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Description
D-AM2004 Energy-autonomous transport trolley for battery module pack handling
D-CT1902 produce more efficiently with Festo AX
D-CT1902 produce more efficiently with Festo AX
D-CT1902 produce more efficiently with Festo AX
D-EE2201 Consumption cut in half: energy-saving systems with Controlled Pneumatics
D-EE2202_EN.pdf
D-EE2203 Pneumatics: highly energy-efficient operation for the first time exactly as required
D-EL2103 "Fast and reliable battery cell production – degassing and sealing"
D-GR2302_DE_EN_V1.pdf
D-VA2301_EN.pdf
Success Story "Water's long journey" Automation in one of the most modern water treatment plants in Europe
Code of Conduct Business Partners_EN_2024.pdf
General Information for Supplier Questionnaires from Customers

This document contains the following information:

  • Headquarter of the Festo Group
  • Commercial Law Information
  • Board of Directors and Supervisory Board
  • Main Figures of the Festo Group
  • Products and Main Product Groups
  • Service worldwide
  • Purchase and Procurement
  • Production and Manufacturing Technologies
  • Sales Organisation and Delivery Service
  • Management System Certifications
  • Product Approvals
Handout ATEX

This document explains the marking of ATEX products according to

Directive 2014/34/EU and EN 60079-0:2018

Information on LkSG

Festo fulfils its obligations under the Act on Corporate Due Diligence Obligations in Supply Chains (Lieferkettensorgfaltspflichtengesetz - LkSG)

Quick order sheet Guided drive DFM Guided drive DFM – Selected products

basic design – Ordering data

Spare parts catalogue Spare parts finder for configured products like valve terminals and service units
VDMA article: Added value by software Facts for making good decisions – AI services for improved quality, greater flexibility and reduced costs

Artificial intelligence such as the Festo Automation Experience AX (Festo AX) brings preventive quality assurance, preventative energy management and preventive maintenance within reach in four steps.
Festo AX delivers the modules needed to collect data from systems and machines. Using machine learning, models are trained to detect anomalies, predict errors or downtimes and correct the energy demand.

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