Engineering Challenges In Manufacturing

Manufacturing processes are often plagued by inefficiency.   Here's why:   Manufacturers cling to old batch habits. ___   Batch Production is a traditional manufacturing method where identical or similar items are produced in batches before moving on to the next step.   Some manufacturers argue that large batches balance workloads and minimize changeovers.   But data often shows otherwise.   Overlong production runs cause overproduction. Operators lose focus working on large batches while equipment drifts out of standards between changeovers.   Main drawbacks:   -Piles of WIP inventory waiting for the next step -Defects hide among the batches -Inefficient space management -Uneven workflow -Long lead times   Those lead to:   -Some stations being overloaded, others waiting -Low responsiveness to customer demand -More scrap and rework -Higher carrying costs -Facility costs up   Switching to One-Piece Flow can bring relief.    Workstations are arranged so that products can flow one at a time through each process step, making changeovers quick and routine.   Main advantages:   +High customer responsiveness +Minimal work-in-process inventory +Quality issues are detected immediately +Reduced wasted space and material handling +Easy to level load production to match takt time   The selection between batch processing and one-piece flow can significantly impact quality, productivity, and lead time in a manufacturing process.   P.S. Some case studies show improvements in labour productivity of 50% or more. Lead times can drop by 80%. And quality can approach Six Sigma.

Supply chains are increasingly complex, spanning upstream suppliers, midstream manufacturers, and downstream distributors and retailers. In such interconnected networks, smooth throughput — the rate at which materials, products, capital and information flow — is critical to business performance. Yet real-world disruptions often lead to throughput compression, where flow slows. One of the less visible challenges in orchestrating throughput under compression is the variability in demand patterns across multiple SKUs. In many consumer goods sectors, demand follows a long-tail distribution: a few SKUs drive the bulk of volume, while a large number of niche SKUs contribute marginally but are critical for market coverage and customer satisfaction. The coefficient of variation (CV) — the ratio of standard deviation to mean demand — differs widely across SKUs. Core, high-volume products often show low variability, making them easier to plan and schedule. In contrast, niche SKUs exhibit very high variability, with unpredictable order cycles and small batch requirements. To serve this diversity, manufacturers are often forced to run very short production runs, sometimes as little as a single pallet lot. This introduces significant complexity: frequent changeovers, increased downtime, higher waste, and fragmented throughput. The constraint is not only in machine time but also in the ability to sequence runs so that the next batch is available just in time for demand — a concept known as “days before next run” (DBNR). DBNR reflects the balancing act between service and efficiency. If a SKU is run too frequently, capacity is consumed by setups. If it is run too infrequently, stockouts occur given the high variability of demand. When throughput compression sets in — whether from upstream shortages or midstream bottlenecks — managing DBNR across dozens or hundreds of SKUs becomes even harder. This is where synchronization across partners is essential. Suppliers must align raw material deliveries with compressed production windows; manufacturers must optimize sequencing across SKUs under constraint; and distributors must accept rationalized availability of niche SKUs during compression periods. In this essay I have tried to look at all three dimensions of the compression: 1.  Upstream (Suppliers and Raw Materials) - Throughput compression upstream often stems from shortages of key inputs, delayed shipments, or price volatility. 2.  Midstream (Manufacturing and Processing) - Midstream compression typically arises from plant shutdowns, labor shortages, or process inefficiencies. A paint manufacturer facing a resin shortage may operate below capacity, producing fewer SKUs and delaying delivery schedules. 3.  Downstream (Distribution and Retail) - Downstream compression manifests as logistics bottlenecks, dealer stockouts, or last-mile inefficiencies. Read my paper to understand the inter-dependencies.   #supplychain #demandvariability #DBNR #throughput

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

Russia’s tank reserves are running out. Satellite data show that out of 7,342 tanks stored before the war, only 92 T-72B remain in decent condition. Most others — thousands of T-64s, T-72s, and T-80s — now sit rusting or stripped for parts. Figures were provided by OSINT researcher @Jonpy99, UNITED24 summarized them. Since 2022, Russia has reactivated about 4,800 tanks, mostly T-80B/BV (1,411), T-72B (1,191), and T-62 (1,012). Each wave to the front empties depots further — leaving little modern armor to replace losses. Today, Moscow’s “repairable stock” is down to 684 tanks — mostly outdated T-62s. Analysts say the quick-to-refit reserves are gone. What’s left is aging, cannibalized equipment that takes months to fix and rarely survives long in battle. The shortage spreads wider. Of 7,723 infantry vehicles, 4,931 are already deployed. Only 39% of prewar artillery and 18% of multiple rocket systems remain in storage. Even the Grads and Uragans are running out. The Defense Ministry has started auctioning destroyed tanks and IFVs as scrap metal. It’s a sign of how deeply Russia drained its armored reserves and how hard it will be to rebuild.

☝️ "In 2025, russia will probably go on the 𝐝𝐞𝐟𝐞𝐧𝐬𝐢𝐯𝐞," Writes The Economist. 📉 Explaining that the russian Federation is approaching the point of "resource exhaustion" - there will be nothing to fight with. "Russia deconserved Soviet weapons, but ~70% of old tanks remained immobile, and others were washed and passed off as new ones. In addition, the russians are removing artillery barrels from old equipment and installing them on self-propelled howitzers." Why otherwise is Putin trying to force some bad peace deal or truce with #Ukraine ? 📊 When the then defence minister, Sergei Shoigu, boasted in December 2023 that 1,530 tanks had been delivered in the course of the year, he omitted to say that nearly 85% of them were not new tanks but old ones that had been taken out of storage and given a wash and brush-up. Since the invasion, about 175 reasonably modern t-90m tanks have been sent to the front line. As those numbers dwindle, production of newly built t-90ms this year might be no more than 28. Pavel Luzin, an expert on Russian #military capacity, reckons that Russia can build only 30 brand-new tanks a year. Mr Luzin reckons that Russia’s ability to build new tanks or infantry fighting vehicles, or even to refurbish old ones, is hampered by the difficulty of getting components. Stores of components for tank production that before the war were intended for use in 2025 have already been raided, while crucial equipment, such as fuel-heaters for diesel engines, high-voltage electrical systems and infrared thermal imaging to identify targets, were all previously imported from Europe and their sale is now blocked by sanctions. The lack of high-quality ball bearings is also a constraint. Chinese alternatives are sometimes available, but are said not to meet former quality standards. 🚫 AKA sanctions work. Furthermore, the old Soviet armaments supply chain no longer exists. Ukraine, Georgia and East Germany were all important centres of weapons and components manufacture. Ironically, Kharkiv was the main producer of turrets for t-72 tanks. The number of workers has also fallen dramatically. 💥 And while russia can produce artillery shells (with help from North Korea) they can't produce artillery barrels. There are just two factories that have the sophisticated Austrian-made rotary forging machines needed to make the barrels. They can each produce only around 100 barrels a year, compared with the thousands needed. Russia has never made its own forging machines; they imported them from America in the 1930s and looted them from Germany after the war. Richard Vereker, an open-source analyst, thinks that by the start of this year about 4,800 barrels had been swapped out. How long the Russians can carry on doing this depends on the condition of the 7,000 or so that may be left" #Intelligence #Journalism #StandWithUkraine 🇺🇦

Conifer's huge $20M seed round just unlocked a smart way around our rare earth dependence. Every EV needs motors, but we rarely talk about them. Most discussions (and funding) focus on batteries, while motors remain tied to China's rare earth monopoly. Conifer's team flipped this by developing electric hub motors using ferrite magnets instead of rare earths. Simple switch, massive implications: The motors deliver 10% better range while being half the size of competitors. What is their smart move? Building automated production lines near customers - no massive factories, just local microfactories cranking out motors. For manufacturers, it's literally plug-and-play. It's exactly the kind of climate tech we need more of: Better performance, simpler supply chains, easy adoption. Sometimes the biggest impact comes from rethinking the basics rather than chasing the next breakthrough. Any hardware founders working on overlooked EV components? Drop a comment.

Here's a question: Why are so many businesses using the exact same off-the-shelf AI tools as their direct competitors and expecting to gain a unique advantage? A real, sustainable competitive edge doesn't come from a shared product. It comes from building your own intellectual property. This is the fundamental difference between 'renting' a generic AI and owning a bespoke one. When you build a custom AI, it’s trained on your most valuable asset: your proprietary data. Your internal process logs, your unique customer interaction history, your specific performance metrics. This is a goldmine that generic tools simply cannot access or understand. Let’s make this practical. Imagine a UK manufacturing firm struggling with machinery downtime. They try a generic predictive maintenance tool. It fails. Why? Because it can't integrate with their proprietary sensors or understand the unique operational stresses of their specific machinery. With a bespoke solution, you build an AI that: ✅ Integrates perfectly with their existing legacy SCADA systems. ✅ Is trained exclusively on their years of historical performance data (vibration patterns, temperature, etc.). ✅ Understands the specific failure signatures of their machines. The result isn't a generic dashboard. It's a pinpoint-accurate prediction that a critical component will fail in three days. Maintenance is scheduled, production isn't disrupted, and the business saves a fortune. That is an advantage your competitors cannot copy. That’s your secret weapon. Read more on our new blog: https://lnkd.in/eHk4tD42 If you could build an AI to solve just one unique, high-value problem in your business, what would it be? #BespokeSoftware #PredictiveMaintenance #AIforManufacturing

If we are to achieve the clean energy transition, we must encourage local renewable energy manufacturing in developing countries. To aid the private sector, investors, and other stakeholders in navigating the manufacturing policy and regulatory landscape in #Kenya and #Ghana, we have developed guides that offer insights and potential solutions to address bottlenecks facing local manufacturers in the solar PV, battery and electric mobility ecosystem. The Kenya guide can be accessed here: https://lnkd.in/dcqRqArK The Ghana guide can be accessed here: https://lnkd.in/db3-j4BD

Who is Failing the Textile Recycling Industry? 🤔 A few years ago, the fashion world woke up to a PR crisis: 📸 pictures of their clothes ending up in deserts of Chile 🏜️ or beaches of Africa 🏖️. This brought the conversation around responsibly managing textile waste to the centre. ♻️ The answer seemed obvious: recycle it. And yet… today, more than 85% of textile waste still ends up burned or buried. I’ve spoken to hundreds of stakeholders across the system — brands, recyclers, waste handlers, designers. And the truth is: everyone wants to recycle. But the system is failing by its own structure. 1️⃣ The brands & manufacturers want to “go circular”… …but their product team is still designing products with five materials, glues, and chemicals. Not because they want to make recycling hard — but because no one told them it mattered. By the time that product is tossed out, it's too late. No one can untangle it. 2️⃣ The collector does their job. They gather tonnes of discarded textiles — but they’ve been trained to look for resale value, not recyclability. The cotton jeans go in the same bag as the sequined tops and the carpets. To a recycler, that’s not feedstock. It’s chaos. 3️⃣The recycler? They’re ready. They’ve built the infrastructure. But they don’t communicate with collectors. They treat their sourcing guidelines as classified information. And right now, they’re receiving bales of unknown fabric types with no digital trail, no fibre breakdown, and no viable way to scale. 4️⃣ And what about governments? They regulate packaging. They tax plastic bags. But textiles? Still considered a "soft problem." The policy is lagging. The funding is thin. The urgency is missing. So we keep going in circles, chasing a vision of circularity — without building the roads to get there. But there’s a different path. 🌱 And we’re already building it. 👋 At Eslando, our marketplace connects the dots 🔗 — between collectors, recyclers, brands, and sorting innovators. Because recycling isn’t magic ✨. It’s infrastructure 🏗️. It’s data 📊. It’s cooperation🤝. Signup here for more details: https://lnkd.in/eeMeKmXm #CircularEconomy #TextileRecycling #SystemDesign #WasteManagement #Circularity #Recycling

"Did you know 🤔 that prior to Nov. 15, 2023, you couldn't install solar ☀️ on the Big Island 🏝️ without a design review and approval by a licensed electrical engineer, regardless of system size? This regulation exemplifies the systemic barriers ⛔ impeding rural LMI communities from harnessing solar energy. The necessity of professional installation, compounded by the costs 💸 of compliance with stringent regulatory standards, significantly inflates the overall expense of transitioning to solar power. Given the typically lower household incomes in these areas compared to urban centers, the additional financial burden can render solar systems an unattainable luxury, despite their potential for long-term savings 💰 and energy independence 🍃. Equitable access to sustainable tech is crucial. Particularly for low-to-moderate income (LMI) rural communities. Case in point: Solar Bill 66 on Big Island. Not just an environmental commitment 🌳, it's a mission for societal transition. It shows the path to reducing energy costs, creating green jobs 🛠️, and shrinking carbon footprints. All while fostering social equity 🤝. The task is huge. Collective effort? Needed. There's an opportunity here beyond environmental impact - a societal transformation 💫. Economic resilience and better quality of life for LMI communities are at stake. Join the Solar Energy Equity Movement ⚡ As industry pros, we can make the shift towards a sustainable, inclusive future real. Solar Bill 66 isn't just a local victory - it's attainable nationwide 🇺🇸 with dedication and teamwork 🤲. We're calling all innovators, investors, policymakers, and leaders to back LMI rural communities. Be it tech development, investments, or advocacy, your support matters. Here's how: 1. Advocate for similar policies locally and nationally. 2. Invest in renewable energy projects that support LMI accessibility. 3. Partner with non-profits to educate rural communities on solar benefits. 4. Provide long-term support and training. Together, we can make renewable energy accessible to all, building a sustainable future where every community thrives. Let's back initiatives like Solar Bill 66 for a brighter, equitable renewable energy future 💡. #EnergyEquity #RenewableEnergy #LMI #JEDI #AJPerkins #MicrogridMentor