We, the Engineers
of the World, in Order to form a perfect Society of engineers,
establish Data Intelligence,
ensure professional Tranquility,
provide for the common Knowledge,
promote the general Welfare,
and secure the Blessings of Progress to ourselves and our Posterity,
do recommend and establish this
Constitution for GearLAB Digital.
This is a new website under construction.
GearLAB Digital is a project to create a smart data management for large businesses.
We are working to find industrial partners & investors to make it happen.
We all, are using digital, everyday, to obtain valuable data & information, to communicate, to entertain, to travel, supported by AI so naturally. This is our world, today.
What about engineering world, like automotive industry? Most people believe that data management is far more efficient in the industry. Really?
It should be. With so impressive 3D pictures and realistic images. Isn’t it?
This is the point: 3D is impressive but is not all. It is the visible part of an iceberg
3D is about 20% of the global cost in engineering. What about the other 80% underneath?
80% of pure data management. Not always as entertaining as 3D pictures, but just vital for the business.
Engineers live in the Jurassic era of data management, despite hundreds of millions of dollars spent. They manipulate mostly wrong data, without any help, any reliability control, any intelligence. Using expensive & sophisticated relational database systems unable to respond properly to their most basic concerns.
We know it deeply & in detail, because we, personally, have intensively practiced data management in our duty as automotive engineers in major car industries.
Carmakers use PLM systems to manage large data. They spent a lot of energy & cash to make it, but they fake it: 60% of data are simply wrong, unusable, unfitted. This is the everyday reality of car engineers, whatever the company.
The series of videos below are created to:
- Expose the problem, to understand the “Why”
- Show the technical foundation of our process to create a new paradigm in data management: the "What".
We claim to think different to go beyond established dogma in computer science, to be result oriented.
We observe, understand, think about the future, then we work make it happen.
We build the future, not waiting until it happens.
Gear LAB solution uses data to build intelligence
This is our vision of the future
Nothing great is possible without a great philosophy
Usually the manager accepts the status quo, and the leader challenges it
We are challengers. We meant to think simply, and, to make it great
Organizing is a journey, not a destination
Gear Board is meant to structure the world.
There is a place for everything and everything is in place
The more the data structure is creepy, the more you waste time using it
The most dangerous kind of waste is the waste we do not recognize
GearLAB Digital is promised to be an new startup about Data Management in large businesses
We are engineers in the first place, in the automotive industry. In this business data management is very demanding: very large data, thousands of engineers, various skills either mechanical, electrical, electronics, manufacturing, marketing, after sale, recycling, submitted to the hardest regulations in term of security, ecology, social responsibility, and big stakes.
In this industry Engineers are constantly under stress to produce the most competitive cars with the highest technology to satisfy clients.
However, i their daily job, engineers are using rudimentary data management tools (called PLM), purchased several hundreds of million $ each, which deliver poor and unreliable results for their duty. PLM systems do not really take advantage of the computer technology to assist engineers but create more complexity, more trouble in their duty.
This is why, we are are working to bring intelligence in engineering, not only in automotive but in other businesses too. We do not propose another dull PLM software, but a unique solution based on our smart data management innovation called IQS.
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