Implementing Change In Manufacturing

Most people still get supply chain wrong. They think it’s just trucks, warehouses, and orders. That's incomplete. ✓ Supply chain is a structured system of core functions, cross-functional enablers, and strategic design. This infographic explains 8 core functions you need to know: (1) Procurement and Purchasing ↳ Strategic sourcing, contract management, supplier development, purchase order processing. (2) Production and Manufacturing Planning ↳ Production scheduling, MRP, process design, quality control, performance metrics. (3) Warehousing and Fulfillment ↳ Warehouse layout, slotting, automation, WMS, same-day fulfillment. (4) Transport and Logistics ↳ Freight management, route optimization, last-mile delivery, GPS tracking. (5) Demand and Supply Planning ↳ Forecasting, S&OP, IBP, inventory policy design. (6) Inventory Management ↳ Economic order quantity, safety stock, cycle counting, inventory classification. (7) Order Management ↳ Order processing, omni-channel sync, customer service, returns management. (8) Risk and Compliance ↳ Risk assessment, trade regulations, customs compliance, cybersecurity controls. 📌You cannot optimize performance without considering the enablers that connect these functions. Cross-Functional Enablers: (1) Sustainability ↳ Reducing emissions across transport, ethical sourcing, waste reduction in packaging. (2) Digitalization ↳ Using AI for forecasting, automating order processing, real-time shipment visibility. Strategic Enablers: (1) Supply Chain Network Design ↳ Location strategy, flow path optimization, scenario modeling for disruption planning. 📝For example, if your inventory team focuses only on stock levels but ignores supplier performance, you risk frequent stockouts. If your transport team optimizes routes but neglects last-mile delivery data, customer service declines. 💡True supply chain strength is built by linking every function with data, technology, and clear strategy. Ask yourself: ✓ Do you have full visibility across all these functions? ✓ Is your supply chain designed to adapt when risks emerge? ✓ Are your cross-functional enablers aligned with your business goals? Use this framework to assess where you stand and where you need to improve. #SupplyChainManagement #SupplyChainPlanning #Logistics #InventoryManagement #Procurement

🔬 UX Concept Testing. How to test your UX design without spending too much time and effort polishing mock-ups and prototypes ↓ ✅ Concept testing is an early real-world check of design ideas. ✅ It happens before a new product/feature is designed and built. ✅ It helps you find an idea that will meet user and business needs. ✅ Always low-fidelity, always pre-launch, always involves real users. 🚫 Testing, not validation: ideas are not confirmed, but evaluated. ✅ What people think, do, say and feel are often very different things. ✅ You’ll need 5 users per feature or a group of features. ✅ You will discover 85% of usability problems with 5 users. ✅ You will discover 100% of UX problems with 20–40 users. 🚫 Poor surveys are a dangerous, unreliable tool to assess design. 🚫 Never ask users if they prefer one design over the other. ✅ Ask what adjectives or qualities they connect with a design. ✅ Tree testing: ask users to find content in your navigation tree. ✅ Kano model survey: get user’s sentiment about new features. ✅ First impression test: ask to rate a concept against your keywords. ✅ Preference test: ask to pick a concept that better conveys keywords. ✅ Competitive testing: like preference test, but with competitor’s design. ✅ 5-sec test: show for 5 secs, then ask questions to answer from memory. ✅ Monadic testing: segment users, test concepts in-depth per segment. ✅ Concept testing isn’t one-off, but a continuous part of the UX process. In design process, we often speak about “validation” of the new design. Yet as Kara Pernice rightfully noted, the word is confusing and introduces bias. It suggests that we know it works, and are looking for data to prove that. Instead, test, study, watch how people use it, see where the design succeeds and fails. We don’t need polished mock-ups or advanced prototypes to test UX concepts. The earlier you bring your work to actual users, the less time you’ll spend on designing and building a solution that doesn’t meet user needs and doesn’t have a market fit. And that’s where concept testing can be extremely valuable. Useful resources: Concept Testing 101, by Jenny L. https://lnkd.in/egAiKreK A Guide To Concept Testing in UX, by Maze https://lnkd.in/eawUR-AM Concept Testing In Product Design, by Victor Yocco, PhD https://lnkd.in/egs-cyap How To Test A Design Concept For Effectiveness, by Paul Boag https://lnkd.in/e7wre6E4 The Perfect UX Research Midway Method, by Gabriella Campagna Lanning https://lnkd.in/e-iA3Wkn Don’t “Validate” Designs; Test Them, by Kara Pernice https://lnkd.in/eeHhG77j UX Research Methods Cheat Sheet, by Allison Grayce Marshall https://lnkd.in/eyKW8nSu #ux #testing

Data without intelligence is potential; intelligence without action is waste. Databricks' 𝟐𝟎𝟐𝟒 𝐒𝐭𝐚𝐭𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐀𝐈 𝐑𝐞𝐩𝐨𝐫𝐭 showcases a decisive shift as industries transition from AI experimentation to widespread production, with manufacturing emerging as a standout sector. Companies are leveraging AI to optimize production, enhance quality control, and integrate operational data into decision-making processes. Key takeaways from the report include: • 𝟏𝟏𝐱 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 in machine learning models reaching production, indicating industries are prioritizing real-world AI applications. • 𝟏𝟒𝟖% 𝐲𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫 𝐠𝐫𝐨𝐰𝐭𝐡 in natural language processing (NLP) use in manufacturing, driving improvements in quality control and customer feedback analysis. • 𝟑𝟕𝟕% 𝐠𝐫𝐨𝐰𝐭𝐡 in vector database adoption, supporting retrieval augmented generation (RAG) to integrate proprietary data for tailored AI applications. • Manufacturing and Automotive lead the charge with a staggering 𝟏𝟒𝟖% 𝐲𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 in adopting Natural Language Processing (NLP).  Would anyone have picked Manufacturing growing the fastest in NLP?!?! 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐃𝐨 𝐰𝐢𝐭𝐡 𝐓𝐡𝐢𝐬 𝐈𝐧𝐟𝐨? If you’re still debating AI’s value, you’re already late to the game. Manufacturers are moving from “what if” to “what’s next” by putting more AI models into production than ever before — 𝟏𝟏 𝐭𝐢𝐦𝐞𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐥𝐚𝐬𝐭 𝐲��𝐚𝐫!  The most successful organizations are cutting inefficiencies, standardizing processes with tools like data intelligence platforms, and deploying solutions faster. This isn’t just about keeping up with the Joneses; it’s about outpacing them entirely. 𝟏) 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Use tools like Retrieval Augmented Generation (RAG) and vector databases to turn AI into a competitive advantage by integrating your proprietary data. Don’t rely on off-the-shelf solutions that lack your industry’s nuance. 𝟐) 𝐀𝐝𝐨𝐩𝐭 𝐚 𝐂𝐮𝐥𝐭𝐮𝐫𝐞 𝐨𝐟 𝐒𝐩𝐞𝐞𝐝: The report highlights a 3x efficiency boost in getting models to production. Speed matters — not just for innovation, but for staying ahead of market demands. 𝟑) 𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 𝐚𝐧𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧:  The rise of open-source tools means you can innovate faster without vendor lock-in. Build smarter, more cost-effective systems that fit your needs. 𝟒) 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐀𝐈 𝐟𝐨𝐫 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐆𝐚𝐢𝐧𝐬: AI isn’t just for customer-facing solutions. Use it to supercharge processes like real-time equipment monitoring, predictive maintenance, and supply chain resilience. 𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/eZCrq_nF ******************************************* • 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!

Marty Cagan dropped a masterclass on AI prototyping. No hype. Just a clear-eyed view: Many people misunderstand the purpose of these tools. They are for discovery, not delivery. In fact... Prototypes are your go-to tools for solution discovery. They help minimize the 4 risks: 1. Value 2. Viability 3. Usability 4. Feasibility But AI prototypes aren't always the right prototype. And even if you go for an AI prototype, it doesn't always need live data. The level of fidelity should match the level of risk. If you don't care about visual fidelity, then getting that right in an AI prototyping tool is overkill. So repeat after me: "The level of fidelity should match the level of risk." Then read the piece yourself: https://lnkd.in/eqiGDRjU If you want to go more into the nuances of this topic, I've put together a bunch of resources: • Guide to AI prototyping: https://lnkd.in/eJujDhBV • Test of the top 5 tools: https://lnkd.in/eEGy9Dri • Podcast with Marty: https://lnkd.in/eb5hbA28 • Exploring Windsurf: https://lnkd.in/eKcpNsCD • Using Cursor: https://lnkd.in/d2pcXD7R • Claude Code: https://lnkd.in/eUyPEAma • What this means for PRDs: https://lnkd.in/eMu59p_z • As well as for AI Strategy: https://lnkd.in/egemMhMF • And vibe coding interviews: https://lnkd.in/e66DrW-h The role of PM is changing fast. Hope this helps you. 📌 Want my view of the AI prototyping tool landscape? Comment 'prototyping landscape' + DM me. What's your take: are folks overhyping AI prototypes?

Meet anyone in manufacturing, and for their top two concerns, you'll hear about:   1. Supply Chain Disruptions: Challenges related to inventory and supply chain management. 2. Operating Costs: Navigating economic headwinds and operational inefficiency.   Our clients in the manufacturing sector work in a fast-paced world where maintaining operational efficiency is crucial. One of our clients faced significant challenges with their Clean-In-Place (CIP) process, which directly impacted their quality check procedures. Frequent unplanned downtimes due to equipment failures were hampering productivity and throughput, highlighting the need for a more proactive maintenance approach. They needed real-time insights to make informed preventive maintenance decisions! To address their challenges, our team developed and implemented an AI-based predictive maintenance solution for the CIP equipment. Leveraging data analytics and machine learning, this solution integrated critical datasets from batch processes, sensors, and maintenance records.   By empowering our client with real-time insights through anomaly detection and a risk scoring system, we enabled them to make informed preventive maintenance decisions. This proactive approach not only improved their operational efficiency but also set a new standard for maintenance practices in the manufacturing industry.   Our client went from reactive and corrective maintenance to predictive maintenance! Would love to hear from the network on what you are seeing in this area. If you have a story, let us talk.

𝗙𝗿𝗼𝗺 𝗥𝗼𝗼𝘁 𝗖𝗮𝘂𝘀𝗲 𝘁𝗼 𝗥𝗲𝗮𝗹 𝗖𝗵𝗮𝗻𝗴𝗲: 𝗧𝗵𝗲 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗦𝗵𝗶𝗳𝘁 𝗧𝗵𝗮𝘁 𝗕𝘂𝗶𝗹𝘁 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 Over a decade ago, I took on a role in an organisation dealing with deep-rooted legacy challenges. Persistent failures in quality, delivery, and cost were damaging our reputation. I tried addressing the issues head-on, but they kept resurfacing in different forms. Escalations grew. Blame started moving across teams. That moment stayed with me. Not for the frustration—but for what it taught me about real 𝘱𝘳𝘰𝘣𝘭𝘦𝘮-𝘴𝘰𝘭𝘷𝘪𝘯𝘨 𝘢𝘯𝘥 𝘭𝘦𝘢𝘥𝘦𝘳𝘴𝘩𝘪𝘱 𝘴𝘺𝘴𝘵𝘦𝘮 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨. 🔍 Were we only fixing symptoms? 🔍 Did we truly understand the impact across departments? 🔍 Were our efforts aligned with the bigger plant goals? Instead of reacting, I shifted gears—addressed root-level issues, brought in collaboration across functions, and helped move the culture from blame to ownership. Within two years, we saw it: ✅ A cultural shift rooted in accountability and clarity ✅ Our 𝗦𝗤𝗗𝗜𝗣𝗖 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝟮𝗫—and more importantly, it was sustainable ✅ In a proud moment, we stood at the National QCC stage and returned with a 2nd Runner-Up Award But for me, the real win wasn’t the award. It was this shift in mindset—from fixing problems to 𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. It’s the same lens I bring even today, leading multi-plant operations and cross-functional teams. No trophy can match the leadership transformation that began back then. Looking back, I’m glad the struggle came first—it taught me to bounce back faster, as a stronger version of myself. With a relentless mindset focused on solutions, not setbacks, it’s helped me lead better every day since. ----------------------------------------------------------------------- If you find it useful, please comment, 👍🏻👏🏻❤️💡🔁 For more insightful content, follow SIVAKUMAR C 🇮🇳 #OwnYourStory #SQDIPC #LeadershipLessons #Leadership #SystemsThinking #PlantExcellence The Career Excellence League #CultureChange

Advanced manufacturing technologies like 3D printing are reshaping traditional approaches to maintenance, offering transformative potential in reducing waste, streamlining logistics, and accelerating innovation in industrial operations. 3D printing minimizes inventory and storage needs by creating parts on demand and enabling rapid response to equipment downtime. Key considerations include proper material selection, CAD design precision, tailored printer investments, and skilled staff training. By aligning predictive maintenance strategies with additive manufacturing, industries can proactively address wear and tear, further optimizing operations. Ensuring compliance with safety and quality standards is critical for long-term success and operational reliability, underscoring the importance of strategic planning in implementing this technology. #3Dprinting #manufacturing #DigitalTransformation

𝗘𝗹𝗲𝘃𝗮𝘁𝗶𝗻𝗴 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: 𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁 𝗼𝗻 𝗜𝗧𝗦𝗠 & 𝗦𝗥𝗘 💡🛠️ In the age of Industry 4.0, digital transformation is reshaping manufacturing in unprecedented ways. The convergence of IT and operations technology (OT) is revolutionizing how we produce goods, and at the heart of this transformation lie IT Service Management (ITSM) processes and Site Reliability Engineering (SRE). Let's delve into how these key elements are propelling the manufacturing sector forward and how monitoring KPIs and site reliability metrics are driving this change. 📌 𝗜𝗧𝗦𝗠: 𝗧𝘂𝗿𝗯𝗼𝗰𝗵𝗮𝗿𝗴𝗶𝗻𝗴 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 🔗 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞: Brings together diverse systems for seamless communication, enabling real-time insights & data-driven decisions. ⚙️ 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Streamlines operations, automates tasks, and addresses IT concerns to reduce downtime. 📈 𝐀𝐠𝐢𝐥𝐞 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Adapts IT resources swiftly, matching fluctuating production needs. 📌 𝐒𝐑𝐄: 𝐓𝐡𝐞 𝐆𝐮𝐚𝐫𝐝𝐢𝐚𝐧 𝐨𝐟 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞 🚦 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞 𝐎𝐯𝐞𝐫𝐬𝐢𝐠𝐡𝐭: Uses state-of-the-art monitoring for early issue detection, ensuring consistent system health. 🚨 𝐒𝐰𝐢𝐟𝐭 𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞: Prioritizes both incident resolution and preventive measures against future incidents. 📊 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐌𝐚𝐬𝐭𝐞𝐫𝐲: Focuses on optimizing vital metrics like MTTD & MTTR to minimize disruptions and uphold reliability. 📌 𝐊𝐏𝐈𝐬: 𝐓𝐡𝐞 𝐏𝐮𝐥𝐬𝐞 𝐨𝐟 𝐏𝐫𝐨𝐠𝐫𝐞𝐬𝐬 📉 𝐁𝐨𝐨𝐬𝐭𝐢𝐧𝐠 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Monitors metrics linked to machine uptime and energy usage for operational excellence. 🏆 𝐔𝐩𝐡𝐨𝐥𝐝𝐢𝐧𝐠 𝐐𝐮𝐚𝐥𝐢𝐭𝐲: Keeps an eye on product quality and defect rates to meet industry norms and consumer expectations. 🔍 𝐅𝐨𝐫𝐰𝐚𝐫𝐝-𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞: Leverages predictive analytics and equipment health KPIs to foresee maintenance needs, slashing downtime. To wrap up, harnessing the power of ITSM, SRE, and KPIs is vital for manufacturers in this digital age. As we move towards a more data-centric era, these key players will continue to redefine the manufacturing landscape. Embrace them to stay ahead in the game! 🏭🔧💡

What happens when smart factories worldwide need to share real-time data with cloud-based business applications across the globe? #BMW tackled this by using #ApacheKafka for real-time data streaming from edge to cloud—connecting PLCs, robots, and IoT devices directly to #MES and #ERP systems. This enables powerful automation, predictive maintenance, and just-in-time logistics at global scale. In a smart factory scenario, #IoT and #edge computing meet #cloudnative data platforms. Confluent Cloud acts as the event backbone—decoupling physical systems from business logic and enabling real-time analytics, automation, and machine learning. This is not just theory. BMW Group streams production and logistics data from dozens of global plants into a Kafka-powered cloud infrastructure. Business units across the company benefit from the same live data streams for analytics, visibility, and decision-making. I recorded a 5-minute lightboard video that shows how data streaming connects smart factories and cloud-native architectures. Check it out and let me know what use cases you’re solving with #DataStreaming in #manufacturing or other edge-to-cloud scenarios. https://lnkd.in/eNwEtJE3 Where do you see the biggest opportunity for streaming data from edge to cloud—in logistics, predictive maintenance, or cross-site visibility? #SmartFactory #Industry40 #DigitalTwin #OPCUA #KafkaConnect #ConfluentCloud #ManufacturingInnovation

As a Senior Expert in Digital Transformation, I am always on the lookout for groundbreaking innovations that shape the future of industries. Recently, I came across a fascinating use case presented by Siemens at Realize LIVE 2023, focusing on battery pack assembly powered by the Industrial Metaverse and Process Simulate software. 🌐 Understanding the Industrial Metaverse The Industrial Metaverse is a fusion of physical and digital realms, bridging the gap between real and virtual worlds. It leverages technologies like IoT, AI, AR, and VR to create seamless interactions between physical assets and their virtual counterparts. 👥 The Role of Real-Time Digital Twin At the heart of this innovation lies the real-time digital twin, enabled by Tecnomatix Process Simulate software. It allows companies to plan, simulate, and validate manufacturing processes, including robotics, automation, and human tasks, throughout the entire product development lifecycle. 🔧 Enabling the Battery Industry Siemens demonstrated how the battery industry can adopt the Industrial Metaverse using Process Simulate software. This example showcases how companies can gain valuable insights and optimize production processes using digital twin technology. 🛠️ Seamless Integration with NVIDIA Omniverse The newly released Tecnomatix connector to Omniverse enables realistic and high-fidelity visualization simulations. It paves the way for a seamless update of digital twins in Process Simulate, reflecting immediate changes on the shop floor. 🏭 The End Result: Realistic Visualization & Closed-Loop Asset Management The ability to visualize the digital twin in its real-world context provides a realistic environment for decision-making. One compelling feature is the integration with real assets in a closed loop, ensuring seamless and efficient operations. 🚀 A Game-Changer for Forward-Thinking Organizations Realize LIVE 2023 unveiled a future that promises to revolutionize industries through the Industrial Metaverse and real-time digital twin technology. Embracing this innovation will undoubtedly be a game-changer for any forward-thinking organization. As we move forward into this exciting era, it's essential for leaders to recognize the potential of these technologies in optimizing production processes, improving collaboration, and gaining valuable insights. Feel free to reach out if you'd like to discuss these and other innovations. Let's shape the future together! More at: https://lnkd.in/g2s2U6du #IndustrialMetaverse #DigitalTwin #Innovation #BatteryIndustry #Manufacturing #FutureTech