How Digitalization can Boost Manufacturing Productivity

McKinsey & Company shows how Danone turns operations into a growth engine. A sharp interview by Pierre de la Boulaye and Søren Fritzen with Vikram Agarwal highlights a structural shift across the FMCG industry. For decades, operations were treated as a cost center. That paradigm is changing. Leading companies now position operations as a driver of growth and competitiveness. The transformation at Danone shows how AI, digital manufacturing and advanced supply chains are reshaping the sector. Several insights stand out. 1) AI turns factories predictive Operators increasingly monitor production lines via tablets instead of control rooms. AI systems detect potential equipment failures before they occur, for example overheating motors in packaging lines. Maintenance shifts from reactive repair to predictive intervention, improving uptime and efficiency. 2) Capacity planning becomes strategic Danone distinguishes three ways to build manufacturing capacity: • Release capacity from existing assets • Transform capacity by converting underperforming lines • Create capacity through new production investments Transforming existing lines enables growth with much lower capital intensity than building new factories. 3) AI reshapes supply chains Danone uses AI models to forecast ingredient costs and supply chain dynamics across global agricultural markets. Instead of analyzing thousands of variables, systems process millions of data points. For a company managing roughly €13.7B in COGS, forecasting accuracy becomes a competitive advantage. 4) Digital manufacturing at scale Danone’s Digital Manufacturing Acceleration program already covers 80+ factories, with 40 more joining soon, across 140+ production sites globally. The ambition goes beyond Industry 4.0 toward Industry 5.0, combining machines, AI and human expertise. 5) People remain central Danone employs 47,000+ people in operations, about half of its workforce. Through its Industry 5.0 Academy, the company has already trained around 20,000 employees in digital manufacturing capabilities. Why this matters The global FMCG industry generates over $4 trillion in annual sales and operates on tight margins. Even small improvements in forecasting, manufacturing efficiency or capacity utilization can translate into billions in value creation. As demand shifts toward health, high-protein and plant-based products, supply chains must become faster and more flexible. AI-driven operations are becoming a strategic advantage. The signal for FMCG leaders is clear: Competitive advantage is increasingly built beyond brands and marketing — in operations. #operations #manufacturing #ai #digitaltransformation #foodindustry #foodtech #retailtech #innovation #procurement #datadriven #danone #france #europe #startup #investors #marketing #sales #technology #logistics

Transformation thrives when people are empowered to make the most of technology. 🚀 My recent visit to the Bosch production facility for automotive and eBike drives in Miskolc, Hungary, showcased this perfectly. I was deeply impressed to see firsthand how their progress in digitalization and the implementation of the Bosch Manufacturing and Logistics Platform (BMLP) is reshaping their manufacturing operations. BMLP is a globally standardized, open IT platform that connects all stages of production and logistics. During an insightful plant tour, I observed a successful example of how the platform leads to significant improvements in efficiency, quality, and data transparency across the plant. What stood out most was seeing the passionate and enthusiastic team at Miskolc leverage this technology in action and achieving great results towards operational excellence. Here are three key areas where BMLP is contributing to the plant’s digital transformation success, powered by our NEXEED IAS: 1️⃣ Enhanced Efficiency & Reduced Downtime: The module Shopfloor Management enables a closed PDCA cycle in production by consequent integration of all relevant information in one system. This leads to quick reaction in case of deviations to minimize downtimes and safeguard the daily performance targets.   2️⃣ Improved Product Quality: Continuous monitoring throughout production stages helps the team identify issues early, ensuring top-tier quality while driving process improvements.   3️⃣ Change Management: Change management plays a crucial role in digital transformation within a plant. As seen in Miskolc, effectively managing change ensures that the workforce is engaged, and equipped to embrace new technologies, driving sustainable success. In Miskolc we have seen solutions using gamification that help to involve all associates, making the transition both engaging and effective.   I was also excited to see AI in action with a live demo of 8D Analysis using GenAI, cutting failure analysis time by half. By automating the root cause analysis process, engineers are now spending less time on administrative tasks and more on proactive problem-solving – a great example of how technology empowers people. Beyond the production lines, the most rewarding part of the visit was engaging with the team. Their passion for digitalization, commitment to upskilling, and their drive for innovation truly brought home the message: technology is only as strong as the people behind it. A special thank you to the entire Miskolc team for the inspiring discussions and warm welcome – along with Volker Schilling, Klaus Maeder, Joerg Klingler, Volker Schiek, Norbert Jung, Stephan Brand, Aemen Bouafif, and everyone who joined us on this great trip. I’m excited to see what’s next on this incredible digitalization journey!

𝗗𝗮𝘁𝗮 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗜𝘀 𝗝𝘂𝘀𝘁 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗗𝗲𝗰𝗮𝘆 You can feel it on every factory floor today — dashboards everywhere, clarity nowhere. We’re drowning in numbers, yet starving for meaning. Stop worshipping at the altar of reporting. Demand 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. Still treating your MES as a digital filing cabinet? That’s yesterday’s strategy. If your operations team can’t predict tomorrow’s bottlenecks today — what’s all that data really worth? 𝗦𝘁𝗼𝗽 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮. 𝗦𝘁𝗮𝗿𝘁 𝗧𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗜𝘁 𝘁𝗼 𝗧𝗵𝗶𝗻𝗸. Manufacturers have been collecting data for decades. Few have taught it how to learn. That’s the leap from digitization to informatization — from recording what happened to engineering what happens next. A global electronics manufacturer recently informatized three years of MES data — lead times, process durations, and equipment logs. Within six months, they cut average production lead time by 14.7% — with zero new capital investment. Their advantage? A deceptively simple metric: the 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗥𝗮𝘁𝗲. It measured how fast their processes were learning — turning hindsight into foresight. When your data starts predicting its own improvement, you’re not just automated —you’re 𝗮𝗻𝘁𝗶𝗳𝗿𝗮𝗴𝗶𝗹𝗲. 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗜𝘀 𝗖𝗹𝗲𝗮𝗿 In the next decade, the winners in manufacturing won’t be the biggest producers — they’ll be the fastest learners. Ignore informatization, and your competitors won’t just outpace you — they’ll 𝗼𝘂𝘁𝘁𝗵𝗶𝗻𝗸 𝘆𝗼𝘂. 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Enough dashboards. Enough diagnostics. Stop reporting what broke. Start predicting what’s about to. Don’t measure the past — manipulate the future. Data gave you sight. 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗴𝗶𝘃𝗲𝘀 𝘆𝗼𝘂 𝘃𝗶𝘀𝗶𝗼𝗻.

🇩🇪 How German Toolrooms Convert 80% Manpower into 100% Efficiency — Lessons for Indian Manufacturing In most Indian toolrooms, we utilize around 80% of our manpower capacity, yet still lose nearly 20% efficiency due to delays, variation, and unbalanced workloads. German manufacturers face the same challenges — but their response is fundamentally different. They don’t push people harder; they design systems smarter. Here’s how they close that gap 👇 🔧 1️⃣ Standardization – #Prozessdisziplin Every operation, from EDM to die spotting, is standardized and documented. Once the best method is found, it becomes the standard across all stations. ➡️ This eliminates personal variation and keeps efficiency consistent above 95%. 🧠 2️⃣ Skill + Autonomy Culture Through their Meister System, even shop-floor technicians understand the why, not just the how. They make small, smart decisions without waiting for approvals. ➡️ No idle waiting, higher ownership, and faster improvement cycles. ⚙️ 3️⃣ Predictive + TPM Integration Machine and tool health are digitally tracked through TPM and predictive analytics. Downtime is anticipated — not reacted to. ➡️ Equipment stays “production ready” 24/7. 🏭 4️⃣ Flow-Oriented Layouts German toolrooms are built for flow. Materials, jigs, and fixtures are positioned by takt time — the rhythm of production. ➡️ Reduces motion losses and operator fatigue, boosting productivity by up to 20%. 📊 5️⃣ Digital Visibility MES dashboards and data analytics create shared visibility for planners, toolmakers, and managers. ➡️ Bottlenecks are seen and solved in real-time. 💪 6️⃣ Human Efficiency = System Efficiency They treat manpower not as cost, but as capital. Cross-training, ergonomics, and reward for process improvements make every minute valuable. ✅ Result > 95% manpower utilization 1.4× productivity 30–40% less scrap and rework Low employee turnover and higher morale 🇮🇳 For our Toolrooms We can close the same 20% efficiency gap — not by pushing people harder, but by building disciplined systems around them. Efficiency isn’t a target. It’s a culture — built through standards, flow, and people who care. #Toolmaking #ManufacturingExcellence #LeanManufacturing #GermanEngineering #Industry40 #Toolroom #ContinuousImprovement #TPM #ProcessOptimization #ManufacturingInnovation #Productivity #SmartFactory #DigitalTransformation #CNC #ToolAndDie #PrecisionEngineering #EngineeringEducation #SkillIndia #VocationalTraining #ShijinInsights

If you’re managing a large-scale Industrial Digitalization program, here is some advice: 1. Automate infrastructure and data management headaches. Managing tools and infrastructure for data collection is a drain on resources. Fully managed data platforms let you focus on what matters—use cases and value creation, not the complexity of cloud, data tools, data lakes, or infrastructure management. 2. Avoid manual data entry wherever possible. Manually collected data isn’t sustainable. If you think adding multiple screens across the factory for people to enter data is smart, ask those who have tried it. You'll likely hear a long list of reasons why it doesn't work. 3. Choose sensors wisely. Many IIoT projects fail due to poor generalization. Don’t assume that one size fits all—there’s a specific sensor for each use case. Spending $10,000 on a sensor for a $5,000 use case won’t justify your business case. 4. Own your data. Remember, the technology you choose today will likely be obsolete in less than two years. Make sure you own your data. A good way to check is by asking the vendor if you can access the data collected outside their hardware or software. Many companies have fallen victim to technology lock-in, leaving them unable to switch because they don’t control their data. 5. Avoid monolithic solutions. While it may seem logical and convenient to have one vendor manage everything, unless you have an unlimited budget and can afford long waits for changes or adaptations, this isn’t a smart approach. Break down your requirements into small, modular business capabilities. Ensure interoperability between these capabilities—it's easier, faster, and cheaper. 6. Standards, standards, standards. The future of the industry lies in connected manufacturing. Everything—from demand analysis and planning to capacity and supply management—will eventually be automated. The business value is huge, and the automotive industry is already leading this change. Data needs to be securely shared across the value chain, and it all starts with standards. If your architecture doesn’t follow standards now, you’ll have to retrofit it later. I encourage all who work in this space to share their thoughts, experiences and ideas. Irrespective of Technology or solutions, companies must succeed on their digitalization journey. Every failed project pushes this industry backwards. I strongly believe the Industrial future will come from open collaboration. Let's do what’s right to move the industry forward. Finally, that's all that matters. IndustryApps IndustryApps DACH

Lean = Profit. Not Philosophy “If digitization shows you the problem… Lean is what solves it.” In the last post, we discussed how many factories already have production scanning and real-time data, yet performance does not improve. So what is the real challenge? It is not a lack of machines or data, but the reality that factories are running with , unstable flow, and constant firefighting, where work moves in batches, problems are discovered late, and decisions remain reactive, slowly turning waste into a norm and firefighting into a culture. This is why Lean is essential—not as a toolkit, but as a strategy to fix flow and build a culture of problem solving. By reducing WIP, balancing lines, and solving problems at root cause every day, flow becomes stable, variation reduces, and teams move from reacting to controlling. The mistake most companies make is jumping straight to digitization and expecting transformation. But digitization only makes problems visible faster—it does not solve them. That’s why many factories have data but no action, visibility but no improvement. The real answer is simple: Lean is the strategy, digitization is the enabler. When combined, digitization accelerates Lean by making flow visible in real time, exposing bottlenecks instantly, and triggering faster response, while also making a culture of problem solving easier to sustain because problems cannot hide and data drives daily action. Most factories miss this synergy. They either have digitization without direction or Lean without speed. But together, they create a system where problems are not just seen, but solved every day. Digitization enables . Lean builds the capability to improve it. Together, they turn data into action and action into results.

The problem with manufacturing digital transformation? "Random acts of digital." Manufacturers are drowning in data—projected to hit 4.4 Zettabytes by 2030. But it's trapped in fragmented legacy systems that can't talk to each other. The unlock isn't more technology. It's three things: 1. Clean, unified data. Focus on what matters: OEE, downtime, bottlenecks. A 10% OEE improvement can create capacity equivalent to 5 new production lines. For free. 2. Empowered people. Your operators need to interpret real-time analytics and make decisions fast—without waiting for the C-suite. AI tools can democratize the data, but humans still need to act on it. 3. Predictive AI. Stop analyzing yesterday's problems. AI continuously monitors performance, flags anomalies, and recommends actions before issues become critical. Think manufacturing GPS, not a map. The payoff? Cost savings from hidden efficiencies. Sustainability gains from reduced waste. Better workplace culture when teams can see their impact. Real-time benchmarking across sites will define who wins over the next decade. Stop the random acts. Build the foundation.

This morning, I watched a leaf collection crew braving the cold, exposed to dust, debris, and traffic hazards, especially the person riding behind their collection truck. It’s a vivid reminder: many manufacturing front line workers face similar risks every day on the shop floor, repetitive, hazardous, and unfulfilling tasks that leave little room for creativity or growth. With today’s digital manufacturing transformation, powered by capabilities such as computer vision, AI, and automation, we have the tools to change that story. Automated systems can already take on the dull, dirty, and dangerous jobs in factories freeing our front line teams for safer, smarter work. Imagine those workers moving from standing at machines all day to focusing on troubleshooting automated cells, analyzing process data, and innovating on the line. That’s real upskilling, and real value creation for both the business and the people who drive it. We need to harness technology not just for productivity, but for the well-being of our people. The best outcome of automation— giving our people dignity, a safe environment, and the chance to do meaningful work, every day. #Manufacturing #Automation #Safety #DigtalTransformation #FrontlineFuture

Seriously, we need to start looking beyond #compliance to unlock the true value in #digital #manufacturing! For too long, the conversation around digital transformation in regulated industries, has centered on compliance. While eBRs, eDHRs, and eLogs are undoubtedly a crucial outcome of digitization, viewing them as the primary goal misses the forest for the trees. Compliance in a truly digital system isn't a premium feature; it's a baseline, inherent capability. It's the expected result of well-designed, integrated digital processes. The real power of digital thinking extends far beyond ticking regulatory boxes. It's about unlocking massive productivity improvements, driving operational excellence, and fostering continuous innovation. When you embed compliance into a digital foundation, you free up resources to focus on: - Optimizing processes: Real-time data and analytics reveal bottlenecks and inefficiencies. - Improving decision-making: Insights from connected systems empower agile responses. - Boosting throughput and yield: Reduced errors, faster cycles, and better resource utilization. - Enhancing overall product quality: Proactive identification and mitigation of risks. Don't just digitize for #compliance; digitize for a #competitive #advantage. The future of life sciences manufacturing is about building in compliance while simultaneously driving unprecedented levels of #productivity. #DigitalTransformation #Manufacturing #Pharma40 #Industry40 #MES #IIoT #Productivity #Compliance #OperationalExcellence

When Manufacturing Becomes Software? Software-defined operations are now the engine empowering modern production. At organizations like Deloitte, SDM represents a paradigm shift that aligns data, automation, and human labor under a unified, software-driven framework, closing critical gaps across smart factory systems. Bosch Research takes this a step further, comparing SDM to a smartphone’s architecture, where hardware remains static while software defines functionality. In their collaborative project, Bosch showed how decoupling control software from physical machines enables rapid reconfiguration in volatile markets. At its core, SDM enables a factory to change what it makes, and how it makes it, without major retooling. Imagine rolling out new production workflows not by swapping out entire machines, but by deploying software updates. Reconfiguration, optimization, and even new product variations can be orchestrated digitally. . The bricks and mortar of this evolution lie in the Industrial Internet of Things, where networks of sensors, actuators, and digital twins bring the physical and digital worlds into real-time conversation. For manufacturers, this means smarter operations, agile supply chains, and factories that can adapt on the fly. Consider what happens when sensors monitor temperature, pressure, and vibration, feeding data into cloud platforms that detect anomalies long before breakdowns occur. Remote monitoring and predictive maintenance keep machines humming, today’s anomalies become tomorrow’s avoided downtime. In fact, predictive and prescriptive maintenance powered by AI and robotics is already saving global manufacturers billions. Startups like Aquant and Gecko Robotics report reductions of up to 23% in annual service costs, helping giants like The Coca-Cola Company and Siemens avoid catastrophic unplanned outages. But manufacturing’s digital transformation is about embedding software deeper into physical products, turning offline widgets into smart, connected systems capable of updates, analytics, and customer engagement long after delivery. This software-led evolution isn’t without its challenges. Realizing SDM demands new competencies, from managing cloud and edge infrastructure to securing increasingly complex digital ecosystems. Cybersecurity risks escalate as more endpoints connect online. And organizationally, the shift from hardware-focused teams to data-driven operations requires both investment and cultural transformation. Yet the upside is compelling. EY-Parthenon forecasts that smart connected products, fueled by software-defined tools, could unlock up to $2.3 trillion in incremental revenue and $1.8 trillion in operational savings by 2030.