Transportation Management System Features

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.

Cities globally still manage their transport with rearview mirrors. Agree? Traffic jams, late deliveries, rising fuel costs, and safety incidents... these aren't just daily headaches. They're symptoms of a system that's very complex. You're constantly reacting. But what if you prevent these problems before they happen? This is the reality of a Transport & Mobility Digital Twin. The diagram isn't just a flowchart. It's a blueprint for turning chaos into control. Here’s how it works, broken down simply: Step 1: Create the Foundation Model This is a dynamic Transport DT which continuously ingests real-time data from every possible source (Measurable Properties in the chart): MP2: Live traffic data MP1: Public transport data MP12: Fleet fuel consumption MP5: Weather conditions MP9: Supply chain information Step 2: Start simulating Once you have a copy with this real-time data inflow, you can ask powerful "what-if" questions without real-world consequences. We call these Adjustments: "What if we re-route 20% of city traffic to test a new traffic flow model? (A1)" "Where will new road infrastructure give us the highest ROI? (A2)" "How will a new sustainability regulation impact our delivery times? (A5)" You test, analyze, and perfect your strategy in the digital world before committing a single resource in the physical world. Step 3: Make Decisions ONLY Tied to your Business Goals Every simulation is measured against the goals that matter for YOU most (Key Performance Indicators): Increase Transport Efficiency (KPI1) Improve Sustainability & Environmental Impact (KPI2) Enhance Traffic Safety (KPI5) Boost your Economic & Financial performance (KPI7) This will stop you from making gut-feel decisions. You start executing real-world strategies for your transport city network. --------------- Follow me for #digitaltwins Links in my profile Florian Huemer

Designing an AI System That Doesn’t Collapse Under Latency Spikes A single user query passes through multiple stages — tokenization → batching → GPU scheduling → model execution → post-processing → response assembly. Now picture this: A few heavy prompts take 5× longer than average. Your batching layer waits to fill the “perfect batch.” Meanwhile, the queue grows. Requests start timing out. Retries stack up. That’s when you realize: You’re not running out of compute. You’re running out of control. Here’s how you design for resilience instead of collapse 👇 1️⃣ Bounded Queues Never let latency scale linearly with load. Bound your input queues and shed load proactively — either by dropping excess requests or serving degraded responses. Unbounded queues are silent killers — they delay backpressure, causing cascading timeouts. Think of it like circuit breakers for inference — graceful denial is better than system-wide collapse. 2️⃣ Adaptive Batching Static batch sizes look great in benchmarks and terrible in production. Instead, make batch sizes dynamic — continuously tuned based on GPU occupancy, queue length, and recent tail latency percentiles (P95/P99). At low load, batch small for lower latency. At high load, batch large for throughput — but with strict timeouts. The goal is elasticity without unpredictability. 3️⃣ Token-Aware Scheduling Batching by request count is naive. In LLM workloads, token length determines cost. A single 10,000-token prompt can stall 15 smaller ones if batched together. Token-aware schedulers measure total token budget per batch and allocate GPU time accordingly. This ensures fairness and consistent latency curves even under mixed workloads. 4️⃣ Partial Caching Most engineers cache final model outputs. That helps little. What actually saves time is pre- and post-compute caching — tokenized inputs, embeddings, and prompt templates. These are deterministic and cheap to reuse, shaving milliseconds off critical paths. Combine that with vector cache lookups to skip redundant reasoning altogether. 5️⃣ Deadline-First Scheduling In multi-tenant inference systems, not all requests are equal. Prioritize requests based on expected completion deadlines instead of FIFO order. This minimizes tail latency and improves QoS across traffic tiers. It’s the same principle airlines use — business class boards first, but everyone still gets there. This is where systems engineering meets AI infrastructure. Because LLM inference at scale isn’t just about throughput — it’s about temporal predictability. Inside my Advanced System Design Cohort, we go deep into these challenges — how to design AI systems that don’t just scale, but stay stable under load. If you’ve been leading distributed systems or AI infra and want to sharpen your architectural depth, there’s a link to a form in the comments — apply, and we’ll check if you’re a great fit.

🚀 Walmart’s E-Commerce Surge: $121B in 2024, Profitability Achieved! 💰 While Amazon often dominates e-commerce headlines, Walmart has been quietly revolutionizing its online presence: • $120.9B in online sales in 2024, marking a 21% year-over-year increase. • E-commerce now constitutes 18% of total revenue, up from 13.6% in 2023. • A remarkable 47% growth in just two years. The catalyst? A strategic overhaul of their supply chain and delivery systems: • Transitioned from traditional ZIP code mapping to a honeycomb-style hexagonal system, enhancing delivery efficiency. • Expanded same-day delivery reach to 93% of U.S. households, with plans to achieve 95% coverage by end of 2025. • The Spark delivery platform, leveraging geospatial technology, added 12 million new households to its network in January alone. These innovations have led to significant operational efficiencies: • 30% of U.S. orders now utilize fast delivery options. • Delivery cost per order decreased by 20% in Q4. • Walmart’s U.S. e-commerce sector achieved profitability for the first time in Q1 2025. David Guggina, Executive VP and Chief eCommerce Officer, emphasized the “flywheel effect”: “When customers choose fast delivery, they shop more frequently, buy a broader range of items, and basket size increases.” This transformation underscores a pivotal lesson for retailers: Seamless integration between physical stores and e-commerce platforms is not just beneficial—it’s essential. Walmart’s journey from a traditional brick-and-mortar giant to a formidable e-commerce contender exemplifies the power of strategic innovation and adaptability. #Ecommerce #RetailInnovation #DigitalTransformation #Walmart #SupplyChain #Logistics #Omnichannel #RetailStrategy

You're sitting in an L5-level system design interview at Google, and you've just been told to design a distributed job scheduler. You’ve done job schedulers before. Great. But it only takes one extra constraint to turn something “simple” into a headache: → Suppose they add DAG-based execution and now you’re managing dependency ordering → Suppose they add millions of jobs/day and suddenly your scheduler table must survive hell → Suppose they add multi-level executors (cheap vs expensive hardware) and now you’re in OS-level scheduling territory Before you know it,  your “simple scheduler” becomes a mini Airflow + Cron + Kafka hybrid. Here’s my personal checklist of 15 things you must get right when designing a distributed job scheduler: 1. Store binaries in object storage Never ship code through your backend. Users upload binaries/scripts → you store them in S3/GCS → executors download directly. 2. Separate Cron jobs and DAG jobs Cron needs predictable time-based triggering. DAGs need dependency resolution + epoch tracking. Do NOT mix both in one table. 3. Topologically sort DAGs on upload Users will dump random graphs. You must determine roots, order, and execution sequence. 4. Pre-schedule only the next Cron run Not all future runs. Only the *upcoming* job instance goes into the scheduler table. 5. Each job must have a “run_at” timestamp Schedulers poll: `SELECT * FROM tasks WHERE run_at <= NOW() AND status = 'pending'` 6. Update run_at as soon as execution starts Add +5 or +10 min. This prevents retry storms and ensures clean scheduling timeouts. 7. Executors pull, not receive pushed tasks Pulling avoids overload, simplifies horizontal scaling, and prevents blind pushes.  8. Use an in-memory message broker for load balancing Kafka = bad for job schedulers (partition lock-in). ActiveMQ/RabbitMQ = executors pick tasks only when idle.  9. Use multi-level priority queues Think OS scheduling: Level 1 → cheap nodes Level 2 → standard Level 3 → high-power nodes Long-running tasks get escalated. 10. Use distributed locks for “run once” semantics Zookeeper lock per job ID → prevents simultaneous execution on multiple executors. 11. Accept that some jobs may run twice Make jobs idempotent. Use versioned writes. Retry logic will inevitably double-fire something. 12. Maintain a status table with final outcomes Users should see: pending, running, success, failed, error logs. 13. Use read replicas for user-facing status Never let users hit the primary scheduler DB. 14. Shard scheduler table by job_id + time range Millions of rows. High churn. Without sharding, your entire system becomes a single-point bottleneck. 15. Use change-data-capture (CDC) instead of 2-phase commits When DAG nodes complete → update DAG table → emit CDC event → enqueue next node. No locking hell. No cross-table multi-row transactions.

During my Master’s thesis, I was lucky enough to visit Ghent University, where I wanted to develop a #MATSim model (a type of simulation that forecasts how people move around a city), to forecast how to enhance Alexandria's informal transport (aka paratransit, or microbus). #Alexandria (for those who don’t know, it’s a Mediterranean city of around 6 million people), I quickly realized it was almost impossible to create a synthetic population representing everyone’s daily movements. Imagine asking six million people where, when, and why they move. #MATSim literature often describes this as a do-it-yourself task, but in a data-scarce context, it felt like a mission impossible. Then I came across #Meta’s Data for Good portal (Formerly Facebook), a platform that provides aggregated Origin–Destination data for users of Meta’s apps. Of course, privacy is fully protected (individual movements cannot be tracked), but it allows studying city-level mobility patterns. Together with Corneel Casier and with the support of our supervisors Prof. Paola Pucci and Prof. Frank Witlox, we built a pipeline to convert Meta mobility data into MATSim-compatible inputs. In my head, I like to think of it as OpenStreetMap for human movement. The best part is that this approach can be replicated anywhere. All you need is: 1- OpenStreetMap data, which is freely available worldwide 2-Meta Data for Good data for your city, available during emergencies or upon request With that, you can build your own city-scale traffic model, just like the one we made for Alexandria. Check the video below. Once the model is ready, you can test any urban change and forecast which streets or areas might be most affected. To make this easier, we created a GitHub repository where you can copy the Python scripts directly, turning the synthetic-population task from DIY to ready-to-use. I’m also happy to share that we are publishing this work in the Journal of Urban Technology (Q1) together with Corneel Casier, Paola Pucci, and Frank Witlox. The full method is available through https://lnkd.in/dMSMbh2G (50 free e-prints). The GitHub Repo is also available here: https://lnkd.in/dMn6CgUw A long wait, but worth it. My first publication in this journal, which wouldn’t have been done without the support of Corneel and the supervision of Profs. Paola Pucci and Frank Witlox. Thank you, I hope you can utilize this work.

Microscopic traffic simulation in 3D using ArcGIS and SUMO 🤔🚗 This is something I’ve been playing around with for a while, and (after a few headaches) I've put together a working proof of concept. The idea is to combine SUMO’s rule-based traffic simulations with ArcGIS’s 3D visualisation, editing, and analytical tools - bringing live, dynamic traffic modelling that you can engage with into a digital twin environment. Right now, I can click a road segment to change its lane state and instantly watch vehicles reroute. I’ve also been playing with heat map views to show traffic density and flow across the network. In the future I’m planning to feed in flood simulation data to automatically close roads as they flood, bring in other SUMO outputs like emissions, and test live network and rule editing. It’s got me thinking about all the ways this could be used - from simulating the impact of new housing and infrastructure developments to planning for floods, road closures, or major events. Would love to hear your thoughts and other ideas or scenarios you’d be curious to test! Built using the ArcGIS Maps SDK for Javascript, SUMO (Simulation of Urban MObility), and Flask.

Exciting research area we’re exploring at Google: simulations on urban mobility. Simulations can help city planners anticipate congestion (and pollution!) before it happens. What's a mobility simulation? Virtual replicas of cities that can simulate and predict traffic patterns, identify bottlenecks, and test potential solutions - all before impacting real-world commuters. Significant Travel Time Reduction Our model achieves up to a 78% reduction in path travel time nRMSE during peak hours (Boston, 7pm) and 76% during non-peak hours (Orlando, 3pm) How Does It Work? We combine our understanding of the road network and with historical Google Maps driving trends to create accurate models (especially of mobility demand, which is one of the key parameters). Our metamodel calibrates traffic simulations 52% better than the baseline method. Our team constructed simulations for six metropolitan areas: - Seattle - Denver - Philadelphia - Boston - Orlando - Salt Lake City. Learn more about this early research: https://lnkd.in/gyQzXqUg Congratulations Carolina Osorio, Chao Zhang, Neha Arora and team!

Strategies for Optimizing First, Middle, and Last Mile Delivery in Logistics What Is First Mile? This is the initial stage of the delivery process, where goods are picked up from the manufacturer or supplier and transported to a distribution center or fulfillment center. It involves activities such as packaging, labeling, and sorting. What Is Middle Mile? The middle mile involves the transportation of goods from the distribution center to local hubs or sorting centers. This stage is essential for consolidating shipments and optimizing transportation routes before the final leg of delivery. What Is Last Mile? The last mile is the final stage of the delivery process, where goods are transported from the local hub or sorting center to the customer's location. This is often the most expensive and challenging part of the delivery process due to the intricacies of navigating urban areas and delivering individual packages to specific addresses. Optimizing Strategies 1. Utilize Technology Implementing route optimization software, GPS tracking, and real-time analytics can help optimize delivery routes and improve efficiency. 2. Partner With 3PL Providers Collaborating with local carriers or last-mile delivery specialists can help streamline the last mile and reduce costs. 3. Implement Flexible Delivery Options Offering customers options such as same-day delivery, click-and-collect, or alternative delivery locations can improve customer satisfaction and reduce the complexity of last-mile logistics. 4. Use Alternative Transportation Methods Experimenting with alternative modes of transportation such as drones, electric vehicles, or cargo bikes can help reduce carbon emissions and congestion in urban areas. Trends • Increased demand for same-day and next-day delivery services • Growth of e-commerce leading to higher parcel volumes and more complex delivery networks • Adoption of sustainable and eco-friendly delivery solutions to reduce environmental impact • Integration of emerging technologies such as AI, IoT, and blockchain to enhance visibility and efficiency in the supply chain Challenges • Traffic congestion and urbanization leading to delivery delays and increased costs • Difficulty in achieving profitability in the last mile due to high operational costs • Labor shortages and turnover in the logistics industry • Security and safety concerns related to theft, vandalism, and accidents during delivery Conclusion First, middle, and last mile delivery are critical components of the logistics supply chain, each presenting unique challenges and opportunities for optimization. By leveraging technology, implementing flexible delivery options, and adapting to emerging trends, companies can improve efficiency, reduce costs, and enhance the customer experience in the increasingly competitive landscape of e-commerce and parcel delivery. #LogisticsOptimization #SupplyChainEfficiency #CustomerExperience #DeliveryInnovation #SupplyChainTrends

Hi LinkedIn community 🙏✨ Jai Shree Krishna to everyone Have you ever thought about how: 💳 Your credit card reminder arrives exactly 2 days before the due date? 📺 Your OTT subscription renews automatically at midnight? 📈 A stock order executes precisely at market open? All of this is powered by job scheduling systems ⚙️ But here’s the twist: Running 10 scheduled jobs is easy. Running 100,000+ jobs per second reliably is a serious system design challenge. 🚀 🟢 Stage 1: Cron Jobs ⏰ (Simple & Effective) At small scale (10–20 users), cron works beautifully: Schedule recurring tasks Minimal setup Predictable execution But as features grow — emails 📩, automated trades 🤖, reports 📊 — the number of scheduled jobs grows rapidly. Challenges appear: Hard to monitor failures Operational overhead increases No centralized visibility Cron isn’t wrong. It’s just not built for massive scale. 🟢 Stage 2: Centralized Scheduler 🏗️ Next step: move scheduling inside the application. Now you: Manage jobs centrally Track states (Scheduled, Running, Completed) Improve observability But a single machine has limits: ⚡ CPU 🧵 Threads 🧠 Memory As users scale from hundreds to thousands: Jobs get delayed Thread pools become bottlenecks Single point of failure emerges Adding more nodes increases capacity — but creates a new problem: ❗ Duplicate execution. In financial systems, that’s unacceptable. 🟢 Stage 3: Coordination for Correctness 🔐 To ensure a job runs exactly once: Persist job metadata in DB Use locking before execution Update job states atomically Correctness improves. But at high scale (100K+ jobs/sec), the real bottleneck appears: ⚠️ Scheduling and execution are tightly coupled. If one node picks up 200K jobs, execution becomes slow — even if correct. 🟢 Stage 4: Decouple to Scale 🚀 The breakthrough is separation of concerns: 🧭 Scheduler → Finds due jobs 📦 Message Queue → Buffers workload ⚙️ Executors → Process jobs Now: Executors scale horizontally Backpressure is manageable Failures are isolated To avoid one workload blocking another: Separate queues per feature Independent worker pools At extreme scale, partitioning (sharding) jobs by execution time prevents database contention. 🔥 Core Principles 1️⃣ Correctness requires coordination 2️⃣ Scale requires decoupling 3️⃣ Isolation prevents cascading failures 4️⃣ Bottlenecks shift as systems grow Cron → Central Scheduler → Distributed, Partitioned Architecture The tools change. The principles remain constant. If you were designing this system, what would you scale first — scheduler, executor, or database? 👇 #SystemDesign #DistributedSystems #Scalability #BackendEngineering #SoftwareArchitecture #Microservices