Geeglee® is the first software which enables you to find the right architecture and reach your objectives. It supports Engineering Design Teams, at the early stages when architecting products or services, and then all along the design life cycle.

From Architecting Problem Setting & Geeglee® principles …

… To Architecting Problem solving:
Geeglee® Engineering Intelligence Platform

Geeglee®'s purpose is to support engineers in their decisions by highlighting risks and attainable performance for every architecture or configuration. It is an expert based software in which the Design Team lists their ideas for solving the problem (an idea can be either functional or technical). Geeglee® then uses System Engineering know-how to perform automatic architecture analysis, from the local assessment, discipline-by-discipline, of technical ideas.

Our Geeglee® methodology consists of:

  • Modeling the Engineering Design Problem (Ideas, as well as, discipline know-how are capitalized into “Geeglee Model”),

  • Connecting to “Geeglee Core” to generate the design space and analyze it.

  • Integrating the overall results into the “Geeglee Explorer”, which can be used as a stand-alone application by Architects and System Engineers. 

Geeglee® enables the digitalization of engineering know-how

Geeglee® is based on the Ph.D. thesis of Vincent HOLLEY (2011). In his Ph.D. thesis, Vincent demonstrates the ability to anticipate multi-disciplinary design risk very early in the design process. After 5 years of relentless coding, industrial based testing, and more scripting, Vincent founded Geeglee® to accelerate the industrial development of his solution. Since then, we continuously improve it to integrate feedback from industrial applications.

As a standard, Geeglee® proposes to:

  • Map the overall Design Space: all the architectures and configurations that can be imagined from Disciplines’ ideas,

  • Assess them on hundreds of performance criteria (Geeglee contains standard performance models),

  • Analyze various points-of-view all along the product lifecycle,

  • Analyze constraints from a multi-disciplinary perspective.

Geeglee® can run more dedicated System Engineering algorithms to:

  • Manage uncertainty: understand by how much the uncertainty must be refined in order to make the good decision,

  • Assess the Should Perform®: define the engineering over-quality inside the system,

  • Analyze the affordance capability: the quick improvement potential to turn over-quality into valuable performance for the client or the interacting system(s),

  • Evaluate the “Requirement cost”, either financial or technical,

  • Optimize product line platforms: define the optimal technological platforms required to meet variable market expectations,

  • Set the Should Target: the just-needed requirements for a sub-system in order to minimize the over-quality of the system

Use Geeglee® to enrich the vision of your design teams

Geeglee® enables you to explore design alternatives and to assess their ability to meet multiple requirements and technical performance criteria, in a systematic way and on a large scale.

It brings added value to the design of systems in many ways:

  • Avoid project delay by anticipating multi-disciplinary optimization conflicts and thus by reducing unexpected iterations,

  • Reduce development costs by keeping engineers focused on the most valuable activities (upstream identification of trade-offs, Pareto Frontier, … and more dedicated analysis),

  • Increase competitiveness: improve end-product performance by exploring more solutions including innovative designs,

  • Reduce project risks for the company,

  • Avoid engineer frustrations,

  • Bring agility to the design process: engineers can efficiently add new ideas, change performance targets, refine uncertainty through day-to-day analyses. All Geeglee® models are reusable, for rapid runs of new design scenarios as well as for entirely new projects.  

Geeglee® also helps your design teams to augment the impact of their expertise:

  • As we map more requirements earlier into the architecture phase, we also request more data from engineers (usually knowledge that is not taken into account until later design phases, when iterations become costly). 

  • We challenge the design teams to create generic models, which are usually higher level than their classical simulation models. This is often achieved with the valuable support of System Value engineers.

It is a new paradigm for finding the best architecture or configuration

Instead of rapidly narrowing the scope to “acceptable” ideas (i.e. compatible with all discipline constraints) and then obtaining a set of performance criteria as a consequence of their engineering decisions, Geeglee® enables engineers to spend more time on potentially interesting ideas from their perspective and then discuss about the trade-offs they want; the architecture choice is the consequence of these high added-value activities.

Engineers will no longer choose an architecture that is supposed to work and then assess its performance. Geeglee® will assess all the performance criteria of all the possible architectures and engineers will chose an architecture based on its evaluated performance. 

Why is this approach so efficient?

Let’s take an example. If you ask a design team to assess the reliability of a “complex” system, like a car, they will spend weeks or months before being able to answer the question. Instead, if you take an experienced mechanical engineer, and if you ask him the reliability of a connecting rod, he can tell you the number of cycles which the connecting rod can withstand. It is much more efficient to ask to engineers knowledge about their own discipline than about cross-disciplines.

That is where Geeglee® is very useful. Geeglee capitalized performance models and systems engineering rules can be applied to evaluate system level performance from local assessment.

What are the principles behind Geeglee®?

Geeglee® is an automated Set-Based Concurrent Engineering software that also integrates the advantage of the Point-Based approach.

The Point-Based approach is still the most deployed one in the industry, mainly due to the fact that it is the most natural one. The most efficient one remains the Set-Based Concurrent approach, because it drives convergence and avoids predictable iterative loops (the ones outside the scope of all disciplines). This strength is also a weakness. Industrial past experiences showed that Set-Based end-products tend to be satisfactory regarding the requirements, but not optimal. Indeed, discipline constraints can sometimes be the results of comfort design rules rather than real constraints. 

Geeglee® first explores each discipline's full scope, regardless of the constraints they may want to impose. With this approach, we can challenge the disciplines' points-of-view to uncover potential performance hidden by constraints that are unnecessarily stringent.

Geeglee® positions itself upstream of the design cycle, for the early architecture decisions. As such, it is the perfect complement to classical design tools and methods which are used more downstream in the design process. For instance, the selection of an architecture with Geeglee® can be followed by activities which focus on optimizing the design parameters related to the architecture (Multidisciplinary Design Optimization, parametric modelling and simulation, ...). Geeglee® can also be smoothly integrated to System Engineering approaches such as Model-Based Systems Engineering (MBSE). 

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Geeglee®  augments your software value chain

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