The World Economic Forum recently took a look a how AI was being used in manufacturing world wide, with comments from six supply chain leaders on the topic.
The report notes that “AI is rapidly transforming the factory floor, accelerating the shift toward smarter, more efficient operations. From predictive maintenance to quality control, adding that “AI-powered systems are optimizing production lines, driving cost savings and reducing emissions.”
We publish highlights below.
Supply Chain Digest Says... |
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Comments from Nihat Bayiz. W, Chief Production and Technology Officer, Beko: Key AI applications include a smart machine learning powered control system that adjusts parameters in real time, reducing scrap and preventing defects in sheet metal forming, resulting in a 12.5% material cost savings.
A decision tree-based model prevents clinching failures from variations in sheet thickness, cutting defect rates by 66%. A closed-loop valve gate control using convolutional neural network algorithms optimizes plastic injection, analyzing over 150K data points and improving cycle time by 18%.
Advanced machine learning algorithms in cleaning cycle design reduced time to market by 46% and achieved 99% optimization in cleaning performance.
Jim Fox, Vice President Sweden Operations and Executive Sponsor for Digital, AstraZeneca: In manufacturing, AI-powered process digital twins optimize the conditions for yield and productivity while reducing the use of raw materials and minimizing tech transfer requirements. The digital twins simulate the relationship between drug substance properties, process conditions and product quality to optimize operating conditions.
Combined with continuous manufacturing, we’ve reduced manufacturing lead times from weeks to hours. And with GenAI-human synergy, we are accelerating regulatory filings, cutting the time to create some documents by more than 70%.
Anand Laxshmivarahan. R, Chief Digital and Information Officer, Jubilant Bhartia Group: At Jubilant Ingrevia, we’ve embraced AI and machine learning across all production stages to boost efficiency, reduce process variations and optimize yield and throughput.
We’ve widely deployed “digital twins” – virtual replicas of critical assets – to model, forecast and manage operations in real time. Specific AI or machine learning models optimize production parameters, leveraging historical and current data to ensure quality and resource efficiency.
Using insights from our Digital Performance Management model, we’ve reduced process variability by 63%
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CATEGORY SPONSOR: SOFTEON |
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Our manufacturing units are equipped with internet of things-based monitoring systems with predictive analytics – superior AI algorithms to predict equipment failures before they occur. This approach has reduced downtime by more than 50%, enhancing our operational efficiency remarkably.
A key first step in doing so has been to ensure all our plants are connected and integrated with an Operational Data Lake to get a real-time and integrated view of data to help us deliver AI or machine learning-based interventions to improve the yield and throughput.
Stephan Schlauss, Global Head of Manufacturing, Siemens AG: At Siemens, we experience AI's transformative impact on manufacturing daily, boosting productivity, efficiency and sustainability. With rising labour costs, skill shortages and a need for eco-friendly solutions, AI is a crucial part of our vision for the industrial metaverse.
AI applications deliver remarkable results across our entire value stream at Siemens Electronics Factory Erlangen. For example, machine learning optimizes testing procedures, significantly increasing first-pass yield and boosting efficiency.
AI-enabled robots that pick and place different parts and materials in our fully automated assembly lines reduce automation costs by 90%. Manual workers are also empowered with AI-guided systems, enhancing productivity and quality.
Our industrial-grade AI infrastructure, built on Siemens hardware and software, simplifies adoption and reduces change management.
 Guoxin Yao, General Manager of Supply Chain, Ambient Business Unit, Mengniu Dairy: Mengniu’s “digitalization 1.0” focused on digitalizing dairy farms and factories to achieve comprehensive digital coverage on the supply side, from raw milk to production.
In its One-Stop Laboratory, AI modules like neural network image recognition and reinforcement learning-based intelligent scheduling replace manual testing, ensuring accuracy and efficiency in critical test stages.
For procurement and cyclic delivery, AI automates supplier order scheduling and vehicle dispatching, increasing inventory turnover by 73% and operational efficiency by 8%.
In predictive maintenance, AI algorithms analyze equipment data to forecast faults and prevent downtime. These systems have enhanced overall production decision-making and operational efficiency.
Simon Zhang, Vice President and Chief Data Officer, Midea Group: Midea washing machines explore and restructure end-to-end green and sustainable new capabilities, widely deploying a variety of digital technologies integrated with AI applications in product design, manufacturing – quality, equipment and energy – and logistics, promoting intelligent operation in various sub-scenarios.
We have achieved a 25% reduction in development cycles, a 53% reduction in poor quality and a 29% optimization of logistics paths. The company is witnessing factory-scale adoption through the use of AI.
The deep application of AI in the entire factory process covers 457 sub-scenarios, mainly through self-developed small sample intelligent algorithms and open AI cloud platforms, significantly reducing sample collection and training time, and lowering scale promotion and operation costs.
SCDigest says: It appears the benefits of AI in manufacturing are very real indeed.
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