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Frequently Asked Questions / FAQ

FAQ photos 3 production planning scheduling

General

QLECTOR LEAP enhances your MES and ERP. MES serves as the data source for building and maintaining the digital twin in QLECTOR LEAP.

There are two ways to implement QLECTOR LEAP: a) a standalone APS augmented with machine learning, or b) adding value to any APS by refining and maintaining master data.

QLECTOR LEAP uses proprietary artificial intelligence and knowledge graph technology to automatically build and maintain a data-driven digital twin of production.

 

Typically, it takes two to six months.

Digital Twin

QLECTOR LEAP uses the digital twin to refine and maintain master data, create realistic plans and schedules, and develop various production scenarios.

The data source is your MES and ERP.

The automatic creation of the digital twin by QLECTOR LEAP is a matter of hours.

The digital twin in QLECTOR LEAP learns automatically and grows with the company. It automatically considers new production lines, new products, and realization.

Production guiding

QLECTOR LEAP facilitates the management of key resources on the shopfloor: plan, machines, tools, and workforce.

QLECTOR LEAP can use its own graphical interface or be integrated with the HR module in ERP or MES.

Maintenance scheduled in MES or ERP can be included in simulations and production scheduling.

Optimization

QLECTOR LEAP uses AI to learn and maintain specifics of processes and workflows from historical data. It uses realistic cycle times, scrap, downtime, and cleaning/setup matrices.

QLECTOR LEAP uses a set of parameters (e.g., planning horizon, frozen period) to run thousands of simulations and criteria (e.g., tool changes, stock level, delays) and select the most optimal one.

With QLECTOR LEAP, you can compare various scenarios and make informed decisions based on your company's KPIs, such as delays, stock value, and equipment utilization.

QLECTOR LEAP IN REAL-TIME

Make your data work for you!

QLECTOR LEAP uses historical data and artificial intelligence to forecast production and suggest optimal measures when unplanned events occur.

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