Rethinking business models for innovation

One of the major challenges confronted by those in charge of technological innovation involves anticipating the value creation model sufficiently early on,in a highly uncertain context both as far as the technology itself is concerned and the potential market. Today, in many industrial sectors, the innovation boundaries have moved towards projects that are more and more exploratory and fuzzy. The simple optimisation of linear processes of the "stage-gate" type is no longer sufficient to build sustainable competitive advantages. The notion of Business Models, when applied to innovation, enables us to describe how a company creates value through innovation, generally within a business ecosystem, and how the value will be distributed between the actors involved. The authors of this book believe that the notions of Business Modelling and value creation are key to all the dimensions of successful innovation, whether technology, marketing, organisational or economically based. Rethinking Business Models for Innovation: this title describes the relationship between thinking, modelling, and also field-testing. The book is based on a series of nine recent cases of innovation involving company managers, often assisted by researchers (the co-authors of each chapter), and how they built and formalised their Business Models and then tested their strategies. After having discovered the variety of the cases, the reader will understand that every innovation situation generates specific questions about Business Models. However, we feel that we can identify three key issues that arise, more or less, in each of these projects. The chapters in this book build on these issues: the identification of sources of value and revenue models (the notion of value creation), the position of the company in the value-network or ecosystem (the sharing of value) and finally the evolution of Business MoDdels over time (the sustainability and the competitiveness of the company). The last chapter goes over all the contributions, exploring the notion of value in the Business Model approach.business model ; innovation ; value ; entrepreneurial project

: Lessons from entrepreneurial projects

International audienceOne of the major challenges confronted by those in charge of technological innovation involves anticipating the value creation model sufficiently early on,in a highly uncertain context both as far as the technology itself is concerned and the potential market. Today, in many industrial sectors, the innovation boundaries have moved towards projects that are more and more exploratory and fuzzy. The simple optimisation of linear processes of the "stage-gate" type is no longer sufficient to build sustainable competitive advantages. The notion of Business Models, when applied to innovation, enables us to describe how a company creates value through innovation, generally within a business ecosystem, and how the value will be distributed between the actors involved. The authors of this book believe that the notions of Business Modelling and value creation are key to all the dimensions of successful innovation, whether technology, marketing, organisational or economically based. Rethinking Business Models for Innovation: this title describes the relationship between thinking, modelling, and also field-testing. The book is based on a series of nine recent cases of innovation involving company managers, often assisted by researchers (the co-authors of each chapter), and how they built and formalised their Business Models and then tested their strategies. After having discovered the variety of the cases, the reader will understand that every innovation situation generates specific questions about Business Models. However, we feel that we can identify three key issues that arise, more or less, in each of these projects. The chapters in this book build on these issues: the identification of sources of value and revenue models (the notion of value creation), the position of the company in the value-network or ecosystem (the sharing of value) and finally the evolution of Business MoDdels over time (the sustainability and the competitiveness of the company). The last chapter goes over all the contributions, exploring the notion of value in the Business Model approach.L'innovation technologique, qu'elle soit conduite par des start-ups ou par de grandes entreprises, n'est plus une condition suffisante de la création de valeur. Créer de la valeur sur des marchés nouveaux nécessite le plus souvent de repenser l'organisation de l'entreprise, sa façon de faire des affaires, ses partenariats stratégiques, autrement dit, son business model. Cet ouvrage se veut un guide pour les porteurs de projets d'innovation en leur fournissant des outils de compréhension et d'analyse de la dimension stratégique de leur projet. Les études de cas présentées sont le fruit d'une collaboration étroite entre les porteurs de chacun des projets et des chercheurs en management de l'innovation reconnus. Au travers de ces cas, trois grandes problématiques sont abordées : l'identification des sources de valeur chez les clients potentiels, la position que l'entreprise pourra prendre dans son écosystème et enfin l'évolution des business models dans le temps. Sur chacun des cas, le lecteur aura accès à une compréhension fine des problèmes stratégiques posés par l'innovation ainsi que des outils de management mis en œuvre pour aider à réfléchir et à agir. (http://www.rethinkingbusinessmodel.net/

INTEND: Intent-Based Data Operation in the Computing Continuum

The European Commission (EC) Digital Decade strategy to gain by 2030 autonomy in the digital economy requires more and more data to be processed in the Cloud-Edge-IoT computing continuum, instead of only in the central cloud. This requires advanced automation and intelligence of the continuum. At the same time, recent breakthroughs in Artificial Intelligence (AI) research have shown unprecedented results in handling creative tasks. Such human-like intelligence will eventually disrupt how people use the cloud and continuum. The European Union (EU) -funded project INTEND aims at bringing such human-like intelligence into the cognitive continuum, to achieve the novel concept of intent-based data operation. The project will deliver 11 novel software tools, which integrate into an INTEND toolbox. The outputs pave the way of migrating EU’s data industry from cloud to the continuum, and implement EC’s strategy of human-centric AI in the domain of data processing and computing continuum

Piloting Tourist Guides in a Mobile Context

Non

On the Use of Air Quality Microsensors for Supporting Decision Makers

In this poster we present how a network of Internet-of-things (IoT) devices facilitated through machine learning can improve decision making. Our application domain is air quality in the municipality of Trondheim. Ambient air pollution poses a major threat to both health and climate with millions of premature deaths occurring every year. To enable solutions to this problem, accurate measurements of the phenomenon are required and tools for decision makers need to be in place to quickly understand situations as well as suggest actions that lead to the best possible outcome

Blind Calibration of Air Quality Wireless Sensor Networks Using Deep Neural Networks

Temporal drift of low-cost sensors is crucial for the applicability of wireless sensor networks (WSN) to measure highly local phenomenon such as air quality. The emergence of wireless sensor networks in locations without available reference data makes calibrating such networks without the aid of true values a key area of research. While deep learning (DL) has proved successful on numerous other tasks, it is under-researched in the context of blind WSN calibration, particularly in scenarios with networks that mix static and mobile sensors. In this paper we investigate the use of DL architectures for such scenarios, including the effects of weather in both drifting and sensor measurement. New models are proposed and compared against a baseline, based on a previous proposed model and extended to include mobile sensors and weather data. Also, a procedure for generating simulated air quality data is presented, including the emission, dispersion and measurement of the two most common particulate matter pollutants: PM 2.5 and PM 10 . Results show that our models reduce the calibration error with an order of magnitude compared to the baseline, showing that DL is a suitable method for WSN calibration and that these networks can be remotely calibrated with minimal cost for the deployer

Blind Calibration of Air Quality Wireless Sensor Networks Using Deep Neural Networks

Temporal drift of low-cost sensors is crucial for the applicability of wireless sensor networks (WSN) to measure highly local phenomenon such as air quality. The emergence of wireless sensor networks in locations without available reference data makes calibrating such networks without the aid of true values a key area of research. While deep learning (DL) has proved successful on numerous other tasks, it is under-researched in the context of blind WSN calibration, particularly in scenarios with networks that mix static and mobile sensors. In this paper we investigate the use of DL architectures for such scenarios, including the effects of weather in both drifting and sensor measurement. New models are proposed and compared against a baseline, based on a previous proposed model and extended to include mobile sensors and weather data. Also, a procedure for generating simulated air quality data is presented, including the emission, dispersion and measurement of the two most common particulate matter pollutants: PM 2.5 and PM 10 . Results show that our models reduce the calibration error with an order of magnitude compared to the baseline, showing that DL is a suitable method for WSN calibration and that these networks can be remotely calibrated with minimal cost for the deployer

The Open Tourism Consortium:: Laying The Foundations for the Future of Tourism

The current highly fragmented multitude of information systems supporting tourism greatly increases the tourist's search costs, and while touring there is almost no information systems support. A more cohesive and integrated approach should enhance the tourist's experience in the three phases of tourism: planning, touring, and reminiscing. The emergence of u-commerce, the ultimate form of commerce, is the backdrop to identifying a series of information products that will improve the searching, management, delivery, and sharing of tourism data. It is proposed that the six products identified (TourDM, TourML, TourStyle, TourCMS, TourImplement, and TourCommunity) be developed using the open source model and the cooperative efforts of a large number of geographically dispersed students. The Open Tourism Consortium has been created to support this collaborative endeavor.Open tourism consortium Tourism Tourist information systems Tourism data