IT Asset Management Essentials

When a data scientist looks at a pump, they see a dataset. When a maintenance technician looks at a dataset, they see gibberish. And therein lies the problem. 😮 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞? Predictive maintenance refers to the use of data analysis tools and techniques to detect anomalies in equipment and predict potential failures before they occur. This approach leverages data from sensors and machines to anticipate maintenance needs, thereby preventing costly downtime and extending the lifespan of machinery. The power of predictive maintenance lies in its ability to ensure operational efficiency and save substantial costs in the long run. By preventing unexpected equipment failures, companies can reduce downtime, enhance safety, and optimize spare parts handling, making operations smoother and more cost-effective. 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: 𝐓𝐰𝐨 𝐖𝐨𝐫𝐥𝐝𝐬 𝐂𝐨𝐥𝐥𝐢𝐝𝐢𝐧𝐠 However, integrating predictive maintenance into business operations isn't without its hurdles. One significant challenge is the cultural and knowledge gap between maintenance teams and AI experts. Maintenance professionals may lack a deep understanding of AI and data analytics, while AI specialists often do not possess firsthand knowledge of the intricate realities of day-to-day maintenance. This disparity can lead to miscommunication and inefficiencies in implementing predictive maintenance solutions. The companies that succeed in predictive maintenance are the ones that don’t just invest in technology—but also invest in breaking down silos between AI engineers and maintenance teams. 𝐀 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐦𝐨𝐝𝐞𝐥 𝐢𝐬 𝐨𝐧𝐥𝐲 𝐚𝐬 𝐠𝐨𝐨𝐝 𝐚𝐬 𝐭𝐡𝐞 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐛𝐞𝐡𝐢𝐧𝐝 𝐢𝐭. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝐌𝐚𝐫𝐤𝐞𝐭 According to IoT Analytics, the predictive maintenance market is growing fast, hitting $𝟓.𝟓 𝐛𝐢𝐥𝐥𝐢𝐨𝐧 in 2022 and is expected to grow by 𝟏𝟕% annually until 2028. The market has evolved to include three main types of predictive maintenance: indirect failure prediction, anomaly detection, and remaining useful life (RUL). Most companies adopting predictive maintenance report a positive ROI, with 𝟗𝟓% seeing benefits and 𝟐𝟕% recouping costs within a year. Successful vendors often specialize in specific industries or assets, and software tools in this space share common features like data collection, analytics, and third-party integration. 𝐅𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞, 𝐡𝐢𝐠𝐡-𝐫𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐢𝐦𝐚𝐠𝐞, 𝐚𝐧𝐝 𝐚𝐝𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬: https://lnkd.in/erQ5HTab ******************************************* • 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!

It's astonishing that $180 billion of the nearly $600 billion on cloud spend globally is entirely unnecessary. For companies to save millions, they need to focus on these 3 principles — visibility, accountability, and automation. 1) Visibility The very characteristics that make the cloud so convenient also make it difficult to track and control how much teams and individuals spend on cloud resources. Most companies still struggle to keep budgets aligned. The good news is that a new generation of tools can provide transparency. For example: resource tagging to automatically track which teams use cloud resources to measure costs and identify excess capacity accurately. 2) Accountability Companies wouldn't dare deploy a payroll budget without an administrator to optimize spend carefully. Yet, when it comes to cloud costs, there's often no one at the helm. Enter the emerging disciplines of FinOps or cloud operations. These dedicated teams can take responsibility of everything from setting cloud budgets and negotiating favorable controls to putting engineering discipline in place to control costs. 3) Automation Even with a dedicated team monitoring cloud use and need, automation is the only way to keep up with the complex and evolving scenarios. Much of today's cloud cost management remains bespoke and manual, In many cases, a monthly report or round-up of cloud waste is the only maintenance done — and highly paid engineers are expected to manually remove abandoned projects and initiatives to free up space. It’s the equivalent of asking someone to delete extra photos from their iPhone each month to free up extra storage. That’s why AI and automation are critical to identify cloud waste and eliminate it. For example: tools like "intelligent auto-stopping" allow users to stop their cloud instances when not in use, much like motion sensors can turn off a light switch at the end of the workday. As cloud management evolves, companies are discovering ways to save millions, if not hundreds of millions — and these 3 principles are key to getting cloud costs under control.

𝐓𝐡𝐞 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐚 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐀𝐬𝐬𝐞𝐭 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 !! Visibility and knowledge truly is a power for cybersecuity. A regularly updated asset inventory—covering all IT and OT devices with an IP address (including IPv6)—forms the backbone of an effective security program, aligning with NIST CSF (ID.AM-1, ID.AM-2, ID.AM-4, DE.CM-1, DE.CM-7) and addressing critical MITRE ATT&CK tactics and techniques (T1200, T0819, ICS T0819, ICS T0883). 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐬𝐨 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥? 1️⃣ Identify Unknown & Shadow Assets: Unmanaged or “shadow” devices create blind spots that adversaries can exploit (T1200). Keeping a thorough inventory shines a light on what’s really on your network. 2️⃣ Rapid Vulnerability Response: An up-to-date list of assets lets you quickly pinpoint which systems might be affected by new threats (T0819, ICS T0819). 3️⃣ Manage Internet-Accessible Devices: Internet-facing endpoints, especially in OT/ICS environments (ICS T0883), are high-value targets for attackers. A strong asset inventory protects these crucial points. As industry frameworks like NIST 800-82, IEC62443, ISO27001, UL2900 maintaining a frequently updated inventory of all IP-based assets ensures you can detect and respond effectively to security issues—whether it’s a new piece of hardware appearing on the network or a critical vulnerability disclosure. 𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫: You can’t protect what you don’t know exists. By improving your asset awareness, you significantly bolster your organization’s ability to manage cyber risks and maintain a resilient security posture. #AssetInventory #Cybersecurity #IT #OT #MITREATTACK #NISTCSF #VulnerabilityManagement #SecurityBestPractices

62 billion kg of e-waste in 2022. Only 22% was recycled. That’s 48 BILLION KGs.. ...either dumped, burned, or forgotten in storerooms. The real issue? Most firms don’t track what they own. Discarded switches, laptops, and servers become invisible liabilities. E-waste isn’t just an environmental issue. It’s a failure in governance, process, and accountability. The solution isn’t new technology. It’s: Better records Smarter workflows Certified partners... ...who show up with trucks and certificates You can automate IT asset disposition inside ServiceNow or OTRS Group. ▸Set end-of-life triggers. ▸Attach recycling certificates. ▸Report WEEE compliance directly. Enviroserve UAE and Sims Limited India are certified ITAD partners. Dell Technologies, Lenovo, and Huawei run take-back schemes with secure data wipes. Do this ↬ Catalogue every IT asset. ↬ Assign an owner and disposal date. ↬ Automate disposition in your ITSM tool. ↬ Partner only with certified e-waste recyclers. ↬ Refurbish and reissue internally where possible. ↬ Use OEM programs to close the loop securely. E-waste is not someone else’s problem. It’s your hardware lifecycle. And ESG recovers real asset value. Track it. Reuse it. Prove it. Save this if you manage infrastructure.

Organizations managing 500+ devices save up to 60% in IT workload. An FMCG company’s procurement manager walked into our office at QuickTech with a concern. “We want to upgrade to Apple devices, but managing hundreds of them seems like a nightmare.” They needed different setups for their sales and tech teams, pre-installed apps, security settings, and minimal IT intervention. Configuring each device manually wasn’t an option. That’s when we introduced them to Apple Business Manager (ABM) and Mobile Device Management (MDM). With Zero-Touch Deployment, their employees could receive a sealed Apple device, turn it on, and everything would be pre-configured, right from apps to security policies. "So, no manual setup? No IT headaches?" he asked. Here are three key features of Apple Business Manager (ABM), which we explained to him: 📍Zero-Touch Deployment – Devices arrive pre-configured and ready to use, with all apps and settings automatically installed. 📍Centralized Device Management – Manage and assign different profiles for sales, tech, or any team from a single platform. 📍Enhanced Security & Compliance – Enforce security policies, remotely wipe data, and ensure all devices stay updated. Today, their teams work seamlessly, and IT no longer spends hours setting up devices. If your business is confused about whether this setup would be helpful to you or not, let’s have a chat :)) #procurement #apple #procurementmanagers #quicktech #it #fmcg

Railways Reimagined: Precision to Performance with LiDAR, BIM & Digital Twins 🚆 LiDAR, BIM & Digital Twins: Revolutionizing Rly Infrastructure & Rolling Stock Management ⚙️📡 IR and Metro systems are witnessing a technological transformation. At the heart of this change are LiDAR, BIM (Building Information Modeling), and Digital Twin technologies—collectively redefining how rail projects are planned, executed, and operated. 🔷 LiDAR (Light Detection and Ranging): LiDAR enables high-precision 3D scanning of rail corridors, station yards, tunnels, and depot infrastructure: Critical for alignment design, terrain mapping, and encroachment detection Offers accurate as-built documentation for existing tracks, OHE masts, bridges, and structures Speeds up feasibility studies for new lines, doubling, electrification, and station redevelopment Enhances planning for automatic track inspection and drone-assisted corridor surveys 🔷 BIM (Building Information Modeling): BIM provides a digital representation of physical railway assets—integrating geometry, specifications, and timelines: Used in station design, platform upgradation, signal room layouts, rolling stock depots, and OHE gantries Facilitates clash detection between track, OHE, signaling, and structures during planning Enhances coordination between civil, S&T, and electrical departments Improves contractor and consultant collaboration during execution and handover 🔷 Digital Twins in Railways: A Digital Twin creates a live, data-driven model of railway assets—powered by real-time inputs from sensors, IoT devices, and BIM: Enables condition-based monitoring of rolling stock, tracks, bridges, and OHE Predictive maintenance becomes possible for LHB/EMU/Trainsets/Locos through real-time diagnostics Improves energy optimization and system reliability for metro and electric traction systems Supports lifecycle asset management and planning of renewals and upgrades 🔗 The Connected Railway Ecosystem: Together, these technologies create a seamless digital workflow: LiDAR feeds accurate data into the BIM platform BIM serves as the static digital blueprint Digital Twins bring that model alive with performance, usage, and sensor data 🎯 Railway-Specific Benefits: ✅ Faster approvals & precision in DPRs for new lines, sidings, and stations ✅ Reduction in rework due to clash-free design integration ✅ Predictive asset management of rolling stock, track geometry, and OHE sag & tension ✅ Remote monitoring of depots, yards, stabling lines, and maintenance facilities ✅ Real-time decision support for railway operations and asset lifecycle optimization 💡 In Summary: 👉 LiDAR provides spatial precision 👉 BIM delivers integrated design 👉 Digital Twins enable real-time intelligence Together, they’re driving Indian Railways toward a digitally enabled, sustainable, and future-ready network. #MetroRail #DigitalTwins #BIM #LiDAR #RollingStock #RailwayProjects #OHE #TrackManagement #SmartRailways

Risk Based Inspection in the age of Industry 4.0 Traditional Risk Based Inspection has served industry well, but the actual risk is not static. We assess risk at a point in time, then wait months or years before reassessing → hoping nothing significant changes in between. In the age of Industry 4.0 and Predictive Analytics, that approach is rapidly becoming obsolete. The evolution from traditional to monitoring-enhanced RBI represents more than just technological advancement → it's a fundamental shift in how we understand and manage asset integrity. Traditional RBI Foundations: Built on API 580 and 581 standards, traditional RBI provides structured frameworks for calculating Probability of Failure (PoF) and Consequence of Failure (CoF). These periodic, static assessments create inspection schedules based on risk rankings at specific moments in time. The monitoring enhanced Evolution: Modern RBI integrates real-time sensor data, predictive analytics, and machine learning to create dynamic risk profiles that evolve continuously. Instead of waiting for scheduled reassessments, risk calculations update automatically as conditions change. Here are the key technological enablers: → Smart sensors and IoT networks providing continuous condition monitoring → Data-driven FMEA models that identify failure patterns humans might miss → Predictive Analytics simulate degradation scenarios under various operating conditions → Risk visualization platforms that make complex data accessible to decision-makers API Standards Integration: This evolution aligns with existing API frameworks → 580/581 for quantitative risk modeling. The transformation delivers tangible benefits: earlier anomaly detection, optimized inspection planning, reduced costs, and enhanced regulatory compliance. Most importantly, it transforms risk management from a periodic exercise into a continuous capability. The technology exists today to make this transition. The question is not when but how fast the organizations will adopt this evolution or wait for others to prove its value. How is your facility preparing to integrate real-time data into your risk-based inspection strategy?

Learn SAP EAM with me Episode# 5: Monitoring Asset Health Imagine a turbine running 24/7. It looks fine on the outside — but inside, the temperature is quietly rising beyond safe limits. By the time humans notice, it’s too late. That’s why Asset Health Monitoring is not a luxury anymore. It’s survival. 👉 With SAP Asset Performance Management (integrated with SAP IoT + S/4HANA), you don’t just maintain assets — you listen to them. Here’s how it works: 🔹 Indicators Sensors on equipment (temperature, pressure, vibration, etc.) stream data continuously. These become Indicators in APM, directly linked to Measuring Points in SAP S/4HANA. 🔹 Alerts When thresholds are crossed, APM creates an Alert — an early warning of anomalies, failures, or risks. You decide whether alerts are based on rules or triggered by equipment alarms. 🔹 Rules Rules act as your always-on watchdog. You set logic (e.g., “If temperature > 90°C for 10 mins → Trigger action”). The system monitors every single data point, 24/7. 🔹 Integration with IoT Your equipment becomes a device in SAP IoT. Each sensor is mapped, each reading flows in real time, and together, IoT + APM turn raw signals into actionable insights. 📌 Why it matters: Instead of reacting to breakdowns, you’re predicting them. Instead of relying on guesswork, you’re using data-driven reliability. ⸻ Takeaway: Monitoring asset health is about moving from “What went wrong?” to “What’s about to go wrong — and how do we prevent it?” ⸻ 👉 I’m breaking down SAP EAM step by step. Follow along if you want to see how real projects use IoT + APM for predictive maintenance. Have you worked on a project where sensor data actually prevented a failure? — ✍️ Avnikant Singh 🇮🇳 Singh

Real-time monitoring isn’t about sensors or dashboards. It starts with people. Before wiring a single machine, sit down with operators, supervisors, and CI leaders. Ask: What information would actually help you hit your goals? Machine states, scrap problems, downtime details. Those answers shape the whole project. Here’s the 12-step framework to monitor your factory in real time: → Step 0: Interview people to define key info to track → Step 1: Map your process, lines, and machines → Step 2: Collect downtime, scrap, and capacity data → Step 3: Define fields from SKUs/work orders → Step 4: Set a heartbeat signal per machine → Step 5: Identify data sources (PLCs, SCADA, OPC…) → Step 6: Connect machines with wiring and networks → Step 7: Configure the system with your process info → Step 8: Train people and involve them in validation → Step 9: Validate data with regular shift/day/week reviews → Step 10: Build CI dashboards with structured agendas → Step 11: Track KPIs and actions tied to improvements → Step 12: Analyze trends to guide strategy High performers don’t start with tech. They start with people, then build the system that makes every meeting, every decision, and every improvement cycle run on facts. Pro tip: Step 0 saves months of wasted effort later. PS: If you had to pick one, what’s the most important data point to track in your plant? Save this framework and repost to help others start monitoring in real time.