Delivery Time Optimization

Yes, CDN improves latencies by caching things closer to the users, but here's an interesting optimization they do to optimize on latencies... TCP suffers from slow starts, i.e., when a new TCP connection is established, it doesn't immediately operate at full bandwidth. Instead, it begins conservatively with fewer segments and doubles them each round-trip time until it detects congestion. This ramp-up can take several iterations to achieve optimal throughput, which is problematic for latency-sensitive applications. This is a big problem for CDN, because even a few additional round-trips for a massive scale costs a lot. CDNs solve this by maintaining persistent connection pools to origin servers. Rather than establishing fresh connections for each user request, they keep a pool of long-lived connections alive between edge nodes and origins. The clever part is pre-warming during low-traffic periods. CDNs periodically send small amounts of data (like health checks or cache validation requests) over these idle connections. This keeps the TCP congestion window at max and prevents it from shrinking due to inactivity. Here's a simple calculation to quantify the impact. By keeping the congestion window pre-warmed, it is operating at 64KB instead of 4KB. This eliminates the 3-7 round-trip ramp-up delay that would otherwise occur. For a connection with 50ms round-trip time, this saves 150-350ms of latency, and that's pretty significant for something that operates at web scale, literally. Hope you found this interesting, and like always, keep digging deeper.

SMED in Logistics – Fast Turnaround for Lorries Waiting trucks = lost time, lost money, and frustrated drivers. In logistics, speed and flow are everything. And that's why SMED (Single-Minute Exchange of Die) isn’t just for manufacturing—it's a game changer in transport and logistics too. Applied correctly, SMED can sharply reduce lorry turnaround times, increase dock availability, and improve supply chain performance. What is SMED in Logistics? SMED in logistics means streamlining and standardizing the steps needed to load or unload a truck, with the goal of completing the process in single-digit minutes (under 10, where possible). It’s about: 🔹 Eliminating delays before and after arrival 🔹 Prepping everything before the lorry even stops 🔹 Reducing manual steps and unnecessary motion 🔹 Creating a consistent, repeatable process How It Works in Practice ✅ Pre-stage materials and paperwork Ensure goods are ready and documents prepared before arrival. ✅ Standardize loading/unloading sequences Use fixed routes, zones, and trained teams. ✅ Visual management Mark bays, pallets, and loading zones clearly to avoid confusion. ✅ Dedicated teams or rapid response units Quick in, quick out—no delays in assigning people or equipment. ✅ Invest in support tools Use conveyors, dock levelers, or flow racks to speed up the physical movement of goods. Results You Can Expect ✔️ Shorter lead times ✔️ Higher throughput per loading bay ✔️ Reduced driver waiting charges ✔️ Improved on-time performance ✔️ Happier carriers and partners

When I was CRO of a $200M SaaS, doing POCs almost destroyed us—months wasted, team exhausted, buyers constantly delaying. Until my VP Sales said, “Kill the POC. We’ll validate value clearly in 3 hours flat”. Here's exactly how we rebuilt our sales process and cut our sales cycle by 50%: BACKGROUND: We were selling 100% enterprise. POCs were the automatic default: Heavy, technical validation lasting 1-3 months. It was painful… - Sales Engineers were overloaded - Buyers kept delaying due to resource issues - Buyers kept wanting more “just one more test” Initially, I thought: It’s Enterprise, that’s the game right? Until our VP Sales, Idan Arealy, joined. Two weeks in, he tells me: “No offense—but these POCs are total overkill.” “Buyers don’t need these endless tests.” “They’re not doubting the tech.” “They’re doubting the value.” “And we don’t need this complexity to prove value.” So he suggested a simpler, smarter alternative: The 'Use Case Workshop'—and it changed everything. Here’s the step-by-step: —— 1. Kickoff (45 min) - AE positions the workshop immediately post-demo: “Here’s what we typically do next to help validate real-world scenarios in just hours—no heavy lift needed. Shall we set it up?" - SE runs deeper disco into problem root causes in a kickoff call - AE sets clear Mutual Action Plan (MAP) 2. Internal Alignment (60 min) - SE & AE clearly define and build initial use-case solutions - Output: Slides outlining impactful solutions & open questions 3. Use Case Co-Design (45 min) - Live session with buyers walking through scenarios - Collaboratively refine solutions LIVE (e.g. Miro, slides): “Walk me through this problem in more detail—we’ll map exactly how solving it looks." 4. Prioritization & Wrap Up (30 min) - Jointly prioritize top 3 impactful use cases clearly: "Which scenarios, solved, would immediately solve [Problem]?" - Lock down committed next steps ↳ Result? - 3 focused hours (instead of months) - Clear, confident buyers ready to champion - 100% faster sales cycles & higher win rates —— POCs are NOT mandatory. Buyers don't want endless tests. Don’t default to what most buyers ask. Design what will solve what they need— With as little friction as possible. That's: Sales Process Design 101. P.S. We built Aligned to help manage the chaos of Complex Sales. 100% FREE Deal Room used by 40K AEs to run POCs, MAPs, etc. Try it https://lnkd.in/d_49kHZE

A sluggish API isn't just a technical hiccup – it's the difference between retaining and losing users to competitors. Let me share some battle-tested strategies that have helped many  achieve 10x performance improvements: 1. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Not just any caching – but strategic implementation. Think Redis or Memcached for frequently accessed data. The key is identifying what to cache and for how long. We've seen response times drop from seconds to milliseconds by implementing smart cache invalidation patterns and cache-aside strategies. 2. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Large datasets need careful handling. Whether you're using cursor-based or offset pagination, the secret lies in optimizing page sizes and implementing infinite scroll efficiently. Pro tip: Always include total count and metadata in your pagination response for better frontend handling. 3. 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is often overlooked, but crucial. Using efficient serializers (like MessagePack or Protocol Buffers as alternatives), removing unnecessary fields, and implementing partial response patterns can significantly reduce payload size. I've seen API response sizes shrink by 60% through careful serialization optimization. 4. 𝗧𝗵𝗲 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗞𝗶𝗹𝗹𝗲𝗿 This is the silent performance killer in many APIs. Using eager loading, implementing GraphQL for flexible data fetching, or utilizing batch loading techniques (like DataLoader pattern) can transform your API's database interaction patterns. 5. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 GZIP or Brotli compression isn't just about smaller payloads – it's about finding the right balance between CPU usage and transfer size. Modern compression algorithms can reduce payload size by up to 70% with minimal CPU overhead. 6. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹 A well-configured connection pool is your API's best friend. Whether it's database connections or HTTP clients, maintaining an optimal pool size based on your infrastructure capabilities can prevent connection bottlenecks and reduce latency spikes. 7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗟𝗼𝗮𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Beyond simple round-robin – implement adaptive load balancing that considers server health, current load, and geographical proximity. Tools like Kubernetes horizontal pod autoscaling can help automatically adjust resources based on real-time demand. In my experience, implementing these techniques reduces average response times from 800ms to under 100ms and helps handle 10x more traffic with the same infrastructure. Which of these techniques made the most significant impact on your API optimization journey?

Here’s a quick breakdown of Kubernetes deployment strategies you should know — and the trade-offs that come with each. But first — why does this matter? Because deploying isn’t just about pushing new code — it’s about how safely, efficiently, and with what level of risk you roll it out. The right strategy ensures you deliver value without breaking production or disrupting users. Let's dive in: 1. Canary ↳ Gradually route a small percentage of traffic (e.g. 20%) to the new version before a full rollout. ↳ When to use ~ Minimize risk by testing updates in production with real users. Downtime: No Trade-offs: ✅ Safer releases with early detection of issues ❌ Requires additional monitoring, automation, and traffic control ❌ Slower rollout process 2. Blue-Green ↳ Maintain two environments — switch all traffic to the new version after validation. ↳ When to use ~ When you need instant rollback options with zero downtime. Downtime: No Trade-offs: ✅ Instant rollback with traffic switch ✅ Zero downtime ❌ Higher infrastructure cost — duplicate environments ❌ More complex to manage at scale 3. A/B Testing ↳ Split traffic between two versions based on user segments or devices. ↳ When to use ~ For experimenting with features and collecting user feedback. Downtime: Not Applicable Trade-offs: ✅ Direct user insights and data-driven decisions ✅ Controlled experimentation ❌ Complex routing and user segmentation logic ❌ Potential inconsistency in user experience 4. Rolling Update ↳ Gradually replace old pods with new ones, one batch at a time. ↳ When to use ~ To update services continuously without downtime. Downtime: No Trade-offs: ✅ Zero downtime ✅ Simple and native to Kubernetes ❌ Bugs might propagate if monitoring isn’t vigilant ❌ Rollbacks can be slow if an issue emerges late 5. Recreate ↳ Shut down the old version completely before starting the new one. ↳ When to use ~ When your app doesn’t support running multiple versions concurrently. Downtime: Yes Trade-offs: ✅ Simple and clean for small apps ✅ Avoids version conflicts ❌ Service downtime ❌ Risky for production environments needing high availability 6. Shadow ↳ Mirror real user traffic to the new version without exposing it to users. ↳ When to use ~ To test how the new version performs under real workloads. Downtime: No Trade-offs: ✅ Safely validate under real conditions ✅ No impact on end users ❌ Extra resource consumption — running dual workloads ❌ Doesn’t test user interaction or experience directly ❌ Requires sophisticated monitoring Want to dive deeper? I’ll be breaking down each k8s strategy in more detail in the upcoming editions of my newsletter. Subscribe here → tech5ense.com Which strategy do you rely on most often? • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well!

E-commerce logistics during peak season is a complex and challenging operation. Here's an overview: Thumb rule - Fast,safe & on time delivery with minimum price operation ,one has to follow to meet the customer satisfaction in all aspects. Peak Season Logistics Challenges: 1. Increased volume (millions of packages per day) 2. Time-sensitive delivery demands 3. Higher customer expectations 4. Limited capacity and resources 5. Supply chain disruptions 6. Weather-related issues 7. Labor shortages 8. Technology and infrastructure constraints Strategies to Meet On-Time Delivery Demands: 1. Scalable Infrastructure: Temporary warehouses, pop-up distribution centers 2. Flexible Workforce: Seasonal hiring, overtime, and flexible scheduling 3. Technology Integration: Automated sorting, tracking, and delivery systems 4. Data Analytics: Predictive modeling, real-time monitoring, and optimization 5. Partnerships and Collaborations*: Carrier partnerships, last-mile delivery networks 6. Dynamic Routing: Real-time route optimization, traffic management 7. Inventory Management: Strategic inventory placement, pre-season stocking 8. Customer Communication: Proactive updates, transparent tracking Best Practices: 1. Pre-Season Planning: Forecasting, capacity planning, and resource allocation 2. Real-Time Visibility: End-to-end tracking, monitoring, and alerts 3. Proactive Issue Resolution: Quick response to delays, exceptions 4. Carrier Diversification: Multiple carrier partnerships for contingency 5. Contingency Planning: Backup plans for unexpected disruptions Innovative Solutions: 1. Drone Delivery: Last-mile delivery acceleration 2. Autonomous Vehicles: Self-driving delivery trucks 3. Robotics and Automation: Warehouse automation, sorting 4. Artificial Intelligence: Predictive analytics, optimized routing 5. Internet of Things (IoT): Real-time tracking, monitoring Key Performance Indicators (KPIs): 1. On-time delivery rate 2. Order fulfillment rate 3. Shipping accuracy 4. Customer satisfaction (CSAT) 5. Return rate 6. Cost per shipment 7. Transit time 8. Supply chain visibility Few major E-commerce Logistics Players: 1. Amazon Logistics 2. UPS 3. FedEx 4. DHL 5. USPS 6. JD Logistics 7. Alibaba Logistics 8. Shopify Logistics 9.Flipkart logistics 10.Delhivery.com. Peak Season Logistics Timeline: 1. Pre-season (July-August): Planning, forecasting, resource allocation 2. Peak season (November-December): Increased volume, expedited shipping 3. Post-peak (January-February): Returns, inventory management By implementing strategies, e-commerce companies can ensure timely delivery and meet customer expectations during peak season.

42% of startups fail because no one wants the product. A successful MVP removes all doubt. Here's how to build one: I've launched MVPs across 5 businesses in different niches,   And the hardest part is never the technical side. For me, it's always the discipline of leaving things out. The founder instinct will always tell you to add more, do more. But that's how you end up wasting 6 months on features no one needs. When Tom Blomfield, founder of Monzo, agreed to invest £100k in Lottie,  He gave us one condition: launch in 14 days. We basically had to strip everything back and ship.  And with sheer speed and effort, we did just that. The signals we got back were completely different from what we expected. He saved us months of wasted time with 1 deadline.  With Searchable, I was more prepared as a founder. When it came time to build our MVP, we asked one question:  What's the single thing we can build that proves this idea works? The answer was prompt tracking. We kept it insanely simple and opened our waitlist in December.  The feedback we got built the next version of the product. This is now the framework I use EVERY time: 1️⃣ Who is your MVP for? ↳ Define your ideal customer before you build anything. ↳ What single problem are you solving, and how does it provide immediate value? 2️⃣ Validate the problem first ↳ Would people actually pay for a solution to this? ↳ If nobody is eager, rethink before you write a line of code. 3️⃣ The Core MVP Rules ✅ Solve one specific problem in the simplest way possible. ✅ Be usable without extensive training. ✅ Provide immediate value. ✅ Be built in 4 to 8 weeks maximum. ❌ Don't build the full product vision from day one. ❌ Don't pack in features nobody asked for. 4️⃣ Get Your First Users ↳ Social media, early access deals, direct email outreach. ↳ Your first users are your best product team. Treat them that way. 5️⃣ The Feedback Loop ↳ Launch. Get feedback. Iterate. Relaunch. ↳ Remove what isn't working before you add anything new. 6️⃣ Signs Your MVP Is Ready to Scale: ✅ Strong retention and repeat users. ✅ Users asking for more features. ✅ Customers willing to pay without being pushed. If in doubt, aim to remove as many things as you can. The simplest version that delivers real value will always teach you more than the most complete version you could imagine. For more on building businesses the right way from day one... 📌 Subscribe to my newsletter: https://lnkd.in/eUTCQTWb Save this post to come back to the framework. ♻️ Repost to help other founders avoid building what nobody asked for. Follow Chris Donnelly for more on building and scaling businesses.

Your software development organization is slow?  Business and customers are complaining? There is an easy fix: WIP limits. Most organizations face a common problem: they are slow. Usually because they are trying to do everything at once. Development teams juggle multiple projects, thinking this maximizes productivity. Traditional fixes? - Throw more resources at it. - Add developers. - Buy new tools. - Reorganize teams. All expensive, all time-consuming, all missing the real issue. The solution is surprisingly simple: Stop starting and start finishing. WIP (Work in Progress) limits force teams to complete current tasks before taking on new ones. It's like traffic flow - cars move faster on an uncrowded highway than in bumper-to-bumper congestion. Here's a real example: Three 6-week projects. With multitasking, Project A finishes in week 16, B in week 17, C in week 18. With WIP limits? A done in week 6, B in week 12, C still in week 18. Same total time, but value delivered 10 weeks earlier. Want to implement WIP limits? 1. Start with one pilot team 2. Set initial WIP limits at 70-80% of current workload 3. Reduce by 10-20% every few weeks 4. Watch delivery times drop while throughput stays steady 5. Visualize the effects! Stop starting new work. Start finishing what's in progress and become as twice as fast. What's your experience with WIP limits? Share your thoughts in the comments.

Two years ago, our first AI system crashed on its very first day in production. We’d built a voice assistant for healthcare claims, back in mid-2023 - when GPT-4 was fresh and excitement about LLMs was at its peak. In the lab, our assistant was flawless. Then the real world hit and our AI system crashed on its very first day in production. Customers called with TVs blasting in backgrounds. Elderly patients mumbled. Cheap Android mics couldn't pick up voices. Accents we'd never trained for broke everything. Network hiccups chopped sentences in half. Our carefully crafted assistant quickly became digital wreckage. That failure taught us something that's shaped everything we've built since: The gap between demo and production isn't a bridge - it's a canyon. Everyone talks about AI ideas. But the real battle? Building systems that perform consistently and reliably, at scale, under pressure. That impressive demo for your investors? Now imagine deploying it in a car going down the highway at 100 km/h. You have only 39 meters to stop safely. Every 100ms delay from AI costs you 2.8 meters of stopping distance. Or think about banking systems handling millions of transactions every minute. Even microseconds of delay can cost millions. Plus, regulators require transparency, and all this needs to be cheaper than traditional methods. At Fast Code AI , we've been building robust, production-grade #ML systems that do exactly this, day in and day out. Here's what we've learned along the way: - Prototypes lie - they don't show real-world problems. - Latency matters a lot - no matter how brilliant your model, if it's slow, it is not better than the original. - Good engineering beats clever ideas - especially when everything's on fire Real-world AI isn't built over a hackathon weekend. It's craftsmanship. It's the boring stuff - testing, retries, logging, fallback logic - that separates toys from tools. Think of it like Formula 1 racing. It's not just the car. It's the driver, the pit crew, the telemetry, and thousands of hours of training. At Fast Code AI , we're building the pit crew for AI. If you're ready to take your AI system from demo to dependable - let's talk. Sridhar Kamath Parth Basole Hrishikesh Jadhav Shubham Kumar

MRP errors are devastating. This document contains a 21-point checklist for Material Requirements Planning (MRP): ↳ # 1 - Ensure Bills of Materials (BOMs) are complete and contain all part numbers and alternates ↳ # 2 - Set up preferred and approved suppliers with accurate details ↳ # 3 Maintain up-to-date supplier lead times for each item ↳ # 4 - Define accurate Minimum Order Quantities (MOQs) for every supplier and part ↳ # 5 - Input and validate accurate freight and incoterms in supplier records ↳ # 6 - Review and confirm open purchase orders (POs) with suppliers ↳ # 7 - Keep ready dates and ETAs (Estimated Times of Arrival) updated regularly ↳ # 8 - Maintain accurate on-hand inventory by location and batch ↳ # 9 - Track and consider inventory in transit and inter-plant transfer lead times ↳ # 10 - Always run MRP with the most updated Master Production Schedule (MPS) ↳ # 11 - Review safety stock levels to reflect true demand and supply variability ↳ # 12 - Set accurate planning horizons (short, medium, and long-term) to match business needs ↳ # 13 - Ensure item planning policies are accurate ↳ # 14 - Validate production lead times for each work center ↳ # 15 - Include alternate sourcing and dual suppliers in the system where applicable ↳ # 16 - Verify substitution rules for interchangeable parts ↳ # 17 - Incorporate shelf-life, expiration dates, and aging rules for sensitive items. ↳ # 18 - Verify exception messages (reschedule in, reschedule out, cancel, expedite) ↳ # 19 - Track planner buy reports and reconcile against supplier acknowledgments ↳ # 20 - Validate supplier confirmations (acknowledgment of PO dates and quantities) ↳ # 21 - Establish feedback loops; when MRP recommendations fail, document root causes and adjust parameters Any others to add?