No matter which carmaker is observed, it appears that very few innovations actually find their way into vehicle development projects, compared to the number ideas originally imagined. Although it is normal practice to filter out many innovations, it is essential to maintain a certain number within the vehicle development projects. Otherwise there is a risk of not being able to keep up with market expectations or of being out of step with the competitors' market offerings. This difficulty of transforming good ideas into innovations that find their place in vehicle development projects may be attributed to the difficulty in converging innovation development with vehicle development, a process that we shall refer to in the rest of this article as the touch-down process (Buet et al., 2008). This term stems from an analogy that may be made with an aircraft (innovation projects) landing on an aircraft-carrier (vehicle development projects). While landing, it is essential to specify all the conditions required, to apply all defined processes, but also to know how to react to events in order to make a successful touch-down. In order to keep abreast of the market, motor manufacturers are faced with coordinating innovations that have not always achieved a sufficient degree of maturity with the vehicle development projects that are likely to be their platform. The notion of touch-down is typified by the integration such innovations in vehicle development projects. 'The integration process itself is proving problematical in that new technology fields, organizations and timescales differ considerably from those applicable to vehicle development projects. […] It is a complex process to successfully converge new technology developments (available at the right moment and at the right level of maturity) with product development projects.' (Buet et al., 2008)
In order to get better results during brainstorming activities participants must respect some rules when they write notes about ideas and when they consider notes written by somebody else. We argue that the respect of these rules can be verified by a multi-agent system analyzing videos and notes produced by the participants in real time. This system can simplify the role of the meeting facilitator. Feedback is sent individually or is addressed globally to the entire team. This paper presents considerations about the necessity of rules, the structure of a multi-agent system for analyzing the respect of those rules and how experiments could show the level of acceptance by people of such a system. The goal of this prospective research is to add a non intrusive system during brainstorming sessions in order to enhance the quality of results.
To be consistent with sustainable innovation, production and consumption evolution, business model design needs to be reconsidered to jointly redefine the value proposition, the productive organization, the remuneration modalities and the customer relationship. This paper shows how business model design could help to manage the transition from intensive innovation toward sustainable innovation. First, we distinguish two visions of business model: a representative vision, and an interactionist vision in which the business model acts as an intermediary object. Then, we choose to adopt the interactionist vision diverting the representative canevas of business model as an intermediary object. This approach is supposed to support the co-evolution of business models and technologies leading to the design of sustainable innovation. We have tested this methodology with industrial partners in order to move toward PSS offers as sustainable innovation. Our results suggest that business model can become a privileged tool for facilitating this transition. This finding leads us to formalize a method supporting decision in order to help industrials to define their sustainable transition.
In this paper, we describe an AI-based system that recognizes the activity status of several people from video streams during brainstorming meetings. Deep learning is often used to recognize video characteristics but requires a huge amount of computer resources. This makes it difficult to keep track of the activities of multiple people whose circumstances change. On the other hand, many trained models of one person's motion recognition have been developed and are available. We propose to use the existing technology but to be able to do that we need to identify a single person's activities within a group context. This is achieved by segmenting the video and cropping the area with a person, identifying the activity using pre-existing trained models. The activity of the group is recognized by a production rule system based on individual activities. To achieve our goal, we introduce the concept of atomic action to describe activities and propose categories of atomic actions. High-level collaborative categories that indicate the status of a group during collaborative meetings are based on the CIAO model. This paper ends with the results of the first experiments we conducted using video recordings of actual students' work sessions.
