Implementing Change In Manufacturing

Most manufacturers aren't failing because they don't want to transform. They're failing because they don't know how far they've come, where the roadblocks are, or what's actually holding them back. That’s why benchmarking matters. A recent report by Onward Partners based on 387 Industry 4.0 assessments across 40 countries provides a rare data-driven snapshot of where manufacturers truly stand. 𝐓𝐡𝐞 𝐚𝐯𝐞𝐫𝐚𝐠𝐞 𝐦𝐚𝐭𝐮𝐫𝐢𝐭𝐲 𝐬𝐜𝐨𝐫𝐞? Just 2.38 out of 6. That means most companies are still stuck in the early or middle stages of their transformation journey. But the story the data tells gets even more interesting when you look at the gaps. 𝐓𝐡𝐫𝐞𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐭𝐡𝐚𝐭 𝐞𝐯𝐞𝐫𝐲 𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐞𝐫 𝐬𝐡𝐨𝐮𝐥𝐝 𝐩𝐚𝐲 𝐚𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐭𝐨: • 𝐈𝐓 𝐬𝐲𝐬𝐭𝐞𝐦 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐭𝐡𝐞 𝐰𝐞𝐚𝐤𝐞𝐬𝐭 𝐥𝐢𝐧𝐤. It received the lowest average score across all dimensions. But it also had the highest maximum score. This means the vast majority are struggling with fragmented systems and data silos, while a small set of front-runners are unlocking real competitive advantage through seamless integration. • 𝐒𝐨𝐜𝐢𝐚𝐥 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐭𝐡𝐞 𝐡𝐢𝐠𝐡𝐞𝐬𝐭 𝐬𝐜𝐨𝐫𝐢𝐧𝐠 𝐚𝐫𝐞𝐚 𝐨𝐧 𝐚𝐯𝐞𝐫𝐚𝐠𝐞, 𝐛𝐮𝐭 𝐧𝐨 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐬𝐜𝐨𝐫𝐞𝐝 𝐧𝐞𝐚𝐫 𝐭𝐡𝐞 𝐭𝐨𝐩. Collaboration exists, but it's not fully enabled by data or technology. Teams are still operating on email threads and meetings rather than AI-supported, real-time decision platforms. Culture may support teamwork, but the tools aren't amplifying it. • 𝐂𝐮𝐥𝐭𝐮𝐫𝐞 𝐥𝐞𝐚𝐝𝐬, 𝐛𝐮𝐭 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐥𝐚𝐠𝐬. Among the four structuring forces that define Industry 4.0 maturity (resources, information systems, organizational structure, and culture), culture comes out on top. Companies have strong leadership buy-in and employee readiness. But without the right systems and processes in place, motivation alone isn’t moving the needle. The most successful manufacturers aren’t just investing in tools. They are aligning their people, systems, and processes in a way that scales. That’s the real path to transformation. 𝐑𝐞𝐚𝐝 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐚𝐧𝐝 𝐚𝐜𝐜𝐞𝐬𝐬 𝐰𝐡𝐢𝐭𝐞𝐩𝐚𝐩𝐞𝐫 𝐡𝐞𝐫𝐞:  https://lnkd.in/erSBysjQ ******************************************* • 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!

The manufacturing landscape is evolving rapidly, driven by AI, sustainability, and agility. My experience at RSWM Limited has shown that progress stems from blending technology with human insight. Beyond automation, success lies in intelligent collaboration. Agentic AI predicts maintenance, optimises supply chains, and boosts efficiency. Value emerges when teams innovate with these systems. Our shift to biofuels and zero-liquid-discharge operations illustrates how discipline transforms waste into value and enhances profitability. Sustainability is core to strategy. Circular models, recycled materials, and bio-fabrication set new standards. GreenStitch’s AI platform supports this by centralising data, automating ESG reporting, and tracking carbon footprints for informed decisions. Agility is vital amid trade shifts and climate disruptions. Market diversification and digital adoption foster resilience: the strength Indian manufacturing has shown across cycles. The future of manufacturing depends on intelligence, agility, and purpose. AI-enabled factories and digital supply chains are becoming standard practice while sustainability is embedded in operations rather than positioned as a CSR initiative. Leadership excels via effective technology integration: data-driven decisions, balanced profitability, responsive systems, and skilled teams. Concerns about AI replacing jobs ignore historical trends. Technology has always redefined roles rather than eliminated work. Supply chains are now AI-driven, equipment uses smart sensors, automated changeovers are standard, and predictive insights have replaced manual inspection. Customer engagement has moved from physical catalogues to digital portfolios, meeting global regulatory and market standards. Today’s manufacturing leaders must ask sharper questions, take informed risks, and build organisations that evolve continuously. Future factories will rely on engineering excellence, strategic clarity, and strong cultural alignment. #manufacturing #AI #agenticAI #technology #leadership #leadwithrajeev

The manufacturing reset: Why November 2025 could redefine Indian manufacturing “You don’t lead change afterwards; you get in front of it.” Today, I believe India’s manufacturing sector is at a pivotal inflection point, and for Greaves Cotton Ltd, it’s not just a wave to ride, but a platform to leap from. So why does November 2025 matter? India’s #manufacturing output is showing renewed traction; the #PMI climbed to 59.3 (a 17.5-year high) signalling strong expansion momentum. Manufacturing still contributes only ~17.2 % of #GDP, with a target of 25 % ahead. The runway is expansive. For Greaves, this “manufacturing reset” aligns with our evolving identity. Under our new strategic roadmap, GREAVES.NEXT, we have moved beyond the idea of being simply ‘engine-makers’. We are building ourselves to offer #Energy Solutions, #Mobility Solutions, and #Industrial Solutions, three areas that will shape how India powers, moves, and builds in the decade ahead. But strategy only matters when it meets execution, and for us, that execution is unfolding every day on the shop floor. Across our plants, Industry 4.0 is no longer a buzzword; it has become an operating reality. Machines now speak to us through IoT-driven dashboards. Data is giving us real-time visibility into performance, quality, and asset health. Automated inspection systems are eliminating guesswork and improving consistency. Predictive maintenance is helping us move from reactive repair to proactive reliability. And our focus on energy efficiency and green manufacturing is building sustainability into the system, not as an afterthought, but as a design principle. All of this supports what GREAVES.NEXT fundamentally stands for: fuel-agnostic powertrains, deeper OEM collaborations, and industrial solutions that are engineered for global relevance. It’s a shift from building products to providing solutions; from chasing efficiencies to creating them. A manufacturing reset doesn’t happen by accident. It requires teams aligned to a long-term view, processes that evolve continuously, and a culture that adapts faster than the world around it. At Greaves, we’re working towards that mindset every day; learning, progressing, and building for a future that rewards flexibility and reinvention.

The future of manufacturing isn’t being built in Silicon Valley. It’s being built in Biel. 🇨🇭 Today at Swiss Smart Factory, I heard the most powerful question: 💡 “What if we stopped optimizing our current business model and started designing for the one we’ll need in 2030?” That question captures why the Swiss Smart Factory model represents the most sophisticated manufacturing innovation approach in Europe. It’s not a technology showcase. It’s a strategic neutrality platform that enables radical collaboration: → Competing automation providers share the same factory floor → Technology vendors design for interoperability, not lock-in → Global corporations and Swiss SMEs access identical capabilities → Academia validates solutions in real production conditions This ecosystem solves Industry 4.0’s biggest failure: The implementation gap. Three shifts happening right now: ⚡ Digital Twins → Cognitive Twins Virtual representations that predict, prescribe, and continuously learn. AI-augmented simulation that gets smarter with every scenario. Automation → Augmentation Industry 5.0 amplifies human capability. Multi-touch collaboration, VR-enabled review, real-time what-if analysis make complex decisions accessible. Integration → Orchestration When 50+ technology partners operate in one innovation space, interoperability becomes survival. Systems must compose and orchestrate, not just integrate. 🎯While other regions compete on labor costs, Swiss manufacturing competes on precision, quality, and innovation velocity. Virtual Twin intelligence combined with SSF’s collaborative ecosystem amplifies exactly these strengths. This is competitive advantage at the system level, not company level. Not future vision. Strategic transformation laboratory. Working today in Switzerland. 🚀 Your question isn’t “What’s our digital transformation roadmap?” It’s “What ecosystems and capabilities enable our future competitiveness?” Are you buying technology or building adaptive capability? #Industry50 #StrategicLeadership #SwissInnovation #ManufacturingExcellence

Many factories lose money on problems they can't even see. Tiny defects, machine breakdowns, and small inefficiencies add up quietly. Regular robots and machines can't spot these issues. But AI can see them. The groundbreaking partnership between Intel and LG Innotek tackles this challenge head-on. We are building a smart factory where AI acts as a "superhuman eye" for real-time visual quality control. This system is powered by a suite of Intel technologies, including Intel® Xeon® processors, the OpenVINO toolkit, and Intel® Arc™ Graphics. This is a leap beyond simple robotics. We're now moving into the era of the self-optimizing production line. What does this look like in practice?  - AI vision systems can detect defects invisible to the human eye. Micro-fractures, subtle color variations, minute misalignments prevent flawed products from reaching the next stage. - As the AI analyzes thousands of units, it learns. It begins to identify patterns that predict a future failure, allowing for preemptive adjustments to the manufacturing process itself. - This creates a continuous feedback cycle. The line doesn't just produce widgets; it produces data. That data fuels the AI, which in turn makes the line smarter, more efficient, and more resilient with every shift. I see this as the fundamental shift from automated manufacturing to cognitive manufacturing. The goal is no longer just speed but intelligent adaptation.  Read more here: https://lnkd.in/gz6tURZz #IntelAI #SmartFactories #IntelXeon #IntelArc #AIInManufacturing

A connected factory without control is just a faster way to make mistakes. When machines share live data, every signal can affect production, so automation needs purpose, reliable data, and clear human responsibility. The point is simple: connecting machines does not automatically make a factory smarter. A connection is useful when it helps production work better, improves maintenance, or makes the process safer. Data is where many problems start. If sensor data is wrong, late, or poorly protected, automated systems can act with confidence on bad information. In a factory, that can affect quality and safety very quickly. Security also needs to move closer to the shop floor. Remote access, controllers, and industrial networks are no longer background infrastructure. They are part of the production system. This is why IT and OT cannot work as separate worlds. Connected production needs shared rules and visible responsibility, so people can understand who owns the data, who controls the process, and how automated decisions are checked. #IndustrialIoT #SmartManufacturing

Additive Manufacturing - Shift from Prototyping to Mission-Critical Production Most people still frame additive manufacturing as a faster way to prototype parts. That framing is now outdated. This week, Chromatic 3D Materials successfully tested a 3D-printed rocket propellant capable of handling more than 1,800 PSI combustion pressure. The breakthrough wasn’t the printer. It was the ability to manufacture mission-critical propulsion systems with new geometries, lower weight, and dramatically faster production cycles. The deeper signal is that additive manufacturing is moving upstream in the value chain. For years, the industry sold efficiency. Now it is selling strategic capability. When supply chains become geopolitical assets, the ability to locally produce complex aerospace and defense components becomes more valuable than marginal cost savings. The winners won’t be printer companies. They’ll be the platforms controlling materials science, digital inventories, and distributed production networks. Recent consolidation across the sector points in the same direction. Investors should stop evaluating additive manufacturing as industrial tooling. The category is evolving into a resilience layer for critical industries. Founders building around advanced materials, defense manufacturing, and on-demand production infrastructure are operating in a much larger market than most forecasts capture. The next decade of manufacturing may look less like factories and more like software-defined production. #AdditiveManufacturing #DefenseTech #AdvancedManufacturing #IndustrialTech #3DPrinting https://lnkd.in/gzsrDPAe

Manufacturing teams: Stop thinking AI is "just for software". I just analyzed how Anthropic's teams actually use Claude across their organization, and the translation to industrial use cases is shocking. Traditional AI → Industrial AI: - Debugging Infrastructure → Sensor logs, MES system bugs, PLC issues - Unit Test Generation → Hardware test planning, QA protocols - Code Reviews → Legacy code in robotic arms, CNC controllers - Data Visualization → Production floor dashboards for operators - Documentation → ISO/FDA protocols, incident playbooks The real insight? Claude is becoming a cool teammate! :) Anthropic uses it across: → Engineering (code reviews, debugging) → Security (risk assessment, config reviews) → Operations (process optimization, SOPs) → Quality (test planning, validation) → Compliance (regulatory docs, audits) This is the future of smart factories. Not more siloed dashboards (please!), but AI teammates positioned across every role in your organization. 5 things manufacturing can steal (proudly) from Anthropic's playbook: 1���⃣ Use AI for edge case identification, not just automation 2️⃣ Replace documentation burnout with AI-first drafting 3️⃣ Help teams think faster, not just work faster 4️⃣ Deploy AI across ALL roles, not just IT 5️⃣ Build organizational memory, not just velocity The companies getting this right aren't waiting for "AI to be ready for manufacturing." They're realizing it already is. We just need to catch up. What's your biggest AI opportunity in manufacturing? 👇 Read more in my Substack post, link in the comments. #ManufacturingAI #IndustrialAI #SmartFactory #Claude #DigiFabAI

Manufacturing Efficiency is More Than Numbers…It’s Transformational Science that Delivers Value. In my experience of deploying continuous process improvement, I’ve seen one truth repeat itself: small changes in cycle time create massive changes in organizational success. Consider a real-world example from a Fortune 500 distribution center. The facility struggled with a 12-hour lead time from order receipt to shipping. When we applied Manufacturing Cycle Time (MCT) and Manufacturing Cycle Efficiency (MCE) analysis, the data revealed that only 35 percent of production time was true value-added work. The rest was waiting, unnecessary movement, or inefficient scheduling. Through Lean tools like value stream mapping, Kaizen events, and standard work design, we cut average lead time from 12 hours to 8 hours. That 4-hour reduction meant faster customer fulfillment, increased throughput capacity, and a remarkable financial impact, more than 3.2 million dollars in annualized savings through reduced overtime, lower inventory holding costs, and fewer expedited shipments. The return on investment went far beyond financials. Employees who once felt pressured by bottlenecks were now empowered to work in a smoother, more predictable system. Morale increased as they could focus on craftsmanship and problem-solving rather than firefighting. When people feel their contributions directly improve performance, you build a culture of ownership and innovation. I have led these transformations across industries, from aerospace to government services and the outcomes are consistent. The combination of measuring cycle efficiency and acting on it with Lean methods delivers scalable success. Organizations gain profitability, employees gain pride, and customers gain trust. Continuous improvement is not just about efficiency metrics. It is about unlocking hidden capacity, protecting margins, and most importantly, enabling people to thrive in environments designed for excellence. That is the real power of Lean.🔋

🙈 “Risks in the Shadow of Change“ 🙉 The basic goal of Management of Change (MOC) is to determine the risks brought by changes to be made in a hazardous process in advance, to eliminate or minimize these risks and to ensure that the change is implemented safely and sustainably. This approach is of vital importance, especially in technical areas. Because even a small change can have major consequences; it can cause rupture, leak, fire or even a major industrial accident. Unfortunately, many change approvers make decisions by evaluating this process only on paper. It is a common mistake to approve without seeing the reflection of the change in the field and without making the necessary analyses and observations. This can ironically turn change management into a process that creates risks rather than reducing risks. MOC is not only a procedural approval process, but also a critical discipline that requires technical expertise, field experience and a multi-faceted evaluation. Therefore, it is essential to adopt a multidisciplinary approach, especially in technical changes. Different areas of expertise such as mechanics, electricity, chemistry, operator, automation, occupational health and environment should come together to make an evaluation. Many industrial accidents in the past have resulted from the implementation of changes without sufficient analysis. For example, a small design change made in a pipeline may not be able to withstand the system pressure and may eventually cause explosions. Similarly, a small error made in software updates may hide alarms of processes that will create risks in PLC or DCS systems. In order to prevent such results, the MOC process must be supported by field observation, engineering calculations, and function tests. Although analyses on paper provide some basic insights, they cannot always reflect the complexity of real conditions. Therefore, conducting onsite inspections, interviewing employees, and observing the physical condition of equipment are critical steps. It should not be forgotten that change inherently involves uncertainty. This uncertainty can only be managed through a planned, systematic, and participatory MOC. It is necessary not only to analyze risks, but also to be prepared for these risks, to provide transparency in processes, and to create systems that can reverse change when necessary. Creating an effective MOC not only prevents accidents, but also paves the way for continuous improvement and innovation. Therefore, it is a critical requirement for change management practitioners to have field awareness as well as technical knowledge. #oil #gas #LPG #refinery #process #safety #learning #engineering #MOC #managementofchange #risks #riskassessment #terminal #safeoperation #safechange #LNG #oilandgas #evaluation.