Optimization of production with AI in the ERP system EVO Competition

AI Application Self-Learning Production Planning Improves Capacity Analysis

2025-10-27 Source: Press release EVO Information Systems | Translated by AI 1 min Reading Time

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In the metalworking industry, different employees are often used for machine setup than for actual production. A new functionality in the ERP system EVO Competition takes this practice into account and automatically derives employee skills from operational data acquisition.

AI-supported personnel planning of qualified and available employees enables optimized resource utilization.(Image: EVO Information Systems) — AI-supported personnel planning of qualified and available employees enables optimized resource utilization.
(Image: EVO Information Systems)

The impetus for the development of this intelligent algorithm came from close observation of practical applications. Jürgen Widmann, CEO of EVO, stated: "Many medium-sized companies are overwhelmed with maintaining the master data of a qualification matrix to deploy employees according to their qualifications and availability. That's why we integrated artificial intelligence algorithms that extract experiential knowledge from operational data in the ERP system and use it for capacity planning," explains Jürgen Widmann, CEO.

Master Data Maintenance is Not Necessary for this

The function does not require manual maintenance of a qualification matrix in the master data. Instead of using outdated or incomplete master data, EVO Competition derives the skills and qualifications of individual employees directly from production and setup operations performed on the machines. The system continuously learns from completed and ongoing orders, distinguishing between different roles and work steps. Additionally, it takes into account vacation, illness, and other reasons for absence in capacity planning.

The advantages for industrial companies are obvious, according to the provider: production orders can be planned more realistically, delivery deadlines can be met more reliably, and existing resources can be better utilized. Machines are used more efficiently, the number of overtime hours is reduced, and the operational effort for personnel planning is minimized. Thus, the combination of AI-supported competency determination and adaptive capacity utilization takes production a decisive step closer to the digitally planned and controlled factory.

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