Trends in Manufacturing Productivity Changes

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 𝐲𝐞𝐚𝐫!  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!

Something’s Not Adding Up in Manufacturing Productivity When I first looked at the numbers, I didn’t quite believe them. Or maybe I didn’t want to. However, over the last two decades, productivity in manufacturing has been in steady decline in the US, despite investments in robotics, automation, and digital transformation. There is a slight post-COVID productivity bump that is moderating. (Charts 1 & 2) That raises a few hard questions. And I’d love to hear your take. Here’s what I’ve seen on the ground: - A general drop in mechanical aptitude - Tribal knowledge quietly walking out the door - Frontline training that’s often inconsistent or deprioritized - Automation and digital initiatives that miss the mark - Outsourcing of high-throughput manufacturing Now, compare that with retail (see Chart 3), an industry with which I spent a significant amount of time in the past. After a dip in the early 2000s, retail has steadily improved its productivity, thanks to considerably better adoption of technology, and also a lower need for a highly specialized labor force. What are we missing in manufacturing? Would love to hear your perspectives. ---- Source: Federal Reserve Bank St.Louis Data: U.S. Bureau of Labor Statistics Labor productivity, or output per hour, is calculated by dividing an index of real output by an index of hours worked of all persons, including employees, proprietors, and unpaid family workers.

Why isn’t AI boosting productivity in manufacturing yet? MIT Sloan just explored this in a must-read piece: “The Productivity Paradox of AI Adoption in Manufacturing.” The key takeaway is that we are seeing the J-curve effect in action. In the early stages of AI adoption, productivity often dips, which is normal: 1. Costs rise due to integration, training, and change management 2. Old processes clash with new tech 3. Gains are isolated in pilots or siloed tools It is only after this initial dip, when workflows are redesigned, people are upskilled, and data foundations mature, that the exponential gains begin to take hold. This is the J-curve of AI transformation: Short-term pain leading to long-term advantage. We see many manufacturers give up too early, when they are just before the curve turns upward. Leaders need to set expectations, invest in capabilities, and commit to scaling AI beyond pilots. Successful firms rethink, not just automate, their operations, though automation may be a necessary first step. What stood out to me: “You can’t bolt AI onto legacy workflows and expect future-ready results.” Makoro demonstrates the success of businesses that have accelerated teams through the J-curve, from pilot to productivity through the implementation of AI-native manufacturing systems. Where are you on the J-curve? Early dip, scaling gains, or riding the upswing? #AI #Manufacturing #Productivity #JCurve #DigitalTransformation #MakoroAI #Industry40 #AIStrategy https://lnkd.in/gJarKHpG

US manufacturing total factor productivity actually decreased an average of .3% per year in the decade prior to COVID. This is driven by an aging and rapidly retiring workforce and not being able to pass on important institutional knowledge to the next generation of skilled tradespeople. The gap in proficiency can lead to substantial loss of productivity. To this point, a study by McKinsey & Company addresses the key talent development problem in advanced industrial manufacturing like aerospace & defense: "How can we decrease time to proficiency to increase resulting productivity?" This can be achieved through specific actions taken in 1. Talent Acquisition. How can we better acquire new talent with a higher skill level or who can achieve proficiency faster? 2. Talent Development. How can we expand the pipeline of "ready" talent by upskilling or reskilling those in roles and in adjacent roles? How can we use "modern" development approaches that make the learning happen faster and better? 3. Performance Management. How can we manage talent differently to increase either skill level or time to proficiency? How can we capture and codify critical knowledge that is at risk of leaving the organization? https://lnkd.in/dN57RDPD #defenseindustry #advancedmanufacturing #talentdevelopment #aerospace #defenseinnovation