IAMM: A maturity model for measuring industrial analytics capabilities in large-scale manufacturing facilities

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledgebased competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility

Model driven validation approach for enterprise architecture and motivation extensions

As the endorsement of Enterprise Architecture (EA) modelling continues to grow in diversity and complexity, management of its schema, artefacts, semantics and relationships has become an important business concern. To maintain agility and flexibility within competitive markets, organizations have also been compelled to explore ways of adjusting proactively to innovations, changes and complex events also by use of EA concepts to model business processes and strategies. Thus the need to ensure appropriate validation of EA taxonomies has been considered severally as an essential requirement for these processes in order to exert business motivation; relate information systems to technological infrastructure. However, since many taxonomies deployed today use widespread and disparate modelling methodologies, the possibility to adopt a generic validation approach remains a challenge. The proliferation of EA methodologies and perspectives has also led to intricacies in the formalization and validation of EA constructs as models often times have variant schematic interpretations. Thus, disparate implementations and inconsistent simulation of alignment between business architectures and heterogeneous application systems is common within the EA domain (Jonkers et al., 2003). In this research, the Model Driven Validation Approach (MDVA) is introduced. MDVA allows modelling of EA with validation attributes, formalization of the validation concepts and transformation of model artefacts to ontologies. The transformation simplifies querying based on motivation and constraints. As the extended methodology is grounded on the semiotics of existing tools, validation is executed using ubiquitous query language. The major contributions of this work are the extension of a metamodel of Business Layer of an EAF with Validation Element and the development of EAF model to ontology transformation Approach. With this innovation, domain-driven design and object-oriented analysis concepts are applied to achieve EAF model’s validation using ontology querying methodology. Additionally, the MDVA facilitates the traceability of EA artefacts using ontology graph patterns

Methods and Models for Industrial Internet of Things-based Business Process Improvement

Over the last three decades, the Internet of Things (IoT) has gained significant importance and has been implemented in many private, public, and business contexts. Leveraging and combining the IoT's capabilities enables far-reaching transformations and disruptive innovations that are increasingly recognized, especially by industrial organizations. In this regard, the Industrial IoT (IIoT) paradigm has emerged, describing the use of IIoT technology in the industrial domain. One key use of the IIoT is the incremental or radical improvement of business processes. This goal-oriented change of business processes with IIoT technology to accomplish organizational goals more effectively is called IIoT-based Business Process Improvement (BPI). Many use cases demonstrate the benefits of IIoT-based BPI for all types of industrial organizations. However, the interconnection between IIoT and BPI lacks theoretical knowledge and applicable artifacts that support practitioners. Moreover, a significant number of related projects fail or do not achieve the anticipated benefits. This issue has drawn attention in recent scholarly literature, which calls for further research. The dissertation at hand approaches this research gap by extending and advancing existing knowledge and providing valuable contributions to managerial practice. Three critical challenges for conducting IIoT-based BPI projects are addressed in particular: First, the essential characteristics of IIoT-based BPI applications are explored. This enables their classification and a foundational comprehension of the research field. Second, the required capabilities to leverage IIoT for BPI are identified. On this basis, industrial organizations can assess their maturity and readiness for implementing corresponding applications. Third, the identification, specification, and selection of appropriate applications are addressed. These activities enable the successful practical execution of IIoT projects with BPI potential

Methods and Models for Industrial Internet of Things-based Business Process Improvement

Over the last three decades, the Internet of Things (IoT) has gained significant importance and has been implemented in many private, public, and business contexts. Leveraging and combining the IoT's capabilities enables far-reaching transformations and disruptive innovations that are increasingly recognized, especially by industrial organizations. In this regard, the Industrial IoT (IIoT) paradigm has emerged, describing the use of IIoT technology in the industrial domain. One key use of the IIoT is the incremental or radical improvement of business processes. This goal-oriented change of business processes with IIoT technology to accomplish organizational goals more effectively is called IIoT-based Business Process Improvement (BPI). Many use cases demonstrate the benefits of IIoT-based BPI for all types of industrial organizations. However, the interconnection between IIoT and BPI lacks theoretical knowledge and applicable artifacts that support practitioners. Moreover, a significant number of related projects fail or do not achieve the anticipated benefits. This issue has drawn attention in recent scholarly literature, which calls for further research. The dissertation at hand approaches this research gap by extending and advancing existing knowledge and providing valuable contributions to managerial practice. Three critical challenges for conducting IIoT-based BPI projects are addressed in particular: First, the essential characteristics of IIoT-based BPI applications are explored. This enables their classification and a foundational comprehension of the research field. Second, the required capabilities to leverage IIoT for BPI are identified. On this basis, industrial organizations can assess their maturity and readiness for implementing corresponding applications. Third, the identification, specification, and selection of appropriate applications are addressed. These activities enable the successful practical execution of IIoT projects with BPI potential

Enterprise reference architectures for higher education institutions: Analysis, comparison and practical uses

Enterprise Architecture (EA) is currently accepted as one on the major instruments for enabling organisations in their transformation processes to achieve business-technology alignment. Despite that over the last years EA has been successfully adopted in many industries, Higher Education still represents one of the sectors with lower levels of adoption and maturity of EA practices. The present thesis puts the emphasis particularly on the study Enterprise Reference Architectures (ERAs), as a particular type of EA artefact, in Higher Education Institutions (HEIs). After formally clarifying the concept of ERAs and giving a panoramic view of the current state-of-the-art of existing HEI-oriented ERAs, the thesis proposes an artefact framework build through a Design Science Research (DSR) approach aimed to facilitate practitioners their (re-)use or application in their own real practical settings. The purpose of the constructed artefact is to support practitioners when conducting the necessary adjustments to exiting HEI-oriented ERAs in order to be successfully applied for their specific needs.La Arquitectura Empresarial (AE) es actualmente reconocida como una disciplina que permite configurar procesos de trasformación organizativa a objeto de alinear el negocio con la tecnología. A pesar de que en los últimos años la AE se ha ido adoptando progresivamente de forma exitosa en diversas industrias, la educación superior representa todavía hoy en día uno de los sectores con menores niveles de adopción y de madurez en lo que se refiere a las prácticas de AE. La presente tesis hace especial hincapié en el estudio de las Arquitecturas de Referencia Empresariales (AREs), entendidas como un artefacto específico de AE, en Instituciones de Educación Superior (IES). Así, después de clarificar formalmente el concepto de ARE y de ofrecer una visión panorámica del estado del arte relativo a las AREs para IES existentes, la tesis propone un framework de trabajo construido a través de un enfoque de investigación basado en la Ciencia del diseño destinado a facilitar su (re-)utilización o aplicación práctica en dominios de trabajo reales. El objetivo del artefacto es proporcionar soporte práctico a los profesionales para realizar los ajustes necesarios a las AREs para IES existentes para que puedan aplicarlas con éxito a sus necesidades específicas.L'Arquitectura Empresarial (AE) és actualment reconeguda com una disciplina que permet configurar processos de transformació organitzatius a fi d'alinear el negoci amb la tecnologia. Tot i que en els darrers anys l'AE s'ha anat adoptant progressivament amb èxit en diverses indústries, l'educació superior representa encara avui dia un dels sectors amb menors nivells d'adopció i de maduresa pel que fa a pràctiques d'AE. Aquesta tesi posa especial èmfasi en l'estudi de les Arquitectures de Referència Empresarials (AREs), enteses com un artefacte concret d'AE, a Institucions d'Educació Superior (IES). Així, després d'aclarir formalment el concepte d'ARE i oferir una visió panoràmica de l'estat de l'art relatiu a les ARE per a IES existents, la tesi proposa un framework de treball construït a través d'un enfocament de recerca basat en la ciència del disseny destinat a facilitar-ne la seva (re-)utilització o aplicació pràctica en dominis de treball reals. L'objectiu de l'artefacte és proporcionar suport pràctic als professionals per realitzar els ajustaments necessaris a les AREs per a IES existents de forma que les puguin aplicar amb èxit a les seves necessitats específiques.Tecnologies de la informació i de xarxe

An industrial analytics methodology and fog computing cyber-physical system for Industry 4.0 embedded machine learning applications

Industrial cyber-physical systems are the primary enabling technology for Industry 4.0, which combine legacy industrial and control engineering, with emerging technology paradigms (e.g. big data, internet-of-things, artificial intelligence, and machine learning), to derive self-aware and self-configuring factories capable of delivering major production innovations. However, the technologies and architectures needed to connect and extend physical factory operations to the cyber world have not been fully resolved. Although cloud computing and service-oriented architectures demonstrate strong adoption, such implementations are commonly produced using information technology perspectives, which can overlook engineering, control and Industry 4.0 design concerns relating to real-time performance, reliability or resilience. Hence, this research compares the latency and reliability performance of cyber-physical interfaces implemented using traditional cloud computing (i.e. centralised), and emerging fog computing (i.e. decentralised) paradigms, to deliver real-time embedded machine learning engineering applications for Industry 4.0. The findings highlight that despite the cloud’s highly scalable processing capacity, the fog’s decentralised, localised and autonomous topology may provide greater consistency, reliability, privacy and security for Industry 4.0 engineering applications, with the difference in observed maximum latency ranging from 67.7% to 99.4%. In addition, communication failures rates highlighted differences in both consistency and reliability, with the fog interface successfully responding to 900,000 communication requests (i.e. 0% failure rate), and the cloud interface recording failure rates of 0.11%, 1.42%, and 6.6% under varying levels of stress

Method support for enterprise architecture management capabilities

"What can our EA organization do and/or what should it be capable of?". In order to answer this questions, a capability-based method is developed, which assists in the identification, structuring and management of capabilities. The approach is embedded in a process comprising four building blocks providing appropriated procedures, concepts and supporting tools evolved from theory and practical use cases. The guide represents a flexible method for capability newcomers and experienced audiences to optimize enterprises’ economic impacts of EAM supporting the alignment of business and IT.„Was muss unser UAM leisten können?“ Als Grundlage für die Beantwortung dieser Frage sollen Konzepte aus dem Fähigkeitenmanagement genutzt werden. Im Rahmen dieser Arbeit wird eine fähigkeitenbasierte Methode entwickelt, welche Unternehmen bei der Identifikation, Strukturierung und Verwaltung von UAM-Fähigkeiten unterstützt. Der Ansatz ist in einen Prozess eingegliedert, welcher vier Hauptbestandteile beinhaltet und die für die Durchführung notwendigen Vorgehen, Konzepte und Hilfsmittel beschreibt, welche wiederrum in Kooperationen mit der Praxis getestet wurden

Enterprise modelling framework for dynamic and complex business environment: socio-technical systems perspective

The modern business environment is characterised by dynamism and ambiguity. The causes include global economic change, rapid change requirements, shortened development life cycles and the increasing complexity of information technology and information systems (IT/IS). However, enterprises have been seen as socio-technical systems. The dynamic complex business environment cannot be understood without intensive modelling and simulation. Nevertheless, there is no single description of reality, which has been seen as relative to its context and point of view. Human perception is considered an important determinant for the subjectivist view of reality. Many scholars working in the socio-technical systems and enterprise modelling domains have conceived the holistic sociotechnical systems analysis and design possible using a limited number of procedural and modelling approaches. For instance, the ETHICS and Human-centred design approaches of socio-technical analysis and design, goal-oriented and process-oriented modelling of enterprise modelling perspectives, and the Zachman and DoDAF enterprise architecture frameworks all have limitations that can be improved upon, which have been significantly explained in this thesis. [Continues.