A majority of the established automotive manufacturers are under severe competitive pressure and their long term economic sustainability is threatened. In particular the transformation towards more CO2-efficient energy sources is a huge financial burden for an already investment capital intensive industry. In addition existing operations urgently need rapid improvement and even more critical is the development of highly productive, efficient and sustainable manufacturing solutions for new and updated products. Simultaneously, a number of severe drawbacks with current improvement methods for industrial production systems have been identified. In summary, variation is not considered sufficient with current analysis methods, tools used are insufficient for revealing enough knowledge to support decisions, procedures for finding optimal solutions are not considered, and information about bottlenecks is often required, but no accurate methods for the identification of bottlenecks are used in practice, because they do not normally generate any improvement actions. Current methods follow a trial-and-error pattern instead of a proactive approach. Decisions are often made directly on the basis of raw static historical data without an awareness of optimal alternatives and their effects. These issues could most likely lead to inadequate production solutions, low effectiveness, and high costs, resulting in poor competitiveness. In order to address the shortcomings of existing methods, a methodology and framework for manufacturing management decision support using simulation-based multi-objective optimisation is proposed. The framework incorporates modelling and the optimisation of production systems, costs, and sustainability. Decision support is created through the extraction of knowledge from optimised data. A novel method and algorithm for the detection of constraints and bottlenecks is proposed as part of the framework. This enables optimal improvement activities with ranking in order of importance can be sought. The new method can achieve a higher improvement rate, when applied to industrial improvement situations, compared to the well-established shifting bottleneck technique. A number of 'laboratory' experiments and real-world industrial applications have been conducted in order to explore, develop, and verify the proposed framework. The identified gaps can be addressed with the proposed methodology. By using simulation-based methods, stochastic behaviour and variability is taken into account and knowledge for the creation of decision support is gathered through post-optimality analysis. Several conflicting objectives can be considered simultaneously through the application of multi-objective optimisation, while objectives related to running cost, investments and other sustainability parameters can be included through the use of the new cost and sustainability models introduced. Experiments and tests have been undertaken and have shown that the proposed framework can assist the creation of manufacturing management decision support and that such a methodology can contribute significantly to regaining profitability when applied within the automotive industry. It can be concluded that a proof-of-concept has been rigorously established for the application of the proposed framework on real-world industrial decision-making, in a manufacturing management context.

Für Investitionsgüterhersteller ist das Geschäft mit industriellen Dienstleistungen eng mit dem Produktgeschäft verbunden: Die Summe der verkauften und im Betrieb befindlichen Produkte – die „installierte Basis“ – stellt die Grundlage für den Absatz produktbegleitender industrieller Dienstleistungen dar. Mit einem Exportanteil von etwa 70 % agieren deutsche Investitionsgüterhersteller stark international, d. h. die installierte Basis ist weltweit verteilt (vgl. VDMA 2004). Um einen möglichst hohen Anteil dieser installierten Basis mit Dienstleistungen zu bedienen, muss das Dienstleistungsgeschäft ebenfalls international erfolgen. Gleichzeitig wird der Anteil des Dienstleistungsgeschäfts am Gesamterfolg der Unternehmen wichtiger. Eine Konzentration auf heimische Märkte, die nur einen geringen Anteil der installierten Basis darstellen, würde also auch ein entsprechend begrenztes Ertragspotenzial bedeuten. Allerdings können weltweite Serviceeinsätze wie z. B. Montage, Störfallbehebung oder Instandhaltung mit sehr hohen Kosten verbunden sein und so die Rendite schmälern. Für die Anbieter industrieller Dienstleistungen stellt sich daher die Frage nach einer geeigneten Form der Internationalisierung. Während große Unternehmen und Konzerne häufig Direktinvestitionen tätigen, benötigen insbesondere kleine und mittlere Unternehmen (KMU) Lösungen mit geringerem unternehmerischen Risiko. Hier bietet sich die Kooperation im Netzwerk an. Diese Lösung erfordert im Vergleich zur Direktinvestition weniger vertragliche Bindungen und geringere Investitionen, gleichzeitig bietet sie jedoch die Chance, von der Erfahrung und den Kenntnissen der Partner über die lokalen Märkte zu profitieren und Ressourcen zusammenfassen zu können (vgl. KUTSCHKER, MÖßLANG 1996; GULATI ET AL. 2000; WARNECKE 2002; ZAHN, STANIK 2003).

In today’s fast-paced industry where fight for market share is fierce and reaching the market ahead of competition imperative, product development is a target for lead-time reductions. In this context, in product development in stage-gate processes, decisions need to be made even though knowledge and information are scarce and flawed. The challenge is how to support the decisions that are made in light of uncertainty and ambiguity. The thesis moves from analysing the role of the stage-gate process within the aerospace industry. The stage-gate process was more than a decision making mechanism, and instead a mechanism that facilitated communication, discussion and knowledge sharing between team members, as well as supported knowledge creation and shaping of the boundaries between people’s different perceptions of the knowledge base. However, the communicative and negotiative function of the stage-gate was highly dependent on the ability of the participating individuals to reflect on the status and quality of the available knowledge assets used throughout the process. To make this reflective activity an explicit part of the stage-gate practice, this thesis proposes the application of a knowledge maturity concept at the gates to raise the decision makers’ awareness of the status of the knowledge assets handled at the decision point. The knowledge maturity concept considers three basic dimensions: input, method/tool and experience/expertise in assessing the knowledge base maturity. The scale is intended to act as a boundary object, facilitating the knowledge creation process by highlighting the current status of the knowledge base and making stakeholders aware of the nature of the project’s uncertainties and ambiguities. In the knowledge maturity concept, its purpose is to support design teams at the gates in taking appropriate action, mitigating risk and focusing their efforts on improving the knowledge assets where it is needed most, regarding the situation at hand and, finally, to make more confident decisions.The thesis was developed within the EU FP6 VIVACE (Value Improvement through a Virtual Aeronautical Collaborative Enterprise) and EU FP7 CRESCENDO (Collaborative and Robust Engineering using Simulation Capability Enabling Next Design Optimisation) projects, and within the Faste Laboratory, a VINNOVA Excellence Centre involving partners from the Swedish manufacturing industry.

Background: Type 2 diabetes mellitus (T2DM) is a complex health condition that impacts multiple organ systems and contributes to both acute and chronic health problems. In the United States (U.S.), T2DM is a growing health concern with increasing prevalence among both adult and pediatric populations (American Diabetes Association [ADA], 2015; Dea, 2011). Developing a comprehensive plan of care that incorporates a multifaceted treatment and prevention plan is necessary to address this growing health concern and reduce overall morbidity and mortality. Problem: The Edmund Primary Care (EPC) practice data for routine annual diabetic foot exams, annual eye exams, annual urine microalbumin, smoking cessation education and recommendations for pneumococcal polysaccharide do not meet the ADA (American Diabetes Association, 2015) recommendations for patients with T2DM.Design: Quality improvement (QI) project applying the Plan-Do-Study-Act (PDSA) cycle to develop a process change to improve diabetic preventative care measures for hemoglobin A1C, urine microalbumin, diabetic foot exams, and optometry referrals. Setting: A small, nurse practitioner owned, family practice clinic targeting patients 18 years and older with a diagnosis of T2DM.Intervention: A fishbone diagram to conduct a root cause analysis led to identification of key factors contributing to the problem. A comprehensive process change integrating a Diabetic Assessment Flow Sheet (DAFS) and diabetic foot exam sheet was developed to address the problem. Expected Outcome: Increase in rates of completion to at least 90% over eight weeks. Results: Analyzed with run charts demonstrating an increase in rates of completion to 100% for A1C, urine microalbumin, diabetic foot exams, and optometry referrals. A positive percent of change for each measure is as follows: A1C 7%; urine microalbumin 43%; diabetic foot exams 150%; and referrals to optometrist 43%. Significance: This QI project emphasizes the importance of implementing a system to evaluate the quality of care being delivered. It also highlights the usefulness of the PDSA cycle as a method to implementing quality improvement measures in health care. Lastly, this QI project demonstrated the effectiveness of flow sheets in improving the quality of care delivered to patients with T2DM.

Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data into useful, personalized decision support knowledge in a flexible, cost-effective, and productive way. The goal of my Ph.D. research is to address some key challenges in current clinical deci-sion support, including (1) the lack of a flexible, evidence-based, and personalized data mining tool, (2) the need for interactive interfaces and visualization to deliver the decision support knowledge in an accurate and effective way, (3) the ability to generate temporal rules based on patient-centric chronological events, and (4) the need for quantitative and progressive clinical predictions to investigate the causality of targeted clinical outcomes. The problem statement of this dissertation is that the size, complexity, and biases of the current clinical data make it very difficult for current informatics technologies to extract individual-specific knowledge for clinical decision support. This dissertation addresses these challenges with four overall specific aims: Evidence-Based and Personalized Decision Support: To develop clinical decision support systems that can generate evidence-based rules based on personalized clinical conditions. The systems should also show flexibility by using data from different clinical settings. Interactive Knowledge Delivery: To develop an interactive graphical user interface that expedites the delivery of discovered decision support knowledge and to propose a new visualiza-tion technique to improve the accuracy and efficiency of knowledge search. Temporal Knowledge Discovery: To improve conventional rule mining techniques for the discovery of relationships among temporal clinical events and to use case-based reasoning to evaluate the quality of discovered rules. Clinical Casual Analysis: To expand temporal rules with casual and time-after-cause analyses to provide progressive clinical prognostications without prediction time constraints. The research of this dissertation was conducted with frequent collaboration with Children’s Healthcare of Atlanta, Emory Hospital, and Georgia Institute of Technology. It resulted in the development and adoption of concrete application deliverables in different medical settings, including: the neuroARM system in pediatric neuropsychology, the PHARM system in predictive health, and the icuARM, icuARM-II, and icuARM-KM systems in intensive care. The case studies for the evaluation of these systems and the discovered knowledge demonstrate the scope of this research and its potential for future evidence-based and personalized clinical decision support.

Thesis (PhD)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: The policing and monitoring of South Africa's coastline and economic exclusion zone is made di cult not only by the size of the area of interest, but also by the limited resources available for maritime detection and policing. As a consequence, illegal activities, such as smuggling, poaching and illegal border crossings, are often conducted with impunity. Conventional approaches to monitoring coastal areas, such as the use of patrol boats, port inspections and aircraft surveillance, may be augmented by advances in technology that are steadily contributing vast amounts of data related to maritime activity. For example, various South African agencies collect auto- matic identi cation system and vessel monitoring system transmissions, and gather additional kinematic data of maritime vessels through a number of strategically placed coastal radars. A command and control centre for actively monitoring these data (outside of the intelligence community) was established by the South African Navy in 2014. Such centres provide surveillance operators with a real-time picture of a maritime region of interest from which they can identify relevant facts of interest through a reliance on experience and domain knowledge. The e ectiveness of this process may, however, be undermined by the vast quantities of data typically under consideration, by the di culty of identifying long-term trends in vessel kinematic behaviour and by the possibility of operator fatigue brought on by the relatively low incidence levels of activities of interest. E ective decision support tools may play a valuable role in this context by the automatic processing of these vast collections of data, by the identi cation of concepts of interest and by the prediction of future occurrences of interest. It is, however, essential that such tools should be exible enough to adapt to changes in typical vessel behaviour over time and that they should be capable of integrating new trends and new types of behaviours. Various approaches to maritime surveillance are investigated in this dissertation from the perspectives of threat detection and anomaly identi cation, with particular emphasis on a systems approach to decision support. A decision support system framework that utilises rule-based and data-driven mechanisms is proposed as a means to separate the interesting from the uninteresting and to provide early warnings of potentially threatening maritime vessel behaviour to operators. This system framework is primarily concerned with kinematic data and is restricted to the identi cation of certain types of activities. Successful classi cation and, ultimately, timely prediction of potentially threatening behaviour would allow for e ective policing by providing early warning to relevant entities, thus potentially leading to more e ective use of available policing resources. / AFRIKAANSE OPSOMMING: Die patrollering en monitering van die Suid-Afrikaanse kusgebied en gepaardgaande ekonomiese eksklusiewe zone word bemoeilik deur die grootte van die tersprake area en die beperkte hulpbronne wat vir patrollie-doeleindes aangewend kan word. Gevolglik gaan onwettige aktiwiteite, soos smokkelary, stroping en onwettige immigrasie dikwels ongestraf. Konvensionele benaderings tot die monitering van kusgebiede, soos die aanwending van patrolliebote, die uitvoer van hawe-inspeksies en gere elde lugpatrollies, kan aangevul word deur tegnologiese vooruitgang wat voortdurend tot groot hoeveelhede data oor maritieme aktiwiteit bydra. Verskeie Suid- Afrikaanse agentskappe ontvang byvoorbeeld outomatiese identi kasiestelsel en vaartuigmoni- teringstelsel uitsendings, en samel ook addisionele kinematiese data oor maritieme vaartuie deur middel van strategies-geplaasde kusradars in. 'n Bevel-en-beheersentrum wat hierdie inligting (buite die intelligensiegemeenskap) aktief ontleed, is in 2014 deur die Suid-Afrikaanse Vloot tot stand gebring. Sulke sentra verskaf 'n intydse blik oor die maritieme gebied onder beskouing aan operateurs wat dan, gebaseer op hulle ervaring en omgewingskennis, relevante inligting oor vaartuie kan a ei. Die doeltre ende uitvoering van hierdie proses kan egter ondermyn word deur die tipiese groot hoeveelhede data, die moeilikheidsgraad van die identi kasie van langtermyn tendense in die kinematiese gedrag van vaartuie om die kus en die moontlikheid van operateur-uitputting as gevolg van lang periodes van relatiewe oninteressante vaartuiggedrag. Doeltre ende besluitsteunhulpmiddels kan 'n waardevolle bydrae in hierdie konteks maak deur die ge-outomatiseerde prosessering van hierdie groot hoeveelhede data, die identi kasie van interessante vaartuiggedrag en die voorspelling van toekomstige relevante insidente. Dit is egter noodsaaklik dat sulke hulpmiddels buigsaam genoeg moet wees om te kan aanpas by veranderings in tipiese maritieme aktiwiteit oor tyd en dat nuwe tendense en tipes aktiwiteite geakkommodeer kan word. Verskeie benaderings tot maritieme oorsig word in hierdie proefskrif vanuit die perspektiewe van die bespeuring van bedreigings en die opsporing van vreemde verskynsels ondersoek, met 'n spesi eke fokus op 'n stelselbenadering tot besluitsteun. 'n Besluitsteun stelselraamwerk wat berus op re el-gebaseerde en data-aangedrewe meganismes word as 'n hulpmiddel voorgestel waarmee interessante maritieme gedrag van oninteressante gedrag onderskei kan word om sodoende 'n vroe e waarskuwing aan operateurs met betrekking tot moontlike bedreigende maritieme aktiwiteite te kan rig. Die werking van hierdie stelselraamwerk berus hoofsaaklik op die gebruik van kinematiese vaartuigdata en is beperk tot die naspeuring van sekere soorte bedreigende gedrag. Die suksesvolle klassi kasie en tydige voorspelling van potensi ele bedreigende maritieme gedrag behoort doeltre ende kusmonitering en verbeterde aanwending van die beperkte, gepaardgaande hulpbronne deur relevante kusagentskappe moontlik te maak.

published_or_final_version / Urban Planning and Environmental Management / Doctoral / Doctor of Philosophy

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / Business Administration / Master / Master of Business Administration