around innovation that remains very active in French engineering schools, and contributes to a field of research that can be described as “innovation engineering” and whose key concepts will be presented in the following section.
2.3. Practical guide to innovation engineering
Innovation processes have radically changed and must take into account the increasing capacity of industrial developments and the growing complexity of surrounding systems. The question of innovation has therefore emerged over time as a fundamental element that can reconcile two requirements: short-term efficiency in the control of an industrial process and long-term development that will reside in the capacity to create value (both in a human and a productive dimension). These changes in the understanding of the concept of innovation in general have naturally contributed to a new field of research: innovation engineering, whose role is to study the mutual involvement of the disciplines that feed into each other and to obtain knowledge that will become levers of growth for tomorrow’s economy.
Following the exercise of inventorying methods and tools in innovation engineering carried out in 2011 by a collective of researchers and practitioners in engineering techniques under the direction of B. Yannou (2011), we have chosen to take this work for granted and to focus on the accepted bases in the piloting of innovative projects. Moreover, starting from the observation that there is a more than sufficient number of definitions of innovation, we will base ourselves on the definition of innovation of the Oslo Manual, version 2018, taken up in the very recent ISO/TC279 standard on innovation management:
An innovation is a new or improved product or process (or combination thereof) that differs significantly from the organization’s previous products or processes5 and that has been made available to potential users (product) or put into use by the organization (process).
2.3.1. First bias: there are no good or bad innovative ideas!
Innovation is a building process. It begins with an intention to create something new because of dissatisfaction, awareness of potential improvements or by surprise/serendipity (i.e. by the involuntary observation of a phenomenon). This is particularly due to the inherent curiosity of every human being and his or her ability to deploy resources to improve living conditions. As De Brabandere (De Brabandere 2014) points out, being creative means taking a different look at an object or a situation, whereas being innovative means achieving the solution.
It is therefore essential to develop one’s ability to explore the ecosystem in which one develops, in particular by being able to understand and integrate the major challenges we face in a mode of exploration commonly known as “from glocal to local”.
As a result, the current trend of frugal innovation cannot be ignored today when it comes to designing an innovation that is accessible to a larger number of people and nor can we ignore the needs expressed or not expressed by users. The analysis of needs and requirements is one of the major tools in innovation engineering. This ability to better understand and identify the context of the innovation is a prerequisite to any innovation engineering process in order to contextualize the project to be developed.
At this stage, it is not a matter of selecting ideas. In fact, it is a mistake to do so because many experiences show that ideas perceived as very attractive at the end of a creativity session turn out to be, in the end, little or not at all accepted by users/consumers. This leads to the interest of integrating the potential users of the innovation upstream of the innovation process, which we will come back to later (second bias).
Engineering research in the field of ideation is now looking for ways to make the process more efficient. To do so, methods are more and more systematic and supported by computer-based tools that make them more robust, such as the association of the TRIZ method with AI, or the development of expert systems associated with ontologies to drive the entire creative process.
Innovation is also the result of confrontation. An idea must go through a phase of enrichment, particularly by confronting the expertise of people with whom a relationship of trust has been built. Managing a network of internal and external experts is a crucial aspect of engineering in the service of innovation. There can be no management of innovative projects without collaboration (third bias).
2.3.2. Second bias: any innovation process requires contextualization of the situation
Each innovation context is specific according to the nature of the innovation, the degree of novelty or the field of study. It is therefore essential, before committing further to the project, to carry out a watch in order to determine the variables of the macro-environment that could positively or negatively influence the innovative project. It is therefore common to use a macro-environmental analysis tool, such as PESTEL, to assess the political, economic, socio-cultural, technological, ecological and legal dimensions associated with the field of study. It can be carried out at several scales depending on the case: local, national and international. Moreover, in order to refine the analysis, the use of a SWOT analysis allows us to qualify the PESTEL data as opportunities and/or as threats and to verify, from this preliminary phase, the resources available to the organization to support future innovation. Evaluating an organization’s capacity for innovation is a prerequisite for the implementation of an engineering approach (Boly 2004). Indeed, an organization can find itself in difficulty if the innovative project takes it too far away from its core competencies or requires human and financial resources that it does not have. Finally, a fortiori today, as the business environment has become prone to “VUCA” (volatility, uncertainty, complexity and ambiguity), taking into account trends and weak signals in a market proves to be an undeniable asset to create, in fine, a truly disruptive innovation.
This aspect of context analysis and assessment has been facilitated in recent years by the increasing availability of information and data. This also makes it more difficult, in particular because of the availability of tools for data visualization, processing and storage. Work on data mining has found a growing echo in improving the understanding of the project context. It is the same for research on AI. In recent years, this has become an essential tool to help innovative project leaders make the “best” decisions in a complex and uncertain world.
Likewise, as we have already pointed out, needs and requirements analysis is one of the major tools of innovation engineering. Based on the concept of “customer pain” and the highlighting of dissatisfactions, the needs analysis makes it possible to refine the first part of an innovation process known as the “front end” or the upstream phase. If, in addition, the needs analysis is combined with a prospective analysis, then the analysis can be refined by looking at implicit and unspoken dimensions, such as trends and new uses. In this way, a clear vision of the needs and expectations of the parties involved in the innovation project can be obtained and potential scenarios can be developed.
Innovation is therefore systemic. Having this awareness at the beginning of the implementation of an innovation process also means having a perception of the associated project risk.
2.3.3. Third bias: there is no innovative project management without collaboration
For several years, the belief that the success of an innovation lays in hiding it and keeping it well controlled within an organization has been transformed into a new way of acting that is more open and requires internal and external collaboration. Individual value is being replaced by collective value. Indeed, research conducted by the engineering community has led to the development of tools and methods to facilitate this collaboration and to support projects integrating multiple stakeholders around the same objective. As a result, research on the interoperability of objects and systems and on collaborative virtual reality and augmented reality has been widely developed to allow the management of distributed, inter-temporal and inter-organizational knowledge and skills.
This work has enabled us to move from a theoretical concept of collaboration between individuals and organizations, and open innovation, to a method equipped to manage knowledge remotely. Indeed, the technological push has undeniably accelerated