Manufacturing Consulting Services

The majority of Industrial AI isn’t going into some futuristic, fully autonomous factory. It’s going into: • Catching defects • Keeping lines running • Fixing machines before they break That’s it. Over half the use cases are sitting right there in quality, production, and maintenance. What I found more interesting wasn’t the top of the list… it was the movement. 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 & 𝐑&𝐃 𝐮𝐩 𝐚𝐥𝐦𝐨𝐬𝐭 𝟑𝐱. 😮 AI is starting to show up before anything hits the floor. Not just improving execution… influencing how things are designed, tested, and brought into production. This means different conversations and different people involved. And then there’s the part that made me laugh a bit…“Other” dropped by 70%. 🤣 Fewer side projects. More focus on the parts of the business that run every day. Also worth noting…You don’t see a category here that screams GenAI. Most of this is: • Vision • Time-series data • Operational models The kind of AI that doesn’t demo well… but does show up in results. My biggest takeaway from this chart: Companies are putting AI where: • The problem already hurts • The data already exists • The outcome actually matters to the business Not everywhere. Just where it counts. I wrote a deeper breakdown of what the latest Industrial AI data and trends reveal based on the huge amount of research conducted by IoT Analytics in their 399-page 2025 Industrial AI Report. 𝐅𝐮𝐥𝐥 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/e2-GJZYJ ******************************************* • 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!

AI agents and physical AI are shifting industrial automation from equipment supply to autonomous, self-optimizing systems. The most mature vendors are moving from pilots to production, with robots navigating complex environments and digital twins optimizing the value chain. This CB Insights brief gives a good view of where the top 20 industrial automation companies stand on AI maturity. Three key trends. 1. Leaders like Siemens Industry and ABB are linking AI systems across design, logistics, manufacturing, and maintenance creating compounding benefits. 2. Optimization dominates near-term priorities, while digital twins are emerging as the backbone for connecting hardware and software. 3. Partnerships with tech companies like Microsoft, Google, and Nvidia are essential, but they create new dependencies that must be managed. Siemens at the top of the ranking, combining copilots, edge platforms, and digital twins. Its work with Microsoft and Nvidia expands capabilities but increases reliance on external tech. Honeywell takes a more focused approach, embedding AI into devices and workflows. Its Qualcomm partnership highlights product-level integration over broad system building. ABB advances through its OmniCore platform and acquisitions such as Sevensense and SensorFact, blending robotics, software, and energy management. Schneider Electric pushes AI in energy management, using digital twins and partnerships with Nvidia, Microsoft, and Itron to extend from factory optimization into grid intelligence. The path forward in industrial AI is moving beyond pilots or isolated tools. It will depend on how well vendors embed AI into their platforms, link technologies across domains, and balance the benefits of external partners with the need for strategic independence. Those that will get it right will turn AI from experimentation into durable advantage. Just as critical is how their customers adopt these technologies. Industrial firms must shift from isolated use cases to embedding AI in design, production, energy, and logistics. Success requires not only advanced tools, but also the data, skills, and processes to make AI scale in complex operations.

There is a lot to unpack with November’s trade data. One pattern that is appearing in sector after sector can only be termed a decoupling of US activity with the rest of the world. This is shown below for seasonally and price adjusted imports (top) and exports (bottom) of industrial and service machinery. This BEA end use category includes (i) Industrial engines & pumps; (i) food & tobacco processing machinery; (iii) machine tools, metal working machines; (iv) industrial textiles, sewing machines; (v) woodworking, glass working machines; (vi) pulp & paper machinery; (vii) measuring, testing & control instruments; (viii) materials handling equipment; (ix) other industrial machinery; and (x) photo & other service industry machinery. Both series are expressed as indexes where 100 = 2023. Thoughts: •The top chart shows a sharp drop in imported industrial and service machinery since February 2025. We have seen especially weak imports in September, October, and November that were down 14.3%, 14.6%, and 12.7% from prior year readings, respectively. •The bottom chart shows a sharp drop in US exports of such machinery. September, October, and November were down 11.5%, 3.8%, and 11.9% year-over-year, respectively. •These are significant trade categories. In 2024, nominal exports were $14.8 billion a month. Nominal imports were $20.7 billion a month. •Before anyone makes a comment in the form of “but this is good, this means companies are buying more American-made machinery,” that’s not how things tend to work with specialized producer goods like those captured in these categories. Rather, we see US manufacturers across many sectors investing less in machinery (hence the declining imports) while our own machinery manufacturers are losing access to export markets. Consistent with this argument, US machinery wholesalers’ price and seasonally adjusted sales were down 6.0% and 7.9% from the prior year in October and November per the Census Bureau’s real wholesale trade program. There is also an asymmetry here in that foreign producers are increasingly concerned about trade policy instability in the USA and will begin to turn elsewhere. US buyers of foreign machinery aren't looking at this issue the same way from my conversations. Implication: declining imports and exports of industrial machinery are a troubling sign. When you then factor in how rapidly machinery prices are rising based on PPI data, I‘m not anticipating strong capital investment by manufacturers in 2026 in new machinery. #supplychain #shipsandshipping #manufacturing #freight #trucking

Are closed industrial systems silently draining your bottom line? New research shows they can cost manufacturers up to $45.18M annually for large enterprises and $11M for mid-sized, but there’s a smarter path forward. Openness isn’t just a tech trend, it’s a business advantage. ✅ Open, software-defined automation (SDA) breaks free from vendor lock-in by decoupling hardware and software. This gives manufacturers the flexibility to choose best-fit solutions, scale at their own pace, and accelerate innovation. ✅ With #EcoStruxureAutomationExpert, companies like Shell, Nestlé, Zicaffè, dhp Technology, and Evonik are already proving that openness drives speed, sustainability, and resilience. ✅ This isn’t just swapping architectures, it’s a new operating model where data flows seamlessly across design, operations, and optimization, enabling faster decisions and continuous improvement. Curious to read more? https://lnkd.in/eU5nh6M9 What’s your take, are open ecosystems the future of industrial automation? Let’s discuss in the comments! 

In today’s fast-evolving digital landscape, the convergence of Information Technology (IT) and Operational Technology (OT) is essential. From my experience working across industries like automotive and manufacturing, I see how integrating IT and OT can transform operations, boost security, and drive innovation. Here’s why this matters: 🔹 Unified Governance: Strong leadership and clear roles help align IT and OT efforts toward shared business goals. 🔹 Enhanced Security: OT systems benefit from IT’s cybersecurity expertise through standardised policies, regular updates, and centralised user controls, closing gaps that were once vulnerabilities. 🔹 Data-Driven Innovation: Seamless data flow across IT and OT enables new digital products and services, unlocking value and creating competitive advantage. Leading companies are already capitalising on this. 🔹 Empowered Teams: Bringing IT and OT professionals together fosters knowledge exchange and agility, which accelerates decision-making and drives business success. As we move deeper into Industry 4.0, embracing IT-OT convergence is a strategic imperative. India’s industries stand to gain massively by accelerating this integration, positioning us as global innovation leaders. Would love to hear your experiences with IT-OT convergence in your organisation. #DigitalTransformation #ITOTConvergence #Industry40

𝗗𝗶𝗱 𝘆𝗼𝘂 𝗽𝗹𝗮𝗰𝗲 𝗮 "𝗖𝗮𝗻𝗮𝗿𝘆" 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻? The sort of early warning detection system which monitors your automated processes and sings when irregularities occur? Why a 🐤 𝗰𝗮𝗻𝗮𝗿𝘆 you ask? Around 1911, miners started to take canary birds into the coal mines to detect the accumulation of toxic gases. These birds, would even sense the smallest traces and emissions, starting to erratically chirp and with that giving miners early warnings to immediately evacuate the mine. Just as the canaries once did in the mines, 𝗮 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 "𝗰𝗮𝗻𝗮𝗿𝘆" can play a vital role in monitoring the health of your automated workflows signalling potential issues before they escalate and perhaps, cause scaled harm. But how do you implement a digital canary into your workflows in your process automation? 𝗜𝗻𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗶𝘁 𝗳𝗿𝗼𝗺 𝘀𝘁𝗮𝗿𝘁: into your design by using code, reconciliation reports, and validation rules to establish effective in-process control checks and monitoring mechanisms and visual dashboards to analyse red flags. Find here 5 examples how to get early alerts in your process automation, even if your automation bots don't know how to sing: ▪️𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗖𝗵𝗲𝗰𝗸𝘀: Implement automated checks at various stages of the process to ensure accuracy and completeness and volume variations. ▪️𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗘𝗿𝗿𝗼𝗿 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Monitor integration and break points like API's for errors or failures to maintain seamless data flow across systems. ▪️𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗦𝗰𝗮𝗻𝘀: Validate for duplicate records or inconsistencies to maintain data integrity and remove manual overrides or corrections. ▪️𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸𝘀: Analyse insights from user feedbacks to check on usability issues, frequent issues and detect sentiment drops with NLP / AI. ▪️𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗖𝗼𝗰𝗸𝗽𝗶𝘁: Create a centralised dashboard to monitor compliance metrics to detect red flags and and detect deviations from policies. By integrating digital canaries into your process automation strategy, you are not only enhance your ability to detect and respond to issues rapidly but also promote a culture of self-monitoring and continuous improvement. So, did you already place a digital "canary" into your process design and automations? If not, maybe it's time to reconsider adding this early warning system to your automation approach ensuring the health and resilience of your tasks, data & process performance. What early warning systems have worked for you best? #processautomation #intelligentautomation #rpa #processexcellence

Why recreate humans when you can redesign the process? Tesla's Robot Strategy: A Manufacturing Reality Check Here's a number that caught my attention: $200K for a humanoid robot vs. $20K for specialized automation that does the job better. I've been working with AI in production environments for years, and Tesla's Optimus approach makes me think... there might be a more efficient way to solve this. Everyone gets excited about humanoid robots replacing workers. But here's the question I keep asking: Why recreate humans when you can redesign the process? What I Learned About Manufacturing Automation In production AI, I discovered something important: the best automation doesn't copy humans: it eliminates the need for human-like movements entirely. During my time at Intel Corporation, the most successful improvements came from: → Redesigning workflows around machine capabilities (not making machines work like humans) → Using specialized tools for specific jobs (not general-purpose solutions) → Working with existing systems (not replacing everything) Tesla's humanoid approach seems like the expensive path. What Manufacturing Really Needs Think about this: Why build a robot with hands when you can change the assembly line to not need hands at all? What actually works in manufacturing: • Pick-and-place systems → 99.9% accuracy, $50K investment • Vision inspection → 24/7 quality control, finds defects immediately • Collaborative robot arms → Work with humans, deploy in weeks not years These solutions aren't as exciting, but they change production lines in months. The Numbers Tell a Different Story This is what I find interesting: A $20K specialized robot often outperforms a $200K humanoid robot for specific manufacturing tasks. Looking at the data: • Specialized automation: 6-month return on investment • General humanoid robots: 5+ years (maybe never) • Process redesign + targeted automation: 3-month return Tesla's Real Opportunity Instead of expensive human-like robots, what if Tesla focused on: Manufacturing AI that: - Predicts when machines will break before it happens - Optimizes assembly steps in real-time - Prevents quality problems through smart process control This approach could transform manufacturing faster. My Take While everyone builds humanoid robots, I see a big opportunity in smart automation that makes existing manufacturing much more efficient. The future of manufacturing might not be robots that look like us. It might be systems so intelligent they make human-like robots unnecessary. Through DigiFab, I work on bridging AI and manufacturing. Sometimes the best solutions don't look like science fiction, they just work much better.

What connects Industrial IoT, Application and Data Integration, and Process Intelligence? During my time at Software AG, my attention has shifted in line with the company's strategic priorities and the changing needs of the market. My focus on Industrial IoT, moved into Application and Data Integration, and now I specialise on Business Process Management and Process Intelligence through ARIS. While these areas may appear to address different challenges, a common thread runs through them. Take a typical production process as an example. From raw material intake to finished goods delivery, there are countless interdependencies, processes and workflows, and just as many data sources. Industrial IoT plays a key role by capturing real-time data from machines and sensors on the shop floor. This data provides visibility into equipment performance, production rates, energy usage, and more. It enables predictive maintenance, reduces downtime, and supports continuous improvement through real-time monitoring and analytics. Application and Data Integration brings together data from across the value chain, including sensor data, manufacturing execution systems, ERP platforms, quality management systems, logistics, and supply chain management. Synchronising these systems with integration creates a unified, reliable view of production operations. This cohesion is essential for automation, traceability, quality management and responsive decision-making across departments and geographies. Process Management, including modelling, and governance, risk, and controls, takes a different yet equally critical perspective. Modelling helps design optimal process flows, while governance frameworks ensure controls are in place to manage quality, risk, and enforce conformance for standardisation. Process mining uncovers bottlenecks, rework loops, and compliance deviations. It focuses on how the production process actually runs, rather than how it was designed to operate. Despite their different vantage points, each of these domains works toward the same goal: aggregating, normalising, and structuring data to transform it into information that can be easily consumed to create meaningful, actionable insights. If your organisation is capturing process-related data through isolated tools, such as diagramming or collaboration platforms, quality management systems, risk registers, or role-based work instructions, it is likely you are only seeing part of the picture. Without a unified approach to integrating and analysing this data, the deeper insights remain fragmented or out of reach. By aligning physical operations, applications & systems, and business processes, organisations can move beyond surface-level visibility to uncover the root causes of inefficiency, unlock hidden potential, and govern change with clarity and confidence. #Process #Intelligence #OperationalExcellence #QualityManagement #Risk #Compliance

🚀 AI-Powered Industrial Revolution: How Rockwell Automation is Shaping the Future of Smart Manufacturing Artificial Intelligence and Generative AI are transforming industrial automation, and Rockwell Automation is at the forefront of this revolution. By embedding AI into manufacturing execution systems (MES), digital twins, industrial IoT, and supply chain optimization, Rockwell is unlocking new levels of efficiency, productivity, and resilience in industrial operations. 💡 Key AI Innovations by Rockwell Automation: ✅ Predictive Maintenance – AI-driven analytics reduce machine downtime and optimize performance. ✅ Generative AI for Industrial Design – AI automates engineering workflows, system design, and PLC programming. ✅ AI-Powered Industrial IoT (IIoT) – FactoryTalk InnovationSuite provides real-time monitoring and predictive insights. ✅ AI in Supply Chain Management – Intelligent forecasting, risk assessment, and logistics optimization. 🌍 The Bigger Picture: AI is driving autonomous manufacturing, edge computing, and human-machine collaboration, making industrial automation smarter, faster, and more resilient. Competitors like Siemens, ABB, Schneider Electric, and Honeywell are also investing in AI, but Rockwell’s integrated approach to AI-powered automation gives it a competitive edge. ⚠️ Challenges & Considerations: 🔹 AI model accuracy and reliability in critical industrial processes. 🔹 Cybersecurity risks in AI-driven industrial control systems. 🔹 Regulatory compliance with NIST, ISO, and the EU AI Act for AI governance. The future of industrial automation is AI-driven, autonomous, and adaptive. Rockwell Automation is shaping that future by blending AI, IoT, and automation to build the factories of tomorrow. 💬 What do you think about AI’s role in industrial automation? How do you see AI transforming manufacturing in the next decade? Drop your thoughts below! ⬇️ #AI #Automation #Industry40 #SmartManufacturing #RockwellAutomation #IndustrialAI

Most third-party risk teams I speak with face the same challenge: Small staff, large vendor portfolios. 💼 The data backs this up: - The average portfolio is ~286 vendors; most TPRM teams have fewer than 10 staff. - 94% of teams say they cannot assess all vendors due to a lack of time or resources. - Nearly 50% of companies admit they don’t even reassess all vendors periodically. - Assessment cycles average 37+ hours per week, with vendor responses dragging 12+ days and 84% needing follow-ups. So, how do you cover more risk without more people? Here are some simple recommendations: ✅ Tier ruthlessly – Auto-tier vendors into 4 levels; reserve full assessments + monitoring for Tier 1. ✅ Use what exists – Accept SOC 2, ISO, or SIG Lite when fresh instead of sending new questionnaires. ✅ Streamline questionnaires – Keep only two: Core and Lite, with “proof selector” options to reduce doc sprawl. ✅ Event-based reassessments – Trigger quick checks after major incidents or CVEs instead of annual reviews for all. ✅ Automate workflows – SLA boards, templates, and parallel legal/security reviews speed decisions. ✅ Blend capacity – In-house for critical vendors, managed services, or external reviewers for overflow. Six metrics to prove efficiency to your board: 1) Coverage – % of Tier 1–2 assessed & monitored 2) Cycle Time – intake → decision 3) Risk Impact – remediation in 30/60/90 days 4) Accepted Risk Backlog – trend line 5) Reviewer Hours – per completed assessment 6) Cost – per Tier 1 decision Bottom line: You don’t need to assess every vendor equally. Focus depth where it matters, streamline the rest, and measure results. #ThirdPartyRiskManagement #TPRM #VendorRisk #OperationalResilience #RiskManagement #CyberRisk #Governance #Compliance #Procurement #SupplyChainRisk