Cecilia Zanni-Merk

An ontological basis for computer aided innovation

Knowledge acquisition and capitalization to solve problems concerning artefact evolution, still called inventive design, has a certain quantity of specific characteristics. It needs the choice of certain pieces of knowledge that may induce evolutions; it leads to reformulating the initial problem to build an abstract model of the artefact. The theoretical approach we are interested in, called TRIZ (the Russian acronym for Theory for Inventive Problem Solving), when translated into a methodological procedure, has not been fully formalized yet. This article proposes an ontology of the main notions of the concepts associated to knowledge acquisition in this framework. This ontology, beyond the clarification it brings to the involved notions, will be the support of a software architecture for implementing the method for knowledge acquisition and problem formulation.

Use of formal ontologies as a foundation for inventive design studies

The paradigm change that rules our industry (currently evolving under the quality paradigm) requires an enterprise to organize innovation in a pragmatic way. Beyond theoretical discourses around the necessity for innovation, methods and tools, based on theories should now be translated into engineering practices to be efficiently applied. One of the consequences of this necessary evolution is that R&D departments (and especially project teams), familiar with methods and tools inherited from quality area, are pushed toward changes in their practices sometimes far from their respective cultures. Software tools for accompanying this evolution are needed. This article proposes the use of ontologies as a base to the development of those software tools. Formalization of the main concepts concerning inventive design is provided by the use of formal ontologies. The tools already developed assist the expert in the conduction of an inventive design study, from problem formulation to the proposal of solution concepts.

Knowledge Transfer for Supporting the Organizational Evolution of SMEs: A Case Study

This article describes the research project MAEOS that has been launched six months ago for a three year duration. The goal of this project is the modelling of the support of the evolution of Small and Medium Enterprises (SMEs). The developed models will constitute the foundations of a knowledge based system that will permit consultants to improve the effectiveness of their missions thanks to the implementation of theoretical and practical domain knowledge.

Reasoning with multiple points of view: a case study

This article presents our approach to reasoning with diversified and voluminous knowledge sources that can eventually be, contradictory. In fact, knowledge sources coming from management sciences are inherently rich and, sometimes, conflicting. We choose to exploit the entire range of this diversity to improve business advice to small and medium enterprises (SMEs).

Towards a Formal Model of the Lean Enterprise

Abstract In this paper, we describe the characteristics of the Lean Enterprise and make the case for modelling it in order to reproduce its successful practices more easily. The literature contains many good descriptions of the Toyota Production System and Lean in general, but no formal model that we can build upon. We then make the hypothesis that Lean is a Complex System, which can be modelled formally. We propose to follow the KREM model which comprises four components. The K (Knowledge) component includes domain knowledge about Lean in the form of several ontologies, the R (Rules) component is expressed by probabilistic rules, the E (Experience) component describes the practices (Kata) and the M (Meta-data) component describes the context of the application of Lean (different types of companies or cultural environments, for example). A practical example modelling the Hoshin Kanri process for setting objectives at the enterprise level demonstrates how to put this approach into practice.

A Semantic Layered Architecture for Analysis and Diagnosis of SME

Abstract This article describes the research project MAEOS, whose purpose is to model the organizational and strategic development of SMEs. The main objective of this project is to improve the efficiency and performance of business advice given to this kind of companies by establishing a set of methods and software tools for analysis and diagnosis. In order to achieve this, a multi-disciplinary team was created in which two main research areas are represented: artificial intelligence and management science. In this work several key questions of the knowledge engineering field are addressed by the team: how to extract theoretical knowledge (e.g. from scientific works in management science) and practical one (e.g. from consultants); how to formalize it and use it to assist consultants in their daily work.

A Hybrid System Combining Description Logics and Rules for Inventive Design

Knowledge acquisition and capitalization to solve problems concerning artefact evolution, still called inventive design, has a certain quantity of specific characteristics. The theoretical approach we are interested in, called TRIZ (the Russian acronym for Theory for Inventive Problem Solving), when translated into a methodological procedure, can be declined into two different steps: problem formulation and problem resolution. This article presents an analysis of two of the most used knowledge bases of TRIZ during the resolution stage. These knowledge bases have been formalized by the construction of an ontology of the informal knowledge sources usually used by the TRIZ experts. This approach has permitted the design of a software architecture that eases the implementation of these bases by means of their declarative manipulation. It combines rules and description logics for populating the ontology and facilitates the access to the compiled generic knowledge that synthesizes, at an abstract level, the already encountered problems and their solutions.

KREM: A Generic Knowledge-based Framework for Problem Solving in Engineering

This article presents a generic knowledge-based framework for problem solving in Engineering, in a broad n nsense. After a discussion about the drawbacks of the traditional architecture used for deploying knowledge-based n nsystems (KBS), the KREM (Knowledge, Rules, Experience, Meta-Knowledge) architecture is presented. n nThe novelty of the proposal comes from the inclusion of experience capitalization and of meta-knowledge use n ninto the previously discussed traditional architecture. KREM improves the efficiency of classic KBSs, as it n npermits to deal with incomplete expert knowledge models, by progressively completing them, learning with n nexperience. Also, the use of meta-knowledge can steer their execution more efficiently. This framework has n nbeen successfully used in different projects. Here, the architecture of the KREM model is presented along n nwith some implementation issues and three case studies are discussed.

A formal ontology for a generalized inventive design methodology

TRIZ the Russian acronym for Theory of Resolution of Inventive Problems is a methodology to guide the search for inventive solutions to one, or a few, difficult problems. Classic TRIZ is not well suited to the examination of complex situations composed of many problems, sub-problems and partial solutions, strongly interconnected. It has therefore been completed to give birth, among others, to the Inventive Design Methodology IDM framework.TRIZ and IDM share many similarities with Artificial Intelligence methods: they both propose to solve a problem by reformulation in an abstract model. Generic solving patterns are applied to this abstract model to produce abstract solutions. The domain-specific knowledge is then used to get the final solution concept. However, neither TRIZ nor IDMs descriptions are formal enough to permit a reliable software implementation and rely mainly on the experts manual work. Therefore this paper proposes an ontological formalization of TRIZ and IDM to overcome these difficulties and allow the development of software tools to assist TRIZ/IDM experts in their work.

Improving the performance of production lines with an expert system using a stochastic approach

In our increasingly competitive world, nowadays companies implement improvement strategies in every department and, in particular, in their manufacturing systems. This paper discusses the use of a global method, based on a knowledge-based approach, aiming at the development of a software tool for modeling and analysis of production flows. The main goal is the improvement of the performance of the production line. This method is based on data-processing and data-mining techniques and will help the acquisition of the meta-knowledge that is needed for finding correlations among different events in the line. Different techniques will be used: a graphical representation of the production, identification of specific behavior and research of correlations among events in the production line. Most of these techniques are based on statistical and probabilistic analyses. Events are expressed in the form of phenomena. To carry out high-level analyses, a stochastic approach will be used to identify breakdown models, which are the expression of specific correlations between phenomena. Breakdowns models will be the basis for, finally, defining action plans to improve the performance of the manufacturing lines.