EOMYS uses the following workflow when providing engineering consultancy and research services:
- First contact: the Customer informs EOMYS about his issue: «I don’t understand why my system behaves
this way», «I want to minimize such attribute of my system», or «I need to integrate a new criterion in my design process».
- Reformulation: EOMYS gathers some additional information to get the whole picture and be sure to identify the core
problem, and then reformulates the Customer technical needs in terms of objectives to achieve and
constraints to fulfill.
- Quotation: EOMYS builds a problem solving strategy based on several activities that are not necessarily
chronological (e.g. study of the causal chain, establishment of the state of the art, development of a model, running of a simulation or test campaign). A deliverable (e.g. technical report, numerical model) is linked to some of these activities. This structure allows to establish a clear and detailed quotation. The quotation is always made of fixed-rate workpackages.
- Launch of activities: the first task of EOMYS generally consists in gathering all the project data (e.g. experimental and numerical data, reports,
design constraints, Customer’s know-how) and analyzing it. This phase can lead to a new definition of the problem in agreement with the Customer, and therefore an update of the quotation. Then, EOMYS runs its different activities as per the quotation and regularly informs the Customer with a technical progress report. As the engineers of EOMYS do not work at the Customer’s workplace,
launch meeting and progress meetings are preferably done at the Customer’s office. This
distance ensures EOMYS to have a brand new eye on the Customer’s project, and allows him
a better control of the information.
For the activities where creativity matters, EOMYS relies on its home made ideation methodology.
EOMYS always validates its models (e.g. study of extreme cases, comparison with analytic solutions, test
data, FEM simulation results for analytical and semi-analytical models), studies their sensitivity and
numerical robustness before running any optimization. Simulation and optimization results are
always criticized with the physical sense.