Alexander Holland

Knowledge integration of collaborative product design using cloud computing infrastructure

The pivotal key for the success of manufacturing enterprises is sustainable and innovative product design and development. In collaborative design, stakeholders are heterogeneously distributed chain-like. Due to the growing volume of data and knowledge, an effective management of the knowledge acquired in the product design and development is one of the key challenges facing most manufacturing enterprises. Opportunities for improving efficiency and performance of IT-based product design applications through centralization of resources such as knowledge and computation have increased in the last few years with maturation of technologies such as SOA, virtualization, grid computing, and/or cloud computing. The main focus of this paper is the concept of ongoing research in providing the knowledge integration service for collaborative product design and development using cloud computing infrastructure. Potentials of the cloud computing to support the Knowledge integration functionalities as a Service by providing functionalities such as knowledge mapping, merging, searching, and transferring in product design procedure are described in this paper. Proposed knowledge integration services support users by giving real-time access to knowledge resources. The framework has the advantage of availability, efficiency, cost reduction, less time to result, and scalability.

Advanced Condition Monitoring Services in Product Lifecycle Management

This paper describes a specialized PLM approach with operation monitoring and maintenance of the product during its usage based on a condition monitoring method framework. A coupling procedure integrates the PLM meta data model with associated condition monitoring and diagnosis method framework results. Graphical models dealing with a priori information play a key part based on modeling, learning from sensor data and probabilistic inference.

Necessity of using Dynamic Bayesian Networks for feedback analysis into product development

Transformations into the modern business world is sustained by enhancement and improvement of strategies, systems and techniques towards evaluating and applying customer knowledge for the integration of Product Use Information (PUI) into product development, and meeting customer and market demands. In this paper the processing and modelling of PUI of many instances of one product type which is captured during the product use phase, e.g. condition monitoring data, failures or incidences of maintenance, raised by different graphical methods on the basis of a praxis and application scenario. Product Lifecycle Management (PLM) ensures a uniform data basis for supporting numerous engineering and economic organizational processes along the entire product life cycle - from the first product idea to disposal or recycling of the product. The processing and modelling of PUI raised by graphical methods like Bayesian Networks (BNs) or Dynamic Bayesian Networks (DBNs). In accordance, the product use knowledge leads back of the product development phase. This is used for discovering room for product improvements for the next product generation. Therefore the PUI of the different instances should be aggregated by applying fusion techniques to deduce/achieve generalized product improvements for a product type which is related to prospective research by focus on quality management systems and defining measures for customer satisfaction. As a result the significant aspect of this paper is to identify which graphical solution brings optimally the best results for the requirements of processing and modeling of PUI.

Knowledge-based feedback integration to facilitate sustainable product innovation

Since real product use information is not available, Design Simulation and Design Methods rely in many ways on assumptions regarding the product use today. These assumptions generally differ from the real conditions of product use. There are various reasons for not feeding back product use information. First, current business models lead to a loss of access to the product after sale. Second, due to their price and size appropriate sensors are only rarely embedded in the product. Third, there is a lack of an integrated framework for feeding back product use information into product development. This paper presents a new solution approach for the integration of product use information into product development. The first part of the paper provides a summary of the developed solution. While aspects like data management and knowledge discovery have been covered in previous work, this paper focuses on the representation of empirical product use information and the use of knowledgebased inference methods in order to carry out “What-If” analyses. These can serve the product developer to improve the design of next generation products.

Applying Rules for Representing and Reasoning of Objective Product Use Information Exemplarily for Injection Molding Machines

Information gathered during daily life can be generally categorized as either of objective or of subjective character. The given work focuses on the objective field of product use information and how to represent and reason the information with the help of Rules or a Rule Based System. The aim of the reasoning process is to infer new knowledge form the given product information and to use this knowledge for the improvement of a new product generation of the product. The application scenario detailed in this work focuses on the objective information gathered from an injection molding machine to improve not only parts of the machine, but indirectly the quality of the produced parts.

Advanced knowledge management concept for sustainable environmental integration

Between knowing sustainability challenges and sustainable acting is still a big difference. What can be done with Knowledge Management (KM) to improve this situation? The sustainable treatment of the resource knowledge includes social, environmental and organizational aspects like converting economic goals to knowledge goals, discharging obsolete knowledge, detecting knowledge gaps or offering automatic mechanisms for knowledge sharing and transfer. This paper illustrates the significance and functionality of KM for sustainable environmental integration from two aspects: maintaining structural knowledge and accessing and integrating external customer knowledge sources. The first aspect in regards to innovations or new markets is with direct impact on organizational knowledge. This produces knowledge gaps, which have to be filled by making available qualifiable operational performance indicators on the strategic layer as decision support component. The pragmatic approach behind this way of proceeding is the determination of decision goals for the initiatives, which leads to measurable alterations. The second aspect treats a new approach to evaluate and apply customer knowledge for the integration of product use information into product development. This is mandatory for the development and improvement of products and services covering the market demands.

Quantitative and qualitative risk in IT portfolio management

The key point of this paper is the proactive management of the whole risk of an IT project portfolio. A portfolio is collection of projects and every project implicates its own risk. Project risks describes how uncertainty and risk are identified, analyzed and cumulated for a single project. The most attention is paid to the quantification of risk that is based on fundamental decision theory. IT project portfolio management clarifies the sense and the benefit of a portfolio for IT projects. Especially topics like implementation of a portfolio and measurement criteria are fundamental discussion points. Portfolio management including risk as measurement tries to figure out how the whole risk for a portfolio could be made up of the single project risks. Especially inter-dependencies and correlations across the projects are considered. Also addressed is the issue of how to minimize the whole risk of an IT project portfolio by diversification. Proactive risk management and mitigation discusses all opportunities for risk mitigation, that includes reduction of the probability that a risk will materialize or reduction of the impact on the business of a risk event if it does occur.

Experience Fusion as Integration of Distributed Structured Knowledge

Provide in this paper we address and discuss the problem of learning and fusion of graphical models using structure learning algorithms. We present a new enhanced parameterized structure learning and experience fusion approach. A concurrent fusion method to aggregate expert knowledge stored in distributed knowledge bases or probability distributions is also described. Experimental results of a case study show that our approach can improve the efficiency of learning structure algorithms for knowledge fusion applications.

Creating graphical models as representation of personalized skill profiles

In this paper we address and discuss the problem of creating graphical models as representation format of personalized skill profiles in different application environments (e.g. skill matching for staff positioning in a project). We can learn employee skills from data based on structured profiles and their representation as Bayesian network structure using an information theoretic dependency analysis approach. Many enterprise resource management systems (ERP) come along with integrated modules for Human Resource Management (HRM). One main task of HRM is to manage, improve and deploy the right skills at the right time. These processes are well known as skill management. Furthermore the problem of finding dependencies between employee skills not obvious in evidence is considered. Using an information theoretic approach to construct a powerful skill representation as graphical model is comprehensible. To demonstrate the achievement of the learned and created network structure, a test scenario concerning historical reference project data is given.

Rule-Based Compilation of Graphical Structures

Among the various types of decision support systems, decision-theoretic models and rule-based systems have gained considerable attraction. Both approaches have advantages and disadvantages. Decision-theoretic models like decision networks dispose of a sound fundamental mathematical basis and comfortable knowledge engineering tools. Rule-based systems provide an efficient execution architecture and represent knowledge in an explicit, intelligible way. In this paper, we consider fuzzy rule-based systems as a special type of condensed decision model. We outline a knowledge transformation and compilation scheme which allows one to transform a decision-theoretic model into a fuzzy rule base and, hence, to combine the advantages of both approaches. An experimental example is given as demonstration of the described techniques.