Abstract. This book demonstrates the significance of domain-specific conceptual modeling through new research and development approaches that are manifested in each of the chapters. They include novel modelling methods and tools that emphasize the recent results accomplished and their adequacy to assess specific aspects of a domain.
Each chapter offers detailed instructions on how to build models in a particular domain, such as product-service engineering, enterprise engineering, digital business ecosystems, and enterprise modelling and capability management. All chapters are enriched with case studies, related information, and tool implementations. The tools are based on the ADOxx metamodelling platform and are provided free of charge via OMiLAB. Furthermore, the book emphasizes possible future developments and potential research directions.
The collection of works presented here will benefit experts and practitioners from academia and industry alike, including members of the conceptual modeling community as well as lecturers and students.
Abstract. The digital transformation is a global mega trend that is triggered by the evolution of digital technology, that has the potential for every organisation to either optimize their current business via a digital innovation or by transforming the business via digital disruption. The challenge for every organisation is therefore to select and personalise the appropriate digital innovation. There is a plethora of methods and assessment frameworks, here we introduce the OMiLAB Innovation Corner that assists in (1) creating new business, (2) design the organisational model and (3) engineer proof-of-concept prototypes as a “communication media”. The unique value proposition of OMiLAB Innovation Corner is the model-based foundation that supports decision makers in key phases of the innovation. First, the creation of new business models by providing digital design thinking tools is assisted. Second, the design of the digital organisation by providing extended modelling capabilities is supported. Third, a proof-of-concept engineering providing robots and sensors is enabled. We share our practical exeriences by introducing (a) how new business models are created in the H2020 project Change2Twin to help manufacturing SMEs in their digital transformation, (b) how conceptual models are design in the H2020 project BIMERR to create digital twins of renovation processes and (d) how proof-of-concept engineering is performed in the FFG project complAI to analyse different robotic behaviour.
Abstract. Haptic storyboarding tools supporting storytelling as a Design Thinking approach, enable early exploration and validation of design alternatives regarding services, new product (features), innovative processes and disruptive business models. They do however not communicate the exact meaning and the importance of each object nor do they show relationships between them. Yet when aiming to materialize an innovative idea these aspects need to be unambiguously described. Diagrammatic models play an essential role here as they capture different aspects of the problem. When computed by means of software they also explicitlyshowdetailswhichinhapticstoryboardsusers implicitly fill with their own world-understanding, thus fostering a clear and transparent representation of the problem space. In addition, diagrammatic models can be enriched by semantics and subsequently be machine-queried, -analysed and -processed. The paper at hand shows the DIGITRANS project approach for an automated transformation of haptic storyboards into diagrammatic models and their provision in a computer-aided design environment.
Type:
Proceedings Article
Authors:
Elena-Teodora Miron and Christian Muck and Dimitris Karagiannis
Abstract. This paper presents the OMiLAB-Node Vienna, which supports the educational and research activities undertaken by the research Group Knowledge Engineering at the University of Vienna, the Node’s host. The OMiLAB Vienna Node is part of a larger community, which is presented subsequently. Last but not least the OMiLAB community’s relevance for the EMISA Special Interest Group is discussed.
Abstract. This position paper introduces a particular angle to address some student preconceptions regarding Conceptual Modelling, by presenting it as a standalone discipline that has a value proposition for any domain. The proposed thesis is that modelling languages should be primarily understood as purposeful knowledge schemas that can be subjected to agile adaptations in support of model-driven systems or decision processes. This thesis is supported by enablers such as the Open Models Laboratory and the Agile Modelling Method Engineering framework, which are also briefly presented.
Abstract. This paper proposes a modeling method (the work system modeling method – WSMM) that addresses key issues related to enterprise and process modeling. Those issues lead to modeling method requirements that call for relaxing common assumptions about the nature of modeling methods and related modeling languages and metamodels. A summary of work system theory (WST) and the work system method (WSM) provides background for understanding WSMM. A design space for modeling positions most applications of WSM in relation to seven purposes of modeling that call for successively more formal approaches. WSMM is presented in relation to the seven purposes, thereby extending WSM in new directions. A final section summarizes how WSMM addresses the issues and requirements from the introduction, explains how coherence is maintained within WSMM, and identifies areas for future research.
Abstract. Competition is steadily increasing in the IT-industry through factors such as globalism and the increase of the development rate of technology. In order to keep up with changing times, an analysis of one’s current sales process is vital in order to critically review and optimize the way an organization’s products are sold. The proposed method for this critical analysis is based on a technique called business process modeling, paired with strategic framework. Business process modeling, in this sense, involves drawing a sequential map representing the activities in a process, therefore giving the analyst a view of each respective activity, both in isolation and in the process’s entirety. Through business process modeling, optimization points for the sales process in the areas of digitization, personalization, and simplification, can be analyzed. From a strategy point of view, such a method enables the responsible decision makers of an organization to analyze the sales process with strategic principles and goals. In the scope of this research project, a German use-case organization with a reach to all German-speaking countries will be used in order to apply the to-be-established method. In addition, academics shall be interviewed, in order to gain an understanding of the viewpoint of the responsible decision makers in this process. Through this pairing, concrete optimization points can be defined in an innovative way.
Abstract. Design Thinking and its tools facilitate the service design process through interpretation and visualization means for design prescriptions. However, Design Thinking artefacts do not connect conceptually to enterprise models or resources yet service design approaches emphasize the need for resource integration mechanisms, which align the enterprise aim and resources with the customer value process. The paper at hand proposes an end-to-end process for the software- supported transformation of tangible Design Thinking artefacts into diagrammatic models with the simultaneous semantic enrichment of the objects aiming to support the aforementioned service design process. This and the ability to connect the Design Thinking artefacts with enterprise resources innovates the way diagrams can be used in industry work environments.
Abstract. Cyber-Physical Systems (CPS) are interconnected, computational control systems which directly interact with the non-deterministic physical world. To manage the confronted uncertainty when dealing with nondeterministic environments, a constant feedback loop of monitoring the environment and consequent adjustment of the system is obligatory. Components of a CPS as well as the communication between its components are prone to malfunctioning leading to system failures. Therefore, to enable an effective integration of CPSs into arbitrary business processes, a conceptual modeling framework which enables the evaluation of the capabilities and functionalities of CPSs is needed. In this paper, such a conceptual modeling framework based on applied category theory is proposed to model CPSs in a given environment.
Abstract. The era of models as executable entities rather than descriptive pictures is on the rise. Nowadays modeling languages provide a variety of additional features like transformation, simulation, or code generation and the consumers of models are persons as well as machines. This development brings about the necessity to make models machine-processable, i.e. to formalize modeling languages. The goal of this research project is to find a suitable formalism for this purpose. This formalism will be grounded on mathematical theories, adopting their unambiguity and expressiveness and enabling the application of established mathematical methods. Therefore the plan for this research project is to examine several promising mathematical theories regarding their possibilities and limitations. A first result of this examination is presented in this paper: the formalization of the semantics of a metamodel based on model theory, a branch of mathematical logic. We outline the analogy between the concepts of metamodeling and model theory and describe how a metamodel can be formalized with this approach. A proof of concept is given by applying the formalism on a simple example, namely the entity-relationship modeling language.
Abstract. Modelling methods are applied for decades focussing on the analysis and design of holistic conceptual representations of entire enterprises. Creation and interpretation of such holistic representations in one single model is unrealistic. It is therefore widely adopted to refer to multi-view enterprise modelling methods. Such methods decompose the holistic model into views that focus only on some aspects while omitting others. The views, however, rather usually than rarely overlap. Usability and utility of multi-view enterprise models therefore significantly depends on consistency between all views. This paper explores enterprise modelling methods with a focus on multi-view consistency, thereby contributing a thorough investigation of the syntactic and semantic overlaps between views. It then abstracts from concrete examples to derive generic consistency patterns which can be specified in a novel formalism. Utility of the formalism is evaluated in two case study applications. One strength of this approach is its conceptual nature, enabling its adoption by method engineers who not necessarily have a computer science background. The consistency patterns facilitate specification of modelling methods while the formalism eases the validation of models, and the implementation of multi-view modelling tools.
Abstract. Konzeptuelle Modelle werden für viele Anwendungsfälle verwendet. Hierzu haben sich verschiedenste Modellierungssprachen, wie BPMN, UML und ER herausentwickelt. Modellierungssprachen sind jedoch nicht immer leicht verständlich und auch Modelle können komplexer werden. Eine Methode um diesem Problem entgegenzutreten, kann ein explorativer Ansatz über einen Web Model Navigator sein, welcher es erlaubt Modelle schrittweise kennenzulernen. Ein Konzept für ein solche Anwendung ist aber bislang nicht verfügbar. Um diese Lücke zu schließen, soll innerhalb dieser Arbeit ein Konzept für eine entsprechende Webanwendung in einem kollaborativen Umfeld entwickelt werden. Die Umsetzbarkeit dieses Konzepts wird anhand einer Implementierung für das Open Models Laboratory (OMiLAB) gezeigt. Für die Konzeptentwicklung wurden verschiedene Ansätze analysiert und geeignete Eigenschaften als Grundlage übernommen. Das Ergebnis der Implementierung und anschließender Evaluierung zeigt, dass sich ein Web Model Navigator mit dem erarbeiteten Konzept entwickeln lässt, dieser jedoch nicht nur Stärken und Chancen bietet, sondern auch seinen Schwächen und Gefahren ausgesetzt ist.
Abstract. Die Verwendung von Suchmaschinen als Service ist in den Zeiten von Industrie 4.0 und der umfassenden Digitalisierung von Prozessen ein zentrales Element in der digitalen Welt. Sowohl Internetsuchmaschinen als auch firmeninterne Intranet Suchmaschinen erleichtern durch die Verarbeitung von Suchanfragen die tägliche Arbeit und erlauben durch immer komplexere Algorithmen eine höchstmögliche Relevanz der Suchergebnisse. Eine semantische Suchmaschine versucht dabei, die natürliche Eingabe eines Benutzers zu interpretieren und deren Semantik mit dem Suchindex zu vergleichen. Gerade in verteilten System-Architekturen ist die Einbindung einer spezifischen Suchmaschine häufig eine Herausforderung. Im Zuge dieser Arbeit werden anhand einer gegebenen Domaine, dem ’Open Models (OMi) Laboratory’, Anbieter von Suchmaschinen Frameworks verglichen und ein darauf passendes Konzept entwickelt. Dieses wird dann als Proof-of-Concept realisiert und in einer Produktivumgebung eingesetzt.
Abstract. The engagement in professional risk management is today a fact for most large organizations. In or-der to satisfy regulation and auditing requirements, an important step thereby is the identification and documentation of risks in an organization and the definition of measures for their mitigation. Thereby, the use of enterprise models provides the foundation for a systematic and holistic analysis of processes, organizational structures and IT systems. In the approach at hand we build upon the SeMFIS approach for semantic annotations of enterprise models with concepts from an OWL2 ontology. By providing an ontology for representing risks and mitigation measures, this additional information can be represented through annotations in arbitrary types of enterprise models without having to adapt the originally used modeling language. In addition, the approach provides a visual modeling language for representing rules according to the SWRL specification. This permits to process the semantic information provided by the annotations. The usage of the approach is illustrated through an example from the domain of risk-aware business process management. Upon the representation of risks in business processes using the semantic annotation approach, it is shown how SWRL rules can be used to automatically generate configurable risk reports.
Abstract. Traditionally, venues that are publishing Knowledge Management research have been separating concerns between two viewpoints that rarely converge into holistic approaches: one is the tradition of Artificial Intelligence research, where “Knowledge Management” is often employed as an umbrella term in relation to a variety of semantic technologies, knowledge representation and knowledge discovery techniques; the other viewpoint is a specialisation of “intangible asset management”, dealing with the business value and the pragmatics of organisational knowledge. Knowledge Management Systems are a catalyst for bridging such complementary perspectives and Design Science artefacts must be employed to facilitate alignments between these viewpoints, specifically between humanoriented and machine-oriented knowledge representations. Motivated by this desideratum and driven by project-based experience, the paper at hand advocates a key role of Diagrammatic Conceptual Modelling methods in enriching the seminal SECI Knowledge Conversion spiral, to the aim of opening it towards Knowledge Management Systems that could not have been envisioned at the time of Nonaka’s original SECI proposal, but can now benefit from state-of-the-art semantics-driven practices. By hybridising the SECI model with a machine-oriented Knowledge Distilling cycle, an extended SECI spiral variant is proposed and analysed in the paper, as a reflection on project-based deployments and experience.
Abstract. Traditional design methods, based on analytical rationale, often cannot address upcoming challenges e.g., related to the digital business transformation in volatile environments. Analytical rationale assumes a particular result and provides the methods and tools for achieving it. Nowadays, however, the result of a business transformation is often not precisely known nor the ways and means to achieve it. As a result, methods and tools are required that foster creativity while allowing customization to specific requirements or stakeholder needs. This paper proposes customized design thinking processes, realized with a conceptual modelling approach. The approach supports creativity in transformative business design. It shows how numerous design thinking tools can be integrated into a single conceptual modelling approach – supported by a modelling platform. The platform facilitates efficient and flexible design of novel business solutions. The created models moreover serve as a formalized knowledge base that enables knowledge processing and reuse.
Abstract. Development of domain specific modeling languages is observed by a growing number of groups emphasizing the implementation of individual modeling languages, methods and approaches for a variety of application domains. Domain orientation allows to express focused models using tailored domain concepts. To raise benefits of domain-specific modeling and in particular the use of developed models, tool support must not be limited to model editors realizing a certain language, but instead must provide full-fledged functionality for domain-specific modeling methods. This paper introduces the Open Models Laboratory (OMiLAB, www.omilab.org), an open environment for method engineering and tool development. In particular, the paper reports on the Secure Tropos method engineering case in OMiLAB, realized using the ADOxx meta-modeling platform as an implementation environment. Secure Tropos is a security-aware software systems development methodology, which combines requirements engineering concepts with security engineering concepts under an unified process to support the analysis and development of secure and trustworthy software systems.
Abstract. The Open Models Laboratory (OMiLAB) provides an open innovation environment for modeling method engineering. Innovation within OMiLAB relates to the engineering of new domain-specific modelling methods. The created modeling methods are shared in a world-wide open and collaborative community. OMiLAB enables prototyping and experimentation in heterogeneous research areas. The backbone of the OMiLAB ecosystem is openness on multiple layers: content, methodology, and technology. Content refers to the sharing of research results, methodology refers to the application domain of modeling methods, and technology refers to the provision and use of open technologies. This paper introduces OMiLAB briefly focusing on the different evolved communities that constitute the OMiLAB ecosystem as it exists today.
Abstract. Enterprise modelling is one of the popular means for capturing organizational knowledge in representations that are both human-readable (that is, diagrammatic) and machine-readable (that is, sufficiently formal and granular). However, the discipline faces certain challenges pertaining to the complexity of the socio-technical system to be modelled. This often requires a separation of concerns, to allow separate modelling of various enterprise facets – work processes, organizational structure, resource descriptions. This separation must be compensated by consistency management approaches, as diagrams of different types become inter-dependent views, enabled by different viewpoints (modelling language fragments with different scopes). The paper introduces novel means for managing the consistency of knowledge captured in multi-view enterprise models. The proposal is based on semantic graphs derived from diagrammatic models, and queries acting upon these. To achieve the goal, the Linked Data paradigm is repurposed and its technological enablers are aligned to the underlying graph nature of enterprise models. Several use cases will be discussed: (a) view transformations through graph rewriting; (b) view synchronization through reasoning; (c) passive view consistency checks. Exemplary cases are extracted from two research projects where the proposal has been successfully applied, therefore background on the projects will be provided to facilitate understanding.
Abstract. The Digital Innovation Environment (DiEn) powered by OMiLAB enables design, engineering and training activities for organisations pursuing Digital Transformation initiatives. Stakeholders from multi-disciplinary backgrounds are supported to create innovative ideas as Digital Business Models, to materialise them in proof-of-concept implementations using Digital Twins and to evaluate their feasibility in a laboratory setting as/through the OMiLAB Innovation Corner, within a corporate or academic context focusing on Digital Innovation.
Abstract. Today’s competitive conditions call for detailed comparative analyzes of manufacturing processes in order to get competitive products. This analysis involves the development of faithful and robust models for the supervision and management of all organizational and operational activities of companies. Efficient modelling involves the selection and use of appropriate tools for modelling, simulation and analysis of manufacturing processes. The diversity of manufacturing processes often makes it necessary to implement specific modelling tools. MM-DSL is a platform independent language for specifying and implementing specific modelling tools. The core objective of the MM-DSL language is the implementation of the modelling method concept. The paper presents the mechanisms underlying the MM-DSL language as well as its use for building the modelling tools specific to the manufacturing systems.
Abstract. The Problem Based Learning (PBL) as student centred approach and learning-by-doing method is suited for the modern higher education. However, the first contact with the method can be overwhelming for the students, in the absence of prior domain knowledge. The preparation of the learning material can be time and resource consuming for the teacher. The goal of the research was the implementation of an environment that should enhance the learning experience for the student and reduce the implementation burden for the teacher. The environment is based on the ADOxx platform and allows the collaboration of the learner teams and the teacher-learner interaction on three levels. The Metamodeling level supports the development of the domain-specific language used in the modelling of the manufacturing system; this activity stimulates and directs the gathering and consolidation of domain-specific knowledge. The modelling level allows the development of alternative design solution using models of the factory components. The Simulation level allows the analysis of these variants. The environment supports the teacher in developing instructional scaffolding and uses cases to ease the learners the first time contact with PBL. The functionality of the environment is presented using the case of designing a flexible food production line.
Dominik Bork and Aurona Gerber and Elena-Teodora Miron and Phil van Deventer and Alta Van der Merwe and Dimitris Karagiannis and Sunet Eybers and Anna Sumereder
Abstract. Third-party innovators, i.e., complementors, in platform enterprises develop and commercialize add-on products which are one of the main attraction points for customers. To ensure a sustainable evolution of the enterprise, the platform owner needs to attract and retain high-quality third-party innovators. We posit that the transaction costs incurred upon joining the enterprise as well as the controls imposed by the platform owner throughout the development and commercialization process shape the innovator’s perceived risk and influence his decision on whether to join or not. Based on a literature review, the paper at hand proposes a conceptual model for complementors to assess their perceived risk and subsequently evaluates the model in a case study of a platform enterprise for IT-based modelling tools. While some of the propositions are validated, i.e., that informational controls decrease the perceived environmental uncertainty and implicitly the perceived risks, other propositions, such as the fact that asset specificity is a deterrent to entering the platform enterprise could not be validated. Further case studies are necessary to provide a conclusive proof of the proposed model.
Abstract. Conceptual modeling is commonly employed for two classes of goals: (1) as input for run-time functionality (e.g., code generation) and (2) as support for design-time analysis (e.g., in business process management). An inherent trade-off manifests between such goals, as different levels of abstraction and semantic detail is needed. This has led to a multitude of modeling languages that are conceptually redundant (i.e., they share significant parts of their metamodels) and a dilemma of selecting the most adequate language for each goal. This article advocates the substitution of the selection dilemma with an approach where the modeling method is agilely tailored for the semantic variability required to cover both run-time and design-time concerns. The semantic space enabled by such a method is exposed to model-driven systems as RDF knowledge graphs, whereas the method evolution is managed with the Agile Modeling Method Engineering framework. The argument is grounded in the application area of Product-Service Systems, illustrated by a project-based modeling method.
2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
Abstract. The continuous urbanization forces city planners to be more creative and supported by approaches that enable an abstract perspective on the complex reality. Metropolitan cities have concrete plans to transform districts or the whole city towards a ‘Smart City’. Emerging technologies and the need to process data of millions of sensors raise challenges for city planners. This paper reports on experiences gained from a Smart City conceptual modelling teaching case we presented at the Next-Generation Enterprise Modelling Summer School. The case is subdivided into three scenarios, each focusing on different ways conceptual modelling can contribute in designing a Smart City or leveraging additional services for its citizens. The scenarios complement a theoretical perspective on conceptual modelling foundations by a practical perspective on their tool-based realization. The aim of the paper is to report on opportunities and challenges of teaching conceptual modelling in a Smart City.
Advancing the Impact of Design Science: Moving from Theory to Practice
Editors:
David Hutchison and Takeo Kanade and Josef Kittler and Jon M. Kleinberg and Alfred Kobsa and Friedemann Mattern and John C. Mitchell and Moni Naor and Oscar Nierstrasz and C. Pandu Rangan and Bernhard Steffen and Demetri Terzopoulos and Doug Tygar and Gerhard Weikum and Monica Chiarini Tremblay and Debra VanderMeer and Marcus Rothenberger and Ashish Gupta and Victoria Yoon
Abstract. The paper tackles the problem of notational heterogeneity in business process modeling. Heterogeneity is overcome with an approach that induces semantic homogeneity independent of notation, driven by commonalities and recurring semantics in various control flow-oriented modeling languages, with the goal of enabling process simulation on a generic level. Thus, hybrid process models (for end-to-end or decomposed processes) having different parts or subprocesses modeled with different languages become simulate-able, making it possible to derive quantitative measures (lead time, costs, or resource capacity) across notational heterogeneity. The result also contributes to a better understanding of the process structure, as it helps with identifying interface problems and process execution requirements, and can support a multitude of areas that benefit from step by step process simulation (e.g. process-oriented requirement analysis, user interface design, generation of business-related test cases, compilation of handbooks and training material derived from processes). A use case is presented in the context of the ComVantage EU research project, where notational heterogeneity is induced by: (a) the specificity and hybrid character of a process-centric modeling method designed for the project application domain, and (b) the collaborative nature of the modeling effort, with different modelers working with different notations for different layers of abstraction in a shared on-line tool and model repository.
Jens Ziegler and Robert Buchmann and Markus Graube and Jan Hladik and Tobias Münch and Patricia Ortiz and Johannes Pfeffer and Florian Schneider and Mikel Uriarte and Dimitris Karagiannis and Leon Urbas
Abstract. Design Thinking and its tools facilitate the service design process through interpretation and visualization means for design prescriptions. However, Design Thinking artefacts do not connect conceptually to enterprise models or resources yet service design approaches emphasize the need for resource integration mechanisms, which align the enterprise aim and resources with the customer value process. The paper at hand proposes an end-to-end process for the software-
supported transformation of tangible Design Thinking artefacts into diagrammatic models with the simultaneous semantic enrichment of the objects aiming to support the aforementioned service design process. This and the ability to connect the Design Thinking artefacts with enterprise resources innovates the way diagrams can be used in industry work environments.
Abstract. There is a need for enterprises to transition from a technology-driven Industry 4.0 paradigm towards more sustainable frameworks that are based on the Society 5.0 concept, emphasizing green and human-centric approaches. The CoDEMO 5.0 project addresses the lack of personnel equipped with the necessary skills to facilitate this transition. As a result, the goal is to develop innovative training and collaboration frameworks by building upon the principle of co-creation among stakeholders with diverse backgrounds and interests. Haptic Design Thinking methodologies, and more specifically, the notion of Digital Twins for Haptic Design Thinking, are introduced as prospective input to support the project objectives. Building upon this notion, CoDEMO 5.0 aims to enhance the co-creative capabilities of decision-makers, thereby contributing to the broader agenda of establishing a workforce skilled for steering organizations towards sustainable, resilient, and human-centric development in the European context.
Alexander Völz and Iulia Vaidian and Christian Muck
Book:
Joint Proceedings of RCIS 2024 Workshops and Research Projects Track co-located with the 18th International Conferecence on Research Challenges in Information Science (RCIS 2024)
Abstract. Servitization is a global trend in the manufacturing industry that requires a challenging and complex transformation. In this context, engineering and design Product-Service Systems (PSS), capturing both product and service perspectives and balancing customer satisfaction and internal efficiency, are becoming more and more essential. To this purpose, the SEEM (SErvice Engineering Methodology) methodology is proposed, and the SEEM Modeling toolkit supporting its implementation is presented. Lastly, this chapter describes the methodology and the tool implementations in a case study on an Italian manufacturing company.
Abstract. Digitalization requires cyber-physical ecosystems to achieve the goals of its transformation process, which should be primarily driven by innovation. OMiLAB (www.omilab.org) supports digital innovation within a community of practice and technical environment, based on a global network of physical laboratory nodes. The Digital Innovation Environment (DiEn) powered by OMiLAB located at industrial and academic organizations responds to digital transformation challenges. It facilitates the co-creation, design, and engineering of early prototypes. Digital innovation is challenged by the OMiLAB community of practice through tool-aided conceptual modelling and elevates model value in domain-specific scenarios and experiments.
Abstract. This paper addresses enterprise modeling needs, in a specific field of industrial management: the design and management of industrial product-service-systems. During the last decade, the industrial sector has undergone a change of the business model, through an increasing internalisation of service activities in manufacturing companies. The objective of this chapter is to develop a generic metamodel, which could be used to further develop IT support solutions for the design and lifecycle management of product-service-systems. The specification of this metamodel together with the associated modeling method are explained then illustrated on an industrial case study.
Abstract. The transition from a product–oriented business towards a PSS-oriented business, known in the scientific literature as ‘servitization’ involves complex changes for decision-makers. Over the past years, the scientific literature has provided consistent advances in PSS decision-support systems including PSS modelling. However, concerning PSS modelling languages or formalisms, most initiatives remain context dependent; to date only a small a piece of literature addresses the need for reproducibility of PSS modelling methods. The objective of this paper is to make a first step forward in this direction, by proposing an iterative procedure dedicated to build generic meta-models and by applying it to generate a first proposal of PSS meta-model, expected to be re-usable in several distinct modelling and decision-making contexts.
Abstract. This paper addresses the needs of generic and shared PSS conceptual modelling approaches. The development of conceptual models for PSS is considered in this research as the basis to develop an integrated approach for PSS design. The objective of the paper is to present a generic PSS-dedicated meta-model, resulting from a procedure of integration of various conceptual models developed in the scientific literature of PSS design methods. The procedure of knowledge extraction and integration is explained and the generic PSS meta-model is presented, together with an associated modelling procedure. These results constitute a path forward towards more generic PSS design methods.
Abstract. This paper presents a framework for addressing the challenge of economic value sharing among actors of Product-Service value networks. More specifically the framework is dedicated to the assessment of alternative collaborative value networks and their associated economic models, at the time of designing a product-service system (PSS). The framework includes three main components: modelling, simulation and uncertainty assessment. The framework is briefly presented as parts of its components were discussed in previous research. The paper provides an illustration with a design project of a PSS solution in the agro-alimentary industry, requiring a balanced configuration of collaborative value network.
Type:
Proceedings Article
Authors:
Xavier Boucher and Khaled Medini and Camilo Murillo Coba
Publisher:
Springer International Publishing
Book:
Collaborative Networks and Digital Transformation
Editors:
Luis M. Camarinha-Matos and Hamideh Afsarmanesh and Dario Antonelli
Abstract. This paper explains the first steps of a smart PSS engineering approach, aimed at eliciting stakeholder needs, prototyping the value proposition, representing how the Original Equipment Manufacturer (OEM) will capture value and share it with a collaborative network of stakeholders while identifying and prioritizing risks from the value proposition. The approach addresses two gaps in the field of smart PSS design: (i) the need of visualizing solutions to support the transformation of value propositions for the stakeholders into a contract mechanism supporting value capture by the offering company and (ii) the importance of integrating risk management during the design of Smart PSS value proposition
Type:
Proceedings Article
Authors:
Camilo Murillo Coba and Xavier Boucher and François Vuillaume and Alexandre Gay and Jesus Gonzalez-Feliu
Publisher:
Springer International Publishing
Book:
Boosting Collaborative Networks 4.0
Editors:
Luis M. Camarinha-Matos and Hamideh Afsarmanesh and Angel Ortiz
Abstract. This position paper introduces a Smart Innovation Environment for experimentation related to digital transformation projects, for the consolidation of a proposed “Digital Engineer” skill profile (with a business-oriented facet labelled as “Digital Innovator”). In the Internet of Things era, this profile implies the ability to perform both digital design and engineering activities, to semantically bridge multiple layers of abstraction and specificity – from business analysis down to cyber-physical engineering. In the paper’s proposal, this integration is enabled by conceptual modelling methods and interoperable modelling tools, tailored to support the creation of Digital Twins for innovative digital business models. The architecture of the proposed environment is guided by a Design Research perspective – i.e., it is a treatment to an education “design problem” regarding the Digital Engineer skill profile in the IoT era. The proposed environment encompasses workspaces and toolkits are currently evaluated in “innovation corners” deployed across the OMiLAB ecosystem.
Type:
Proceedings Article
Authors:
Dimitris Karagiannis and Robert Andrei Buchmann and Xavier Boucher and Sergio Cavalieri and Adrian Florea and Dimitris Kiritsis and Moonkun Lee
Publisher:
Springer International Publishing
Book:
Boosting Collaborative Networks 4.0
Editors:
Luis M. Camarinha-Matos and Hamideh Afsarmanesh and Angel Ortiz
Abstract. Over the recent years, the growing research interest in Product-Service Systems (PSS) design and development methods has generated a large background of theoretical knowledge, conceptual methods and application case studies. After analysing the research gaps emerging for these advances, this paper propose a new integrated PSS design method, which intends to associate to 3 key contributions: an increased degree of integration among all components of the method; a higher applicability in industrial companies; specific added-value in balancing economic models of the stakeholders of the PSS delivery network. The proposition is based on extending the Functional Analysis (FA) approach (NF X 50-100), which is commonly used in product engineering, in order to cover the requirements for PSS design and development. The paper intends to provide a conceptual justification, then an industrial verification, of the pertinence and applicability of the method proposed. The industrial experimentation is developed on an industrial PSS case study dealing with the design of an industrial cleaning solution.
Abstract. Although the literature is full of research on the transition of industry towards Product-Service Systems (PSS), the question of how to effectively support PSS engineering is poorly addressed. The compelling need for decision support throughout the various stages of the engineering process is particularly challenging due to the inherent complexity of PSS. In this sense, visualisation and modelling at large have been put forth as promising means for supporting PSS engineering. This paper proposes a method for specifying a modelling language for PSS engineering, putting together PSS domain-specific knowledge and modelling concepts inherited from conceptual modelling and model-based engineering. It relies on a recursive transformation process of the underlying PSS meta-model using knowledge from case studies and the literature. The method has proven to be a practical means for gradual enrichment of the modelling language leading to successful experimentations in the industrial context.
Abstract. Digital technologies are encouraging manufacturing companies in their servitization process by accelerating the offer of integrated product and service to create new value and grow relationships with customers in the Industry 4.0 era. In parallel, digitalization and servitization research streams are merging and paving the way for the Smart Product-Service System (Smart PSS). By analyzing the concept of Smart PSS, this paper questions the convergence between digital and service orientations and considers how digital technologies are used to enable decisions along the PSS lifecycle and/or at different planning levels. Applying topic modeling, a semi-systematic literature review on the combination of digital technologies with PSS has highlighted five main research streams: PSS design, digital servitization, assessing tool for PSS decisions, knowledge management along the lifecycle, sustainability business models. The development over time of these research streams has been tracked leading to the definition of a new research agenda on Smart PSS.
Abstract. The recent convergence between two industrial transitions towards digitalization on the one side and servitization on the other side led to the new business strategies of digital servitization and smart PSS delivery. While inheriting from the previous scientific literature on PSS, because of the multiple impacts of digitalization in the overall system, the processes of ensuring the design and engineering of smart PSS solutions poses new challenges. This research addresses the specific needs to develop conceptual prototypes of smart PSS value offers, at early stages of the design process. The paper presents the development and experimentation of a modelling language and its associated modelling toolkit (sPS²Modeller). The application case study addresses the design of a smart PSS in the field of heating appliances, developed in collaboration with the company elm.leblanc, Bosch Group – France.
Abstract. This position paper introduces a Smart Innovation Environment for experimentation related to digital transformation projects, for the consolidation of a proposed “Digital Engineer” skill profile (with a business-oriented facet labelled as “Digital Innovator”). In the Internet of Things era, this profile implies the ability to perform both digital design and engineering activities, to semantically bridge multiple layers of abstraction and specificity – from business analysis down to cyber-physical engineering. In the paper’s proposal, this integration is enabled by conceptual modelling methods and interoperable modelling tools, tailored to support the creation of Digital Twins for innovative digital business models. The architecture of the proposed environment is guided by a Design Research perspective – i.e., it is a treatment to an education “design problem” regarding the Digital Engineer skill profile in the IoT era. The proposed environment encompasses workspaces and toolkits are currently evaluated in “innovation corners” deployed across the OMiLAB ecosystem.
Type:
Proceedings Article
Authors:
Dimitris Karagiannis and Robert Andrei Buchmann and Xavier Boucher and Sergio Cavalieri and Adrian Florea and Dimitris Kiritsis and Moonkun Lee
Publisher:
Springer International Publishing
Book:
Boosting Collaborative Networks 4.0
Editors:
Luis M. Camarinha-Matos and Hamideh Afsarmanesh and Angel Ortiz
Abstract. Deliverable D2.1 specifies the design requirements for the first relevant stage in the Horizon Europe project FAIRWork aiming at fair decision making within complex systems in the production domain. From the planned use cases of the industrial partners – FLEX “Automated Test Building”, “Worker Allocation”, “Machine Maintenance After Breakdown” and CRF “Workload Balance”, “Delay of Material” and “Quality Issues” – the report constitutes and specifies the basic design decisions for research and development directions in the project. A major contribution is the provision of the initial architecture of an innovative service framework for decision support systems.
The first part of the report is dedicated to the design thinking approach as an efficient choice for the analysis of user requirements. The planned use case scenarios were primarily examined from a user-driven perspective. In several workshops a participatory process was applied in order to deduce the most substantial design decisions from the results of the highly interactive sessions. A model-based approach of the design process was chosen in order to determine the user requirements that were consequently defined and described in detail. In this way, the knowledge about a use case was externalised in the form of conceptual representations, using domain-specific modelling languages that are suitable and provide the required construct for representation and processing. In a further step, the high-level scenarios were designed in a collaborative, interactive, and agile environment involving experts from different backgrounds. Processes are the outcome of this structured approach requiring support for: “finding similar projects”, “find relevant experts”, “simulate production process”, “allocate worker”, “map workers with profiles”, “find similar problems”, “reschedule production line”, “allocate order to production line”, “assess the impact”.
In the second part, we give an overview about the key challenges of FAIRWork. In the current production industry there is a need to make the current automated and hierarchical structured production processes more flexible. At the same time digitalization with AI support is seen as a key enabler for more energy efficient and resource efficient services, products or business models, by also enabling process optimization in the overall production process. Therefore, we describe in more detail the main challenges technical challenges such as configuration, resource allocation, and selection aspects. These three challenges are highly relevant for making the process more flexible, adaptive, and resource efficient by using the relevant AI-based decision strategies in our complex distributed decision-making. At the end, the trustworthy AI aspect is a further key challenge to get AI accepted by the involved humans and also utilize its potential.
In the third part, the overall methodology of making complex decision-making is outlined. Within this chapter, we describe the overall procedure for implementing complex decision-making processes. Therefore, this chapter gives an overview about relevant concepts for the research direction and implementation of such complex decision making by using AI services. In FAIRWork, AI is used in all our scenarios to automate processes or to make their processes more resource-efficient. Since humans are an important part of the overall decision process, trust in AI and human factors plays an essential role, therefore these aspects are explained. Finally, the technical concepts for a concrete implementation such as digital knowledge base, digital twin, digital shadow, will be discussed as well. Finally, it follows the explanation about the orchestration of decision-making processes by using Microservices.
In the fourth part, the initial architecture of the project is presented based on the overall project objectives and requirements. Key components of the FAIRWork service framework are motivated, described, and their relevant features are presented. A detailed description of these components is given in Deliverable D4.1 including the technical implementation of the basic core services or application specific services.
Finally, the initial design of the FAIRWork’s architecture is compared with most relevant technical architectures that are commonly used in the industry environment domain, such as, Gaia-X, FIWARE, International Data Space, and RAMI.
Aleassandro Cisi, Roland Sitar, Wilfrid Utz, Christian Muck, Patrik Burzynski, Robert Woitsch, Magdalena Dienstl, Marlene Mayr, Remi Lanza, Rishyank Chevuri, Sylwia Olbrych, Johanna Werz, Alexander Nasuta, Stefan Böschen, Noushin Gheibi, Higor Rosse, Lucas Paletta
Abstract. This report focuses on the deliverable “D3.1 – DAI-DSS Research Specification”, part of the Horizon Europe project FAIRWork. The deliverable aims to describe the specific research factors in selected use cases of industrial partners FLEX and CRF. It presents a research strategies and factors catalogue that serves as a framework for conducting research within the Democratized AI-based Decision Support System (DAI-DSS). DAI-DSS research specifications are closely related to deliverables “D2.1 Specification of FAIRWork Use Case and DAI-DSS Prototype Report” and “D4.1 DAI-DSS Architecture and Initial Documentation and Test Report”.
The first part of the report provides an overview of the relevant literature related to the research intended within the frame of this project. It covers the most significant research domains, such as the democratization of decision-making and digital shadows and twins for human experts. Additionally, it explores technical approaches like Artificial Intelligence (AI) and Multi-Agent System (MAS) crucial for improving Decision Support Systems (DSS). This section also presents the state-of-the-art crucial aspects of today's technology, particularly reliability and trustworthiness in AI. The output of this literature review leads to research questions in multiple domains addressed within the FAIRWork project.
The second part of the report focuses on the research methodologies and strategies employed to investigate the technical aspects of decision-making processes, human aspects, and digital human factors measurements. It presents research approaches for successfully implementing AI and MAS-based technologies into DSS. Methods such as data-driven modelling, prototyping, and testing are proposed within the AI and MAS domains. Additionally, the report outlines the use of sensors to capture critical information about humans' mental, affective, and motivational states, including implementation details of the Intelligent Sensor Box (ISB). Furthermore, a novel framework using Personas as Human Digital Twins for Decision Making in the context of Industry 5.0 is described.
The final part of the report presents the key research factors identified in the industrial use cases and potential AI services to address them. These research factors are categorized into two main perspectives: the human perspective and the technical perspective. The human perspective factors are derived from the research plan and are observed in given use cases. On the technical side, the requirements for modelling and testing new concepts using AI and MAS technologies primarily focus on data availability (process-relevant and expert knowledge) and the DSS architecture necessary to enable information flow and decision models related to the use cases.
The report also provides a strategy for communication and dissemination in the context of the research methodology of the FAIRWork project. The objective is to continuously disseminate project achievements, raise awareness about the project, and gather feedback to improve the created research artefacts.
Herwig Zeiner, Lucas Paletta, Michael Schneeberger, Gustavo Vieira, Sylwia Olbrych, Alexander Nasuta, Johanna Werz, Noushin Gheibi, Stefan Böschen, Magdalena Dienstl, Marlene Mayer, Christian Muck
Abstract. This report focuses on deliverable “First DAI-DSS Research Collection”, which is part of the Horizon Europe project FAIRWork. The deliverable aims to describe a first iteration on guidelines, methods and tools for democratising the production process in the light of their flexibilisation while using Artificial Intelligence, Optimisation, Human Factors Analytics, and Multi Agent Systems (MAS) as mediators in form of prototypes, physical experiments in laboratories, implemented questionnaires, modelling tools or semantic model of criteria catalogues. It presents a collection of concepts, methods, studies, and services of a research framework within the Democratised AI Decision Support System (DAI-DSS). The DAI-DSS research collection is based on the fundamental principles related to the research intended within the frame of this project that was described in Deliverable “D3.1 DAI-DSS Research Specification” and incorporates cross-connections with Deliverable “D4.1.1 DAI-DSS Architecture and initial Documentation and Test Report“ on the basis of functional components of the DAI-DSS system architecture.
The first part of the report provides an overview of the individual research tracks related to the various research strategies that incrementally shape and extend the research collection within the frame of this project and in the context of the scientific as well as industrial communities. It covers the most significant research domains such as democratisation of decision-making as well as digital shadows for resilience risk stratification or human experts. Additionally, it explores technical approaches like Artificial Intelligence (AI) and MAS crucial for improving Decision Support Systems (DSS). This section also presents the state-of-the-art in key aspects of today's technology, particularly reliability and trustworthiness in AI. The output of this research overview leads to the outline of research activities in multiple domains that will be addressed within the FAIRWork project.
The second part of the report focuses on the research collection in terms of the concrete research methods and services employed to investigate the technical aspects of decision-making processes, human aspects in the process, and digital Human Factors measurements. It presents research approaches for the successful implementation of AI and MAS-based technologies into DSS. Methods such as data-driven modelling, prototyping, and testing are proposed within the AI and MAS domains. Additionally, the report outlines the use of wearable sensors to capture critical information about the physiological, cognitive-emotional, and resilience state of humans, including implementation details of the Intelligent Sensor Box (ISB). Furthermore, the novel framework using Personas as Human Digital Twins for Decision Making in the context of Industry 5.0 is described in detail.
The third part covers initial observations on explainability and fairness in FAIRWork from an algorithmic point of view and summarises some reviews and surveys in the field.
The report also provides results of the strategy for scientific dissemination in the context of the research methodology of the FAIRWork project. The objective is to continuously disseminate project achievements, raise awareness about the project, and gather feedback to improve the created research artefacts.
Lucas Paletta, Herwig Zeiner, Michael Schneeberger, Julia Tschuden, Martin Pszeida, Andreas A. Mosbacher, Gustavo Vieira, Sylwia Olbrych, Alexander Nasuta, Johanna Werz, Noushin Gheibi, Stefan Böschen, Magdalena Dienstl, Marlene Mayer, Christian Muck
Abstract. This deliverable shows the “Final DAI-DSS Research Collection” as part of the Horizon Europe project FAIRWork. The deliverable aims to describe guidelines, methods and tools for democratising the production process in the light of their flexibilization utilizing artificial intelligence (AI), Optimisation, Human Factors Analytics, and multi-agent systems (MAS) as mediators in the form of prototypes, physical experiments in laboratories, implemented questionnaires, modelling tools or semantic model of criteria catalogues. Importantly, this collection is published in the FARIWork Innovation Shop and represents the key support features for the Democratic AI-based Decision Support System Support System (DAI-DSS). The FAIRWork Innovation shop is accessible online as Deliverable 3.3. This document is considered the accompanying document of the deployed online version.
https://innovationshop.fairwork-project.eu/
This deliverable also presents the scientific basis for the FAIRWork project across seven research tracks: The “Democratization of Decision-Making in Socio-Technical Settings” examines the dynamics of democratization in industry through MAS by exploring the contextual conditions for implementing a DSS within socio-technical frameworks. The “Decision-Making Using Multi Agent Systems” explores the potential of MAS for decentralized, adaptive decision-making in industry. By balancing technical, human-centric, and ethical aspects, MAS enhances efficiency, inclusivity, and scalability in complex systems. The “Digital Human Factors Analytics” outlines the use of wearable sensors to capture critical information on human physiological, cognitive-emotional, and resilience states, including the intelligent sensor box (ISB). It also details a novel framework using Personas as Human Digital Twins for Decision Making in Industry 5.0. The “Optimization in Decision Support Systems” is crucial for defining clear goals in AI-driven manufacturing, addressing challenges such as process optimisation, automation, and resource allocation. Various techniques, including AI, new algorithms and heuristics, support decision-making and efficiency in manufacturing companies. The “AI-Enriched Decision Support Systems” explores how AI methodologies, particularly machine learning (ML), can optimise decision-making in manufacturing, with an emphasis on dynamic tasks. It also addresses the gap between industry and developers by proposing a structured categorisation of DSS, enabling developers to select appropriate AI methods for industrial applications. The “Model-based Knowledge Engineering for Decision Support” presents a structured approach to AI adoption in enterprises by proposing a three-layered framework: Identification, Specification, and Configuration. It highlights the role of conceptual and technical models from the identification of the problem setting to the configuration of AI to ensure the alignment with business needs. Furthermore, it also reflects the integration of different AI techniques, such as retrieval-augmented generation (RAG) and large language models (LLMs), within use-case-specific prototypes, demonstrating how model-based methodologies can support AI configuration. It also investigates how such design models can be reused to support the explanation of decision scenarios on a high abstraction level using OMiLAB’s Scene2Model tool. The “Reliable and Trustworthy AI” demonstrates the importance of transparency for trusting AI systems and that transparency needs to be adapted to the target group. AI systems have to be understandable, which is why a system-dependent approach that sets the user in the center is recommended. A developed transparency matrix with additional individual consulting workshops for the developers has shown to be successful in implementing transparency and accuracy communication into AI services.
Lucas Paletta, Herwig Zeiner, Gustavo Vieira, Sylwia Olbrych, Alexander Nasuta, Johanna Werz, Johannes Zysk, Noushin Qeybi, Stefan Böschen, Marlene Mayr, Christian Muck
Abstract. This deliverable “D4.1 – FAIRWork Architecture and initial Documentation and Test Report” for is the first description of the architecture, which will be updated in M20 and M30 in form of updated deliverables.
First the high-level architecture is mapped to the use case scenarios that are for FLEX “Automated Test Building”, “Worker Allocation” and “Machine Maintenance After Breakdown” as well as for CRF “Workload Balance”, “Delay of Material” and “Quality Issues”. Based on the process analysis described in more detail in “D2.1 – Specification of FAIRWork and Initial DAI-DSS Architecture” we identified generic challenges to the aforementioned use case scenarios. This ensures that the architecture and the implemented solutions will not exclusively fit to those use cases but also serve similar use cases that are out of the project. The challenges are: “finding similar projects”, “find relevant experts”, “simulate production process”, “allocate worker”, “map workers with profiles”, “find similar problems”, “reschedule production line”, “allocate order to production line”, “assess the impact”. Such challenges are targeted by Microservices that may use Artificial Intelligence (AI) when appropriate. Hence, we propose a list of services that either individually or in a cooperative manner target the aforementioned list of challenges.
The architecture distinguishes therefore between (a) the core components that allow the selection, configuration, deployment, and operation of selected (AI) services and (b) a list of (AI) services the user can select and compose a solution that targets the requirements of the particular use case.
The (a) cores components are (i) the user interface that is a framework enabling user interface widgets to be deployed in environment like web-pages of MS TEAMS, (ii) the orchestrator that is a framework enabling the controlling of individual services or the orchestration of services, (iii) the knowledge base that integrates data from legacy applications, sensor data streams that require no protection and sensor data stream that require protection in form of an Intelligent Sensor Box (e.g. for human-related information); (iv) the externa data asset marketplace complements the data coming from inside the use case with available data form outside, (v) the configurator that enables the selection and configuration of services to a use-case specific solution and finally (vi) the so-called AI-enrichment which is a service catalogue providing different AI-based services that fulfill the end users’ needs.
The (b) list of (AI) services is a collection of available services, commercial products and research prototypes that are partly used from outside the consortium where feasible and partly created during the project. Hence, we currently propose an initial list of services that target the aforementioned challenges and propose different AI realizations to demonstrate the flexibility of our core components and to enable a selection of appropriate AI solutions. This list is therefore seen as indicative, and it is expected that in the duration of the project it will evolve. The final set of services will also be provided as projects results in the so-called innovation shop.
An initial plan for testing and reporting procedures is presented, which is currently seen as a plan and will be detailed once the deliverable will be updated in M20. The updated version of the deliverable includes the details of implemented components and services and updated security implementations. as well as implemented architecture testing and reporting methodologies.
Remi Lanza, Rishyank, Herwig Zeiner, Lucas Paletta, Michael Schneeberger, Robert Woitsch, Magdalena Dienstl, Jose Barbosa, Gutavo Vieira, Higor Rosse, Sylwia Olbrych, Alexander Nasuta, Christian Muck
Abstract. This deliverable “D4.1.1 – FAIRWork Architecture and initial Documentation and Test Report” is the second iteration of the deliverable D4.1 which has been submitted in the month M6 of the project. This iteration of the deliverable builds upon it and provides a comprehensive overview of the current status of the implementation of DAI-DSS components and services required for the decision support.
This iteration outlines key performance indicators for FLEX, covering "Automated Test Building," "Worker Allocation," and "Machine Maintenance After Breakdown," as well as for CRF, focusing on "Workload Balance," "Delay of Material," and "Quality Issues." These key performance indicators (KPIs) have been revised to align with the FAIRWork goals and objectives. These metrics will be used as benchmarks for evaluating the solutions proposed by the DAI-DSS for the described challenges.
The list of AI services proposed to support the use case challenges are revised in this iteration and further classified into three categories based on their development status: "Initial Prototype", "Development in Progress" and "Planned for Implementation" indicating their status of implementation. In addition to this the prerequisites and usage descriptions for the services have been added. These additional descriptions outline the specific circumstances under which the service is designed to operate, which aids in the selection process, ensuring that end users choose the most appropriate service for their specific scenarios.
This iteration further specifies the software and hardware requirements essential for the operation of DAI-DSS components and provides the status of the functional capability’s implementations, including User Interface, Configurator, Orchestrator, Knowledge Base, AI Enrichment, and Sensor Boxes.
Furthermore, the information related to the security considerations critical to the DAI-DSS architecture, highlighting mechanisms for data protection, authentication, and authorization has been described. This includes employing robust security practices such as SSL certificates, two-factor authentication, role-based access control, and secure data transmission protocols, which ensures the system is safeguarded against unauthorized access and data breaches, ensuring the protection of sensitive information. Additionally, an outlook on the necessary security measures and implementations aimed at enhancing the DAI-DSS's security has been described.
Finally, the iteration provides an overview of the testing methods employed to evaluate the integrity and functionality of the DAI-DSS architecture. Describing the methodologies for assessing the interactions between both internal and external components, the functional evaluation of each distinct component, and the integration testing of the components. This ensures that the DAI-DSS operates seamlessly, maintaining high standards of quality and reliability in its performance. The contents of this deliverable will contribute to the “D4.3 Final DAI-DSS Prototype, Documentation and Test Report”, scheduled for M30.
Rishyank Chevuri, Herwig Zeiner, Lucas Paletta, Michael Schneeberger, Robert Woitsch, Magdalena Dienstl, Gutavo Vieira, Sylwia Olbrych, Lauwigi Johanna, Alexander Nasuta, Christian Muck, Roland Sitar
Abstract. This deliverable, “D4.2 – Initial DAI-DSS Prototype”, is the first reference implementation focusing on the synthesis of fundamental components to establish a shared comprehension of the operational principles behind DAI-DSS. It aims to describe how the different DAI-DSS components fit together, forming a complete and cohesive structure in which the functionalities of the blocks are well-defined, the complementarity of the structure is explored, and the technological component is described in terms of fulfilling its purposes and integrating with the neighbouring structures that are part of the architecture.
The first part of this documentation deals with the description of the building blocks that form part of the architecture described in deliverable “D4.1 – DAI-DSS Architecture and Initial Documentation and Test Report“. Initially, a quick overview of what the building blocks are and a brief explanation of their general structure are given. We then move on to the integration of the DAI-DSS User Interface (UI) into the system. Taking Industrial Partner CRF's Workload Balance Use Case Scenario as a reference, an overview of the UI in this setting is provided. It describes how the UI offers the possibility of visualising the important components directly linked to decision-making in this example, as well as the added value offered by this building block. The UI is essential for efficient decision-making. It provides the appropriate interface between the decision-maker and the system in order to enable a clear visualisation of the decision-making process. Some images provide an introductory view of the UI in this initial prototype.
Next is the DAI-DSS Orchestrator. The Orchestrator plays a very important and central role in the architecture of this system. Its purpose is to provide coordination in the exchange of information and decisions between the various parts of the system. Like the conductor in an orchestra, the DAI-DSS carries out the decision-making process, and the involved individual services retrieve the relevant data from the knowledge database. This currently takes place in two parallel ways. At a more advanced stage is the Workflow-based Orchestrator. In this orchestrator, a workflow engine offers the possibility of executing workflows, i.e., the execution of tasks that follow a well-defined path leading to the triggering or realisation of an AI service that offers a recommendation for decision-making on issues identified in the business process for which the system is built. For demonstration purposes, a workflow was built in which worker data is retrieved, and the order of a part is requested in the Workload Balance Use Case Scenario. The step-by-step execution of the orchestration is documented in order to elucidate how the orchestration of the building blocks can take place in a real, concrete case from the manufacturing industry in this example.
It presents the DAI-DSS Configurator, a tool designed to enhance decision support systems through efficient configuration and integration frameworks. It explains the configurator's components: the Configuration Framework, which assists in creating decision models and strategies, and the Configuration Integration Framework, which generates system configurations from these models. The report details the prototype's configuration steps, illustrated for clarity. It also allows for microservices and workflow configuration, featuring a user-friendly interface with a wizard for UI components combination.
The DAI-DSS Knowledge Base is highlighted as a central data repository, storing user properties, sensor data, and processed data. It plays a key role in the system's data flow, integrating with the Configurator and using REST API for data retrieval. The report covers the integration of AI services using REST-API endpoints, emphasising ease of access and industry-standard practices like Swagger documentation. It explores various algorithms, including Neural Networks and Decision Trees, and the integration of human factors data for assessing worker resilience. Deployment details of the AI services are discussed, focusing on hosting, management, and future enhancements like data catalogues and decision pattern services for resource allocation and predictive maintenance.
Finally, the report addresses the extension of the DAI-DSS with new services for various industrial use cases. It discusses customising services through conceptual modelling and the advantages of rule-based approaches over machine learning methods. The services aim to optimise resource allocation, improve productivity, and address human factors, thereby enhancing the system's decision-making capabilities in complex industrial scenarios.
Abstract. This deliverable, “D4.3 – Final DAI-DSS Prototype, Documentation and Test Report”, provides a comprehensive overview of the final implementation of the DAI-DSS. Building on the foundations established in “D4.1 – DAI-DSS Architecture and Initial Documentation and Test Report” and “D4.2 – Initial DAI-DSS Prototype”, this document details the integration, orchestration, and deployment of the components for AI-based decision-making within industrial applications. The prototype is designed to enhance operational efficiency and support decision-making for various scenarios. In this last version, the DAI-DSS Prototype extends its applicability across multiple industrial use cases, including workforce allocation, production planning, machine maintenance, and validation of documents. The demonstration materials can be accessed via the following link:
https://innovationshop.fairwork-project.eu/
By leveraging a modular and scalable architecture, the system facilitates the interaction between AI services, UIs, and structured data repositories. The implementation of DAI-DSS consists of several integrated building blocks:
The DAI-DSS User Interface collects multiple UI components for the different scenarios and AI services to enable stakeholders to visualize data, interact with decision-making tools, and monitor industrial workflows.
The DAI-DSS Orchestrator component serves as the central coordination engine, managing workflows, microservices, and AI-driven recommendations to ensure the system operation. It includes different approaches that range from centralized to decentralized prototypes.
The DAI-DSS Configurator consists of a tool designed to enhance decision support systems through configuration and integration frameworks. It consists of the Configuration Framework, which assists in creating decision models and strategies, and the Configuration Integration Framework, which generates system configurations. It allows for microservices and workflow configuration, featuring an interface with a wizard for UI components combination.
The DAI-DSS Knowledge Base is highlighted as a central data repository, storing user properties, sensor data, and processed data. It plays a key role in the system's data flow, integrating with the Configurator and using REST API for data retrieval.
The DAI-DSS AI Enrichment incorporates various decision-making techniques and AI services, including neural networks, decision trees, constraint programming, Multi-Agent Systems (MAS), Large Language Model (LLM), and retrieval-augmented generation (RAG), to provide tailored recommendations and automation support.
The final DAI-DSS Prototype delivers several advancements over previous iterations by 1) supporting AI-driven decisions that aim to enhance decision-making and information access with different AI techniques while including reflections on AI and data reliability, 2) ensuring scalability and adaptability of the system by its component-based architecture that allows for integration with various applications and expansion into new domains, 3) advancing data utilization and processing with efficient storage, retrieval, and processing of industrial data, in the Knowledge Base and Vector Databases and 4) proposing a flexible approach to enable the extension with new prototypes.
The DAI-DSS marks a step forward in AI-powered decision support for industrial environments. Its modular and scalable architecture provides a foundation for future AI enhancements, data integration, and broader enterprise adoption. In particular, the results and prototypes aim to be used as starting point for use cases in the area of robots in manufacturing settings for example supporting decisons in maintenance or optimal robot-task and line allocation. Furthermore, findings and implementations documented in this deliverable contribute to advancing intelligent, and effective decision-support solutions in industrial ecosystems, and aim to contribute to future European AI research and reference architectures.
Herwig Zeiner, Lucas Paletta, Julia Tschuden, Michael Schneeberger, Gustavo Vieira, Rui Fernandes, Johanna Lauwigi, Alexander Nasuta, Damiano Falcioni, Marlene Mayr, Christian Muck, Rishyank Chevuri
Abstract. This document is Deliverable D5.1, which focuses on the FAIRWork Knowledge Base at the Use Case Site. The deliverable encompasses the results of Task T5.1 “Modelling FAIRWork for Production Processes” and Task T5.2 “Creating FAIRWork Knowledge Base”. The document needs to be seen in the context of the FAIRWork objective to provide a decision support system that (a) integrates digital twins to optimize the overall production process according to multiple parameters, and (b) democratizes decision making granting human worker and machines a say during decision making. The project combines (a) model-based approaches to transparently design, simulate and improve decision making, (b) a co-creation laboratory using models and physical experiments as communication media to all actors and, (c) reliability indication of data and AI algorithms.
This deliverable serves as a public demonstration of the project's progress. It provides a concise overview of the work completed in Task T5.1, which involved creating decision process models for two Use Case providers, FLEX and CRF. These models cover various use case categories such as "Automated Test Building," "Worker Allocation," "Machine Maintenance After Breakdown," "Workload Balance," "Delay of Material," and "Quality Issues." These models incorporate details about the production environment, decision-making processes, and involved actors. In this deliverable, Chapter 2 corresponds to the Task 5.1 and provides a comprehensive overview of the methodology employed for decision process modeling in the FAIRWork project, as well as the tools utilized to implement these models. Additionally, there is information provided on how to access these models for demonstration purposes. Section 2.1 delves into the methodology of decision process modeling within the specific context of the FAIRWork project. It outlines the steps and approaches taken to develop effective decision models that can enhance decision-making processes. This section provides insights into the iterative nature of the modeling process, emphasizing the need for clear understanding of a problem, identification of concrete decisions, and relevant decision parameters and aspects.
Section 2.2 focuses on the tools utilized to implement the decision process models in the FAIRWork project. It discusses the technological infrastructure leveraged to create these models effectively. The section highlights the utilization of modeling techniques, such as BPMN 2.0, to ensure consistency and compatibility across different decision trees and scenarios. Furthermore, Section 2.3 and 2.4 provide an overview of the decision process models developed specifically for the end users of the FAIRWork project, FLEX and CRF. These sections also present the relevant data inputs to facilitate decision-making.
Concurrently, task T5.2 focused on collecting and storing the data and decision models required for each Use Case scenario to establish a common Knowledge Base. The Knowledge Base is built on internationally available open standards, with the ISO 10303 (STEP) standard for data exchange serving as the foundation for the developed repositories. The Knowledge Base provides REST APIs that enable data access and exchange. Furthermore, the deliverable includes the demonstration of the Knowledge Base through figures presented in chapter 3, showcasing the preliminary project setup, and data accessing methods using REST API and also providing information on the demonstrator videos that are made to show the data accessing and sharing capabilities of the Knowledge Base.
The contents of this deliverable will contribute to "D5.2 DAI-DSS Infrastructure and Setup Report at Use Case Site" and "D5.3 Demonstration Report of FAIRWork with DAI-DSS", scheduled for M20 and M36, respectively. These subsequent deliverables will further adapt the Knowledge Base to suit the specific needs of the use cases.
Abstract. The purpose of the document, "D5.2 – DAI-DSS INFRASTRUCTURE AND SETUP REPORT AT USE CASE SITE" is to provide an update on the progression and current state of implementation on use case sites of the Democratic AI-based Decision Support System (DAI-DSS) Architecture as part of the FAIRWork project.
Section 2 of the deliverable describes the overall infrastructure setup process at the use case partner side. It also describes the setup of the isolated computing system for the DAI-DSS tool to avoid risks to the corporate network.
Section 3 then details the technical preparation tasks such as data collection, human expert training, user selection and implantation of the current DAI-DSS prototype as a use case site and integration with the legacy systems.
Section 4 describes the overall testing procedure. This includes a brief description of the user evaluation and the corresponding KPIs for testing the use cases in general. In summary, this deliverable provides a brief update on the deployment of the first iteration of the FAIRWork DAI DSS. The first iteration has clearly shown that the DAI-DSS is a useful tool for future decision making in Flex and CRF.
Abstract. This report is the accompanying document for deliverable 8.1 (D8.1), in which communication and exploitation tools were created and instantiated for the FAIRWork project. They will be used to communicate information during the project runtime and support exploitation of created artefacts within and after the project runtime. Additionally, the document discusses the FAIRWork innovation shop, which is not yet available, but will be established in D8.2.
The following list provides an overview of the tools, which are discussed in this document and includes important links to where they can be found:
– Website: https://fairwork-project.eu/
– Flyer: https://zenodo.org/record/7677298/files/FAIRWork_Flyer_1.0.pdf?download=1
– Brochure: https://zenodo.org/record/7673832/files/FAIRWork_Brochure_1.0.pdf?download=1
– Social Media:
+ LinkedIn: https://www.linkedin.com/company/fairwork-project/
+ Twitter: https://twitter.com/fairwork_eu
+ YouTube: https://www.youtube.com/@fairwork_eu
– Zenodo: https://zenodo.org/communities/fairwork/
– Webinars:
+ Publicly available webinar recordings on YouTube: https://www.youtube.com/playlist?list=PLDKnDRTHllZrGrXZsiePXmvylV1gHvh9K
+ Event subpage of the webpage (including the webinars): https://fairwork-project.eu/events/
– Innovation Shop: https://fairwork-project.eu/innovation-shop/ (future link)
