At Rise of AI Summit 2020, Thibault de Assi, General Manager and Global Head of Industrial Computing at Siemens Digital Industries Factory Automation, presents Siemens' approach to deploying AI on the factory shop floor. He opens with figures on the scale of the opportunity: 85 percent of potential industrial assets remain unconnected, 87 percent of machine learning projects never reach production, and he expects 41.6 billion industrial IoT devices within five years. He describes Siemens' stack of control layer, edge layer and cloud layer (built on MindSphere and Mendix) and walks through six client use cases spanning die-casting defect prediction, bakery visual inspection, additive manufacturing quality checks, automated guided vehicle failure prediction, beer quality optimization and PCB inspection, each with concrete before-and-after metrics such as defect-rate reductions and cost savings. He answers audience poll results showing most shop-floor customers still run only proof-of-concept AI projects, and discusses lack of trust, insufficient in-house AI expertise and keeping models updated as the top barriers to adoption, along with a typical six-to-twelve-month payback period.
Key takeaways
- De Assi cites 87 percent of machine learning projects failing to reach production as the core problem Siemens' approach targets.
- An edge-deployed AI model for die-casting defect prediction cut defective parts by 30 percent and reduced the defect rate by 40 percent.
- A single stopped automated guided vehicle costs 500 euros per minute, motivating Siemens' failure-prediction use case for AGVs.
- PCB inspection with an AI model achieved 30 percent fewer X-ray tests and roughly half a million euros in reduced capital investment.
- De Assi estimates that around 50 percent of Siemens' customers are still only at the proof-of-concept stage for shop-floor AI.
Detailed notes
The scale of the opportunity
De Assi opens with figures framing why manufacturers need a systematic approach to AI: 85 percent of potential industrial assets remain unconnected, 87 percent of machine learning projects never make it into production, and he projects 41.6 billion industrial IoT devices within five years. He says Siemens' approach starts by understanding a customer's data flow across the value chain and how operational technology integrates with information technology, then combines an AI model, a software application with domain know-how and a human-machine interface, and industrial-grade hardware such as industrial PCs and edge devices to run it, with Siemens also managing the full lifecycle from connectivity to deployment, monitoring and retraining.
The technology stack
De Assi describes a layered architecture: a control layer of industrial PCs and PLCs controlling field devices such as power plants, production machines or elevators; an edge layer that he says is not a simple gateway but covers connectivity, openness, local data pre-processing, security and machine learning; and a cloud layer built on Siemens' MindSphere and Mendix platforms for developing and deploying industrial IoT applications. In a body-shop example, he shows automation and control (Simatic and third-party products), edge computing running AI and non-AI applications close to the machine, a centralized edge management system for mass and security updates, and an app store where Siemens and partners provide applications for deployment on the shop floor.
Six client use cases
De Assi presents six examples with results: defect prediction in die-casting, where an edge-deployed AI model cut defective parts by 30 percent and reduced the defect rate by 40 percent; visual quality inspection on a bakery line, improving counting accuracy and reducing manual inspection time; layer-by-layer defect detection in binder-jet 3D printing, saving about 2.5 hours of rework per day; failure prediction for automated guided vehicles, where a single stopped vehicle costs 500 euros per minute, avoiding unplanned stops; process optimization for beer quality, correlating raw-material and process parameters against a minimum quality grade of 6.8; and PCB inspection in an electronics plant, achieving 30 percent fewer X-ray tests, a 100 percent quality rate and roughly half a million euros in reduced capital investment.
Q&A highlights
Live poll results shown during the session indicate most attendees' companies are not yet using AI on the shop floor at all; de Assi says across Siemens' own customer base roughly 50 percent are running proof-of-concept projects, about 30 percent are actively scaling implementations, and 20 to 30 percent are just starting to explore where AI could help. On the top-cited barriers, lack of trust and insufficient AI expertise, he says Siemens addresses trust by monitoring deployed models for performance drift and retraining them with new data, and addresses the expertise gap by pairing its own machine learning experts with customers' process knowledge. He estimates a typical return on investment of six to twelve months, depending on customer and industry, contingent on jointly aligning on expectations and KPIs from the start.
Quotes
85 percent of potential assets remain unconnected, yet 87 percent of machine learning projects never make it into production.
Thibault de Assi
AI enabled us to trigger tasks that human beings are not able to do before.
Thibault de Assi
Questions answered in this talk
How does Siemens help manufacturers implement AI on the shop floor?
According to Thibault de Assi at Rise of AI Summit 2020, Siemens combines an AI model, software with domain know-how and a human-machine interface, and industrial-grade edge hardware, layered on top of a control, edge and cloud (MindSphere/Mendix) architecture, and manages the full lifecycle from connectivity to deployment and retraining.
What results has AI achieved in die-casting quality inspection?
De Assi states in this talk that an edge-deployed AI model for defect prediction in die-casting reduced defective parts by 30 percent and cut the overall defect rate by 40 percent for a Siemens customer.
What stops manufacturers from adopting AI on the shop floor?
De Assi identifies lack of trust in AI solutions and insufficient in-house AI expertise as the top two barriers cited by the audience at this talk, addressed by continuous model monitoring and retraining, and by pairing Siemens experts with customers' process knowledge.