Lead Time Analysis

Can your team answer these two questions? 1) Once we commit to doing something, how long does it take for us to finish it? 2) Once we start working on something, how long does it take for us to finish it? The first measures your team’s lead time, and the second measures cycle time. They’re very different, even though many think they’re synonymous. If you can’t answer these two questions, it makes it nearly impossible to answer questions like: 🔵 Will we be able to hit promised milestones and releases? 🔵 How long will it take to deliver that new high-priority feature? 🔵 How long until that new art asset is ready for the marketing campaign?  🔵 How long will the customer wait for their support request to be answered? and most important… ⭐️ How can we improve, remove bottlenecks, and deliver faster? To start measuring lead times and cycle times: 1 - Determine and visualize your workflow in a tool like Trello, Asana, or Favro 2 - Be sure to have a clear Commitment point (Selected, Ready, Committed, etc.) and a clear In-progress point (Developing, Doing, Resolving, etc.) 3 - Determine when something is actually Done and delivered to the “customer” in your flow 4 - Measure the time it takes for each item (card) to go from Committed to Done’and Started to Done (a good tool will track this for you) 5 - Visualize your lead and cycle times in a scatter plot, histogram, and/or control chart From there, you can determine averages and probable ranges of how long your team will take to deliver something new. Your team will then be well equipped to: ✅ Better forecast delivery dates for batches of work ✅ Accurately answer: when will this be done? ✅ Begin reducing lead times and cycle times ✅ Start building a more predictable flow Remember, the difference between your lead times and cycle times can be huge, so it’s critical to understand the difference and measure both.

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.🔋

Lead time variance. A lot of brands still plan around a single lead time number. Usually something like: “Production plus transit, about 50 days.” But a single number can be dangerously incomplete. Because the real issue is not whether average lead time is 50 days. It is whether that 50 can swing to 65+ with enough frequency to break your inventory plan. Once that happens, the downstream impact compounds fast: • missed revenue from stockouts • reactive over-ordering • weaker cash conversion • more inventory aging in the wrong places • lower confidence in the planning team’s own numbers This is why lead time should be managed like a risk metric, not just an operational metric. What I’d want to know if I were evaluating an apparel brand: • how variable are lead times by vendor? • how quickly does the team detect slippage? • what happens to reorder logic when variability rises? • which vendors are “cheap” only because delay costs are not being measured properly? For example: • if lead time slips by more than 7 days, review reorder timing immediately • if variability keeps increasing, reduce order size and increase order frequency In unstable supply chains, variance matters more than averages.

Every supply chain system works within a cycle of planned inputs and realized outputs, and the variation between the two determines how much instability must be managed. Demand variability reflects how accurately the organization interprets and translates customer behavior into actionable forecasts. Lead time variability reflects how consistently the system executes against those plans. Both are measurable, controllable, and directly linked to how safety stock functions. DEMAND VARIABILITY is the statistical fluctuation in customer demand over a defined period, measured as the dispersion of actual demand around its forecasted mean. It captures how consistently the market behaves relative to expectations and reflects the precision of forecasting systems, data aggregation, and segmentation. High demand variability indicates unstable consumption patterns or weak forecasting discipline, while low variability reflects predictability and process alignment. LEAD TIME VARIABILITY is the variation in the elapsed time between when an order is initiated and when it is fulfilled. It represents the consistency and reliability of the supply process across procurement, production, and logistics activities. High variability indicates unstable supplier or transportation performance that disrupts replenishment timing and amplifies inventory exposure. Low variability shows controlled, repeatable execution where supply operates predictably within its planned cycle. Demand and lead time variability both act simultaneously on the system, with changes in one dimension influencing the impact of the other. The relationship is non-linear, meaning that small fluctuations can create disproportionate changes in safety stock requirements. But it can also work the opposite direction where improvements in variability control can yield outsized reductions in required inventory. Variability is quantified statistically through standard deviation, but measurement without action has no value. The interpretation should be operational, reflecting the degree of alignment between demand signal accuracy, production responsiveness, and replenishment timing across the network. You can't eliminate variability, but you can understand and control it. What’s the practical application? 🟢 Quantify demand and lead time variability separately using standard deviation. 🟢 Monitor both through control charts to proactively identify emerging instability. 🟢 Recalculate safety stock on a cadence, but update immediately when measured variability shifts. 🟢 Determine which variable contributes most to total uncertainty and correct the underlying process first. 🟢 Reduce variability before increasing inventory. Safety stock does not exist independently of the system that creates it. It is a direct outcome of how demand and lead time variability interact. When safety stock levels rise or fall, they reflect not just shifts in demand or supply, but the system’s capacity to anticipate and respond.

Over years of working with teams at foundries and equipment suppliers, I see the same frustration from executives. Tape-out dates get all the attention, yet strong designs sit on the shelf for months because teams failed to secure tool slots far enough ahead. One competitor locks in priority access, starts their ramp, and captures the market while others wait, even with better architecture. Lead times for essential tools like ASML EUV systems can extend 12 to 24 months when orders stack up. A rival that committed to multi-year agreements earlier gains real months in their production timeline. Product teams can gain ground by investing in approaches that speed tool qualification. Tokyo Electron applies digital twins to predict adjustments and cut downtime risks. When process engineers partner early with suppliers on these virtual models, qualification times shorten, and suppliers often favor those collaborations with better allocation priorities. Another area worth focus is elevating ties with equipment vendors to the level given to foundry partners. Executives negotiate wafer capacity years out with TSMC or Samsung through detailed contracts, but equipment discussions sometimes stay lower in the organization. That gap has created avoidable delays. Capacity influences far more than launch dates - it affects competitive positioning. Top designs lose impact if delayed. Benchmark your timelines against peers, pilot digital twin qualification on one tool family, and bring supplier strategy into executive reviews.

Series 3: Inside the Planner’s Toolbox – Tactics I’d Bring to Your Team Title: How I’d use lead time analysis to cut expedites Most “urgent” freight isn’t caused by demand — it’s caused by bad assumptions. I’ve seen teams spend weeks scrambling on last‑minute expedites, only to realize the root cause wasn’t planning effort — it was lead times that didn’t reflect reality. If I joined your team as a planner, here’s how I’d attack that problem 👇 1️⃣ Actual vs. planned Track real lead time from PO creation to receipt for critical parts. Then compare it to what the system thinks it is. The gap often explains the firefights. 2️⃣ Measure variability Even a simple range or standard deviation shows how “risky” that supplier or lane really is. 3️⃣ Translate into planning terms 🔹 High variability? Add safety stock or update the planning lead time. 🔹 Consistently shorter than expected? You might be able to reduce safety stock safely. 4️⃣ Tie it to cost Look at which items drove the most expedite freight in the last 6–12 months. Then check — do they have underestimated lead times or inconsistent suppliers? Aligning planning parameters with real lead time behavior can quietly cut both shortages and expensive “surprises.” Because in the end, most supply crises start with one bad parameter. How often does your team refresh lead times — and what have you learned from the real data? #LeadTime #Procurement #RiskManagement #SupplyPlanning #CostControl

Why does a 1-day task turn into 10… 50… or even 100 days? When I was implementing the Theory of Constraints at Cisco Systems, I had the opportunity to meet then-CEO John Chambers. He asked me: “Why does it take so long to get things done — and so long to catch up?” It’s a question many leaders quietly struggle with. Here’s what I showed him. Imagine a task that should take 1 day. • At 0% utilization → 1 day • At 50% utilization → 2 days • At 80% utilization → 5 days • At 90% utilization → 10 days • At 99% utilization → 100 days Same task. Completely different outcome. As utilization increases, lead times don’t grow linearly — they grow exponentially. Why? Because overloaded systems create queues. Work waits. Priorities compete. Tasks get stopped and restarted. Coordination becomes harder. And performance begins to deteriorate. First gradually. Then suddenly. • Due-date performance starts near 100% • Then it declines • Then it collapses At high utilization, the system enters what I call Chaos. You see it when: • lead times keep increasing • deadlines are constantly missed • priorities shift daily • people are always busy but progress is slow It becomes almost impossible to manage reliably. What’s counterintuitive is this: More demand does not increase output forever. Beyond a certain point, it actually reduces it. There is a critical zone — what I call the Edge of Chaos — where: • lead times are still reasonable • due-date performance remains high • people stay fully engaged • work flows through the system Push beyond that point, and performance falls off a cliff. This is one of the core principles behind the Theory of Constraints: Overloading resources is the fastest way to slow everything down. If things feel stuck, late, or constantly overwhelming — it may not be a people problem. It may be a utilization problem. And the solution is rarely to push harder or demand more effort. The solution is to create capacity, reduce overload, and protect flow. Because getting 1 day tasks to take at most 5 days not 100 days, does not come from keeping everyone busy — it comes from not loading any resource more than 80%. I would be interested to know: why do you think we focus so much on keeping busy and keeping others busy? To get free access to a simulator I developed that shows why a 1-day task can take up to 100 days, comment 1DAYTASK #TheoryOfConstraints #SystemsThinking #DecisionMaking

Lead Time vs Cycle Time- The Two Metrics Every Agile Team Should Use In Agile delivery, we often track velocity or story points. But if a team wants true predictability and flow, two metrics matter more than anything else: ✅ Lead Time The total time from when a request is made → until it’s delivered to the customer. It reflects how fast the business gets value. ✅ Cycle Time The time from when the team starts working → until the work is completed. It reflects how efficiently the team delivers. 🌟 Why these metrics matter 🔹 Better forecasting. Shorter, stable cycle times make planning and commitments far more accurate. 🔹 Faster value delivery. Shorter lead times mean happier customers and competitive advantage. 🔹 Spot bottlenecks quickly. You can see exactly where work gets stuck such as reviews, QA, design, or handoffs. 🔹 Improves flow. Ideal for teams practicing Kanban or DevOps who want continuous delivery. 🔹 Drives better habits. Smaller work items, clearer slicing, and fewer blockers naturally emerge. If your team has ever asked: 🕒 “When will this be done?” 🌪️ “Why do things take so long?” 🔄 “How do we improve our delivery?” Lead Time and Cycle Time provide the answers. Start measuring them. Start visualizing them. And watch your delivery speed, predictability, and team confidence grow. 🚀

Lead Time analysis starts with Process Mapping The review of Lead Time is a process that businesses should regularly re-visit. It might be prompted due to a “blow-out” in Lead Time or a challenge from a competitor or just a need to remain competitive. Regardless of the prompt, it’s something that should be reviewed, not taken for granted. Lead Time is a critically important business aspect that is typically market-led but unfortunately many businesses dictate this to their customer. I believe a review could simply start with Process mapping. In this case I facilitated an exercise where there were 3 primary outcomes expected: 1) understand the current state, 2) find ways to reduce the Lead Time & 3) create the first Value Stream Map. In this case the prompt was the market calling for a shorter Lead Time. Using a brainstorming mechanism with stakeholders from each department (who brought data with them), starting from order receipt and ending with dispatch. It was really interesting that the actual Lead Time was longer than we all thought and there were new bottlenecks identified along the supply chain. Although the photo only shows part of the initial brainstorming process mapping exercise, the exercise turned into 2 kaizen events which resulted after 3months in an improvement to average Lead Time from 15 days to 10 days. The Team had the courage to review the Lead Time and push through to making and sustaining improvements. If you’re not using a VSM to measure Lead Time, is a Process Mapping activity something you could do to start the review? Please find in the comments section, a link to a video explanation on Process Mapping.

(🎓𝟗𝟗) Understanding the relationship between Demand and Planned Orders is crucial for anyone involved in supply chain and production planning. But when you add lead times and production capacities into the mix, things can quickly become complex. Recently, I was looking at an example that sparked a great conversation about production scheduling. In a scenario where 1800 units were planned to be produced starting on Day 8 with a 2-day lead time, what actually happens to the capacity on Days 8 and 9? Here's the core insight:  If a planned order takes 2 days (ProdDur) to complete, it uses up the production capacity of both those days. This means if you have 1800 units planned on Day 8, Day 9 will also be utilized for completing that order, leaving no capacity for other planned orders. Now, what if there is also a demand of 200 units planned for Day 9? Would the production plan have room to accommodate it? The short answer: No. Because the capacity is already maxed out due to the continuation of the 1800-unit order from Day 8. The Takeaway: Understanding how lead time impacts production scheduling is key to avoiding bottlenecks. Effective planning means balancing demand fulfillment while optimizing the use of available capacity. Sometimes, it might be better to split large planned orders or adjust schedules to avoid overloading production days. This kind of nuanced planning can make or break on-time delivery and resource utilization. ----- If this post added value to your day, 𝐚 𝐥𝐢𝐤𝐞 would convey me the same. 👍 #RecursiveVidya