Leaders at Siemens, Beko and Jubilant Bhartia share how they’re using AI at their Manufacturing ‘Lighthouse’ sites as awarded by WEF.
AI’s impact on manufacturing can be felt and seen across operations.
From enhancing quality control to driving cost savings to reducing emissions to optimising production lines, AI is making the factory floor a smarter and more efficient place.
Nowhere is this more evident than with the manufacturers who are a part of the World Economic Forum’s Global Lighthouse Network.
Cofounded with McKinsey, this pioneering initiative examines how Industry 4.0 technologies like AI are shaping the future of manufacturing operations.
Recently welcoming 22 new members, the Global Lighthouse Network is home to the trailblazing manufacturers who have embraced digital transformation to drive profound operational improvements and efficiency.
Their advanced sites set new standards for technological integration, productivity, sustainability and workforce development, enhancing human-machine collaboration.
Six manufacturing leaders whose sites were recognised by the Global Lighthouse initiative sat down with the World Economic Forum to expand on their use of AI.
Their insights demonstrate the holistic impact AI can have on operations, fundamentally transforming the factory itself.
Here’s what the first three had to say.
Stephan Schlauss, Global Head of Manufacturing, Siemens AG
Siemens is undoubtedly one of the most technologically agile and trailblazing manufacturers.
Across its vast operations, the company widely adopts Industry 4.0 technologies as part of its strategy to create and build its Industrial Metaverse.
“At Siemens, we experience AI’s transformative impact on manufacturing daily, boosting productivity, efficiency and sustainability,” Stephan says.
“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.”
Manufacturing Digital recently reported on how Siemens’s Electronics factory in Erlangen had been recognised as a Digital Lighthouse.
The third Siemens site to be recognised by the WEF, this award came after Siemens announced a staggering €500m (US$549.45m) in research and infrastructure in Erlangen, intending for the hub to drive development and research for the industrial metaverse.
We covered how Siemens Green Lean Digital approach was essential to this, an approach which was shaped by their strategy towards AI.
“AI applications deliver remarkable results across our entire value stream at Siemens Electronics Factory Erlangen. For example, machine learning optimises testing procedures, significantly increasing first-pass yield and boosting efficiency,” adds Stephan.
“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.”
Stephan also describes AI as having a positive impact on the workforce, enhancing training and learning as the industry struggles with a hiring and retention gap.
“Automated training and deployment pipelines minimise the efforts for updates, while continuous automated monitoring ensures reliability and trust in AI algorithms,” he concludes.
“This enhances scalability, lowers adoption barriers and fosters trust.”
Nihat Bayiz, Chief Production and Technology Officer, Beko
Beko is another global manufacturer recognised by the WEF for its pioneering utilisation of Industry 4.0 technologies.
Nihat Bayiz, Chief Production and Technology Officer at the company explained how AI adoption has profoundly enhanced Beko’s manufacturing operations.
“Through the integration of AI-driven innovations, we have not only optimised our manufacturing processes and design but also empowered our workforce,” Nihat says.
“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%. and a closed-loop valve gate control using convolutional neural network algorithms optimises plastic injection, analysing over 150K data points and improving cycle time by 18%.”
Beko’s Ankara Dishwasher plant has been recognised as a key case study in the WEF Global Lighthouse Network. The plant’s integration of over 35 in-house solutions with its IoT platform FLOW led to a myriad of quantifiable improvements.
These included a 46% reduction in time-to-market, a 29.2% drop in field failure rates and a 26.1% decrease in conversion costs.
AI is delivering Beko similar impressive results, according to Nihat.
“Advanced machine learning algorithms in cleaning cycle design reduced time to market by 46% and achieved 99% optimisation in cleaning performance,” he says, noting the positive impact AI has had on the workforce at Beko as well.
“Training programmes covering basic AI principles to advanced machine learning applications have led to 3,160 training hours completed in six months.
“A global automation programme guides factory-scale adoption and use-case sharing, governed by central and local digital transformation offices, with plans to establish a lighthouse factory for each product group.”
Anand Laxshmivarahan. R, Chief Digital and Information Officer, Jubilant Bhartia Group
Jubilant Bhartia Group, the Indian conglomerate with operations spanning manufacturing, pharmaceuticals, food, agribusiness and energy was recognised by the WEF Global Lighthouse Network earlier this year for its Ingrevia facility.
Dedicated to speciality chemical manufacturing, the facility based in Gujarat has embraced 4.0 technologies, exhibiting more than 30 integrated use cases leveraging AI, predictive platforms and digital twins.
“At Jubilant Ingrevia, we’ve embraced AI and machine learning across all production stages to boost efficiency, reduce process variations and optimise yield and throughput,” says Anand Laxshmivarahan. R, Chief Digital and Information Officer, Jubilant Bhartia Group
“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 optimise 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%.”
According to Anand, Jubilant Bhartia Group have experienced significant improvements through AI adoption, specifically in the field of predictive maintenance.
“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,” he says.
“Soft sensors, powered by AI, enhance data collection and analysis, improving product quality and optimising process conditions. The AI-driven analytics system manages energy consumption, reducing operational costs and achieving a 20% cut in Scope 1 emissions, supporting sustainability goals.”
By pursuing AI integration holistically across operations, the group has experienced profound benefits, with plans to increase use of the technology moving forward.
“Integrating AI throughout our production process enhanced automation and boosted operational efficiency, laying the groundwork for a more sustainable and environmentally friendly future,” Anand says.
“Diving in headfirst across all 50 of our plants, we plan to deploy 10-12 use cases involving emerging technologies throughout our global operations this year and next.
“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.
“Our JUMP (Jubilant Model Plant) serves as a digital lighthouse to perfect AI models before broader deployment, while our Digital Centre of Excellence drives this transformation with AI experts and Digital 101 training for all employees. Recognition is also key in this journey – our rewards programme nurtures digital champions while DigiScoop spreads success stories, helping scale innovation.”
Anand believes that the group’s adoption of AI will: “Redefine what’s possible in chemical manufacturing.”
AI has the potential to do the same across a diversity of manufacturing verticals, as trailblazers like Siemens, Beko and the Jubilant Bhartia Group demonstrate.
What experts at AstraZeneca, Mengniu Dairy and Midea Group told the WEF
Welcome to part two of our coverage of how manufacturers recognised by the World Economic Forum’s (WEF) Global Lighthouse Network are using AI.
Our last article covered the successes of Siemens, Beko and the Jubilant Bhartia Group, with comments from key executives at the organisations.
This piece today examines how three new manufacturers- AstraZeneca, Midea Group and Mengniu Dairy- are pursuing AI integration to improve manufacturing operations and maximise results.
Speaking to the WEF in a blog, here’s what three key executives from these companies had to say.
Jim Fox, Vice President Sweden Operations and Executive Sponsor for Digital, AstraZeneca
AstraZeneca has had two of its most advanced manufacturing sites – one in Sweden and one in China – awarded WEF Global Lighthouse Network status.
It’s site in Wuxi, China has integrated more than 30 digital tools and AI-powered solutions, boosting output by 55% and lead times by 44%.
Today it is independently ranked in the top 10% of more than 800 world class pharmaceutical sites in quality, speed and efficiency.
“Today, AstraZeneca is using AI to revolutionise how we develop, make and supply medicines,” says Jim Fox, Vice President Sweden Operations and Executive Sponsor for Digital, AstraZeneca.
“In drug development, predictive modelling helps optimise the physical and chemical properties of our active pharmaceutical ingredients and predict the performance of formulated products during manufacturing.
“Generative AI (GenAI), machine learning and large language models are already helping reduce development lead times by 50% and reduce the use of active pharmaceutical ingredients in experiments by 75%.”
At AstraZeneca’s other site in Södertälje, Sweden the company is utilising more than 50 digital solutions and has upskilled the digital capabilities of 3,000 employees. At this site AI-based digital twins and machine learning has helped boost productivity by 56% and reduced development lead times for launching new products by 67%.
“In manufacturing, AI-powered process digital twins optimise the conditions for yield and productivity while reducing the use of raw materials and minimising tech transfer requirements,” says Jim.
“The digital twins simulate the relationship between drug substance properties, process conditions and product quality to optimise 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%.”
Södertälje accounts for 40% of AstraZeneca’s global production volume and is one of the largest pharmaceutical manufacturing sites in the world. The progress made with AI at this site will be replicated across AstraZeneca’s manufacturing and influence the wider sector.
“As we head towards a net zero carbon footprint, AI-powered tools will also help us achieve this by allowing us to interrogate life-cycle data for our medicines and providing visibility of deviations and emissions “hotspots” so that we can mitigate these across the whole supply chain,” concludes Jim.
Guoxin Yao, General Manager of Supply Chain, Ambient Business Unit, Mengniu Dairy
Mengniu Dairy was recognised by the WEF for its creation of the world’s first fully-intelligent dairy factory in Ningxia, China.
“Mengniu’s “digitalisation 1.0” focused on digitalising dairy farms and factories to achieve comprehensive digital coverage on the supply side, from raw milk to production,” says Guoxin Yao, General Manager of Supply Chain, Ambient Business Unit, Mengniu Dairy.
“Digitalisation 2.0 shifted to optimising management by building a digital marketing and consumer-side operations system, creating precise consumer profiles to enhance service experiences and marketing.”
At the Ningxia factory over 30 advanced Industry 4.0 use cases were explored, from flexible automation to intelligent decision-making. These initiatives spanned milk testing, processing and packaging and resulted in a 60% decrease in quality defects, 32% reduction in operational costs and a 55% reduction in delivery lead time. Frequently incorporating AI, they significantly enhanced product quality and efficiency.
“With digitalisation 3.0, Mengniu integrates AI across the supply and consumer sides to optimise supply chain processes and boost efficiency,” adds Guoxin Yao, highlighting three specific AI use cases that have emerged.
“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 analyse 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 Group, the leading electrical appliance manufacturer has been recognised by the WEF three times for pioneering the adoption of Industry 4.0 tools in manufacturing.
It’s factory in Jingzhou, China which specialises in smart home appliances embraced digital transformation when it found traditional manufacturing processes didn’t allow them meet customer needs quickly enough.
Flexible automation. loT and AI was adopted at the factory, with more than 2,000 digital transformation initiatives pursued.
These initiatives have increased labour productivity by 52% and reduced failure rates by 53%, production lead time by 25% and the utility consumption per unit by 20%.
Many of these initiatives focused on using technological innovation to enhance the performance of key products, like Midea’s washing machines.
“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,” says Simon Zhang, Vice President and Chief Data Officer, Midea Group.
“We have achieved a 25% reduction in development cycles, a 53% reduction in poor quality and a 29% optimisation of logistics paths. The company is witnessing factory-scale adoption through the use of AI.”
AI has played a significant role in Midea Group’s results, integrated across factory operations.
“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,” adds Simon Zhang.
These manufacturers recognised by the WEF speak to the sheer benefits that come with embracing Industry 4.0 technologies like AI.
Understanding how to harness it’s potential and tailor its benefits to your distinctive manufacturing needs is what sets the trailblazers like the companies above apart.
