Networking In Manufacturing

The Smart Factory: When AI, automated systems and employees work hand-in-hand 🤝 At Porsche AG we follow a stringent course to further extend the Smart Factory in Zuffenhausen and Leipzig. We have implemented many different measures and innovative ideas to create the fully digital and connected factory. The Smart Factory enables leaner processes, helps us to achieve greater efficiency and supports us to ensure production quality. Let me take you on a short tour through our fully-connected factory. Have a look at these examples: ⤵️ 1️⃣ The basis for the Smart Factory is the “Digital Production Platform”, which gives us a clear overview about all production processes: From material inflow to the status of each robot to the current factory output. 2️⃣ The “Digital Twin” allows us to plan factories and production lines virtually and simulate production processes digital – already before they exist in real life, 3️⃣ or think of cloud-based control platforms such as the MHP “Fleet Executer”, which centrally controls our Automated Guided Vehicle transport systems at the factory logistics. 4️⃣ As part of our Smart Factory, we also use AI systems such as “Virtual Build to Order”, where we simulate order intakes for dealership cars based on existing order books to forecast how future customer vehicle configurations might look like. 5️⃣ Deep learning technologies help us to speed up workflows and ensure production quality – at our V8 engine production, for example, camera-equipped robots make sure via algorithms that engine auxiliary components are applied correctly. 6️⃣ Equally data throughput and consistency are essential to implement the Smart Factory: Not just within our factories, but also with our partner companies, such as the Smart Press Shop. So, the factory of the future is all about technology? 👉 It is about the people at Porsche Production and Logistics, who come up with innovative ideas and new technologies – they bring the Smart Factory to life. I’m curoius: What interests you the most regarding our Smart Factories?

🔌 Understanding 2-Wire, 3-Wire & 4-Wire Transmitters in Industrial Automation 🛠️ 4 to 20 mA transmitters are everywhere! They are used to measure and send signals for temperature, pressure, level, and many other process parameters to controllers like PLCs. But here is where many engineers and technicians get stuck: WIRING CONFIGURATIONS! 👇 ✅ What is a Transmitter? A transmitter takes a signal from a sensor (like temperature or pressure) and converts it into a standard control signal most commonly 4 to 20 mA. 4 mA = Minimum value 20 mA = Maximum value Now, let us talk about how these transmitters are wired and connected to a PLC (Programmable Logic Controller). There are 3 types of configurations: 2-Wire Transmitter 👉 Uses just two wires: one for power and one for signal, same line does both jobs. 👉 Powered by 24V DC. 👉 The current flows in a loop from power supply ➝ transmitter ➝ PLC ➝ back to supply. Pros: ✔ Simple & cost-effective ✔ Low maintenance ✔ Saves wiring effort Cons: ✘ Less accurate ✘ Can’t go below 4 mA even if there's a fault – can't detect zero-current faults. 3-Wire Transmitter 👉 Uses separate wires for power and signal. 👉 Shares a common ground between the power supply and signal return. Pros: ✔ Requires only 3 wires ✔ Same power source used for both transmitter & PLC Cons: ✘ No electrical isolation ✘ Can be affected by interference ✘ Tricky wiring for beginners 4-Wire Transmitter 👉 Has two pairs: one for power, one for signal 👉 Can be powered by AC or DC 👉 Signal wiring is fully isolated from power Pros: ✔ High accuracy & stable signals ✔ Excellent electrical isolation ✔ Easy for technicians to wire ✔ Fewer false alarms Cons: ✘ Higher cost ✘ Not ideal for hazardous areas due to risk of ignition if powered by high voltage Understanding how to wire these transmitters correctly ensures safe, accurate & reliable measurement in your automation systems. 📌 If found useful please repost / share in your network ----------------------------------------------------------------------------------------- 🎇 Knowledge shared is wisdom gained !! Happy Learning 👉 Whats App Channel: https://lnkd.in/gYkf9pRv 👉 Telegram Channel: https://lnkd.in/d473jAEz 👉 Linkedin Page: Instrumentation Blogs 👉 Linkedin Group: https://lnkd.in/dY3QQYfg 👉 Website: 🌐 www.instrumentationblog.in #instrumentation #automation #plc #transmitters #processcontrol #engineeringstudents #4to20mA #industrialautomation #electricalengineering #fieldinstruments #industrialmaintenance #plantengineering #controlsystems

Demystifying PLC Communication Protocols: The Backbone of Industrial Connectivity In today’s smart manufacturing environments, communication protocols are essential for enabling seamless interaction between PLCs, HMIs, sensors, and control systems. They define the language and rules devices use to share data across networks. Without these protocols, devices may be physically connected—but not truly communicating. Here’s a quick breakdown of common PLC communication protocols and where they shine: Common Protocols: Ethernet: High-speed, internet-capable, and supports up to 255 devices. Ideal for modern industrial networks requiring fast, real-time data transfer. Profibus: A robust fieldbus protocol supporting up to 127 devices over 15 km. Widely used in process automation. RS-232 & RS-485: Serial communication protocols for short-distance, point-to-point (RS-232) or multi-drop (RS-485) communication. Still useful in legacy systems. MPI & PPI: Proprietary Siemens protocols. MPI is used for multi-master communication; PPI for basic point-to-point links in smaller applications. ControlNet & DeviceNet: Allen-Bradley/ Rockwell protocols designed for deterministic data exchange (ControlNet) and device-level networking (DeviceNet). USB/PC Adapters: Often used for programming or initial setup. Not typically suited for large-scale communication. Key Factors to Consider: 1. Baud Rate: Speed of data transfer (e.g., Ethernet = 100 Mb/s, RS-232 = 19.2 kb/s) 2. Network Length: How far communication can reach (e.g., ControlNet = 30 km) 3. Number of Nodes: How many devices can connect (e.g., Profibus = 127) Tip: Use Ethernet for high-speed, scalable networks. Choose ControlNet when long-distance and real-time reliability are critical. #PLCProgramming #CommunicationProtocols #IndustrialAutomation #ControlSystems #SmartManufacturing #IIoT #Ethernet #Profibus #RS232 #RS485 #AutomationEngineer #FactoryAutomation

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

Trillions in GPUs are only as fast as what connects them. This week’s Spotlight is: Optical Connectivity Is AI's Next Infrastructure Bet GPUs are no longer the hard part of scaling AI infrastructure. Connecting them is. At today’s bandwidth requirements, copper cabling hits a physical wall at about a meter and a half. As a result, optical AI connectivity has become the most important infrastructure opportunity to emerge after GPUs. Optical connectivity, moving data at the speed of light, is the connective tissue of AI infrastructure. Optical moves data at scale, cuts latency, and controls power consumption in ways copper can't. The market that follows is expected to exceed $100B over the next five to seven years. There are two distinct but intertwined networking challenges: – Scale-out: connecting thousands of GPUs across racks, rows, and clusters. Every GPU requires multiple optical transceivers. – Scale-up: connecting GPUs within a rack at ultra-high bandwidth and low latency, directly chip-to-chip. Our portfolio company Lumilens recently came out of stealth - announcing more than $900 million in total funding at a valuation of $5.5 billion. Lumilens designs and manufactures optical transceivers and NPO/CPO for scale-out and scale-up, respectively, and treats manufacturing as a product in its own right, from chip assembly to high-volume production. It is building a connectivity platform for AI infrastructure. The market structure here mirrors earlier moments in Internet infrastructure in telecom: a small number of hyperscalers account for most AI infrastructure purchasing. Winning a hyperscaler can define a company. In the PC era, Intel and Microsoft captured the platform. In the Internet era, Cisco and Juniper powered the backbone. In the cloud era, Arista emerged. Each platform shift consolidates infrastructure first, then triggers an explosion of application innovation on top of it. AI is following the same pattern, only faster and on a greater scale. Bottom line: Optical connectivity is the missing infrastructure layer that makes AI clusters economically and performance-viable at scale, and the window to define that category is right now. Key signals from this week's roundup: Google assembled a roughly $200 billion financing machine to supply Anthropic with TPUs, Broadcom hardware, and data center capacity, and Anthropic followed by hiring its own chip design team, making model-hardware co-design table stakes for frontier labs. SpaceX and Tesla committed $16.8 billion to a Texas chip fab, the FCC began drafting a ban on new Chinese data center optical transceivers, and London's Olix raised  $312 million at a $3.3 billion valuation for photonic interconnect silicon. Per Crunchbase data, July ranked as the third-largest funding month of the year, with 14 billion-dollar rounds and $65 billion in global funding, 53% of it into AI. Full Weekend Edition below. 👇

Exploring the Nuances of Communication Protocols: A Deep Dive into I2C, SPI, and UART In the rapidly advancing field of electronics, understanding communication protocols is crucial for designing efficient and reliable systems. In my latest article, I delve deep into three fundamental protocols—I2C, SPI, and UART—that form the backbone of modern electronic communication. I2C (Inter-Integrated Circuit): I2C shines with its minimal pin requirement, utilizing just two lines: SDA (Serial Data Line) and SCL (Serial Clock Line). This simplicity enables easy integration of multiple devices on the same bus, making it perfect for compact circuits and short-distance communications. The addressing scheme allows up to 127 devices on a single bus, facilitating complex sensor networks in embedded systems. However, I2C operates at relatively lower speeds, typically up to 3.4 Mbps in High-Speed Mode, which might not suffice for high-bandwidth applications. Its reliance on pull-up resistors and open-drain configuration makes it sensitive to capacitance and interference, potentially leading to signal degradation over longer distances. Careful bus design and termination are essential to mitigate these limitations. SPI (Serial Peripheral Interface): SPI stands out with its high speed and full-duplex capabilities. It uses separate lines for data transmission (MOSI - Master Out Slave In and MISO - Master In Slave Out), along with a clock line (SCLK) and one or more chip select lines (SS). This configuration allows for data rates exceeding 10 Mbps, making SPI ideal for high-speed peripherals like TFT displays, SD cards, and wireless modules. The trade-off with SPI is the increased pin count and complexity. Each slave device typically requires a dedicated chip select line, consuming valuable GPIO pins on the microcontroller and complicating the circuit when multiple slaves are involved. Unlike I2C, SPI lacks a standardized protocol for addressing multiple devices on the same bus, necessitating careful planning and additional hardware like multiplexers or shift registers for complex systems. UART (Universal Asynchronous Receiver/Transmitter): Lastly, UART's simplicity and flexibility make it a go-to for point-to-point serial communication over longer distances. With only two wires (TX and RX) and asynchronous communication, UART eliminates the need for a shared clock signal, allowing devices to communicate even if they operate at different clock speeds, provided they agree on the baud rate. #Electronics #CommunicationProtocols #I2C #SPI #UART #EmbeddedSystems #HardwareDesign #Engineering #TechInnovation #ExpertInsights

𝗣𝗟𝗖 𝘃𝘀 𝗥𝗧𝗨 𝘃𝘀 𝗜𝗘𝗗 — 𝘄𝗵𝗲𝗿𝗲 𝗲𝗮𝗰𝗵 𝗱𝗲𝘃𝗶𝗰𝗲 𝗳𝗶𝘁𝘀 𝗶𝗻 𝗢𝗧 In OT security, we often say “controllers” — but not all controllers do the same job. A PLC, RTU, and IED may all sit close to Level 1 of the Purdue Model, but their purpose, environment, protocols, and risk profile can be very different. ▪️ 𝗣𝗟𝗖 – 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗮𝗯𝗹𝗲 𝗟𝗼𝗴𝗶𝗰 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗿 Best suited for fast local control in machines, skids, packaging lines, production lines, utilities, and process automation. Typical focus: → deterministic control → local I/O handling → machine automation → interlocks and sequencing ▪️ 𝗥𝗧𝗨 – 𝗥𝗲𝗺𝗼𝘁𝗲 𝗧𝗲𝗿𝗺𝗶𝗻𝗮𝗹 𝗨𝗻𝗶𝘁 Best suited for remote monitoring and supervisory control across distributed field sites. Common in: → power and water networks → oil and gas pipelines → pumping stations → remote wells → geographically distributed assets Typical focus: → telemetry → remote data acquisition → WAN / radio / cellular communication → sending field data upward to SCADA or control centres ▪️ 𝗜𝗘𝗗 – 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗘𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰 𝗗𝗲𝘃𝗶𝗰𝗲 Best suited for protection, control, measurement, event recording, and monitoring in electrical systems. Common in: → substations → switchyards → protection relays → bay controllers → meters → electrical protection systems Typical focus: → protection logic → fault detection → event and disturbance recording → IEC 61850 communication → bay-level control and monitoring The quick rule of thumb: 𝗣𝗟𝗖 = 𝗹𝗼𝗰𝗮𝗹 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗥𝗧𝗨 = 𝗿𝗲𝗺𝗼𝘁𝗲 𝘁𝗲𝗹𝗲𝗺𝗲𝘁𝗿𝘆 𝗜𝗘𝗗 = 𝗽𝗼𝘄𝗲𝗿 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻 & 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 From an OT security perspective, this distinction matters. Because the security approach cannot be the same for all three. A PLC risk conversation may focus on logic changes, engineering workstation access, firmware, and local process impact. An RTU risk conversation may focus on remote connectivity, weak authentication, telemetry links, exposed services, and communication reliability. An IED risk conversation may focus on protection settings, substation communication, IEC 61850 GOOSE/MMS traffic, time sync, and the consequences of false trips or protection failure. This is why asset inventory alone is not enough. We need asset context. 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗲 𝗱𝗲𝘃𝗶𝗰𝗲 𝗰𝗼𝗻𝘁𝗿𝗼𝗹? 𝗪𝗵𝗲𝗿𝗲 𝗶𝘀 𝗶𝘁 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱? 𝗪𝗵𝗼 𝗰𝗮𝗻 𝗰𝗵𝗮𝗻𝗴𝗲 𝗶𝘁? 𝗪𝗵𝗮𝘁 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 𝗱𝗼𝗲𝘀 𝗶𝘁 𝘂𝘀𝗲? 𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗳 𝗶𝘁 𝗳𝗮𝗶𝗹𝘀 𝗼𝗿 𝗶𝘀 𝗺𝗶𝘀𝘂𝘀𝗲𝗱? That is where real OT security starts. Not by treating every Level 1 asset the same. But by understanding the operational role, communication path, and consequence of compromise. #OTSecurity #IndustrialCybersecurity #PLC #RTU #IED #SCADA #ICSsecurity #IEC62443 #PowerSystems #CriticalInfrastructure #CyberSecurity #OTSecurityHuddle

𝗣𝘂𝗿𝗱𝘂𝗲 𝗠𝗼𝗱𝗲𝗹 𝟮.𝟬 -- 𝗥𝗲𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗣𝘆𝗿𝗮𝗺𝗶𝗱 𝗶𝗻 𝘁𝗵𝗲 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗘𝗿𝗮 The #PurdueModel has been the foundational framework for structuring and segregating #IndustrialAutomation systems, networks, and operations for three decades. However, the inception of #Industry40 has made it imperative to rethink the automation pyramid to accommodate #digital technologies (and their effects) into the model. 𝗧𝗵𝗲 𝗜𝗻𝗲𝘃𝗶𝘁𝗮𝗯𝗹𝗲 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 The Purdue Model outlines a hierarchical, layer-based framework: ▪ Level 4 (L4): Business/Enterprise #IT systems (e.g., #ERP, #SCM) ▪ Level 3.5 (L3.5): Demilitarized Zone (#DMZ) ▪ Level 3 (L3): Operational systems (e.g., #MES, #APC, #Historians) ▪ Level 2 (L2): Supervisory control systems (e.g., #SCADA, #DCS) ▪ Level 1 (L1): Sensors, instrumentation, and basic control systems (e.g., #RTUs, #PLCs) ▪ Level 0 (L0): Physical processes However, increased #data volume and decentralized data processing needs expose the model's limitations. An "extended" framework, the Purdue Model 2.0, proposes new additions, such as a Level 5 (L5) for #cloud services, an #edge / #fog computing infrastructure distributed across the "operational/control" zones, and the integration of #IIoT-enabled devices, while prioritizing open protocols for maximum interoperability. 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗕𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀 Cloud computing gets rid of the need for large on-premises infrastructure, introducing scalable storage and powerful computational capabilities. It redefines L4 and L5, serving as the hub for #analytics, #AI / #ML engines, and enterprise-wide data integration: ▪ Public cloud (Level 5): Accessible, scalable, and managed by hyperscalers. ▪ Private cloud (Level 4): Integrated into local #OT networks, provide controlled environments for sensitive operational data. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗮𝘁 𝘁𝗵𝗲 𝗦𝗼𝘂𝗿𝗰𝗲 Edge computing processes data at or near its source—on devices like industrial PCs, minimizing the need to send data to centralized servers and solving the latency problem. It aligns with L2-L3, enabling localized intelligence while sharing data upstream to enterprise layers, supporting real-time analytics and open protocols like #OPC UA and #MQTT. 𝗕𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝗖𝗹𝗼𝘂𝗱 𝗮𝗻𝗱 𝗘𝗱𝗴𝗲 Fog computing is an intermediary layer that decentralizes data processing and storage across a network of nodes, aggregating data from multiple sources before it reaches the cloud and helping improve contextual awareness. It aligns with L3.5. 𝗧𝗵𝗲 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲 IIoT connects physical assets to digital networks, aligning with L1 but with an extended outreach to L4/L5, blurring the traditional boundaries between operational and enterprise layers. Smart sensors provide the foundational data for analytics-driven use cases. ***** ▪ Follow me and ring the 🔔 to stay current on #IndustrialAutomation and #Industry40 Insights!

I've always wondered why adding one new system to a plant feels like rewiring half the building 🔌 After working with communication protocols, control devices, and PLCs, I realized something: it's not about the brand, the protocol, or SCADA. It's about how devices talk to each other Most plants run on point-to-point integration with every producer wired directly to every consumer. So imagine having N producers and M consumers, that is N × M integrations to build, secure, debug, and maintain. Five devices and five consumers result in 25 connections. Add one more device, five new integrations. Add one more consumer, and that is 5 new ones. The graph grows exponentially. Every change affects the entire system Whereas Pub/Sub flips the model. Producers publish to a central data bus once. Consumers subscribe to what they need once. N + M total connections. 5 devices and 5 consumers result in 10 connections. Add a device, 1 new connection. Add a consumer, 1 new connection, the graph grows additively The difference is structural: 🔸 Adding a new system doesn't require touching the existing ones 🔹 Producers don't need to know who's listening 🔸 Consumers don't need to know who's publishing 🔹 Security and access live in one place 🔸 Data resolution scales without rewiring Look at the left side, every PLC, every sensor, every gateway has its own line to every consumer. Each one is a separate integration to build, secure, debug, version, and maintain. Now look at the right side: every device publishes once. Every consumer subscribes once. The diagram is doing the entire argument before you read the math #IndustrialAutomation #IIoT #Industry40 #DigitalTransformation #PLCs

What makes a manufacturing publication worth reading isn’t the design or the headlines. It’s whether the people contributing actually live with the consequences of the ideas they put forward. I recently rediscovered this Intelligent Enterprise Leaders Alliance issue from July, and I’m just as impressed today as I was when it first came out. 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐕𝐞𝐫𝐬𝐢𝐨𝐧: https://lnkd.in/e5gjfyTC What makes it strong isn’t the cover or the headlines. It’s the people. I personally know many of the contributors, and they’re people I trust, look up to, and regularly learn from. These are leaders shaping smart manufacturing strategy, defining Industry 4.0 frameworks, building and deploying automation, advancing robotics, setting standards, and dealing with what AI really looks like once it meets real operations. What I appreciate most is how the topics collide. MES, AI, robotics, IIoT, workforce, and supply chain aren’t treated as separate conversations. They show up the way they do in real life; interconnected, constrained by execution, and shaped by organizational readiness as much as technology. And yes, I’m honored to be on the cover. But honestly, any one of these contributors could be there, and I have no doubt many of them will be in the future. That’s how influential (and how important) these voices are. • John Dyck • Henrik von Scheel • Tom Rozema • Chris Caldwell • Lisa Anderson • Meaghan Ziemba • Claire Fallon • Sainath H. • Tom Davenport • Lora Cecere • David R. Brousell If you’re serious about modern manufacturing, don’t just read what these people have to say... 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞𝐦.  𝐅𝐨𝐥𝐥𝐨𝐰 𝐭𝐡𝐞𝐦.  𝐋𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞𝐦. And to the IELA team, keep up the great work! Looking forward to what’s next. ******************************************* • 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!