Best of LinkedIn: Smart Manufacturing CW 19/ 20

Smart Manufacturing is shifting from ambition to industrial execution. Physical AI, software-defined automation and digital twins are becoming the core levers for scalable, resilient and ROI-driven operations, while AI value increasingly depends on the industrial backbone that turns insights into shop-floor action.

Date

May 20, 2026

Smart Manufacturing

Methodology: Every two weeks we collect most relevant posts on LinkedIn for selected topics and create an overall summary only based on these posts. If you´re interested in the single posts behind, you can find them here: https://linktr.ee/thomasallgeyer. Have a great read!

Listen to our podcast

If you prefer listening, check out our podcast summarizing the most relevant insights from Smart Manufacturing CW 19/ 20:

Smart Manufacturing Execution

Smart Manufacturing shifted from broad Industry 4.0 narratives toward execution, ROI, resilience, and scalable deployment
Physical AI moved closer to factory reality through autonomous welding, humanoid robotics, adaptive factory systems, and robotics use cases in logistics
Software-defined automation gained traction through virtual PLCs, portable control logic, containerized control, and vendor-agnostic automation platforms
Digital twins became more operational, supporting factory planning, batch-failure prediction, virtual commissioning, throughput improvement, and production-change validation

‍

Physical AI & Robotics

Siemens, Humanoid, and NVIDIA tested humanoid robotics in live operations at Siemens’ Electronics Factory Erlangen
Rove highlighted autonomous welding as a concrete Physical AI use case with direct relevance for industrial production environments
UPS’ automation investments showed robotics moving deeper into logistics workflows, including trailer unloading and AI-enabled warehouse operations
Robotics adoption was framed as selective and ROI-led, with stronger relevance in structured environments where safety, tasks, and payback are measurable

‍

Software-Defined Automation

Schneider Electric positioned EcoStruxure Automation Expert as a vendor-agnostic platform for portable control logic and adaptable automation
Audi and Siemens demonstrated that virtual PLCs can scale in automotive production, moving vPLC deployment beyond concept-stage experimentation
Software-defined automation was framed as a way to decouple control software from hardware and increase flexibility across changing production needs
Hannover Messe signals reinforced that industrial AI requires robust control architectures, edge execution, real-time decisions, and operational redundancy

‍

Industrial AI & Agentic Manufacturing

Industrial AI was increasingly positioned as an orchestration challenge rather than a model-only challenge
Agentic manufacturing gained visibility through SAP Sapphire 2026 sessions and SAP Digital Manufacturing use cases across production stages
Multi-agent AI was framed as a way to replicate specialized manufacturing expertise and support complex operational decision-making
AI value depended on reducing friction between signal, decision, and action through strong data, systems, and process foundations

‍

Digital Twins & Digital Thread

PepsiCo achieved a reported 20% throughput improvement through Siemens Digital Twin Composer and virtual validation of production changes
Siemens Digital Twin Composer unified 3D layouts, IoT sensor streams, simulation models, and point cloud data into a governed factory twin
Digital twin use cases expanded across pharma, aerospace, shipbuilding, yacht manufacturing, and virtualized production-line collaboration
Digital thread examples connected engineering parameters, manufacturing execution, simulation, and production validation across platforms

‍

MES & Manufacturing Platforms

MES remained central as the connective layer between machines, operators, and systems for real-time production monitoring and optimization
SAP Digital Manufacturing 2605 launched with POD 2.0 and expanded documentation, strengthening the execution layer for manufacturing operations
Siemens Teamcenter gained visibility as an AI-embedded PLM platform supporting faster product engineering from concept to prototype
MES selection was framed less around feature lists and more around business problems, measurable outcomes, and operating-model fit

‍

Product Launches

SAP Digital Manufacturing 2605 introduced POD 2.0 and strengthened manufacturing execution capabilities through expanded documentation
Siemens Eigen Engineering Agent was positioned as an AI-based accelerator for complex engineering workflows
Siemens Digital Twin Builder and Digital Twin Composer reinforced the role of simulation, AI, and real-time data in factory planning
Intel Core Series 3 processors supported ultra-low-power AI compute at the factory edge for robotics and industrial workloads

‍

Partnerships & Ecosystems

Siemens and NVIDIA stood out through humanoid robotics testing and broader collaboration around adaptive, AI-driven manufacturing
Audi and Siemens provided a production-grade software-defined automation signal through scaled virtual PLC deployment
Siemens, NVIDIA, and AWS appeared together around real-time factory twins and virtual production-change validation
MathWorks and Siemens linked model-based design workflows with automation engineering through MATLAB, Simulink, and SIMATIC Target for Simulink

‍

Subscribe to newsletter

Subscribe to receive the latest blog posts to your inbox every week.