Inno­va­tion Event Process Industry

What is it about?
The demon­strator shows the seam­less inte­gra­tion of Digital Cali­bra­tion Certifi­cates (DCCs) into the concepts and archi­tec­ture of the Asset Admin­is­tra­tion Shell (AAS), enabling auto­mated and fully digital cali­bra­tion work­flows.

Bene­fits at a Glance

• Signif­i­cant savings when exchanging cali­bra­tion data between systems and orga­ni­za­tions
• Fully digital cali­bra­tion processes, elim­i­nating manual steps and time-consuming proce­dures
• Improved quality and enhanced safety

What is it about?
The demon­strator shows how AI can be used to auto­mat­i­cally digi­talize existing plants (brown­field), generate Asset Admin­is­tra­tion Shells (AAS) and create digital twins, including 3D models, while inte­grating data spaces and secure IT/OT connec­tivity.

Bene­fits at a Glance

• Fast and auto­mated digi­tal­iza­tion of existing plants
• Contin­uous use and updating of plant docu­men­ta­tion
• Stan­dard­ized inter­op­er­ability through the AAS across different systems and tools
• Effi­cient data exchange while preserving data sover­eignty
• Improved automa­tion, analytics and process opti­miza­tion
• Inte­grated cyber­se­cu­rity for secure IT/OT connec­tivity

What is it about?
Expe­ri­ence how AAS-based data intel­li­gently extends the COMOS real device catalog, enabling seam­less, data-driven engi­neering from process plan­ning and instru­men­ta­tion to elec­trical engi­neering.

Bene­fits at a Glance

• Seam­less data inte­gra­tion: AAS-based data is inte­grated directly into the COMOS real device catalog and is consis­tently avail­able across all engi­neering disci­plines.
• More effi­cient engi­neering: A unified data foun­da­tion accel­er­ates and simpli­fies processes from process plan­ning and instru­men­ta­tion to elec­trical engi­neering.
• Higher data quality and trans­parency: Up-to-date, stan­dard­ized device data reduces errors, increases plan­ning reli­a­bility and improves trace­ability throughout the entire project.

What is it about?
The demon­strator show­cases the imple­men­ta­tion of a NOA-based digi­tal­iza­tion concept using Asset Admin­is­tra­tion Shells (AAS) for a brown­field process plant, creating a contin­uous digital twin without modi­fying existing systems.

Bene­fits at a Glance

• Contin­uous engi­neering data flow and consis­tent as-is docu­men­ta­tion
• Greater trans­parency and improved deci­sion-making through central­ized data avail­ability
• Step-by-step digi­tal­iza­tion without disrupting ongoing oper­a­tions
• Foun­da­tion for appli­ca­tions such as predic­tive main­te­nance and process opti­miza­tion

What is it about?
The demon­strator show­cases AI-powered capa­bil­i­ties around the Asset Admin­is­tra­tion Shell (AAS), including stan­dard­ized gener­a­tion of digital twins, intu­itive inter­ac­tion through a chatbot and auto­mated analysis of plant perfor­mance.

Bene­fits at a Glance

• AI-gener­ated AAS based on NE196: Stan­dard­ized and modular Asset Admin­is­tra­tion Shells signif­i­cantly reduce engi­neering effort for creating and instan­ti­ating digital twins, with life­cycle infor­ma­tion already struc­tured and linked to estab­lished repos­i­to­ries such as bsDD and ECLASS.
• Chatbot for AAS inter­ac­tion (BaSyx): An intu­itive inter­face enables oper­a­tors to interact natu­rally with digital twins, access rele­vant infor­ma­tion on demand and reduce the need for manual data searches or special­ized IT knowl­edge.
• Auto­mated perfor­mance assess­ment: The tool auto­mat­i­cally gener­ates KPIs, trends and perfor­mance overviews, elim­i­nating manual engi­neering effort while enabling faster iden­ti­fi­ca­tion of faults and oppor­tu­ni­ties for energy opti­miza­tion.

What is it about?
The demon­strator explores the poten­tial of using Large Language Models (LLMs) to support plant oper­a­tors, taking into account data avail­ability, expert knowl­edge and the constraints of brown­field envi­ron­ments.

Bene­fits at a Glance

• Proof of feasi­bility: Eval­u­ates which ques­tions can be answered using existing brown­field data and where expert knowl­edge is still required.
• Struc­tured data foun­da­tion: Defines require­ments for data models that connect general AI knowl­edge with plant-specific context.
• Effi­cient knowl­edge manage­ment: System­at­i­cally contex­tu­al­izes existing datasets to improve the avail­ability and use of oper­a­tional knowl­edge.

What is it about?
Expe­ri­ence the next gener­a­tion of modular automa­tion with the ZETA Cubes. The demon­strator shows how produc­tion plants can be config­ured using the Module Type Package (MTP) stan­dard, where indi­vidual cubes repre­sent modular process units such as mixing, buffering or chro­matog­raphy. Each module is auto­mat­i­cally detected via RFID and inte­grated into the process control system without manual engi­neering, making the plant imme­di­ately avail­able in the Copa-Data zenon POL envi­ron­ment.

Bene­fits at a Glance

• Plug & Produce in prac­tice: Modules are auto­mat­i­cally detected and inte­grated without manual engi­neering.
• Reduced engi­neering effort: Stan­dard­ized MTP inte­gra­tion signif­i­cantly simpli­fies plant config­u­ra­tion and commis­sioning.
• Maximum flex­i­bility: Produc­tion plants can be easily recon­fig­ured or expanded by adding or replacing modular units.
• Open and scal­able archi­tec­ture: Demon­strates how modular concepts and open stan­dards support the future of the process industry.

What is it about?
Festo and Beck­hoff jointly show­case their Module Type Package (MTP) solu­tions, demon­strating seam­less modular plant inte­gra­tion. In a live demon­stra­tion, a Festo module is inte­grated into the Beck­hoff system to illus­trate simple, stan­dard­ized and vendor-inde­pen­dent inter­op­er­ability.

Bene­fits at a Glance

• Stan­dard­ized objects with uniform inter­faces
• Defined work­flows through a stan­dard­ized service state machine
• Auto­mated inte­gra­tion
• Vendor-inde­pen­dent inter­op­er­ability

What is it about?
The demon­strator show­cases the stan­dard­ized inte­gra­tion of mobile robots into process plants based on NAMUR Open Archi­tec­ture (NOA) and Module Type Package (MTP). The goal is scal­able, vendor-inde­pen­dent robotic inte­gra­tion, partic­u­larly for brown­field envi­ron­ments.

Bene­fits at a Glance

• Plug & Produce: Fast inte­gra­tion of mobile robotics
• Scal­able: Easily trans­fer­able across different plants and loca­tions
• Brown­field-ready: Enables digi­tal­iza­tion without exten­sive plant modi­fi­ca­tions
• Vendor-inde­pen­dent: Stan­dard­ized inte­gra­tion of different robotic systems into various soft­ware envi­ron­ments
• Mobile sensing: Uses estab­lished process sensors mounted on mobile robots

What is it about?
The demon­strator show­cases Soft­ware Defined Automa­tion in prac­tice: an open, scal­able infra­struc­ture that enables container­ized automa­tion appli­ca­tions to be devel­oped inde­pen­dently of hard­ware, deployed through a central DevOps plat­form and orches­trated across devices and vendors.

Bene­fits at a Glance

• Decou­pled soft­ware and hard­ware: Flex­ible, portable automa­tion solu­tions that work across vendors and can be updated with ease
• Scal­able deploy­ment and life­cycle manage­ment: CI/CD and central­ized manage­ment enable contin­uous deploy­ment and oper­a­tion of automa­tion appli­ca­tions
• Open, inter­op­er­able ecosystem: A multi-vendor approach accel­er­ates inno­va­tion, reduces frag­men­ta­tion and increases effi­ciency in indus­trial produc­tion

What is it about?
The Fraun­hofer IPT “Juggler” demon­strator show­cases how a complex control task is imple­mented using a tradi­tional automa­tion approach. Over the coming months, the system will be trans­formed into an SDA-enabled demon­strator, illus­trating how real-time, time-synchro­nized commu­ni­ca­tion between distrib­uted, container­ized OT control func­tions and IT/IoT appli­ca­tions on edge devices can be real­ized. The project also invites inter­ested industry part­ners to contribute, with the goal of jointly presenting the results at SPS 2026.

Bene­fits at a Glance

• For machine builders and automa­tion engi­neers: Deter­min­istic, inter­op­er­able IT/OT commu­ni­ca­tion enables flex­ible, soft­ware-based machine concepts.
• For end users: Clearly defined use cases, eval­u­ated stan­dards and refer­ence archi­tec­tures provide prac­tical guid­ance for Soft­ware Defined Automa­tion.
• For devel­opers and oper­a­tors: Tech­nology eval­u­a­tions and compat­i­bility assess­ments (e.g. SDA/Margo) provide a solid foun­da­tion for inter­op­er­able, real-time edge control solu­tions.

What is it about?
The demon­strator show­cases an intel­li­gent agent that moni­tors produc­tion, detects disrup­tions at an early stage and auto­mat­i­cally replans batches and resource allo­ca­tion.

Bene­fits at a Glance

• Reduces manual effort
• Mini­mizes down­time
• Enables data-driven deci­sion-making during live oper­a­tions

What is it about?
The demon­strator shows how auto­mated data exchange based on the Factory‑X stan­dard reduces the effort required for process inspec­tion manage­ment and execu­tion.

Bene­fits at a Glance

• Reduced effort for plant oper­a­tors and service providers: Less time spent creating data, handling follow-up ques­tions, preparing inspec­tion reports and locating rele­vant infor­ma­tion on site
• Improved data reuse: Inspec­tion data can be directly reused for inspec­tion prepa­ra­tion, defect manage­ment and shut­down plan­ning

What is it about?
The demon­strator shows how an AI agent becomes a context-aware assis­tant through an AAS-based digital twin rather than simply acting as a data search tool. Using a 3D printing process as an example, live data, quality metrics, main­te­nance infor­ma­tion and busi­ness KPIs are seman­ti­cally combined, analyzed in Inter­Sys­tems IRIS and made acces­sible through a natural language chat inter­face. Instead of providing raw data alone, the agent under­stands context such as asset rela­tion­ships, units, thresh­olds, data sources, fresh­ness and permitted actions, enabling mean­ingful recom­men­da­tions without navi­gating multiple systems and dash­boards.

Bene­fits at a Glance

• Faster root cause analysis for quality issues, disrup­tions and oper­a­tional devi­a­tions
• Better deci­sion-making by combining live data, KPIs, quality, main­te­nance and cost infor­ma­tion
• Reduced manual effort for data collec­tion, analysis and action plan­ning
• Greater trans­parency into asset condi­tion, OEE, scrap rates, main­te­nance needs and oper­a­tional perfor­mance
• Scal­able approach across multiple machines, produc­tion lines, sites and asset types
• Improved trace­ability of recom­men­da­tions through clear data sources, data fresh­ness and docu­mented assump­tions
• Support for secure oper­a­tions through defined permis­sions and autho­rized actions
• Foun­da­tion for use cases such as predic­tive main­te­nance, quality improve­ment and produc­tion opti­miza­tion