MES Fragmentation Issues in Manufacturing Operations

I've discussed #MES/#MOM project difficulties with hundreds of people. Here's my take on the challenges that crop up time and time again: 1. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Getting master data across systems (like ERP) to be at the right level of detail, or to be abstracted to be relevant for MES/MOM. Connecting to legacy equipment and databases. A lack of standardisation or contextual information in file formats. 2. 𝗖𝗵𝗮𝗻𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Operators and supervisors resist new processes. Without proper training and buy-in from the shop floor, even the best #MES system becomes unused. Onus is on leadership to set the vision and align teams. 3. 𝗨𝗻𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀: Companies expect immediate ROI and perfect data from day one. Manufacturing transformation takes time, and data quality improves gradually as processes mature and people use the system more effectively. 4. 𝗜𝗻𝗮𝗱𝗲𝗾𝘂𝗮𝘁𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Underestimating the internal resources needed. IT teams are stretched thin, and manufacturing engineers often lack the bandwidth to support implementation properly. Often the people needed are already the busiest people, and sometimes the relevant resources don't exist in the business at all, so hiring them and getting them up to speed is a bottleneck. 5. 𝗦𝗰𝗼𝗽𝗲 𝗖𝗿𝗲𝗲𝗽: Lack of strong leadership and governance can lead to a mentality of trying to implement every suggestion - this leads to complexity that overwhelms teams and dilutes project focus. This is worst when trying to replicate functionality from old or homegrown systems. The successful projects I've observed share common traits: they build strong teams with a clear vision, invest heavily in training, set realistic timelines, and maintain strong executive sponsorship throughout. Most importantly, they treat MES implementation as a business transformation project, not just a technology deployment 💪 What's been your biggest challenge when implementing manufacturing systems? I'd love to hear your experiences in the comments. p.s. I know about the typos - but I just loved the image so much so went with it 😂

You have 5,000 sensors, an MES, a cloud platform, and a shiny ERP. Yet you still can’t answer the most basic questions. You didn't build "data fabric", you are now just drowning in data. Everyone’s talking about “data fabrics.” Vendors pitch them like magic glue for your data chaos. Here’s the truth tho: The data fabric isn’t a product, it’s a strategy. And that misunderstanding is killing 7 out of 10 data modernization projects in manufacturing. For years, manufacturers and asset-heavy industries were told to connect everything. They did. Factories now have sensors, historians, MES, ERP, IoT platforms, and a half-dozen dashboards. But most are drowning in data and still can’t answer basic questions like: “Why did Line 3 stop?” “Which supplier caused that quality issue?” “Why don’t the finance numbers match what ops is reporting?” The data exists, it just lives in 20 different systems that don’t talk. Same happening in biotech and pharma. So when vendors promise a “data fabric,” execs think they’re buying the solution. What they’re actually buying is another layer of complexity, built on top of chaos. The real problem is... Data fabric isn’t a thing you install. It’s a way you operate. It’s how you decide: -What data to collect (and what not to) -How to apply business context at the source -How to connect machine, process, and cost data into one traceable flow -How to act (automation, trigger, products) on it without waiting for IT/Data. We call it “Ingest → Store → Contextualize → Act,” that’s not a diagram. That’s an operating model, a new muscle for the business to develop. It's how you turn data into intelligence to impact the business. Because collecting data in your 'fancy' data foundation is not valuable. If not leverage it to it's fullest. Forget the buzzwords. If you want your manufacturing data strategy to work, do this instead: 1. Start with the business pain, not pipelines . To many IT/CTO director start with tools and pipelines. Find the top 2–3 decisions that take too long because your data doesn’t line up. Find problems, start there. E.g. - "Line downtime reports that take days to verify." 2. Define ownership at the source. Maintenance owns asset data. Quality owns inspection data. Finance owns cost data. No more downstream cleanup. This is how you prevent 90% of your “data quality” problems before they happen. 3. Transform at ingestion. Don’t dump raw telemetry into the cloud. Fix units, names, and formats before it leaves the plant. Stop storing garbage you’ll never use. 4. Connect context, not just systems. A sensor reading is meaningless without context or where from. Define the schema once, deploy it across. Context creates intelligence. 5. Build one pilot, prove ROI, then scale. Recently got a question how fast can built out data foundation and get value? I said - "weeks" Don't “boil the ocean.” One use case. One outcome. One win.

My recent post on hybrid project methodology triggered good discussion about the roles IT and OT should play. Here’s what we’ve seen work—and what doesn’t. MES projects create more friction than any other industrial technology deployment because it’s the only system that straddles both worlds—IT and OT. IT’s World: Infrastructure, security, scalability, enterprise integrations (ERP, PLM, quality systems), data governance, vendor management, and budget accountability. A poorly architected MES creates security vulnerabilities, integration nightmares, and compounding technical debt. OT’s World: Shop floor operations, equipment integration, real-time production decisions, operator usability, and manufacturing process expertise. An MES that doesn’t work for operations becomes expensive shelfware, no matter how well it integrates with ERP. The Problem: When IT leads alone → You get a system that checks enterprise boxes but doesn’t work on the floor When OT leads alone → You get a system that works locally but creates enterprise integration and security problems Why MES is Different: Every other industrial technology sits clearly on one side: SCADA/HMI and PLCs are OT domain. ERP and identity management are IT domain. But MES collects real-time data from PLCs while posting to ERP. It manages shop floor workflows while enforcing enterprise compliance. It makes split-second production decisions while maintaining audit trails for regulators. It needs both perspectives from day one. What we’ve seen work: - IT defining architecture, security, and integration standards - OT defining workflows, usability, and operational requirements - Both collaborating on vendor selection and implementation - Clear accountability: IT owns infrastructure and enterprise integration, OT owns operational outcomes The worst deployments? One side decides, the other lives with it. The Challenge: In many organizations, IT reports to Finance, driving consolidation, standardization, and cost reduction. But manufacturing is about managing variability, responding to real-time conditions, and continuous improvement. MES sits at the intersection. It fails when we pretend it belongs to just one world. Less than 10% of manufacturers globally have achieved true MES maturity. The gap isn’t technology capability—it’s that IT purchased an off-the-shelf “configurable” MES without understanding operational requirements, or OT built something that can’t scale or integrate. When IT alone selects MES, they often buy a solution that reduces operational efficiency rather than enhancing it. MES success requires both IT rigor and OT expertise working together from requirements through deployment.

𝗪𝗵𝘆 𝗠𝗘𝗦 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗿𝗲𝗮𝗹𝗹𝘆 𝗴𝗲𝘁 𝗱𝗲𝗹𝗮𝘆𝗲𝗱? It’s not the software. It’s what’s underneath: – Master data that’s wrong or missing – Process steps vary by shift – Rework flows not mapped – Excel still doing critical work – No one owns the process end-to-end – Equipment not even available for commissioning – "Exceptions" happen 40% of the time – Test cases passed — but don’t match the real floor – People trained on screens, not workflows The plan looks clean. The floor is not. #MES #Manufacturing #ManufacturingExecutionSystem #ImplementationReality #DigitalTransformation #ShopFloorTruth #IT #Operations

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.

𝗠𝗘𝗦 -- 𝗔𝗿𝗲 𝗬𝗼𝘂 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆? #Manufacturing processes are often hindered by legacy equipment, disconnected data systems, and manual processes, resulting in a lack of visibility, inefficiencies in scheduling and workflows, and challenges with data collection. An #MES solution helps overcome these issues by providing a single source of truth for production data and integrated workflows. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗣𝗮𝗶𝗻 𝗣𝗼𝗶𝗻𝘁𝘀 𝗶𝗻 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 ▪ Operators are usually burdened with time-consuming manual data entry that is prone to errors and delays. ▪ Without real-time data, identifying and mitigating production bottlenecks becomes a challenge. ▪ The absence of real-time synchronization between production schedules and #ERP systems creates operational inefficiencies. ▪ Traditional workflow management, typically manually outlined, fails to adapt to real-time operational changes. ▪ For regulated industries, the inability to track materials and production stages accurately could result in compliance risks. 𝗧𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗠𝗘𝗦 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 The foundation of any successful MES implementation lies in understanding business needs and ensuring alignment across the organization, including key stakeholders across operations, quality control, and management. Before jumping into implementation: 𝟭. Clearly define the goals of the MES project and how it will impact each department. 𝟮. Conduct a people, process, and technology assessment to identify potential gaps in readiness. Ensuring that employees are prepared to handle new technologies is as critical as choosing the right technology stack. 𝟯. Assess the existing technology stack and operational readiness. A comprehensive platform with a common user interface, dashboards, reporting tools, and data architecture is preferable to a series of standalone solutions. 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗮𝗻 𝗠𝗘𝗦 𝗳𝗼𝗿 𝗟𝗼𝗻𝗴-𝗧𝗲𝗿𝗺 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 A full-scale MES implementation can be overwhelming for smaller operations; therefore, an agile implementation plan is critical. Instead of implementing a one-size-fits-all MES solution, manufacturers can start with key use cases that yield the highest ROI, such as #OEE tracking or downtime monitoring. Focusing on these targeted areas first will allow manufacturers to demonstrate early wins and build support for further MES integration, address immediate pain points while building a foundation for future scalability, and achieve measurable results without overhauling their entire production infrastructure. Source: https://shorturl.at/mB6hI ***** ▪ Enjoy this content? Follow me and ring the 🔔 to stay current on #IndustrialAutomation, #IndustrialSoftware, #SmartManufacturing, and #Industry40 Tech Trends & Market Insights!