Introduction to TIPS: a theory for creative design

A highly intriguing problem in combining artificial intelligence and engineering design is automation of the creative and innovative phases of the design process. This paper gives a brief introduction to the theory of inventive problem solving (TIPS) selected as a theoretical basis of the authors' research efforts in this field. The research is conducted in the Stevin Project of the Knowledge-Based System Group of the University of Twente (Enschede, The Netherlands) in cooperation with the Invention Machine Laboratory (Minsk, Belarus). This collaboration aims at developing a formal basis for the creation of an automated reasoning system to support creative engineering design

Engineering Analytics: Research into the Governance Structure Needed to Integrate the Dominant Design Methodologies

In the ASEM-IAC 2014, Cotter (2014) explored the current state of engineering design, identified the dominate approaches to engineering design, discussed potential contributions from the new field of data analytics to engineering design, and proposed an Engineering Analytics framework that integrates the dominate engineering design approaches and data analytics within a human-intelligence/machine-intelligence (HI-MI) design architecture. This paper reports research applying ontological engineering to integrate the dominate engineering design methodologies into a systemic engineering design decision governance architecture

Case-based Reasoning for Knowledge Capitalization in Inventive Design Using Latent Semantic Analysis

International audienceNowadays, innovation represents one of the most crucial factors driving the success of companies. The Theory of Inventive Problem Solving (also known as TRIZ) is a well-established method to facilitate systematic inventive design. Although, TRIZ allows solving inventive problems through a panoply of knowledge sources, it may make inventive problem solving a time-consuming, experience demanding process and lead to waste of resources of the companies. To avoid the use of these tools and to help new users in solving their inventive problems without completely mastering TRIZ, we propose in this paper an approach based on the use of the Case-based reasoning (CBR) in order to capitalize experience. CBR is a knowledge paradigm that solves a new problem by finding the old similar cases and reusing them. The retrieval is conducted in order to find the old similar cases, and the old solutions of the retrieved cases are adapted to solve the new problem. In this paper, a systematic three-level adaptation is proposed to reduce the effort required of the users in choosing the suitable solution to solve their problem. An example is used to illustrate in detail the proposed approach

A combined use of TRIZ methodology and eco-compass tool as a sustainable innovation model

The authors acknowledge Fundacao para a Ciencia e a Tecnologia (FCT-MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).In recent years, there has been an increase in the adoption of quality tools by companies. As such, there has been a commitment to innovation by the organizations to obtain competitive advantages by the development of new products and technologies focused on the creation of economic value but also on delivering sustainability. This study aims to develop an application model of the inventive resolution theory in conjunction with the Eco-Compass ecological innovation tool, in order to allow solutions to be obtained systematically, and to present a performance increase of certain environmental parameters, promoting thus sustainable innovation. The case study research methodology is used to frame the research. The company under study is Nokia enterprise, located in Portugal, which offers a set of services related to telecommunications infrastructures. The unit of analysis is the department of transformation and continuous improvement, and the study illustrated the application of combined use of theory of inventive problem solving (TRIZ) and Eco-compass to develop innovative solutions systematically. The results show that it is possible to achieve innovation according to a certain level of established sustainable environmental parameters, while at the same time solving the identified inventive problem.publishersversionpublishe

Collaborative problem solving within supply chains: general framework, process and methodology

The Problem Solving Process is a central element of the firms' continuous improvement strategies. In this framework, a number of approaches have succeeded to demonstrate their effectiveness to tackle industrial problems. The list includes, but is not limited to PDCA, DMAICS, 7Steps and 8D/9S. However, the emergence and increasing emphasis in the supply chains have impacted the effectiveness of those methods to solve problems that go beyond the boundaries of a single firm and, in consequence, their ability to provide solutions when the contexts on which firms operate are distributed. This can be explained because not only the problems, but also the products, partners, skills, resources and pieces of evidence required to solve those problems are distributed, fragmented and decentralized across the network. This PhD thesis deals with the solving of industrial problems in supply chains based in collaboration. It develops a general framework for studying this paradigm, as well as both a generic process and a collaborative methodology able to deal with the process in practice. The proposal considers all the technical aspects (e.g. products modeling and network structure) and the collaborative aspects (e.g. the trust decisions and/or the power gaps between partners) that simultaneously impact the supply chain operation and the jointly solving of problems. Finally, this research work positions the experiential knowledge as a central lever of the problem solving process to contribute to the continuous improvement strategies at a more global level

An ontology for defining and characterizing demonstration environments

Demonstration Environments (DEs) are essential tools for testing and demonstrating new technologies, products, and services, and reducing uncertainties and risks in the innovation process. However, the terminology used to describe these environments is inconsistent, leading to heterogeneity in defining and characterizing them. This makes it difficult to establish a universal understanding of DEs and to differentiate between the different types of DEs, including testbeds, pilot-plants, and living labs. Moreover, existing literature lacks a holistic view of DEs, with studies focusing on specific types of DEs and not offering an integrated perspective on their characteristics and applicability in different contexts. This study proposes an ontology for knowledge representation related to DEs to address this gap. Using an ontology learning approach analyzing 3621 peer-reviewed journal articles, we develop a standardized framework for defining and characterizing DEs, providing a holistic view of these environments. The resulting ontology allows innovation managers and practitioners to select appropriate DEs for achieving their innovation goals, based on the characteristics and capabilities of the specific type of DE. The contributions of this study are significant in advancing the understanding and application of DEs in innovation processes. The proposed ontology provides a standardized approach for defining and characterizing DEs, reducing inconsistencies in terminology and establishing a common understanding of these environments. This enables innovation managers and practitioners to select appropriate DEs for their specific innovation goals, facilitating more efficient and effective innovation processes. Overall, this study provides a valuable resource for researchers, practitioners, and policymakers interested in the effective use of DEs in innovation

An ontology for defining and characterizing demonstration environments

Demonstration Environments (DEs) are essential tools for testing and demonstrating new technologies, products, and services, and reducing uncertainties and risks in the innovation process. However, the terminology used to describe these environments is inconsistent, leading to heterogeneity in defining and characterizing them. This makes it difficult to establish a universal understanding of DEs and to differentiate between the different types of DEs, including testbeds, pilot-plants, and living labs. Moreover, existing literature lacks a holistic view of DEs, with studies focusing on specific types of DEs and not offering an integrated perspective on their characteristics and applicability in different contexts. This study proposes an ontology for knowledge representation related to DEs to address this gap. Using an ontology learning approach analyzing 3621 peer-reviewed journal articles, we develop a standardized framework for defining and characterizing DEs, providing a holistic view of these environments. The resulting ontology allows innovation managers and practitioners to select appropriate DEs for achieving their innovation goals, based on the characteristics and capabilities of the specific type of DE. The contributions of this study are significant in advancing the understanding and application of DEs in innovation processes. The proposed ontology provides a standardized approach for defining and characterizing DEs, reducing inconsistencies in terminology and establishing a common understanding of these environments. This enables innovation managers and practitioners to select appropriate DEs for their specific innovation goals, facilitating more efficient and effective innovation processes. Overall, this study provides a valuable resource for researchers, practitioners, and policymakers interested in the effective use of DEs in innovation

Résolution collaborative de problèmes au sein des chaînes logistiques : cadre conceptuel, processus et méthodologie

La Résolution de Problèmes est l'un des piliers des stratégies d'amélioration continue des entreprises. Dans ce cadre, un certain nombre des méthodes ont réussi à démontrer son efficacité pour adresser des problèmes particulièrement complexes. Parmi ces méthodes, on peut distinguer le PDCA, le DMAICS, le 7Steps et le 8D/9S. Pourtant, l'apparition des réseaux distribuées de partenaires, ainsi que le positionnement du concept d'entreprise étendue, ont obligé les entreprises à aller au-delà de ses frontières pour travailler en synergie avec tous les partenaires en amont et en aval de sa chaîne. Dans ce contexte, l'efficacité de ces méthodes de résolution des problèmes a été fortement impactée. Ceci car non seulement les problèmes, mais aussi les produits, les partenaires, les ressources et l'information nécessaires pour sa résolution sont extrêmement fragmentés et décentralisés. Cette thèse s'intéresse donc à la résolution collaborative de problèmes au sein des chaînes distribuées de partenaires et son objectif est de proposer un processus et une méthodologie adaptés à ces contextes. Les propositions faites prennent en compte les aspects techniques (e.g. la modélisation des flux et la configuration de la chaîne) ainsi que les aspects collaboratifs (e.g. le niveau de confiance et/ou le rapport de pouvoir entre les partenaires) que conditionnent l'opération et l'efficacité du réseau. Finalement, cette thèse s'intéresse à l'articulation d'un système de retour d'expérience dans la résolution de problèmes distribués afin d'améliorer son efficacité. ABSTRACT : The Problem Solving Process is a central element of the firms' continuous improvement strategies. In this framework, a number of approaches have succeeded to demonstrate their effectiveness to tackle industrial problems. The list includes, but is not limited to PDCA, DMAICS, 7Steps and 8D/9S. However, the emergence and increasing emphasis in the supply chains have impacted the effectiveness of those methods to solve problems that go beyond the boundaries of a single firm and, in consequence, their ability to provide solutions when the contexts on which firms operate are distributed. This can be explained because not only the problems, but also the products, partners, skills, resources and pieces of evidence required to solve those problems are distributed, fragmented and decentralized across the network. This PhD thesis deals with the solving of industrial problems in supply chains based in collaboration. It develops a general framework for studying this paradigm, as well as both a generic process and a collaborative methodology able to deal with the process in practice. The proposal considers all the technical aspects (e.g. products modeling and network structure) and the collaborative aspects (e.g. the trust decisions and/or the power gaps between partners) that simultaneously impact the supply chain operation and the jointly solving of problems. Finally, this research work positions the experiential knowledge as a central lever of the problem solving process to contribute to the continuous improvement strategies at a more global level

Advances on Mechanics, Design Engineering and Manufacturing III

This open access book gathers contributions presented at the International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing (JCM 2020), held as a web conference on June 2–4, 2020. It reports on cutting-edge topics in product design and manufacturing, such as industrial methods for integrated product and process design; innovative design; and computer-aided design. Further topics covered include virtual simulation and reverse engineering; additive manufacturing; product manufacturing; engineering methods in medicine and education; representation techniques; and nautical, aeronautics and aerospace design and modeling. The book is organized into four main parts, reflecting the focus and primary themes of the conference. The contributions presented here not only provide researchers, engineers and experts in a range of industrial engineering subfields with extensive information to support their daily work; they are also intended to stimulate new research directions, advanced applications of the methods discussed and future interdisciplinary collaborations