Bernard Yannou

The UNPC innovativeness set of indicators for idea or project selection and maturation in healthcare

Abstract A great deal of work has been done to characterize entire sets of ideation indicators as well as isolated factors of innovativeness. Nevertheless, entire sets of innovativeness indicators are essential in business innovation competitions, as well as within companies in order to select promising innovation seeds. In this paper, a complete set of innovativeness indicators in the context of innovating in healthcare is proposed. The UNPC innovativeness model, standing for usefulness, newness, profitability and (proof of) concept, has been tested and validated over a period of 4 years within the largest European innovation cluster in the silver economy. Four authentic examples of innovation selections are analyzed. They illustrate how the UNPC model is able to provide clear and efficient guidance for better decision-making in the context of innovating for the elderly. In addition, a framework for monitoring an innovative idea or project, and for increasing its maturity, is proposed. The UNPC monitoring process starts with the Strengths, Weaknesses, Opportunities, and Threats analysis of competing ideas. It then becomes dynamic, looking for new evidence for increasing the certainty and impact of UNPC proofs of the competing ideas. The model has been successfully used for upgrading a popular smart application on activity tracking.

Capturing the relevant problems leading to pain- and usage-driven innovations: The Dependency Structure Modeling Value Bucket algorithm:

The Dependency Structure Modeling Value Bucket tool is integrated into Radical Innovation Design methodology in order to explore the front end of innovation in need seeker mode. The determination of innovation opportunities, here called value buckets, has been automated by matrix representations of dependencies among problems or pain points, usage situations, and existing solutions. Three matrices are built along the problem-setting stage of a Radical Innovation Design process. The first matrix expresses which problems occur during usage scenarios, the second how far existing solutions cover problems, and the third to what degree existing solutions are useful in usage situations. Combining these three matrices results in a matrix of value buckets, which represents the combinations of important problems which occur during characteristic usage situations and for which few existing solutions are useful or efficient. This outcome allows focused creativity workshops to be run, resulting in usage innovations with a high likelihood of market success.

A template for sustainable food value chains

This paper proposes a template to assist food value chain actors in their collaborative efforts to develop common sustainable strategies and business models. Inspired by the simplicity of the Business Model Canvas, the template reintroduces sustainable practices as a support for management solutions for sustainable food value chains. The template requires cooperation between actors and stakeholders and comprises three steps: (1) identification of needs for sustainability; (2) development of value chain practices aimed to deliver sustainable value, and assignment of responsibilities to actors for these practices; and (3) formulation of a sustainable value proposition. The template also allows a simple graphical representation of sustainability in value chains, which helps improve communication between actors, and allows stakeholders to be kept informed. The template is applied to a sustainable pork value chain to illustrate how it captures various aspects of sustainability in the pork industry.

A DATA-AND KNOWLEDGE-DRIVEN METHODOLOGY FOR GENERATING ECO-INDUSTRIAL PARK ARCHITECTURES

Industrial symbiosis can be understood as the substitution of new resources used in an industrial process by another resource that would otherwise be discarded. Industrial symbiosis can thereby create new revenue streams and at the same time reduce environmental impact. The initial step in creating an industrial symbiosis is the identification of potential substation relationships between production plants. This step is challenging, as information about the companies is often not available. Several software tools have been developed in order to identify potential symbiosis opportunities. However, these tools have the shortcoming that they require extensive data input from companies owning the production plants. This requirement limits the number of companies for which symbiosis opportunities are identified. In this paper, we propose a data-driven methodology for identifying industrial symbiosis and generating eco-industrial park architectures. The methodology is based on meta-models of industrial plants for identifying plant attributes for certain types of plants, correlations that estimate the rough amount of resource supply and demand of a plant, and a rule-based system that identifies symbiosis opportunities based on knowledge from successful symbioses. Based on the symbiosis opportunities, approach generates eco-industrial park architectures that are optimal in terms of economic and environmental performance. Finally, we apply the methodology to a case study of the existing Kalundborg eco-industrial park to evaluate if the methodology is capable of finding existing symbioses. We conclude that the methodology can be applied to screening industrial zones with standard types of industrial plants. However, the results depend on the types of existing industrial plant meta-models in the database. Future work will focus on extending the data and knowledge base; and validating the methodology by its application to other existing eco-industrial parks.

An activity-based modelling framework for quantifying occupants’ energy consumption in residential buildings

Abstract The residential building is a major energy consumer and pollution source worldwide. The shift towards constructing energy-efficient buildings is impelling higher performance. In sustainable building, occupants become a major source of uncertainty in energy consumption. Yet, energy simulation tools often account for occupant behaviour through predefined fixed consumption profiles. Therefore, energy and buildings experts are in need for more precise methods for better forecasting the influence of occupants on the building performance. An activity-based framework for quantifying occupant-related energy consumption is proposed. The energy consumption is quantified per domestic activity as a function of households’ socio-demographic and economic attributes. The aggregation of such domestic activity energy consumption provides an accurate estimation of the household energy consumption per daily, monthly and annually periods. First, a literature review about residential energy consumption and the existing modelling approaches is presented. Second, a systematic breakdown structure of energy end-uses is proposed. The activity-based framework is then introduced. An application example is demonstrated together with simulation results. Finally, model’s utility is outlined and its possible implications are discussed.

Usage-driven problem design for radical innovation in healthcare

While the diffusion and evaluation of healthcare innovations receive a lot of attention, the early design stages are less studied and potential innovators lack methods to identify where new innovations are necessary and to propose concepts relevant to users. To change this, we propose a structured methodology, Radical Innovation Design (RID), which supports designers who want to work on the unstated needs of potential end users in order to create superior value. In this article, the first part of RID is introduced with its two subprocesses: Problem Design and Knowledge Design. In this first period, RID guides innovators to systematically explore users’ problems and evaluate which ones are most pressing in terms of innovation, taking into account existing solutions. The result is an ambition perimeter, composed of a set of value buckets, that is, important usage situations where major problems are experienced and the current solutions provide little or no relief. The methodology then moves on to Solution Design and Business Design (which are not detailed in this article) to address the value buckets identified. With its emphasis on problem exploration, RID differs from methods based on early prototyping. The RID methodology has been validated in various industrial sectors and is well-adapted for healthcare innovation. To exemplify the methodology, we present a case study in dental imagery performed by 10 students in 8 weeks. This example demonstrates how RID favours efficiency in Problem Design and allows designers to explore unaddressed and sometimes undeclared user needs.

Towards an Automatized Generation of Rule-Based Systems for Architecting Eco-Industrial Parks

In this article we present the matchmaking problem in industrial symbiosis where wastes from one company are matched with resources of another company that could be substituted. Identifying potential matches is difficult, as it is based on knowledge that certain wastes can substitute certain resources. Capturing this knowledge in the form of waste-resource matching rules manually is time-consuming. Therefore, we argue that a Natural Language Processing (NLP)-based approach of semi-automatically extracting rules from domain-specific data sets could be a viable approach to solving this problem. The basic NLP problem to solve is to find similar concepts (synonyms), part-whole relationships (meronyms), and “is a” relationships (hyponyms). Synonyms are important for finding wastes and resources that are named differently but refer to the same object. For example, water and its chemical formula H2O are often used interchangeably. Meronyms are part-whole relationships that may help to identify wastes with components that could be used as a resource. For example, methane is a component of natural gas. Hyponyms allow for building taxonomies. For example, wood is a kind of biomass. We present the results of an initial literature survey of algorithms that are able to find these relationships in large sets of unstructured text documents. Furthermore, we propose a research approach for further extending the literature survey and testing the existing algorithms on small test cases and a realistic matchmaking case. For future work, additional problems that fall into the NLP category can be addressed such as semi-automatically identifying processes for converting wastes into resources.