What is systemic innovation?

The term ‘systemic innovation’ is increasing in use. However, there is no consensus on its meaning: four different ways of using the term can be identified in the literature. Most people simply define it as a type of innovation where value can only be derived when the innovation is synergistically integrated with other complementary innovations, going beyond the boundaries of a single organization. Therefore, the term ‘systemic’ refers to the existence of a co-ordinated innovation system. A second, less frequent use of the term makes reference to the development of policies and governance at a local, regional or national scale to create an enabling environment for the above kind of synergistic, multi-organizational innovations. Here, ‘systemic’ means recognition that innovation systems can be enabled and/or constrained by a meta-level policy system. The third use of the term, which is growing in popularity, says that an innovation is ‘systemic’ when its purpose is to change the fundamental nature of society; for instance, to deliver on major transitions concerning ecological sustainability. What makes this systemic is acknowledgement of the existence of a systems hierarchy (systems nested within each other): innovation systems are parts of economic systems, which are parts of societal systems, and all societies exist on a single planetary ecological system. Collaboration is required across organizational and national boundaries to change the societal laws and norms that govern economic systems, which will place new enablers and constraints on innovations systems in the interests of sustainability. The fourth use of the term ‘systemic innovation’ concerns how the people acting to bring about an innovation engage in a process to support systemic thinking, and it is primarily this process and the thinking it gives rise to that is seen as systemic rather than the innovation system that they exist within or are trying to create. It is this fourth understanding of ‘systemic’ that accords with most of the literature on systems thinking published between the late 1970s and the present day. The paper offers an overview of what systems thinkers mean by ‘systemic’, and this not only enables us to provide a redefinition of ‘systemic innovation’, but it also helps to show how all three previous forms of innovation that have been described as systemic can be enhanced by the practice of systems thinking

Developing a revenue sharing method for an operational transfer-operate-transfer project

© 2019 by the authors. The transfer-operate-transfer (TOT) project model is used widely as a commercial framework for public-private-partnerships to support provision of infrastructure and enable the delivery of services. However, operational delivery of such projects can encounter certain challenges, such as the need for improved revenue sharing between governmental and private partners. The purpose of this paper is to design a revenue sharing method (RSM) that satisfies the revenue-sharing forecast in the contract design stage and the realized revenue sharing in the contract execution period for an operational TOT project. This approach identifies the impact of external uncertainty and effort level as well as the input ratio on revenue sharing of participants, distributes and reasonably minimizes the project revenue uncertainty among the participants, and achieves an improved matching of the participants' revenue sharing with their risk-sharing, resource input and effort level. The paper utilizes the fuzzy-payoffs Shapley value method for revenue distribution for an operational TOT project, where the fuzzy alliance and input ratio coefficient are adopted to gradually optimize the Shapley value and form the RSM of an operational TOT project. The RSM allows prediction of the revenue sharing of participations under uncertain conditions of project revenue and supports improved decision-making by participants

Living Innovation Laboratory Model Design and Implementation

Living Innovation Laboratory (LIL) is an open and recyclable way for multidisciplinary researchers to remote control resources and co-develop user centered projects. In the past few years, there were several papers about LIL published and trying to discuss and define the model and architecture of LIL. People all acknowledge about the three characteristics of LIL: user centered, co-creation, and context aware, which make it distinguished from test platform and other innovation approaches. Its existing model consists of five phases: initialization, preparation, formation, development, and evaluation. Goal Net is a goal-oriented methodology to formularize a progress. In this thesis, Goal Net is adopted to subtract a detailed and systemic methodology for LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps. Big data, crowd sourcing, crowd funding and crowd testing take place in suitable steps to realize UUI, MCC and PCA throughout the innovation process in LIL 2.0. It would become a guideline for any company or organization to develop a project in the form of an LIL 2.0 project. To prove the feasibility of LIL Goal Net Model, it was applied to two real cases. One project is a Kinect game and the other one is an Internet product. They were both transformed to LIL 2.0 successfully, based on LIL goal net based methodology. The two projects were evaluated by phenomenography, which was a qualitative research method to study human experiences and their relations in hope of finding the better way to improve human experiences. Through phenomenographic study, the positive evaluation results showed that the new generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf

A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio