WorldLab™ Technology

the WorldLab™ systemic intelligence workshop

To support our vision, we developed the WorldLabTM patented technology – built on the proven infrastructure of the AltaRica safety & reliability analysis tool, developed by Antoine RAUZY during the last 20 years and industrially used in many industrial sectors – which is a systemic intelligence workshop that offers enterprise systemic modelling and scenario stochastic simulation & evaluation capabilities.

The WorldLabTM technology especially allows to produce automatically systemic digital twins from a high level specification designed in our formal modeling language ΣTM. These systemic digital twin shall then accessible in software-as-a-service mode or through a local deployment our customer servers, if necessary (for instance for data confidentiality issues).

The key unique features of the WorldLab™ technology
  • Simplicity & Maintainability: A systemic digital twin is specified in the object-oriented modeling language Σ™ which is quite simple to use to any person with an algorithmic-design background. This choice also allows to easily maintain the evolution of a systemic digital twin among time which becomes similar to software engineering.
  • Heterogeneity: A systemic digital twin can integrate various heterogeneous features, such as technical functions, maintenance policies, societal behaviors, financial market evolutions, regulatory strategies or meteorologic conditions, into a single unique systemic model, allowing to analyze a given industrial system from all these various perspectives.
  • Concurrency & Time: This modeling language especially allows to manage concurrent industrial activities and express explicit durations for timed transformation activities of an industrial system, which is currently not offered by the existing similar languages.
  • Hazards: Hazards can be effectively captured in a systemic digital twin: each variable specified in the Σ™ modeling language can be a random variable with a specific probability distribution – either explicit or pragmatic – allowing to capture random quantities & random delays and to manage stochastic simulations for a given industrial system.
  • Data Abstraction: Operational data are managed through abstraction mechanisms that allow to avoid dealing with details when they are not mandatory, while focusing on the most important trends captured by the data. This choice also allows to gain into execution performance when one needs to deal with complex simulations.
  • Automatic Generation: A given systemic digital twin can be automatically generated from its Σ™ specification which can support systemic simulation due to its underlying formal fundamentals & semantics and mathematical framework.
  • Quick Development: The flexible mechanism provided by the Σ™ specification language allows to quickly develop, typically within a few weeks, a first usable version of a systemic digital twin, typically based on 1-2,000 of lines of Σ™, as soon as the system modeling phase is finished.
  • Scenarios Evaluation & Prioritization: The WorldLab™ platform proposes dedicated features for evaluating & prioritizing business evolution scenarios which allow to achieve multi-criteria optimization, e.g. maximizing production when minimizing delays & energy consumption, with respect to a given industrial system.
  • Dashboards & Alerts: Dashboarding and alerting mechanisms allow to respectively support operational & strategic decisions and identify the deviations of a given industrial system when in operations with respect to its normal trajectory depending on its environment behavior.
  • Methodology: Last, but not least, a strong methodological environment, covering design & development techniques, environment & world modeling methods and systemic data modeling, is offered to all users of the WorldLab™ and Σ™ technology.
Manage the Systemic Digital Twin of an industrial Enterprise with WorldLab™ & Σ™

Use a systemic digital twin consists in creating & simulating evolution scenarios for the industrial system of interest, executing the corresponding simulations and analyzing their results, in order to manage continuous business improvements and to prove the value of a systemic digital twin. This deliverable is a set of key performance indicators for various evolution scenarios of the considered industrial system. This paper intends therefore to present our approach to digital twins which integrates all core features that we just sketched here above.