How to Use Automation in Production Processes

𝗗𝗼𝗻’𝘁 𝗝𝘂𝘀𝘁 𝗥𝗲𝗮𝗱 𝗔𝗯𝗼𝘂𝘁 𝗔𝗜 𝗶𝗻 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴. 𝗔𝗽𝗽𝗹𝘆 𝗜𝘁. The AI headlines are exciting. But if you're a founder, engineer, or educator in manufacturing, here's the question that actually matters: 𝗪𝗵𝗮𝘁 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗱𝗼 𝘵𝘰𝘥𝘢𝘺 𝘁𝗼 𝘁𝘂𝗿𝗻 𝘁𝗵𝗲𝘀𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻? Let’s get tactical. 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗱𝗲𝗺𝗮𝗻𝗱 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 Tool to try: Lenovo’s LeForecast A foundation model for time-series forecasting. Trained on manufacturing-specific datasets. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re battling supply chain volatility and need better inventory planning. 👉 Tip: Start by connecting your ERP data. Don’t wait for perfect integration: small wins snowball. 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝘄𝗶𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝘂𝘆𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗻𝗲𝘅𝘁 𝗿𝗼𝗯𝗼𝘁 Tools behind the scenes: NVIDIA Omniverse, Microsoft Azure Digital Twins Schaeffler + Accenture used these to simulate humanoid robots (like Agility’s Digit) inside full-scale virtual factories. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re considering automation but can’t afford to mess up your live floor. 👉 Tip: Simulate your current workflows first. Even without a robot, you’ll find inefficiencies you didn’t know existed. 𝟯. 𝗕𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗤𝗔 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝟮𝟬𝟮𝟬𝘀 Example: GM uses AI to scan weld quality, detect microcracks, and spot battery defects: before they become recalls. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You’re relying on spot checks or human-only inspections. 👉 Tip: Start with one defect type. Use computer vision (CV) models trained with edge devices like NVIDIA Jetson or AWS Panorama. 𝟰. 𝗘𝗱𝗴𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝘆𝗺𝗼𝗿𝗲 Why it matters: If your AI system reacts in seconds instead of milliseconds, it's too late for safety-critical tasks. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're in high-speed assembly lines, robotics, or anything safety-regulated. 👉 Tip: Evaluate edge-ready AI platforms like Lenovo ThinkEdge or Honeywell’s new containerized UOC systems. 𝟱. 𝗕𝗲 𝗲𝗮𝗿𝗹𝘆 𝗼𝗻 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 The EU AI Act is live. China is doubling down on "self-reliant AI." The U.S.? Deregulating. 𝗨𝘀𝗲 𝗶𝘁 𝗶𝗳: You're deploying GenAI, predictive models, or automation tools across borders. 👉 Tip: Start tagging your AI systems by risk level. This will save you time (and fines) later. Here are 5 actionable moves manufacturers can make today to level up with AI: pulled straight from the trenches of Hannover Messe, GM's plant floor, and what we’re building at DigiFab.ai. ✅ Forecast with tools like LeForecast ✅ Simulate before automating with digital twins ✅ Bring AI into your QA pipeline ✅ Push intelligence to the edge ✅ Get ahead of compliance rules (especially if you operate globally) 🧠 Each of these is something you can pilot now: not next quarter. Happy to share what’s worked (and what hasn’t). 👇 Save and repost. #AI #Manufacturing #DigitalTwins #EdgeAI #IndustrialAI #DigiFabAI

8 out of 10 businesses are missing out on Ai. I see this everyday in my calls. They jump straight to AI tools without understanding their processes first. Then wonder why their "automations" create more problems than they solve. Here's the proven framework that actually works: STEP 1: MAP YOUR PROCESSES FIRST Never automate a broken process. → List every touchpoint in your workflow → Identify bottlenecks and time-wasters → Note who handles each step → Find communication gaps Remember: You can only automate what you understand. STEP 2: START WITH HIGH-ROI TASKS Don't automate because it's trendy. Focus on what saves the most time: → Data entry between systems → Client onboarding workflows → Report generation → Follow-up sequences One good automation beats 10 fancy tools that don't work together. STEP 3: BUILD YOUR TECH FOUNDATION Most companies use 10+ disconnected tools. AI can't help if your data is scattered everywhere. → Centralize data in one source (Airtable works great) → Connect your core systems first → Then layer AI on top STEP 4: DESIGN AI AGENTS FOR SPECIFIC PROBLEMS Generic AI = Generic results. Build precise agents for precise problems: → Research and data analysis → Customer support responses → Content creation workflows → Internal process optimization Each agent needs specific inputs and defined outputs. STEP 5: TEST SMALL, SCALE SMART Don't automate your entire business at once. → Start with one small process → Get team feedback → Fix bottlenecks as you go → Scale what works Build WITH your team, not without them. The biggest mistake I see? Companies hire someone to build exactly what they ask for. Instead of finding someone who challenges their thinking and reveals what they're missing. Good automation is just process optimization. Nothing more. The result? → 30+ hours saved per month on onboarding → Delivery time cut in half → Capacity increased by 30% → Revenue multiplied without adding team members Your competitors are stuck switching between apps. You'll be dominating with seamless systems. Follow me Luke Pierce for more content on AI systems that actually work.

60 control actions. 90+ sensors. 12 years to become an expert. Every traditional automation attempt in a Fortune 500 glass manufacturing process failed until they tried a different approach: The experts taught the system. Here’s how they did it: ▸ Expertise capture We worked side-by-side with "Alex," the plant’s most experienced operator. His 12 years of tacit knowledge got distilled into seven skills that were translated into seven specialized "skill agents" that would be orchestrated together to replicate his decision-making process. ▸ Simulation at scale We collected sample historical data (OSI Soft PI) AND a full range of edge cases designed through experimentation to create a robust simulation to train the AI systems. ▸ Parallel training in simulation The AI systems practiced the process millions of times by running multiple tests in parallel. What once took over a decade to master was now taught in weeks. ▸ Deployment like an operator We took the most successful agent systems and deployed them live on the edge for testing. They weren’t evaluated as software, but like any other new operator. On test six, Alex hit pause, stared at the screen, and said: “I never knew you could do it like that.” The agent system had just taught its teacher. This wasn’t automation replacing expertise. It was the start of a new apprenticeship between humans and machines. If a system can master 12 years of expertise in six weeks… What would it look like to scale every expert in your enterprise? (read the full story here: https://hubs.li/Q03vG6tW0) #industrialautomation #industrialAI #OperationalExcellence

Manufacturing Automation – "Next!" The Lowest Hanging Fruit in Automation is ALWAYS Machine Load-Unload! -- Often addressed with Robot arms, Machine Load-Unload applications remain the most profitable "no brainers" as they increase PRODUCTIVITY in 2 ways: 1. Eliminating / Reducing Labor 2. Increasing Machine Uptime with a predictable cycle Whether Standalone Machine or Continuous Production Line, the TRUE value most often lies in the 2nd as the machine utilization is maximized with the only limitation being the finite time of Unloading a Finished part and Loading the next part. As any preparatory work or NEXT part conditioning can be done OFF-LINE and buried within the machine's cycle time, in the IDEAL, production rate can be significantly and economically justified INCREASED. Achieving this GOAL is best accomplished with a Custom, Industry 3.5 Solution, tailored to the part's UNIQUE Form Factor and NOT with General Purpose Solutions. Lowest hanging Fruit in Automation is ALWAYS Machine Load-Unload! --- "Finally, by designing these custom systems to be portable, (on wheels with docking features), it is also possible to have a common platform that can be deployed from machine to machine within the framework of the common product form factors. (It's not unlikely that a particular process has several systems operating on 2 or even 3 shifts with the attendant high labor requirements.) In Summary: Manual Machine Load-Unload and Feeding operations exist in many legacy and even newer production lines and the deployment of robotic solutions is often a justifiable approach to automating this operation. However, there are many more applications when either the cycle times are too short or too long, (a relative measure), where a custom designed system will be both more cost effective and more importantly, designed exactly to the application without paying for the excess functionality/flexibility provided by a robot which is not required for the particular application. In addition, the generally simpler design of a custom electro-pneumatic-mechanical solution leads to lower technology support and personnel training requirements. This is especially important in SME operations that don't necessarily have the required technical and other skills resources in-house but can still significantly benefit and improve productivity while reducing labor content through “low tech” load - unload automation." -- How do you approach Machine Load-Unload Automation? Your thoughts are appreciated and please SHARE this post if you think your connections will find it of interest. 👉 Comment, follow or connect to COLLABORATE on your automation for increased productivity. Adding value on the WHY, WHAT and HOW of Automation! What are you working on that I can help with? https://lnkd.in/eYqDX-Nd #industry40 #automation #productivity #robotics

What’s harder to track: a $3,000 custom guitar 🎸or a $30 pizza? (Automation Tip Tuesday 👇) A custom guitar manufacturer was manually copying order details between department spreadsheets and crafting individual customer updates. For EACH guitar. Through woodshop, paint, and assembly. 20 times a week. 😵 That's 4 hours of pure admin work that should've been building guitars. The fix? A simple automation that: ➡️ Automatically moves products between department queues when marked "complete" ➡️ Triggers personalized customer updates with progress photos  ➡️ Maintains a central dashboard for all orders The result? 💥  4 hours saved weekly  💥 Zero missed updates  💥 Delighted customers who feel like VIPs  💥 Craftsmen who can focus on crafting And the best part?  This same workflow works for any staged production process — from custom furniture to marketing agencies to home builders. Sometimes the simplest automations have the biggest impact. They just need someone to spot the pattern. Which manual updates are eating your team's time? -- Hi, I’m Nathan Weill, a business process automation expert. ⚡️ These tips I share every Tuesday are drawn from real-world projects we've worked on with our clients at Flow Digital. We help businesses unlock the power of automation with customized solutions so they can run better, faster and smarter — and we can help you too! #automationtiptuesday  #automation #workflow #customerexperience