Industrial Engineering Process Optimization

The hardest step in Goldratt's Theory of Constraints isn't finding the bottleneck. It's step three: subordinate everything else to the constraint. Translation for software teams: if your code review process can absorb 1.2x the current volume, don't generate 2x. If you can't measure outcomes on twice the features, don't commit twice the features to production. This means deliberately throttling AI output. I'll say that again because it sounds heretical. It means looking at a tool that can generate code faster than ever, and choosing to slow it down. Not because the tool is bad. Because the system downstream can't absorb what the tool produces. Most organizations do the opposite. They celebrate the increased commit volume. They trumpet the PR throughput numbers. They showcase the individual productivity gains. Meanwhile, review queues grow. CI recovery time gets worse. Deployment problems increase. 96% of the most frequent AI users end up working evenings and weekends. They optimized the part of the system that wasn't the bottleneck. That's not progress. That's inventory accumulation. The question isn't "how much code can we generate?" It's "how much code can our system absorb, validate, and deliver?" Generate up to that limit. Not beyond it. If that feels wasteful, you've identified the real investment priority: increasing the capacity of the constraint. But exploit and subordinate come first. Throwing money at capacity before you've maximized what you already have is just expensive inventory management. #TheoryOfConstraints #EngineeringLeadership #AIReadiness

Running simulations: base model vs. lookahead model I see people posting on the use of “simulations” for planning inventory policies. If you are using a lookahead model (which is typical for most real-world inventory problems), there are two models where simulation can be used:   1.    The base model, which can be a simulator or the real world. 2.    The lookahead model, which is used in the policy for planning the future to make a decision now. See the figure below - I use the same notational style for both models, but the lookahead model uses tildes on each variables, which also carry two time subscripts: the point in time we are making the decision, and the time period within the lookahead model.   The base model is used to evaluate the policy, and is needed to perform any parameter tuning. The base model can be based on history or a simulation of what you think the future can be.   When simulating inventory policies, special care has to be used because we do not have historical data on market demand – we typically just have sales, which can be “censored” (a topic that has been recognized in the inventory literature for over 60 years). For example, if we run out of product (and there is no back ordering), we lose the sales, which typically means that we do not see (or record) them.   I find it is generally best to run simulations using mathematical models of uncertainty so that we can run many simulations, testing different policies. Stockouts depend on properly simulating the tails of distributions, along with market shifts, price changes and supply chain disruptions. There are, of course, settings where you have no choice but to test your ideas in the field. It is expensive, risky, and slow, but sometimes you just have no choice, especially when you have to capture human behavior.   If your policy requires planning into the future, you really need to be using a stochastic (probabilistic) model of the future which properly captures the tails of distributions. With long lead times, you should also plan for the possibility of significant disruptions, which can mean that you also have to capture the decisions you might make in the future. See chapter 19 of:   https://lnkd.in/dB99tHtM (“tinyurl.com/” with “RLandSO”)   for an in-depth treatment of direct lookahead policies. #supplychain #inventory  Nicolas Vandeput Joannes Vermorel

Operational bottlenecks are often mistaken for minor distractions. In textiles, challenges such as machine downtime, dye-house delays, working capital spikes, or capacity mismatches between spinning and weaving are not just inconveniences. They are critical leverage points for value creation and significant professional impact. Many leaders focus on optimising every area. However, sustainable throughput comes from identifying and rigorously managing the single constraint that governs the entire system. We apply the Theory of Constraints (TOC) at RSWM to convert operational friction into performance gains. TOC shows that local efficiency can be misleading. Keeping every department busy often creates excess work-in-progress, disrupting flow, increasing costs, and delaying deliveries. Instead, we follow a disciplined process: -First, identify what sets the pace of the value chain. This may include machinery misaligned with current market needs or process challenges like low Right First Time (RFT) rates in the dye house that reduce effective capacity. -Second, exploit the constraint by precise scheduling, strengthening discipline, and improving efficiency to extract more output without immediate capital deployment. -Third, align the rest of the organisation to the bottleneck’s pace to ensure smooth material flow across departments. Fourth, elevate the constraint through capital investment or process redesign, addressing capacity mismatches or refining product lines. -Finally, repeat the cycle, since the constraint shifts as performance improves. This approach has delivered tangible results at RSWM. Addressing dye-house bottlenecks increased throughput, reduced working capital requirements, and improved EBITDA. However, constraints change over time. Market shifts, such as China’s shift from a major yarn importer to an exporter, or recent U.S. tariffs affecting demand, can pose new challenges. In response, we adapt by exploring alternative markets, leveraging domestic opportunities, or innovating products to sustain growth. Our goal is to eliminate internal friction so operational excellence drives expansion. When the market is the only constraint, the organisation is positioned to thrive. #TheoryOfConstraints #OperationalExcellence #Textiles #Leadership #RSWM

𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: 𝐓𝐮𝐫𝐧𝐢𝐧𝐠 𝐁𝐨𝐭𝐭𝐥𝐞𝐧𝐞𝐜𝐤𝐬 𝐢𝐧𝐭𝐨 𝐁𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡𝐬 In manufacturing, performance isn’t limited by how much we do — but by where the system slows down. The 𝑻𝒉𝒆𝒐𝒓𝒚 𝒐𝒇 𝑪𝒐𝒏𝒔𝒕𝒓𝒂𝒊𝒏𝒕𝒔 (𝑻𝑶𝑪) teaches that every organization is only as strong as its weakest link. Instead of optimizing every function, TOC focuses all energy on identifying, exploiting, and strengthening that one constraint — the real gatekeeper of throughput. Now, when TOC is integrated with the 𝐎𝐊𝐑 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤, it becomes a system of continuous and focused progress. 𝐓𝐎𝐂 pinpoints what truly matters now — the single constraint limiting performance. 𝐎𝐊𝐑𝐬 define how we move forward — setting measurable objectives that align every team toward that constraint. This synergy replaces scattered initiatives with targeted, compounding improvement. Every quarter, we realign goals to the current constraint, ensuring no effort is wasted, and every move strengthens the system as a whole. In the end, strategy isn’t about doing more — it’s about doing what matters most, with absolute clarity and commitment. ----------------------------------------------------------------------- If you find it useful, please comment, 👍🏻👏🏻❤️💡🔁 For more insightful content, follow SIVAKUMAR C 🇮🇳 #StrategyExecution #ManufacturingExcellence #TheoryOfConstraints #OKRFramework #OperationalExcellence #ContinuousImprovement #Leadership

This is the moment simulation becomes more important than prototyping. In our last posts, Pascalis and I showed two things: First, how you can generate a full production and warehouse environment in NVIDIA Omniverse using Claude Code and the USDA data format. Second, how NVIDIA’s new Kimodo model can generate robot motions from simple text prompts. Now we are taking the next step: Transferring robot motion into Omniverse and merging both use cases. Omniverse is not just for static visualizations. It allows dynamic simulation of movements, interactions and behavior with CAD components inside a virtual environment. And this is where it gets interesting for future product development. The vision is clear: If we can model production environments, warehouses, and real operating environments of products, we can simulate mechatronic products in realistic conditions before they physically exist. Environment → Sensor & actuator interaction → Model-in-the-loop simulation. Very similar to how autonomous vehicles are developed today, but applied to all kinds of mechatronic products. The effects are huge: • Less physical prototyping • Earlier insights without building hardware • Faster iteration cycles • Better product decisions earlier in development • Simulation becomes the main development environment Omniverse already shows how granular these simulations can be created today. Not through months of manual modeling, but increasingly through prompts that generate environments, movements and soon maybe even control logic. We are moving from designing products to designing behavior in simulated worlds first. And that will fundamentally change how we develop products. Curious to hear your thoughts! When will simulation become the primary development environment in your industry? Vlad Larichev | Rüdiger Stern | Rick Bouter | Ruben Hetfleisch | Dr.-Ing. Tobias Guggenberger

The next VP Ops won’t sit in an office. He’ll sit on the constraint. For years, I thought the job of an operations leader was to be “available.” -Available for meetings. -Available for emails. -Available for escalations. Their calendars looked important. Their plants looked chaotic. One day I told a VP Ops client: “You’re always here when we need a signature. You’re almost never here when we need a decision.” That was the difference. He was present in the system. Absent at the constraint. Most factories don’t have a generic performance problem. They have one or two brutal bottlenecks that quietly dictate everything. – The line that’s always late – The machine everyone tiptoes around – The planning decision that explodes into chaos downstream And yet most VP Ops spend their time nowhere near those points. The future VP Ops will look different. They won’t ask, “What’s our OEE this quarter?” They’ll ask, “Where is the constraint today and why am I not standing there?” Here’s what that looks like in practice. 1️⃣ Redesign the week around the constraint One simple rule: Two mornings a week, the VP Ops had to be physically at the main constraint. Not a tour. Not a photo. Just watching the work. Talking to operators. Listening to supervisors. Patterns surfaced fast: -the same changeover ran long, -the same material arrived late, -the same maintenance was deferred. None of it was clear in dashboards. All of it was obvious on the line. 2️⃣ Move key meetings to where the work breaks Daily huddle. Maintenance priorities. Short-term planning. All held at the constraint. Trade-offs became tangible. Excuses thinner. Decisions sharper. Planning saw what reshuffling orders did to setups. Maintenance saw the stoppages their delays created the same day. 3️⃣ Redefine leadership work Instead of explaining variance after the fact, this VP Ops spent time killing its root causes: Bad changeover rules. Insane product mix decisions. Planning logic that forced heroics every Friday. These weren’t technical issues. They were leadership defaults. Six months later : Uptime up double digits. Expedites down. The mood of the plant changed. Because leadership showed up where it hurt. Here’s the truth: You can run a billion-dollar operation and still be a stranger to the one line that makes or breaks your quarter. The next generation won’t accept that. They’ll measure themselves differently: – How much time did I spend at the constraint? – How many chronic problems did we remove? – How calm does the operation feel this month? If your calendar and your constraints don’t overlap, that’s not a scheduling issue. That’s a strategy issue. You don’t need permission to change this. Just decide where you actually lead from. The office. Or the constraint. ♺ Reshare this, every VP Ops and VP Supply Chain knows this gap is real. ► For more no‑BS manufacturing and supply chain transformation stories: Join the newsletter → https://lnkd.in/dMGaUj4p

"𝗛𝗼𝘄 𝗠𝗮𝗻𝘆 𝗜𝗘𝘀 𝗗𝗼𝗲𝘀 𝗧𝗼𝘆𝗼𝘁𝗮 𝗛𝗮𝘃𝗲?" - 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘇𝗶𝗻𝗴 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 A famous anecdote tells us everything we need to know about Toyota's revolutionary approach to manufacturing excellence: 𝗧𝗵𝗲 𝗦𝘁𝗼𝗿𝘆 A General Motors employee visited a Toyota site and asked, "How many Industrial Engineers do you have?" The Toyota representative responded with a question: "What does an IE do?" After the GM employee explained the typical IE responsibilities—process improvement, waste elimination, efficiency optimization—the Toyota person simply replied: "We have 3,000 IEs 'cause we all do that." 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: 𝗧𝗼𝘆𝗼𝘁𝗮'𝘀 𝗣𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝘆 𝗼𝗻 𝗜𝗘 This wasn't just a clever response—it reflects a fundamental business philosophy. Taiichi Ohno, the father of the Toyota Production System, had a specific vision for what he called "Money-Making IE (MIE)". In his book "Toyota Production System," Ohno wrote: "Toyota-style IE is 'Money-Making IE.' In the Toyota Production System, IE has no meaning unless it reduces costs and creates profits." While American companies typically confined IE functions to specialized departments, Toyota took a radically different approach. They recognized that industrial engineering—at its core—is about systematic improvement of processes, methods, and systems. Why limit this powerful capability to just a few specialists? 𝗧𝗵𝗲 𝗧𝗼𝘆𝗼𝘁𝗮 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 Ohno understood that IE isn't just about technical analysis—it's about creating a "manufacturing technology that directly impacts management and encompasses the entire enterprise." This is why Toyota invested in training every employee in IE principles: -Process observation and improvement -Waste identification and elimination -Standardization and optimization -Problem-solving methodologies The result? A workforce where every person thinks like an industrial engineer, continuously improving their work and the company's performance. 𝗧𝗵𝗲 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 When every employee can identify waste, suggest improvements, and optimize processes, you don't just have a manufacturing system—you have a learning organization that adapts and evolves constantly. This democratization of IE capabilities became one of Toyota's most sustainable competitive advantages. As Ohno noted, the Toyota Production System succeeded precisely because it embedded IE thinking throughout the entire organization, making it "management technology that directly connects to business results." 𝗠𝗼𝗱𝗲𝗿𝗻 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 In today's rapidly changing business environment, this lesson is more relevant than ever. Organizations that can democratize continuous improvement capabilities—making every employee a process improver—will outperform those that rely solely on specialized departments. #TaiichiOhno #TPS #Money_Making_IE

👖 Work Study: Method + Motion Study How one small hand motion saved 3 seconds per operation in a denim pants sewing line. 🔍 Real Example – Back Pocket Attachment Operation During a work study on a denim sewing line, we observed that the operator picked up the back pocket from a bundle placed on the left side, then reached to the right side to pick up the denim panel before sewing. This unnecessary cross-body movement added approximately 3 seconds to every operation. ✅ Method + Motion Improvement Instead of asking the operator to work faster, we improved the method: - Moved the pocket bundle directly in front of the operator within the normal reach zone. - Placed the denim panel beside the sewing machine in the sequence of use. - Standardized the pick-up and sewing motion to eliminate unnecessary reaching and hand crossing. 📈 Results ✔️ 3 seconds saved per operation ✔️ Reduced operator fatigue ✔️ Smoother material handling ✔️ Better line balance and consistency ✔️ Increased daily output without additional manpower or machines 💡 The Impact If this operation is performed 1,200 times per shift, saving 3 seconds each cycle means: ⏱️ 3,600 seconds = 60 minutes saved in a single day at one workstation! Work Study is not about working harder—it's about removing wasted motion and creating the most efficient method. Small improvements, repeated thousands of times, lead to significant productivity gains. Have you ever improved a denim sewing operation with a simple method change? Share your experience in the comments! #IndustrialEngineering #GarmentIndustry #Denim #WorkStudy #MethodStudy #MotionStudy #LeanManufacturing #TimeStudy #LineBalancing #IE #Productivity #ContinuousImprovement #NPT #LossTime #SaveTime

⏱️ TIME CALCULATION: The Foundation of Operational Excellence In manufacturing and steel plant operations, performance improvement is not only about increasing production—it starts with understanding and managing time effectively. Every minute on the shop floor has a purpose, and measuring the right time metrics helps drive better planning, resource utilization, productivity, and profitability. Some of the most important operational metrics include: ✅ Lead Time – Total time from order receipt to delivery ✅ Takt Time – Production pace required to meet customer demand ✅ Cycle Time – Actual time taken to complete one unit ✅ Process Time – Time spent on value-adding activities ✅ Setup Time – Time required to prepare equipment for production ✅ Waiting Time – Delays caused by material, machine, or manpower constraints ✅ Move Time – Time spent moving materials or resources ✅ Inspection Time – Time used for quality verification ✅ Touch Time – Direct operator working time on the product ✅ Idle Time – Time when resources are available but not utilized ✅ Run Time & Uptime – Indicators of equipment utilization ✅ Downtime – Lost production time due to stoppages ✅ Availability, Performance & OEE – Key measures of operational effectiveness 📊 The true power of these metrics lies in identifying losses, eliminating waste, balancing workflows, and continuously improving processes. As professionals in manufacturing, steel plants, rolling mills, and industrial operations, mastering these time calculations enables us to make data-driven decisions that improve productivity, reliability, and customer satisfaction. Measure the Time. Manage the Time. Master the Performance. What time-related KPI has had the biggest impact on your operations—Cycle Time, OEE, Downtime, or Takt Time? #Manufacturing #SteelIndustry #OperationsManagement #LeanManufacturing #OperationalExcellence #IndustrialEngineering #ContinuousImprovement #OEE #Productivity #RollingMill #SteelPlant #Leadership #ProcessImprovement #TimeManagement #Engineering

𝗔𝗜 𝗶𝘀 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 "𝗧𝗶𝗺𝗲 & 𝗠𝗼𝘁𝗶𝗼𝗻 𝗦𝘁𝘂𝗱𝗶𝗲𝘀" Artificial Intelligence is opening a new frontier for 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁. Traditionally, time and motion studies required manual observations, stopwatches, and hours of data collection. Today, AI-powered video analytics can automatically track 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿 𝗺𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀, 𝘁𝗮𝘀𝗸 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀, 𝗰𝘆𝗰𝗹𝗲 𝘁𝗶𝗺𝗲𝘀, 𝗮𝗻𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲. As shown in this video, AI can: - Detect operators and workstations - Measure task durations and waiting times - Track material and motion flows - Identify bottlenecks, inefficiencies, and unnecessary movements For industrial engineers, this means shifting from 𝗺𝗮𝗻𝘂𝗮𝗹 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝘁𝗼 𝗱𝗲𝗲𝗽𝗲𝗿 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁. Instead of spending hours collecting data, we can focus on 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀, 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀, 𝗮𝗻𝗱 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝘄𝗮𝘀𝘁𝗲. Combined with Lean Manufacturing principles, AI becomes a powerful tool to improve productivity, ergonomics, and operational efficiency. The future of continuous improvement will not replace engineers; 𝗶𝘁 𝘄𝗶𝗹𝗹 𝗮𝘂𝗴𝗺𝗲𝗻𝘁 𝘁𝗵𝗲𝗶𝗿 𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 𝘀𝗲𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗺𝗼𝗿𝗲 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗿 𝗯𝗲𝗳𝗼𝗿𝗲. #industrialengineering #IE #leanmanufacturing #lean #continuousimprovement #CI #artificialintelligence #AI #timeandmotion #operationalexcellence #OpEx #processimprovement #smartmanufacturing #technology