Order Processing Efficiency

Know about Apache Hudi via Scenario: Real-Time Customer Transactions Analysis. ✅ Project Overview: Imagine you are working for an e-commerce company that processes thousands of customer transactions every minute. You need to build a system that can: ✔ Ingest and store real-time transaction data. ✔ Support real-time updates to the transaction data. ✔Allow incremental processing to generate analytics and reports. ✔ Ensure data consistency and efficient querying. 𝐔𝐬𝐢𝐧𝐠 𝐀𝐩𝐚𝐜𝐡𝐞 𝐇𝐮𝐝𝐢, 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐚𝐜𝐡𝐢𝐞𝐯𝐞 𝐭𝐡𝐞𝐬𝐞 𝐠𝐨𝐚𝐥𝐬 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭𝐥𝐲. Apache Hudi is a data lake storage framework that enables efficient data management and real-time data processing with support for upserts, deletes, and incremental data ingestion. ✅ Steps to Implement the Project: (𝐅𝐨𝐫 𝐂𝐨𝐝𝐞 𝐜𝐡𝐞𝐜𝐤-𝐨𝐮𝐭 𝐆𝐢𝐭𝐡𝐮𝐛) 1. 𝐒𝐞𝐭 𝐔𝐩 𝐀𝐩𝐚𝐜𝐡𝐞 𝐇𝐮𝐝𝐢 Environment: Use a cloud platform like AWS EMR, Google Dataproc, or Azure Databricks, or set up a local environment with Apache Hudi. Dependencies: Ensure you have Hudi dependencies added to your Spark or Hadoop environment. 2. 𝐈𝐧𝐠𝐞𝐬𝐭 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐃𝐚𝐭𝐚 You receive real-time transaction data from various sources (e.g., Kafka, Kinesis). Each transaction record includes details such as transaction ID, customer ID, product ID, amount, timestamp, and status. 3. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐔𝐩𝐝𝐚𝐭𝐞𝐬 Transaction statuses can change (e.g., from "pending" to "completed"). Apache Hudi supports upserts, allowing you to efficiently update existing records. 4. 𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 With Hudi, you can perform incremental queries to fetch only the data that has changed since a specific timestamp, reducing the need to reprocess the entire dataset. ✅ Benefits of Using Apache Hudi in This Scenario: ✔ Upserts and Deletes: Handle updates and deletes efficiently without reprocessing the entire dataset. ✔ Incremental Processing: Process only new or updated data, saving computational resources and time. ✔ Data Consistency: Ensure data consistency with ACID transactions. ✔ Scalability: Handle large volumes of data and scale horizontally. ➡ Github Link: https://lnkd.in/gadKksag ➡ Docs: https://hudi.apache.org/ Image Source: https://hudi.apache.org/ If you find this insightful, please like or repost ♻. For any questions or clarifications, feel free to comment. Direct messages are always welcome! 🤝Follow Nishant Kumar #dataengineer #bigdata #apachehudi #apache

Some shipments demand an extra level of tracking - whether for enhanced security, quality assurance or simply greater visibility. That’s where an advanced live tracking system can make all the difference, providing real-time updates on location, temperature, pressure and more. In the photo, I am holding a FedEx SenseAware tracking device. This technology offers real-time insights, helping you stay informed with data on your shipment’s journey. It also provides proactive alerts, allowing you to respond swiftly to any issues that arise. Here’s how this kind of technology can elevate your logistics operations: 1. Security & Compliance: Keep valuable and sensitive shipments secure with continuous monitoring and heightened protection. 2. Quality & Integrity: Maintain confidence in your shipment’s condition with constant updates on environmental factors like temperature and shock, ensuring quality throughout transit. 3. Operational Visibility: Precise route tracking keeps you informed of your shipment’s exact location, making it easier to optimise planning and mitigate disruptions. Live tracking is not just a tool; it’s a strategic advantage for logistics managers looking to secure their supply chains and deliver exceptional service. Could this be of use to you? What do you think? Let me know below 👇 #logistics #shipping #data #technology #operations #supplychain #fedex

Hospitals are making less money because of these mistakes! In healthcare, managing inventory to align with real demand is a constant challenge. With items billed to in-patients, out-patients, or not billed at all, the risk of overstock or stockouts can be high. Consider the impact of one hospital’s approach: This issue affects cost, resource allocation, and patient care. But what if healthcare facilities could analyze consumption patterns and align supply with actual demand? Here’s how leading hospitals are using data-driven strategies to reduce waste, ensure fulfillment, and cut costs. Many hospitals stock up to avoid shortages. The first step? Analyzing usage across the board. Track demand through metrics like bed days, duration of stay, department, and care provider, hospitals gain a complete view of supply needs, item by item. With this data, they can build statistical models that accurately forecast inventory levels, applying correction factors based on operational changes. Here’s how this data-driven model is transforming inventory management: 1) Demand-driven forecasting: Tracking metrics such as patient stay duration and care provider needs enables precise demand planning. 2) Item-level alignment: Each department and provider receives supplies matched to actual usage, reducing waste and unnecessary stock. 3) Correction factors: By adjusting for seasonal or operational changes, hospitals avoid costly overstocks and stockouts. 4) Financial impact: Reduced inventory costs mean more resources for direct patient care. The outcome? A supply chain where inventory is optimized, every item accounted for, and every dollar maximized. In this way hospitals save time and money to work effectively across all the channels.

What’s harder to track: a $3,000 custom guitar 🎸or a $30 pizza? (Automation Tip Tuesday 👇) A custom guitar manufacturer was manually copying order details between department spreadsheets and crafting individual customer updates. For EACH guitar. Through woodshop, paint, and assembly. 20 times a week. 😵 That's 4 hours of pure admin work that should've been building guitars. The fix? A simple automation that: ➡️ Automatically moves products between department queues when marked "complete" ➡️ Triggers personalized customer updates with progress photos  ➡️ Maintains a central dashboard for all orders The result? 💥  4 hours saved weekly  💥 Zero missed updates  💥 Delighted customers who feel like VIPs  💥 Craftsmen who can focus on crafting And the best part?  This same workflow works for any staged production process — from custom furniture to marketing agencies to home builders. Sometimes the simplest automations have the biggest impact. They just need someone to spot the pattern. Which manual updates are eating your team's time? -- Hi, I’m Nathan Weill, a business process automation expert. ⚡️ These tips I share every Tuesday are drawn from real-world projects we've worked on with our clients at Flow Digital. We help businesses unlock the power of automation with customized solutions so they can run better, faster and smarter — and we can help you too! #automationtiptuesday  #automation #workflow #customerexperience

Why #DDMRP is Superior to #MRP Forecast vs. Real Demand: The Case for Demand Driven Institute #DDMRP One of the biggest challenges in supply chain management is balancing demand variability and supply variability while ensuring optimal inventory levels. Traditional Material Requirements Planning (#MRP) systems rely heavily on forecasts, which, while useful, are inherently inaccurate due to demand unpredictability. Demand Driven MRP (#DDMRP), on the other hand, shifts the focus to real demand, enabling a more responsive and resilient supply chain. MRP: Forecast-Driven but Flawed #MRP systems depend on forecasts to plan inventory and production. While forecasts are based on historical data and market trends, they are rarely precise. Factors like market disruptions, seasonality, and demand spikes make forecasts unreliable. 😟 Key Limitations of MRP: 1. Forecast Inaccuracy: Leads to overproduction or stockouts. 2. Bullwhip Effect: Amplifies demand variability across the supply chain. 3. Inflexibility: Struggles to adapt to real-time changes in demand or supply conditions. 🚫 MRP’s reliance on forecast data often results in inflated inventory levels or frequent shortages, directly impacting customer satisfaction and operational efficiency. #DDMRP: The Power of Real Demand 🚦 DDMRP fundamentally changes the game by focusing on real demand rather than relying on forecast accuracy. Here’s why it’s more effective: 1. Strategic Decoupling Buffers: DDMRP places buffers at key points in the supply chain to absorb demand and supply variability. These buffers decouple dependencies, allowing for a smoother flow of materials and preventing disruptions. 2. Adaptability to Real Demand: DDMRP dynamically adjusts buffer levels based on consumption patterns, ensuring the right inventory is available at the right time. This minimizes both overstocking and understocking. 3. Reduction of Variability: Buffers mitigate the impact of demand spikes and lead time fluctuations, providing stability to the supply chain. 4. Customer-Centric: By prioritizing availability based on real consumption, DDMRP ensures higher service levels and customer satisfaction. Why Real Demand Matters 🚫 MRP’s Dependence on Forecasts: Forecast errors ripple through the supply chain, leading to inefficiencies. Without buffers, variability in demand or supply directly impacts production schedules and inventory levels. 🚦 DDMRP’s Real Demand Focus: With decoupling buffers, DDMRP isolates variability and ensures the supply chain responds to actual consumption. This agility allows companies to maintain optimal inventory levels, even in volatile markets.

Ever wondered how Netflix, Uber, or Flipkart process millions of events in real time? They all rely on one thing—Kafka. Here’s why. 🛠️ Back when I worked on high-scale systems, we struggled with real-time order tracking. Delays led to customer complaints, and debugging was a nightmare. Then we adopted Kafka, and it changed everything—here’s how: 🔍 Why Kafka is a Game-Changer: 📡 Real-Time Data Streaming → Process millions of events per second, just like Netflix! 🔗 Decoupling Microservices → No more service dependencies slowing you down! ⚡ Fault Tolerance → Even if a node crashes, Kafka keeps your data safe. 📈 Scalability → From startup to unicorn—Kafka scales with you. 🛠️ Stream Processing → Turn raw data into real-time insights, instantly. 💡 The Real Impact: - Handled 1M+ messages/sec during Flipkart’s Big Billion Day sale. - Reduced system latency from seconds to milliseconds. - Enabled seamless fraud detection in real-time. What’s your biggest challenge when working with Kafka or real-time data streaming? Let’s discuss in the comments! 👇 Mastering Kafka = Mastering real-time data. 🚀 If this post helped you, repost to help others understand Kafka better! 📌 Follow Abhishek Kumar for more such tech posts!

🚀 How Swiggy & Zomato Use Kafka to Power Real-Time Food Delivery 🍕🍔 Ever wondered how Swiggy & Zomato manage millions of orders, real-time tracking, and restaurant updates seamlessly? The secret ingredient —Apache Kafka! Let’s break it down with a simple food delivery journey: 🥡 Step 1: Order Placement • When you place an order, Kafka instantly streams the request to multiple services—restaurant, delivery partners, and payment systems. • Ensures zero lag and real-time order processing. 🏪 Step 2: Restaurant Processing • Kafka notifies the restaurant instantly to start preparing your food. • Menu updates (like “Out of Stock” items) are also streamed in real-time. 🏍️ Step 3: Assigning a Delivery Partner • Kafka streams GPS data of all nearby delivery partners. • Algorithm picks the best rider based on location, traffic, and restaurant prep time. 📍 Step 4: Live Order Tracking • Your app continuously fetches real-time location updates of the delivery rider via Kafka, ensures smooth tracking without hammering backend servers. 📊 Step 5: Post-Delivery Analytics & Feedback • Kafka powers reviews, ratings, and customer feedback systems. • Helps in dynamic pricing, demand forecasting, and personalized recommendations. 🔹 Why Kafka? ✅ Real-time event streaming ✅ Scalable & fault-tolerant ✅ Handles millions of concurrent users ✅ Ensures low-latency order processing So next time you track your food delivery, thank Kafka for making it smooth! #systemdesign #kafka

𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐭𝐢𝐧𝐠 𝐏𝐃𝐂𝐀 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐲 𝐟𝐨𝐫 𝐂𝐨𝐧𝐭𝐢𝐧𝐨𝐮𝐬 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 🎯 Are your processes truly improving, or are you just firefighting? The PDCA Cycle (Plan-Do-Check-Act) is a simple yet powerful methodology for problem-solving and continuous improvement. It helps organizations move from reactive fixes to sustainable improvements. 🔄 What is PDCA? PDCA is a four-step, iterative cycle used for continuous improvement in processes, products, and systems. It ensures that changes are planned, tested, verified, and standardized before full-scale implementation. 📌 Also known as: Deming Cycle or Shewhart Cycle ❶PLAN - Build the Foundation Focus: Identify problems and develop an effective action plan. Plan Steps: ✅ Identify – Define the problem or opportunity for improvement. ✅ Observe – Gather data, facts, and insights. ✅ Analyze – Use tools like Fishbone Diagrams, 5 Why’s, and Pareto Analysis to find root causes. ✅ Action Plan – Develop solutions and define measurable goals, responsibilities, and timelines. 🔹 Example: A manufacturing company identifies high defect rates in its final product. After analysis, it finds that poor material handling is the root cause. ❷ DO - Implement the Solution Focus: Execute the plan on a small scale to test its effectiveness. ✅ Implement changes in a controlled environment. ✅ Train employees and document the process. ✅ Monitor real-time data to assess impact. 🔹 Example: The company introduces a new material handling procedure in one production line to test if defect rates decrease. ❸CHECK - Measure the Results Focus: Verify whether the changes lead to improvement. ✅ Compare results against planned objectives. ✅ Conduct inspections, audits, and feedback sessions. ✅ Identify any gaps or unintended issues. 🔹 Example: After one month, defect rates drop by 20%, confirming the effectiveness of the new process. ❹ ACT - Standardize & Scale Up Focus: Implement successful changes across the organization. ✅ Standardize the improved process. ✅ Create SOPs (Standard Operating Procedures) and training materials. ✅ Plan for continuous monitoring and future improvements. 🔹 Example: The new material handling procedure is rolled out across all production lines, and employees receive training to maintain consistency. 🔥 Hot Tips for PDCA Success: ✔️ Data First! Never assume—use facts and evidence. ✔️ Think Big, Start Small. Pilot solutions before full-scale implementation. ✔️ Involve Your Team. Collaboration leads to better problem-solving. ✔️ Measure Everything. If you can’t measure it, you can’t improve it. ✔️ Keep Iterating. PDCA is a cycle, not a one-time activity! 🔍 Are you using the PDCA cycle in your organization? Share your experiences in the comments! 👇 =============== 🔔 Consider following me at Govind Tiwari,PhD #Quality #PDCA #ContinuousImprovement #Lean #ProblemSolving #ProcessImprovement #qms #iso9001

OMS is changing fast. For years, Order Management Systems have been rule-based and structured. You set sourcing rules, define SLAs, and hope the system follows instructions. But commerce doesn’t work like that anymore. Too many variables. Too much unpredictability. And yet, most don’t know how to introduce AI to their back office to reduce the manual processes in their day to day. To do this, you have to rethink how orders should me managed. Here’s how AI can start to change the game: Predictive Forecasting OMS: Merchandisers manually analyze order data to predict inventory needs. OMS + AI: Recommends purchase decisions and inventory adjustments based on multiple factors—sales velocity, regional demand, and even on-site product views. Proactive SLA Risk Management OMS: Flags SLA breaches after they happen. OMS + AI: Predicts risks and suggest reprioritized picking, fulfillment, and carrier selection to avoid failures in the first place. Proactive Customer Service Traditional OMS: Customer service teams manually screen orders for edits, changes, and returns. OMS + AI: Translate order relevant tickets into requests and integrate directly with the OMS to instantly action customer requests—based on terms and conditions—so service teams can focus on complex cases. But let’s be real—AI isn’t ready for everything yet. ❌ Making on-the-fly order redirects ❌ Fully automated inventory reordering ❌ Deciding when to optimize pricing based on SKU order frequency …But it will be someday. The question is—OMS evaluations are increasing but are teams planning for how they implement this stuff? I think no, but at Orium our job is to help you look around the corner and validate what can and can’t be done.

Lean Six Sigma in Warehouse Management Lean Six Sigma (LSS) is a powerful methodology that improves warehouse management by minimizing waste, reducing errors, and enhancing efficiency. It combines Lean (which focuses on eliminating waste and improving process flow) and Six Sigma (which reduces defects and variability). Key Benefits of Lean Six Sigma in Warehousing Reduced Errors – Fewer picking and shipping mistakes. Faster Order Fulfillment – Streamlined processes reduce delays. Lower Costs – Eliminating waste leads to cost savings. Optimized Space Utilization – Efficient inventory storage and layout. Improved Safety – Standardized procedures reduce workplace hazards. Higher Customer Satisfaction – Fewer delays and errors lead to better service. Applying Lean Six Sigma in Warehouse Management 1. Identifying Waste (Lean Principles) Lean principles help identify and eliminate the 8 Wastes (DOWNTIME): Defects – Picking, packing, or shipping errors. Overproduction – Stocking excess inventory. Waiting – Delays in order processing or transportation. Non-utilized talent – Poor workforce utilization. Transportation – Unnecessary movement of goods. Inventory – Overstocking or understocking. Motion – Unnecessary employee movements. Extra processing – Unnecessary steps in order fulfillment. 2. Implementing Six Sigma (DMAIC Approach) The DMAIC (Define, Measure, Analyze, Improve, Control) approach is used to identify and fix warehouse inefficiencies: Define – Identify key warehouse challenges (e.g., high error rates, slow fulfillment). Measure – Collect data on warehouse performance (e.g., order accuracy, cycle time). Analyze – Identify root causes of inefficiencies using tools like Pareto charts, fishbone diagrams, and process mapping. Improve – Implement solutions like automation, standardized processes, and optimized layouts. Control – Maintain improvements through SOPs, KPIs, and continuous monitoring. Lean Six Sigma Tools for Warehouse Management 5S (Sort, Set in Order, Shine, Standardize, Sustain) – Keeps the warehouse organized. Kaizen (Continuous Improvement) – Small, incremental improvements in operations. Value Stream Mapping (VSM) – Visualizing and improving process flow. Kanban – Real-time inventory control system. Root Cause Analysis (5 Whys, Fishbone Diagram) – Identifying and fixing recurring problems. Real-World Example Amazon & Lean Six Sigma – Amazon optimizes its warehouses using automation, real-time inventory tracking, and Six Sigma methodologies to reduce errors and improve order fulfillment speeds. Conclusion Implementing Lean Six Sigma in warehouse management helps reduce costs, improve efficiency, and enhance customer satisfaction. By eliminating waste and reducing variability, warehouses can achieve higher productivity and streamlined operations.