Using Technical Data in Modern Manufacturing

𝗧𝗵𝗲 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗵𝗿𝗲𝗮𝗱: 𝗔 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗟𝗲𝘃𝗲𝗿 𝗳𝗼𝗿 𝗦𝗺𝗮𝗿𝘁 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 As manufacturers strive for agility, traceability, and faster innovation, the Digital Thread emerges as a critical enabler—turning disconnected data into an intelligent, continuous flow across the entire product lifecycle. From design and sourcing to production, service, and end-of-life, it connects PLM, ERP, MES, CRM, and IoT systems—now enhanced with AI to deliver real-time insights and smarter decisions. 𝗛𝗼𝘄 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀: Capture data across systems and stages Connect it through structured relationships Analyze with AI to surface insights and answer queries Deliver role-based, contextual access Improve continuously via lifecycle feedback 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝗺𝗽𝗮𝗰𝘁 𝗔𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗩𝗮𝗹𝘂𝗲 𝗖𝗵𝗮𝗶𝗻: Engineering: Faster design-change impact analysis Shorter NPI cycles Living, evolving product models Manufacturing: Automate handoffs (CAD to CNC, CMM, MES) Reduce errors and rework Boost throughput and quality Supply Chain & Quality: Full traceability Connected supplier and compliance data Proactive risk management Customer Service: End-to-end part/service history Faster issue resolution Continuous feedback to design Leadership: Real-time operational visibility Reduced cost of quality Resilient, future-ready enterprise Sustainability: Map environmental impact across lifecycle Support carbon and waste reduction goals 𝗛𝗼𝘄 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗜𝘁: Align stakeholders across functions Identify and map critical data sources Connect them via structured, scalable architecture Apply AI for insight generation Secure and govern with enterprise-grade controls The image shows how systems, data, and AI converge in the Digital Thread framework to power the future of smart manufacturing. This is more than integration—it's the intelligent nervous system of modern industry. Ref: https://lnkd.in/gpnHq5Q3

Many manufacturers today have invested heavily in data infrastructure: PLCs, SCADA, MES, historians, dashboards. Yet when you dig into the architecture, especially on high-speed or complex lines, a common gap emerges. Critical short-duration events are not being captured accurately or with enough context to drive actionable insights. This is not due to lack of technology. Modern PLCs, edge devices, and platforms are more than capable. The problem is architectural. Many plants still rely on SCADA and MES systems that poll PLCs at relatively slow intervals, typically 1000 milliseconds. That polling interval creates a blind spot. Meanwhile, PLC scan cycles typically run between 3 and 5 milliseconds. In high-speed lines, servo-based systems, robotics, and motion applications, critical events happen on sub-second timescales. Operator inputs, cascading alarms, motion faults, and intermittent product jams often occur and resolve in less than a second. If these events are not buffered properly at the PLC layer or edge, they are simply lost to higher-level systems. This leads to a familiar pattern. • OEE reports that do not explain why downtime occurred • Fault logs that fail to show which fault triggered first • Product loss and yield issues that cannot be traced to specific machine behaviors • Maintenance teams spending hours reviewing PLC logic and guesswork post-mortems The bigger risk is that leadership decisions get made on incomplete data. Continuous improvement efforts stall. Predictive maintenance strategies fail to get off the ground. McKinsey & Company data suggests that manufacturers who close this gap and build modern data architectures can reduce unplanned downtime by up to 50% and improve productivity by 10 to 20%. But this requires capturing data with the right fidelity, at the right layer, and with the right context. From my experience, this is true not only on high-speed systems where products are moving faster than the eye can see and $100,000 high-speed cameras are used to diagnose failures. It is equally true on slower lines where operators and engineers struggle to explain recurring issues because key data is missing. If you are running below 60 percent OEE, you likely have more foundational work to do first. But if your goal is to move from reactive to proactive operations, to reduce variability, and to enable next-generation capabilities like advanced analytics and machine learning, this is an architectural conversation that needs to happen. I work with manufacturers who want to modernize these architectures and close this visibility gap. If you are looking at these challenges or want to benchmark your current architecture against best practices, feel free to reach out. I would be happy to share insights and lessons learned.

India’s manufacturing sector is undergoing a transformation, fueled by data analytics, AI, and IoT. As global 𝐬𝐮𝐩𝐩𝐥𝐲 𝐜𝐡𝐚𝐢𝐧𝐬 𝐟𝐚𝐜𝐞 𝐝𝐢𝐬𝐫𝐮𝐩𝐭𝐢𝐨𝐧𝐬 and increasing 𝐝𝐞𝐦𝐚𝐧𝐝𝐬 𝐟𝐨𝐫 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲, Indian industries are turning to data-driven solutions to stay competitive. 🔹 Predictive Analytics for Demand Forecasting Manufacturers are leveraging predictive analytics to analyze historical data, market trends, and external factors like weather and geopolitical risks. This helps them anticipate demand fluctuations, reduce overproduction, and optimize inventory—ensuring that goods are produced and distributed more efficiently. 🔹 AI-Powered Optimization AI-driven automation is streamlining production lines, detecting bottlenecks, and recommending process improvements in real-time. Machine learning models are reducing downtime by predicting equipment failures before they occur, saving costs on maintenance and minimizing disruptions. 🔹 IoT for Real-Time Supply Chain Visibility With IoT sensors integrated across supply chains, manufacturers can track shipments, monitor storage conditions, and ensure quality compliance. Real-time data from connected devices enhances transparency, allowing swift decision-making and reducing losses due to spoilage, theft, or delays. 🔹 Reducing Waste & Enhancing Sustainability Data analytics is helping manufacturers reduce material waste by optimizing production processes. AI-powered quality control ensures that defects are detected early, lowering rejection rates. Companies are also using data to implement sustainable practices, such as reducing energy consumption and improving recycling efficiency. 🔹 Empowering MSMEs with Data-Driven Insights Micro, Small, and Medium Enterprises (MSMEs), which form the backbone of India's manufacturing sector, are increasingly adopting cloud-based analytics solutions. These tools enable small businesses to optimize procurement, manage inventory efficiently, and compete with larger players through data-backed decision-making. India’s march toward becoming a global manufacturing powerhouse depends on how effectively industries harness data analytics. The future lies in an intelligent, connected, and efficient supply chain ecosystem. 𝑯𝒐𝒘 𝒅𝒐 𝒚𝒐𝒖 𝒔𝒆𝒆 𝒅𝒂𝒕𝒂 𝒂𝒏𝒂𝒍𝒚𝒕𝒊𝒄𝒔 𝒔𝒉𝒂𝒑𝒊𝒏𝒈 𝒕𝒉𝒆 𝒇𝒖𝒕𝒖𝒓𝒆 𝒐𝒇 𝒎𝒂𝒏𝒖𝒇𝒂𝒄𝒕𝒖𝒓𝒊𝒏𝒈? #SCM #DataDrivenDecisionMaking #DataAnalytics #DataAnalyticsinManufacturing #dataanalyticsinsupplychain

Manufacturing data is more than CAD. Yes, CAD defines the ideal outcome, but you can’t just go pick parts that meet those specs off a tree somewhere. In the case of machining you’re going to start with a block and take pieces off until it looks like your end product. It’s not readily apparent, but HOW you take those pieces off is actually pretty important too and requires its own documentation. Then, once you’ve figured out how to remove material effectively, you’ve also got to measure the actual outcome you were able to achieve. Current state of manufacturing silos each of these important (and valuable) pieces of information. Yes, the CAD remains the same regardless of who manufactures the part, but the process and reporting can be vastly different depending who does the work. Each manufacturer that produces a part invests heavily in the “how do we make this” and “how do we measure this” portion of the process but this information is never captured in a way that can be shared or sold. I had the opportunity to speak with Shane S. Campbell about a solution to this problem yesterday. Shane is one of those rare people who knows engineering, manufacturing, software and shop practices in a way that can only come from 3 generations of manufacturing entrepreneurship. He shared that the recently released quality information frameworks (ISO 23952 and ANSI QIF 3.0) lay out a process for tying manufacturing data back to engineering data in a way that retains the value added by each stakeholder in the process of going from model to part. He'll be watching comments on this post if you have questions on specifics and you're welcome to reach out directly as well. The cool thing about this standardization is that mass adoption will make manufacturing information portable and machine readable. This type of thing will never get as much press as humanoid robots do but data infrastructure development is the hidden work that will enable the next generation of manufacturing companies to scale rapidly.

With my recent posts diving deep into the roles of Historians and Time Series Databases (TSDBs) in manufacturing, perhaps the most important thing to remember is: Historian AND TimeSeriesDB, not Historian OR Time Series DB 🔄. Given a question I have often been asked, i.e., can I replace a Historian with a TimeSeriesDB, I wanted to give a clear "No" ❌. The Crucial Role of Historians 🌟 Historians are indispensable on the plant floor. They are designed to offer operational simplicity and rapid data retrieval, which are vital during urgent downtime incidents ⏰🔧. Tools like Canary, AVEVA Historian, and offerings from Tatsoft Frameworx Siemens and Inductive Automation are engineered to allow plant personnel to swiftly generate insights without requiring deep technical skills. This feature is paramount at 02:30 AM when a line goes down and immediate action is needed 🌙. Time Series Databases: Expanding the Scope 🌐 Time Series DBs do belong in manufacturing and on the plant floor; the two use cases I most commonly work with are: Enterprise level, Reporting, AI, and Machine Learning 📈: At the enterprise level, TSDBs like InfluxData QuestDB and Timescale come into play. They provide the data fidelity necessary for advanced analytics, supporting machine learning and AI initiatives that drive modern manufacturing forward 🤖. Moreover, the inherent scalability and cost-effectiveness of TSDBs make them suitable for extensive data analysis and reporting across the enterprise 🌍. At the Edge, providing simple, robust, and often open source and free dashboards that deliver immediate insights to team members on the plant floor 🖥️. Unified Namespace: Bridging the Gap 🔗 Integrating both Historians and TSDBs under a Unified Namespace (UNS) offers a comprehensive solution. A UNS acts as a single source of truth for current state, and can provide the input into multiple historical storages (Historian, Time Series DB, Data Lake) simultaneously, ensuring that all follow the same semantic hierarchy and are built using the same input information. This setup not only supports real-time operational control but also enables sophisticated data analysis: Streamlining Plant Operations 🏭: By integrating a UNS with both a historian and a TSDB, manufacturers can maintain continuous plant operations with tools that simplify data visualization directly on the plant floor. Enhancing Flexibility for Advanced Analytics 🔍: The combination of a TSDB with a UNS allows for complex data analytics necessary for predictive maintenance and production optimization. And Even better, by leveraging the #UNS you can build an architecture with the historian First, then add in a #TSDB, then change the #Historian, and even add multiple more TSDB, or use HighByte to collect data from the plant level , model it and push to the #Cloud for #MachineLearning. #ManufacturingData #Industry40 #DigitalTransformation #OperationalTechnology#DataManagement#IIoT

The problem with manufacturing digital transformation? "Random acts of digital." Manufacturers are drowning in data—projected to hit 4.4 Zettabytes by 2030. But it's trapped in fragmented legacy systems that can't talk to each other. The unlock isn't more technology. It's three things: 1. Clean, unified data. Focus on what matters: OEE, downtime, bottlenecks. A 10% OEE improvement can create capacity equivalent to 5 new production lines. For free. 2. Empowered people. Your operators need to interpret real-time analytics and make decisions fast—without waiting for the C-suite. AI tools can democratize the data, but humans still need to act on it. 3. Predictive AI. Stop analyzing yesterday's problems. AI continuously monitors performance, flags anomalies, and recommends actions before issues become critical. Think manufacturing GPS, not a map. The payoff? Cost savings from hidden efficiencies. Sustainability gains from reduced waste. Better workplace culture when teams can see their impact. Real-time benchmarking across sites will define who wins over the next decade. Stop the random acts. Build the foundation.

In manufacturing, some of the 𝐦𝐨𝐬𝐭 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐥𝐢𝐯𝐞 𝐨𝐧 𝐭𝐡𝐞 𝐬𝐡𝐨𝐩 𝐟𝐥𝐨𝐨𝐫. Technicians, operators, and engineers see issues and opportunities in real time. But often, these insights never make it to the C-suite—or when they do, they’re buried in technical jargon that’s disconnected from business strategy. 𝐖𝐡𝐞𝐫𝐞 𝐭𝐡𝐞 𝐃𝐢𝐬𝐜𝐨𝐧𝐧𝐞𝐜𝐭 𝐇𝐚𝐩𝐩𝐞𝐧𝐬: 🏭 Shop Floor Perspective: Metrics like downtime, OEE, yield, or vibration anomalies are the focus. These are essential for operational decisions but rarely tied to strategic goals. 💼 C-Suite Perspective: Leaders want to know how these issues impact revenue, profit margins, customer satisfaction, or long-term competitiveness. Without this connection, valuable technical insights often fall flat. When this gap isn’t bridged, 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 𝐬𝐮𝐟𝐟𝐞𝐫: Operational challenges remain unresolved because they’re seen as “just technical issues.” Investments in tools like AI or IIoT aren’t fully leveraged because executives can’t see 𝘰𝘳 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘩𝘰𝘸 𝘵𝘰 𝘶𝘯𝘭𝘰𝘤𝘬 their strategic value. 𝐇𝐨𝐰 𝐭𝐨 𝐁𝐫𝐢𝐝𝐠𝐞 𝐭𝐡𝐞 𝐆𝐚𝐩: 1️⃣ Translate Metrics into Business Impact: Instead of reporting downtime as “4 hours on Line 3,” say, “This downtime cost $50,000 in lost production and delayed delivery to key accounts.” Framing technical data in terms of revenue, costs, or customer outcomes creates alignment. 2️⃣ Use Relatable Analogies: Replace highly technical terms with simple comparisons. For example: “This predictive maintenance alert is like getting a check engine light—fix it now, or risk a costly breakdown later.” If you can quantify the cost of this breakage, even better. 3️⃣ Make Data Actionable: Executives don’t need every detail—they need a clear summary paired with a recommendation. For instance: “We’ve identified a bottleneck that could be eliminated with a $10,000 investment in automation. The ROI would be $100,000 in the first year.” 4️⃣ Involve Cross-Functional Teams: Foster collaboration between technical and leadership teams. Regularly schedule shop floor walks for executives to connect directly with operational challenges and successes. 𝐓𝐡𝐞 "𝐒𝐨 𝐖𝐡𝐚𝐭?": When technical teams and executives speak the same language, organizations unlock the full potential of their data, systems, and people. Leaders make smarter decisions faster, and technical teams feel valued and aligned with business goals. 𝐀 𝐐𝐮𝐢𝐜𝐤 𝐓𝐢𝐩: Great leaders bridge the gap between data and decisions. By connecting operational insights to strategic priorities, they create a culture of alignment and innovation that drives results. #Leadership #Manufacturing #industry40 #digitaltransformation

𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗠𝗘𝗦: 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗚𝗮𝗽 #MES #technology For 15 years, MES have been the workhorses connecting production and enterprise data. They've reduced errors and manual processes, but the ISA-95 standard is due for an upgrade. We can't leverage the full potential of Industry 4.0 without rethinking the entire factory tech stack. The answer lies in an 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 that unifies information across the shop floor. Cloud, Edge, and IoT are the building blocks, allowing for: Centralized data storage: All systems can access and utilize the same data for improved efficiency and cost reduction. Deeper MES integration: Jendamark's ODIN Workstation redefines MES by bridging the gap between PLCs, ERPs, and operators. Here's how ODIN empowers your MES journey: 👉 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗞𝗮𝗶𝘇𝗲𝗻: Empower industrial engineers with a no-code tool for continuous improvement based on real-time data. 👉 𝗘𝗱𝗴𝗲 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗴𝘂𝗶𝗱𝗮𝗻𝗰𝗲: Provide intuitive instructions for operators, optimizing production processes. 👉 𝗟𝗶𝘃𝗲 𝗹𝗶𝗻𝗲-𝘀𝗶𝗱𝗲 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴: Share real-time data with your ERP for seamless inventory management. 👉 𝗖𝗹𝗼𝘂𝗱-𝗯𝗮𝘀𝗲𝗱 𝗺𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲: Simplify and centralize maintenance operations. The future of MES is here. 𝗪𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆? #DigitalKaizen #Jendamark #Cloud #Edge #IoT

In today's fast-paced manufacturing sector, data interoperability and streamlined workflows are not just goals—they're necessities. The Manufacturing Data Model API from Autodesk Platform Services represents a leap forward in how we manage and utilize data across systems. Empower Your Processes with Structured Data The Manufacturing Data Model API is designed to provide a standardized, yet flexible structure for your manufacturing data, encompassing everything from materials and processes to the final product specifications. This standardization is key to unlocking efficient data exchange and automation. Why this matters for your projects: - Seamless Integration: By adopting a standardized data model, you can ensure seamless integration between different systems and tools used in your manufacturing process, from CAD software to ERP systems. - Automation and Efficiency: With all your data structured and interoperable, automating various aspects of the manufacturing process becomes more straightforward. Whether it's auto-generating work orders or streamlining the supply chain, the possibilities are endless. - Data-Driven Decision Making: A unified data model means you can more easily aggregate, analyze, and derive insights from your data. Make informed decisions faster, identifying efficiencies and areas for improvement in real-time. Dive into the Manufacturing Data Model API documentation to understand the schema and how it can be applied to your current data. Identify key areas of your workflow that can benefit from better data integration and automation. Start small to see immediate benefits. Ensure your team is on board with these changes. The success of implementing new technologies often hinges on adoption and adaptation at the human level. Final Thought: Embracing the Manufacturing Data Model API is not just about enhancing your current processes; it’s about setting a foundation for future innovation and growth. As manufacturing continues to evolve with Industry 4.0 technologies, being ahead in data management will place you at the forefront of this transformation. #Manufacturing #DataModeling #APIs #Industry40 #TechCommunity Centre for Computational Technologies (CCTech) #systemintegrator

MES/MOM Solutions: Elevating Manufacturing Efficiency  Implementing a MES/MOM Solution can revolutionize your manufacturing, driving functional improvements for enhanced efficiency, visibility, and decision-making. Here's a condensed overview:   Real-time Data Visibility: Gain insights into machine status, production rates & quality metrics. Enable faster decision-making through real-time monitoring. Production Scheduling and Sequencing: Optimize processes, minimize downtime, & enhance resource utilization. Improve efficiency through advanced scheduling. Quality Management and Traceability: Ensure adherence to quality standards with real-time inspection. Enable traceability throughout the production process. Workflow and Process Standardization: Establish standardized workflows, reducing errors. Enhance consistency with standardized processes.    Work Order Management: Prioritize, assign, & track tasks effectively for streamlined operations. Ensure efficient work order management. Resource Management: Optimize manpower, equipment, & material allocation. Achieve efficient resource utilization. Reduced Lead Times  Streamline processes for reduced lead times. Respond quickly to market demands. Inventory Management: Minimize stock-outs through efficient inventory management. Enhance supply chain efficiency. Automated Data Collection and Reporting: Reduce manual data entry with automated reporting. Ensure accuracy and timeliness. Non-Conformance & Corrective Action Management: Identify and manage non-conforming products swiftly. Enhance product quality and compliance. Resource Maintenance & Equipment Efficiency  Gain insights into equipment performance, improving OEE. Optimize maintenance schedules. Energy Consumption Optimization: Track & analyze energy consumption data for cost reduction. Identify opportunities for energy optimization. Labor Tracking & Performance Analysis: Monitor workforce performance & measure productivity. Enhance labor efficiency through data-driven insights. Regulatory Compliance & Reporting: Ensure compliance with industry regulations. Streamline regulatory compliance processes.   Continuous Improvement Initiatives: Leverage data-driven insights for continuous improvement. Foster a culture of operational excellence. Integration with Enterprise Systems: Seamlessly integrate with ERP, SCM, PLM, & other systems. Enhance data flow & decision-making. Embrace MES/MOM capabilities to drive operational efficiency, elevate product quality, and achieve superior manufacturing performance #mes #strategy #manufacturers