Optimizing Manufacturing Performance

Everyone wants AI. But what are they actually funding? According to Deloitte’s latest survey of 600 manufacturing executives, the answer is clear: They’re funding data 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬. They’re funding 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐯𝐢𝐭𝐲. They’re funding automation 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞. They’re not buying the hype—they’re building the 𝐛𝐚𝐜𝐤𝐛𝐨𝐧𝐞. • 𝟕𝟖% of manufacturers are spending more than 20% of their improvement budgets on smart manufacturing. • 𝟒𝟎% say data analytics is a top investment priority. • 𝟐𝟗% are putting cloud and AI next. • 𝟑𝟒% are focused on active sensors—the eyes of their factories. Why? Because without clean, connected, contextualized data, none of the shiny stuff works. This isn’t a pilot phase. This is the build phase—and it’s quietly transforming how factories think, sense, and act. Despite all the tech, the lowest maturity score? 𝐇𝐮𝐦𝐚𝐧 𝐜𝐚𝐩𝐢𝐭𝐚𝐥. Manufacturers know the systems are coming online. Now they’re scrambling to bring the people along. So if you're a manufacturer still working off spreadsheets and tribal knowledge—know this: Your competitors aren’t just automating. They’re upgrading their operational IQ. And if you’re not investing in your digital foundation today… You’re budgeting for irrelevance tomorrow. 𝐑𝐞𝐚𝐝 𝐟𝐮𝐥𝐥 𝐫𝐞𝐩𝐨𝐫𝐭:  https://lnkd.in/e6_QsJcw ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

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!

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

Manufacturing processes are often plagued by inefficiency.   Here's why:   Manufacturers cling to old batch habits. ___   Batch Production is a traditional manufacturing method where identical or similar items are produced in batches before moving on to the next step.   Some manufacturers argue that large batches balance workloads and minimize changeovers.   But data often shows otherwise.   Overlong production runs cause overproduction. Operators lose focus working on large batches while equipment drifts out of standards between changeovers.   Main drawbacks:   -Piles of WIP inventory waiting for the next step -Defects hide among the batches -Inefficient space management -Uneven workflow -Long lead times   Those lead to:   -Some stations being overloaded, others waiting -Low responsiveness to customer demand -More scrap and rework -Higher carrying costs -Facility costs up   Switching to One-Piece Flow can bring relief.    Workstations are arranged so that products can flow one at a time through each process step, making changeovers quick and routine.   Main advantages:   +High customer responsiveness +Minimal work-in-process inventory +Quality issues are detected immediately +Reduced wasted space and material handling +Easy to level load production to match takt time   The selection between batch processing and one-piece flow can significantly impact quality, productivity, and lead time in a manufacturing process.   P.S. Some case studies show improvements in labour productivity of 50% or more. Lead times can drop by 80%. And quality can approach Six Sigma.

𝗪𝗵𝗲𝗻 𝗘𝗕𝗢𝗠 𝗮𝗻𝗱 𝗠𝗕𝗢𝗠 𝗱𝗿𝗶𝗳𝘁, 𝗰𝗵𝗮𝗼𝘀 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝗳𝗼𝗹𝗹𝗼𝘄𝘀. Engineering thinks the product looks one way. Manufacturing builds something slightly different. And the gap only shows up when defects appear, stations stall, or customers receive the wrong version. Teams underestimate how fragile EBOM ↔ MBOM alignment really is. This breakdown captures what each BOM actually does and why even small mismatches can cascade across design, planning, and production: 𝗘𝗕𝗢𝗠 (𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗕𝗢𝗠) ↠ Built by engineering to describe 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗶𝘀 - the design structure, CAD assemblies, specs, and revisions. ↠ Its purpose is accuracy, intent, and completeness before anything reaches the factory. 𝗠𝗕𝗢𝗠 (𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗕𝗢𝗠) ↠ Built by manufacturing to describe 𝗵𝗼𝘄 𝘁𝗵𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗶𝘀 𝗯𝘂𝗶𝗹𝘁 - operations, sequences, tooling, consumables, and station-level realities. ↠ Its purpose is feasibility, routing flow, and build readiness at scale. 𝗪𝗵𝘆 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 ↠ EBOM changes ripple fast - structure shifts, new parts appear, revisions close. ↠ MBOM absorbs those changes slowly - line balancing, tooling constraints, supplier lead times, packaging, routing. 𝗪𝗵𝗲𝗻 𝘁𝗵𝗲𝘆 𝗱𝗿𝗶𝗳𝘁: · The factory builds the wrong revision. · Work instructions fail. · Kitting groups no longer match assemblies. · Quality escapes multiply. · Production delays become “mystery issues.” All because the digital definition and physical execution stopped speaking the same language. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗹𝗲𝘀𝘀𝗼𝗻 Engineering defines the product. Manufacturing defines the reality. Great companies don’t choose between EBOM and MBOM, they keep them synchronized. For a deep dive into PLM, MES, or CAD and to elevate your understanding of PLM, connect with us at PLMCOACH and Follow Anup Karumanchi for more such information. #plmcoach #plm #teamcenter #siemens #3dexperience #3ds #dassaultsystemes #training #windchill #ptc #training #plmtraining #architecture #mis #delmia #apriso #mes

Now that the trade war has fully begun (China just placed 84% tariffs on US exports (https://lnkd.in/g58ZrWqw)), I wanted to share data showing the realities of manufacturing payroll changes as of 2018 relative to 1998 using the NBER-CES Manufacturing database (https://lnkd.in/ezzPVsF). This avoids any issues with COVID and, moreover, combines all data in a consistent structure. I've also included change in industry output (measured as change in deflated shipments for all sectors except computers, where I use value of shipments), as well as change in labor productivity over this period. One table. Thoughts: •There is no doubt manufacturing payrolls declined sharply over this period, with sectors associated with apparel and textiles (NAICS 313-316) being especially affected. Declines in paper (NAICS 322) and printing (NAICS 323) were due more to a secular drop in demand. •Change in output tells a different story to some degree. Production in food, petroleum & coal products, chemicals [including pharmaceuticals], primary metals, machinery, transportation equipment, and miscellaneous [including medical devices] was actually higher in 2018 than 1998. •The rightmost column is critical to understand why manufacturing employment won't ever get back to 1998 levels: changes in labor productivity. Most industries have seen 30% or more increases in labor productivity over this period. Implication: there is no chance that the current "reciprocal" tariff regime causes manufacturing payrolls to return even close to their levels in the 1990s. Labor productivity growth alone ensures this. For example, these data indicate manufacturing payrolls dropped by 5.228 million between 1998 and 2018. Yet, if you applied 2018 levels of productivity to 1998 levels of output, you would have had a drop of payrolls of 4.886 million (almost the full magnitude observed). The challenge, which Richard Baldwin has extensively written about, is automation and trade liberalization occurred at the same time, yet we have vilified trade liberalization in the USA (and ignored the many positives it has brought us). #supplychain #markets #economics #shipsandshipping #freight

𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀𝗻'𝘁 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗵𝗲𝗰𝗸 －it's a continuous contract enforced across the various data layers to avoid breakage. Think about it. Planes don’t just fall out of the sky when they land. Crashes happen when people miss the little signals that get brushed off or ignored. Same thing with data. Bad data doesn’t shout; it just drifts quietly—until your decisions hit the ground. When you bake quality checks into every layer and, actually use observability tools, You end up with data pipelines that hold up. Even when things get messy. That’s how you get data people can trust. Why does this matters? Bad data costs money → Failed ML models, wrong decisions. Good monitoring catches 90% of issues automatically. → Raw Materials (Ingestion)  • Inspect at the dock before accepting delivery.  • Check schemas match expectations. Validate formats are correct.  • Monitor stream lag and file completeness. Catch bad data early.  • Cost of fixing? Minimal here, expensive later.  • Spot problems as close to the source as you can. → Storage (Raw Layer)  • Verify inventory matches what you ordered.  • Confirm row counts and volumes look normal.  • Detect anomalies: sudden spikes signal upstream issues.  • Track metadata: schema changes, data freshness, partition balance.  • Raw data is your backup plan when things go sideways. → Processing (Transformation)  • Quality control during assembly is critical.  • Validate business rules during transformations. Test derived calculations.  • Check for data loss in joins. Monitor deduplication effectiveness.  • Statistical profiling reveals outliers and distribution shifts.  • Most data disasters start right here. → Packaging (Cleansed Data)  • Final inspection before shipping to warehouse.  • Ensure master data consistency across all sources.  • Validate privacy rules: PII masked, anonymization works.  • Verify referential integrity and temporal logic.  • Clean doesn’t always mean correct. Keep checking. → Distribution (Published Data)  • Quality assurance for customer-facing products.  • Check SLAs: freshness, availability, schema contracts met.  • Monitor aggregation accuracy in data marts.  • ML models: detect feature drift, prediction degradation.  • Dashboards: validate calculations match source data.  • Once data is published, you’re on the hook. → Cross-Cutting Layers (Force Multipliers)  • Metadata: rules, lineage, ownership, quality scores  • Monitoring: freshness, volume, anomalies, downtime  • Orchestration: dependencies, retries, SLAs  • Logs: failures, patterns, early warning signs Honestly, logs are gold. Don’t sleep on them. What's your job? Design checkpoints, not firefight data incidents. Quality is built in, not inspected in. Pipelines just 𝗺𝗼𝘃𝗲 data. Quality 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝘀 your decisions. Image Credits: Piotr Czarnas 𝘌𝘷𝘦𝘳𝘺 𝘭𝘢𝘺𝘦𝘳 𝘯𝘦𝘦𝘥𝘴 𝘪𝘯𝘴𝘱𝘦𝘤𝘵𝘪𝘰𝘯.  𝘚𝘬𝘪𝘱 𝘰𝘯𝘦, 𝘳𝘪𝘴𝘬 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 𝘥𝘰𝘸𝘯𝘴𝘵𝘳𝘦𝘢𝘮.

Bloomberg just published the conversation I had with their team about how we're using AI and robotics to transform manufacturing, and they captured something important that often gets lost in these discussions.   When people hear "AI in manufacturing," they often picture robots replacing workers. That's not what we're building.   At the Hyundai Motor Group Innovation Center Singapore (HMGICS), we are exploring what some call a "dark factory" due to its high level of automation. The goal isn't eliminating human jobs. It's elevating human work.   We don't need more people tightening bolts repetitively. We need more engineers designing systems, more technicians maintaining intelligent equipment, more problem-solvers optimizing production. AI and robotics handle the repetitive tasks. Humans handle judgment, creativity, and continuous improvement.   As I mentioned in the conversation, "We are a tech company that happens to be in the automotive business." That shift, from purely mechanical manufacturing to software-defined production, changes everything about how we serve customers.   We can produce ten different models on the same line at HMGICS and switch between ICE, hybrid, and EV in real-time based on what markets want. We can respond quickly because our manufacturing systems are intelligent enough to adapt.   That flexibility, powered by AI, is what lets us deliver the right vehicle to the right customer at the right time, not force customers to accept what we happen to be producing.   We're scaling this approach from Singapore to Hyundai Motor Group Metaplant America (HMGMA) and beyond. Sixty percent of HMGICS innovations are already deployed in Georgia. This isn't pilot-stage experimentation, it's industrial transformation in practice.   Thanks to Angie Lau and the Bloomberg team for the conversation and for helping tell this story. In an age of extremes, the companies that thrive will be those that use technology to maximize human potential, not replace it. It's a great time to be with Hyundai Motor Company!

PRODUCTION PERFORMANCE ACTIVITIES: 1. Productivity Improvement: OEE Monitoring – Tracks machine availability, performance, and quality. Line Balancing – Distributes tasks evenly to reduce idle time. Cycle Time Reduction – Minimizes time per unit. Kaizen – Ongoing small improvements by operators. Time & Motion Study – Removes wasted motion. Bottleneck Removal – Use VSM, Takt Time, TOC to fix constraints. 2. Quality Improvement: First Pass Yield – Measures products without rework. In-Process Checks – Ensures quality at every step. Root Cause Analysis – Identifies defect causes (5 Whys, Fishbone). Poka Yoke – Error-proofing devices or techniques. Defect Analysis – Tracks trends and types of defects. 3. Cost Reduction: Material Yield – Reduces scrap and wastage. Energy Monitoring – Cuts power cost per unit. Tool Life Management – Lowers tool costs and downtime. Inventory Control – Uses FIFO, Kanban to manage stock. Lean Waste Removal – Eliminates non-value-added work. 4. Delivery Improvement: OTD Tracking – Measures actual vs. planned delivery. Production Scheduling – Aligns with customer demand. SMED (Quick Changeover) – Reduces setup times. Logistics Optimization – Streamlines material flow. 5. Safety Enhancement: 5S Implementation – Clean, safe, and organized workplace. Safety Audits – Identify and reduce risks. Incident Tracking – Record and act on near-misses. Safety Kaizens – Employee-led safety improvements. 6. Morale & Engagement: Daily Meetings – Share targets and issues. Suggestion Scheme – Reward employee ideas. Skill Matrix – Enable cross-training and flexibility. Recognition Programs – Appreciate team achievements. 7. Environmental Improvement: Waste Segregation – Improve recycling. Utility Savings – Conserve water and energy. Emission Control – Reduce dust, noise, fumes. Green Practices – Use eco-friendly materials/processes. Supporting Activities: Hourly Boards & Dashboards – Monitor daily performance. Tier Meetings – Escalate and solve issues. SOP Audits – Ensure process compliance. Gemba Walks – Management on the floor to guide teams.

Integrating sustainability beyond compliance 🌎 Driving meaningful, long-term sustainability actions requires a comprehensive approach that moves beyond basic regulatory requirements. It involves the development of a strategic vision supported by rigorous target-setting, collaborative networks, robust monitoring frameworks, and continuous improvements in products, processes, and internal capabilities. Establishing a strong foundation begins with assessing material ESG issues to determine priorities for environmental, social, and governance areas, followed by integrating sustainability considerations into core strategies. Ensuring alignment between organizational decision-making and ESG objectives supports effective engagement with stakeholders and partners, enabling value chains to evolve through more responsible sourcing and community collaboration. Achieving progress involves setting science-based, ambitious targets that adhere to global standards, which drives accountability through expanded internal capacity. Performance-linked incentives, coupled with ongoing training, anchor these efforts. In parallel, measuring, monitoring, and disclosing results creates transparency and builds trust, while redesigning products and processes fosters circularity, reduction of waste, and innovation that leads to sustainable outcomes. Expanding these efforts across the entire organization scales the overall impact and lays the groundwork for recognizing achievements, nurturing a sense of collective advancement and shared purpose. Regular refinement of these efforts, along with open communication of progress, ensures that strategies remain relevant and effective in a rapidly evolving sustainability landscape. In conclusion, integrating sustainability beyond compliance emerges as an iterative journey that aligns strategic thinking, stakeholder engagement, continuous innovation, and transparency. This approach ultimately strengthens the resilience of operations, enhances brand value, and contributes to a more sustainable global economy. #sustainability #sustainable #business #esg #climatechange #climateaction