The demand for high-quality and low-cost products with short development time in the dynamic global market has forced researchers and industries to focus on various effective product development strategies. The authors are carrying out research studies to explore the applicability of fuzzy logic and knowledge-based systems technologies to today’s competitive product design and development, with an emphasis on the design of high quality products at the conceptual design stage. A framework of a fuzzy FMEA (failure modes and rffects analysis) based evaluation approach for new product concepts is proposed in this paper. Based on the proposed approach and methodologies, a prototype system named EPDS-1, which can assist inexperienced users to perform FMEA analysis for quality and reliability improvement, alternative design evaluation, materials selection, and cost assessment, thus helping to enhance robustness of new products at the conceptual design stage. This paper presents the underlying concepts of the development and shows the practical application with the prototype system with a case study.

/pdf/development-of-a-fuzzy-fmea-based-product-design-system-4987r6itn2.pdf

Development of a fuzzy FMEA based product design system

Knowledge is a very important resource for preserving valuable heritage, learning new things, solving problems, creating core competences, and initiating new situations for both individual and orga...

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.kmrp.8500016.pdf

Employee relationship and knowledge sharing: a case study of a Taiwanese finance and securities firm

Feature model has been used to support requirements analysis and domain engineering in Software Product Line by representing variability. This paper proposes a knowledge base method to validate feature model. Validation of feature model has been split into two main processes, automated consistency check (by defining rules control the variation selections considering cross-tree constraint dependencies) and the second process is automated error detection(two types of errors, dead feature and inconsistency, was defined and validated). Variability was described among feature model, and then variability was represented as a knowledge base containing predicates and rules. In addition to validation, the proposed method can be used to identified and provide auto support for some operations on the automated analysis of feature models (propagation, cardinality validation, explanation, and optimization).

https://www.isa.us.es/aspl08/?download=W5_P06_Osman_Knowledge_Based.pdf

Knowledge Based Method to Validate Feature Models.

Purpose



– This paper aims to determine whether the implementation of knowledge management (KM) is linked to e‐business performance and to identify the nature of the relationship existing in the different components of knowledge‐sharing and application and internet‐based KM.




Design/methodology/approach



– This paper establishes a new model of the practices and results of the KM which has been tested in European companies. For that purpose, a structural equation modelling analysis was used.




Findings



– The results show that product innovation and external employees’ access to databases have a strong positive effect on the maximization of internet‐based KM and that internet‐based KM has also a positive impact on e‐business performance.




Research limitations/implications



– Limitations of this study include the need for more research into the KM cycle. This paper contributes to the research on this topic with new evidence in a broad sample.




Practical implications



– These results point to KM's usefulness in improving every day e‐business processes. Therefore managers should be aware of these benefits.




Originality/value



– The present study advances knowledge on the nature of the relative importance of different components of internet‐based KM as drivers of e‐business performance.

Knowledge management: An overview of European reality

Modern software systems are becoming larger and more complex every day. One of the most challenging steps for designing a good architecture for a certain piece of software is the analysis of requirements, usually written in natural language by engineers not familiar with specific design formalisms. The main problem related to this task is the conceptual gap existing between low-level requirements and higher views of the system decomposing its functionality. In this paper, we introduce an approach for mining and grouping functionality from textual descriptions of requirements using text mining techniques aiming at helping software designers with this complex and time-consuming task. The knowledge discovered starting from informally written requirements using a combination of natural language processing (NLP) and text clustering algorithms can be then easily mapped into design concerns of a possible architecture for the system. Experimental validation in three case studies suggests a great potential of the proposed approach for providing assistance to software designers during early stages of the software development process.

Functional grouping of natural language requirements for assistance in architectural software design

The success of requirement specification in new design projects largely depends on an accurate match between customer requirements and company product and process knowledge. Despite the recent developments in the domain there is still a lack of transparency and consistent definition and integration of the activities in requirement engineering (RE). There is also a lack of structured methods for capturing relevant enterprise knowledge and deploying it in support of decision making for requirement specification. This paper reports on the knowledge acquisition and sharing for requirement engineering (KARE) approach for requirement specification of one-of-a-kind complex systems. The approach provides a generic view of key RE processes clustered into three groups of activities: requirement elicitation, analysis and negotiation. The process is supported by a set of knowledge functions aimed at facilitating the requirement engineers in matching customer requirements to product characteristics. The reported research has been developed as part of the ESPRIT collaborative project KARE funded by the European Commission.

I. Moulek

Papers

Knowledge based requirement engineering for one-of-a-kind complex systems