Case Retrieval Nets as a Model for Building Flexible Information Systems

Im Rahmen dieser Arbeit wird das Modell der Case Retrieval Netze vorgestellt, das ein Speichermodell für die Phase des Retrievals beim fallbasierten Schliessen darstellt. Dieses Modell lehnt sich an Assoziativspeicher an, insbesondere wird das Retrieval als Rekonstruktion des Falles betrachtet anstatt als eine Suche im traditionellen Sinne. Zwei der wesentlichen Vorteile des Modells sind Effizienz und Flexibilität: Effizienz beschreibt dabei die Fähigkeit, mit grossen Fallbasen umzugehen und dennoch schnell ein Resultat des Retrievals liefern zu können. Im Rahmen dieser Arbeit wird dieser Aspekt formal untersucht, das Hauptaugenmerk ist aber eher pragmatisch motiviert insofern als der Retrieval-Prozess so schnell sein sollte, dass der Benutzer möglichst keine Wartezeiten in Kauf nehmen muss. Flexibilität betrifft andererseits die allgemeine Anwendbarkeit des Modells in Bezug auf veränderte Aufgabenstellungen, auf alternative Formen der Fallrepräsentation usw. Hierfür wird das Konzept der Informationsvervollständigung diskutiert, welches insbesondere für die Beschreibung von interaktiven Entscheidungsunterstützungssystemen geeignet ist. Traditionelle Problemlöseverfahren, wie etwa Klassifikation oder Diagnose, können als Spezialfälle von Informationsvervollständigung aufgefasst werden. Das formale Modell der Case Retrieval Netze wird im Detail erläutert und dessen Eigenschaften untersucht. Anschliessend werden einige möglich Erweiterungen beschrieben. Neben diesen theoretischen Aspekten bilden Anwendungen, die mit Hilfe des Case Retrieval Netz Modells erstellt wurden, einen weiteren Schwerpunkt. Diese lassen sich in zwei grosse Richtungen einordnen: intelligente Verkaufsunterstützung für Zwecke des E-Commerce sowie Wissensmanagement auf Basis textueller Dokumente, wobei für letzteres der Aspekt der Wiederbenutzung von Problemlösewissen essentiell ist. Für jedes dieser Gebiete wird eine Anwendung im Detail beschrieben, weitere dienen der Illustration und werden nur kurz erläutert. Zuvor wird allgemein beschrieben, welche Aspekte bei Entwurf und Implementierung eines Informationssystems zu beachten sind, welches das Modell der Case Retrieval Netze nutzt.In this thesis, a specific memory structure is presented that has been developed for the retrieval task in Case-Based Reasoning systems, namely Case Retrieval Nets (CRNs). This model borrows from associative memories in that it suggests to interpret case retrieval as a process of re-constructing a stored case rather than searching for it in the traditional sense. Tow major advantages of this model are efficiency and flexibility: Efficiency, on the one hand, is concerned with the ability to handle large case bases and still deliver retrieval results reasonably fast. In this thesis, a formal investigation of efficiency is included but the main focus is set on a more pragmatic view in the sense that retrieval should, in the ideal case, be fast enough such that for the users of a related system no delay will be noticeable. Flexibility, on the other hand, is related to the general applicability of a case memory depending on the type of task to perform, the representation of cases etc. For this, the concept of information completion is discussed which allows to capture the interactive nature of problem solving methods in particular when they are applied within a decision support system environment. As discussed, information completion, thus, covers more specific problem solving types, such as classification and diagnosis. The formal model of CRNs is presented in detail and its properties are investigated. After that, some possible extensions are described. Besides these more theoretical aspects, a further focus is set on applications that have been developed on the basis of the CRN model. Roughly speaking, two areas of applications can be recognized: electronic commerce applications for which Case-Based Reasoning may provide intelligent sales support, and knowledge management based on textual documents where the reuse of problem solving knowledge plays a crucial role. For each of these areas, a single application is described in full detail and further case studies are listed for illustration purposes. Prior to the details of the applications, a more general framework is presented describing the general design and implementation of an information system that makes uses of the model of CRNs

TRIZ Future Conference 2004

TRIZ the Theory of Inventive Problem Solving is a living science and a practical methodology: millions of patents have been examined to look for principles of innovation and patterns of excellence. Large and small companies are using TRIZ to solve problems and to develop strategies for future technologies. The TRIZ Future Conference is the annual meeting of the European TRIZ Association, with contributions from everywhere in the world. The aims of the 2004 edition are the integration of TRIZ with other methodologies and the dissemination of systematic innovation practices even through SMEs: a broad spectrum of subjects in several fields debated with experts, practitioners and TRIZ newcomers

The Management of Direct Material Cost During New Product Development: A Case Study on the Application of Big Data, Machine Learning, and Target Costing

This dissertation thesis investigates the application of big data, machine learning, and the target costing approach for managing costs during new product development in the context of high product complexity and uncertainty. A longitudinal case study at a German car manufacturer is conducted to examine the topic. First, we conduct a systematic literature review, which analyzes use cases, issues, and benefits of big data and machine learning technology for the application in management accounting. Our review contributes to the literature by providing an overview about the specific aspects of both technologies that can be applied in managerial accounting. Further, we identify the specific issues and benefits of both technologies in the context management accounting. Second, we present a case study on the applicability of machine learning and big data technology for product cost estimation, focusing on the material costs of passenger cars. Our case study contributes to the literature by providing a novel approach to increase the predictive accuracy of cost estimates of subsequent product generations, we show that the predictive accuracy is significantly larger when using big data sets, and we find that machine learning can outperform cost estimates from cost experts, or produce at least comparable results, even when dealing with highly complex products. Third, we conduct an experimental study to investigate the trade-off between accuracy (predictive performance) and explainability (transparency and interpretability) of machine learning models in the context of product cost estimation. We empirically confirm the oftenimplied inverse relationship between both attributes from the perspective of cost experts. Further, we show that the relative importance of explainability to accuracy perceived by cost experts is important when selecting between alternative machine learning models. Then, we present four factors that significantly determine the perceived relative importance of explainability to accuracy. Fourth, we present a proprietary archival study to investigate the target costing approach in a complex product development context, which is characterized by product design interdependence and uncertainty about target cost difficulty. We find that target cost difficulty is related to more cost reduction performance during product development based on archival company data, and thereby complement results from earlier studies, which are based on experimental studies. Further, we demonstrate that in a complex product development context, product design interdependence and uncertainty about target cost difficulty may both limit the effectiveness of target costing

Supporting the management of electronic engineering design teams through a dynamic contingency approach

The contribution to knowledge presented in this thesis is the dynamic contingency approach, supported through software, which supports the management of the early, conceptual stages of electronic engineering team design. 1he term contingency pertains to the design environment being in a contingent state, that is "dependent on uncertain issues" (Hayward & Sparkes, 1991). These issues are typically dynamic, that is ''pertaining to forces not in equilibrium, forces that produce motion" (Hayward & Sparkes, 1991). The concept for the dynamic contingency approach was developed through a soft systems analysis. This analysis drew upon an ethnographic study conducted in parallel with the present work by another researcher. Both the present work and the ethnographic study were carried out within a multidisciplinary research team in collaboration with an industrial partner (company A). This thesis discusses the evolution of this multidisciplinary research method, including the development of a software prototype (EDAPT), which enabled the requirements for the dynamic contingency approach to be established. Through this research method key issues were identified which affect the ability of design managers, and to a lesser extent design engineers, to adequately perceive the current situation of a design project; and to determine appropriate corrective responses to potential problem situations. The work indicates that this is particularly true when under pressure in such a complex, interdependent and dynamic environment. This thesis illustrates how the environment of design can be dependent upon these key issues which are often uncertain, that is, the environment is in a contingent state. Furthermore, the thesis depicts the dynamic nature of these issues. The dynamic contingency approach was developed in response to these issues in partnership with the industrial collaborator. The approach synthesises a variety of such issues to support the coordination of interdependencies, provide a view of the current project situation, alert stakeholders to potential problem situations, and present possible responses to potential problem situations. In short, what has been achieved is a design management worldview with sufficient detail to help people expect and anticipate what might happen, and how others may behave in a team design environment, together with the foundations for a system which enables and supports this perspective. In essence the approach provides a way of conceptualising the design environment which should enable improvements in the management of design teams at the early, conceptual stages of electronic engineering design projects

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers