Editorial Computer-supported innovation pipelines: Current research and trends Even though the term ‘‘innovation’’ is blurred into many different definitions and perceptions, our society has clearly identified the necessity to organize the means to transform new and relevant ideas into valued products, processes, services or business opportunities successfully accepted by the market. To meet such a challenge, companies are tempted to adopt new emerging models like open innovation, where not only their own internal ideas but also those from other firms drive them to new markets outside their traditional businesses. Without discipline and structured processes, the very best ideas and open approaches may fail to attain the target, as more rapid responses to the market, shortened product life cycles, enhanced product performance and user friendliness requirements tend to increase the risk of falling short. Computer-aided innovation is a new community challenging these current realities. One of its roles is to support innovation pipelines that enable enterprises to effectively implement a complete innovation process throughout the entire product life cycle. The aim of this special issue is to continue to contribute to the foundation of the theoretical background and practical implemen- tation of computer-supported innovation pipelines, not only with methods and tools that integrate scientific fields, but also with practical applications that aim to increase the innovation success rate in an orchestrated manner. The term ‘‘pipeline’’ has been newly employed in our community as we realize that innovation is characterized by a lengthy process along which a great deal of associated data flow from one stage to another through various paths. Even if milestones of innovation paths have been clearly identified (idea synthesis phase, selection of the most promising and challenging ones, transforming an idea into a robust concept through multidisciplinary calculations), there are multiple ‘‘ways’’ of moving from idea to innovation. Consequently, the term ‘‘pipeline’’ has been used in the plural. The computer methods and tools for supporting these innovation pipelines are the central theme in this special issue, starting at the fuzzy front end of perceiving customer demands that challenge traditional design drivers and open new business opportunities and continuing through the entire product develop- ment process up to the successful introduction into the market. This special issue leads with a survey paper that summarizes past contributions in the CAI domain and contains a research agenda for computing developments associated with innovation pipelines. It analyzes how innovation has evolved to assert itself in the form of tangible, measurable tools and methods at every phase of the innovation process. This lead article identifies the emergence of a new age of tools for computer-aided artefact creation and proposes a research agenda for the scientific communities involved. Additional eight papers present different aspects of the innovation pipeline starting at the fuzzy front end and continuing on through to the quality management system. Hu ¨ sig and Kohn demonstrate how CAI has been driven by two major recent developments: on the one hand, the technological possibilities in the software field commonly referred to as ‘‘Web 2.0’’ and, on the other, how CAI is becoming a strategic paradigm shift from closed to open innovation in many companies. The authors show how company attention is shifting from employees as main suppliers of new ideas to customers and other users outside the company. It concludes that integrating new ideas from customers and other users into the internal NPD system is becoming a major task for companies. Tan Runhua’s paper discusses how computer-aided innovation systems based on TRIZ can be applied to solve some ill-structured problems that appear in an innovation pipeline by establishing an analogy between ill-structured problems and inventive problems. A case study demonstrating the proposed approach is presented. Jing Xu et al. describe an approach to help designers innovate more efficiently based on an integrated distributed knowledge- management system for innovation. The authors developed a prototype based on their integrated approach to demonstrate its effectiveness and applicability in practice. Duran-Novoa et al. show how the ability to solve inventive problems is at the core of the innovation process and how it can be supported by new conceptual frameworks. They also present the ‘‘Dialectical Negation Algorithm’’ based on new dialectical nega- tion operators in evolutionary algorithms and modified TRIZ inventive problem-solving principles generating a coherent framework as the basis for developing a computer-aided innova- tion shell, where conflict identification and dialectical system negation are at the core. One case study is devoted to crankshaft design and demonstrates how the designer may recognize through new emerging shapes that the old design paradigm can be questioned and how a dialectical negation of the existing comprehension emerges, allowing the derivation of new concepts. Verhaegen et al. examine the use of automatically distilled product characteristics, called product aspects, as a way to automatically and systematically identify candidate products for design-by-analogy. They demonstrate through case studies that given a target product to improve, automatic product characteri- zation with product aspects makes it possible to identify candidate source products for a possible knowledge transfer to the target product. The methodology allows the designer to focus the Computers in Industry 62 (2011) 375–376 Contents lists available at ScienceDirect Computers in Industry journal homepage: www.elsevier.com/locate/compind 0166-3615/$ – see front matter ß 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.compind.2010.12.011