacturing activities. A variety of factors, including climate change, public awareness, and increasingly strict regulations have compelled companies to design and manufacture more sustainable products. While the design phase typically accounts for only 5-7% of a product’s cost, 70-80% of product life cycle costs are determined (or locked in) during design, for example due to the consumption of materials and other resources. In addition to financial costs, however, environmental and social costs are similarly set during product design. Thus, the decisions made during the design phase have a significant impact on the life cycle sustainability performance of a product. To improve product sustainability performance, it has been recognized that supply chain and manufacturing activities should be analyzed during the design phase. Supporting methodologies and tools have been developed to aid in evaluating sustainability performance during product design phase. However, these existing methods and tools are often developed for domain experts and not well-suited for educating non-expert decision makers (e.g., engineering students and engineering practitioners), who do not possess specialized knowledge in sustainability analysis of product designs and manufacturing processes, for several reasons. First, relating sustainability information to conceptual product designs is challenging. Second, eco-design software tools often require costly licensing and training, limiting their ease of access. Third, domain expertise (e.g., knowledge of eco-design and manufacturing methods and tools) and extended analysis times are required to produce meaningful results. Thus, the objective of the research presented herein is to facilitate simultaneous analysis of sustainability performance impacts of different manufacturing processes and systems through unit manufacturing process modeling within an easy-to-use, publicly-available product design and manufacturing analysis tool. To achieve the objective of this research, a framework is developed that considers a cradle-to-gate life cycle scope consisting of four phases: (1) product development, (2) supply chain configuration, (3) manufacturing process design, and (4) manufacturing process and system sustainability analysis. To implement this sustainability assessment framework and to address the identified limitations of sustainability assessment tools, a proof-of-concept Manufacturing Process and System (MaPS) Sustainability Analysis Tool is developed. The proof-of-concept tool is implemented as spreadsheet models (MS Excel) comprising four modules, each mapping to a phase of the developed framework. In addition to environmental impacts, the tool can be used to investigate economic and social impacts. These analyses are demonstrated by quantifying energy and associated carbon footprint, the cost of goods sold, and worker safety, respectively. Further, the operational performance of the MaPS Sustainability Analysis Tool was evaluated in terms of ease-of-use and usefulness metrics by undergraduate and graduate engineering students at Tampere University (Finland) and Oregon State University (USA). Study participants found the tool easy-to-use and indicated it would be useful in the task of analyzing the product design, supply chain, and manufacturing process sustainability performance (i.e., environmental, economic, and social impacts). Several opportunities for future work have been identified to build upon the research undertaken as a part of this dissertation. First, the framework developed herein can be expanded to include other phases of the product life cycle. In addition, key software tool operational characteristics and graphical user interfaces should be investigated to improve efficiency, effectiveness, satisfaction, and learnability of the MaPS Sustainability Analysis Tool, especially by engaging broader and more representative groups of users. To improve tool flexibility and functionality, numerical models of different unit manufacturing processes should be developed, validated, and implemented for sustainability assessment of manufacturing process and systems