Batch Production Scheduling

"Just use Redis TTL for scheduling" is the kind of solution that sounds brilliant at 2 PM in a design review and terrible at 2 AM in production. I see this pattern constantly in system design interviews. The requirement comes up: send a reminder in 24 hours, retry a failed payment after 5 minutes, check order status every hour. And like clockwork (pun intended), candidates propose: "We'll use Redis TTL and listen for expiry events!" It's an attractive trap. The logic seems clean: set a key with expiration, listen for the notification when it expires, trigger your job. One system, minimal code, what could go wrong? A lot, actually. Here's why this pattern fails: 1. Redis processes key expiration in the background. Your notification might arrive seconds or even minutes after the actual expiration time - completely undermining time-sensitive operations. 2. If Redis is under heavy load, it might delay checking for expired keys. This unpredictability makes it impossible to guarantee scheduling precision. 3. A Redis restart means all pending notifications are permanently lost. This isn't just an edge case - it's a critical reliability issue for any production system. More fundamentally, you're using a caching system as a job scheduler. It's like using a hammer to turn a screw - yes, you might eventually get it in, but that's not what the tool was designed for. What should you use instead? For smaller systems I'd keep it light and go with: - Bull/BullMQ (Node.js): Purpose-built for job queuing. Uses Redis too, but properly - with sorted sets and polling instead of key-space notifications. - Amazon SQS with delay queues: Simple, serverless, and it just works For larger systems, especially those requiring more complex workflows: - Temporal: Rock-solid reliability, great for complex workflows (this is what we use extensively at Hello Interview) - Apache Airflow: Perfect if you need visual workflow management Moral of the story. Whether in an interview or a production system, use tools designed for the job. Redis is fantastic at what it does - being a cache and fast data store. But when you need reliable scheduling, reach for a proper scheduler.

Scheduling in Kubernetes happens in various ways. Depending on the workload, you might need different algorithms like 𝗚𝗮𝗻𝗴 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴. Volcano, a CNCF project, supports this and can optimize complex workflows such as AI training, inference pipelines, and distributed data processing.  🚀 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗚𝗮𝗻𝗴 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴? Gang scheduling ensures all pods in a group ("gang") start simultaneously or none do. This prevents partial execution, which is critical for interdependent tasks like distributed training or multi-stage AI pipelines. Without it, a single delayed pod could stall an entire workflow, wasting resources. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: In distributed AI training, if three worker pods are needed, Volcano’s gang scheduler waits until all 3 are available. If even one fails to schedule, the scheduler releases reserved resources to avoid cluster deadlocks. ⚡ 𝗪𝗵𝘆 𝗩𝗼𝗹𝗰𝗮𝗻𝗼? Volcano extends Kubernetes’ default scheduler to handle batch workloads and multi-pod dependencies. It’s ideal for: → AI/ML workflows (e.g., TensorFlow/PyTorch jobs). → Big Data processing (Spark, Flink). → High-performance computing (HPC). Key features: ✅ PodGroup orchestration: Treats multiple pods as a single schedulable unit. ✅ Fair-share resource allocation: Balances cluster resources across teams. ✅ Preemption/Reclaim: Prioritizes critical workloads without manual intervention. 🌟 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 Imagine training a large language model (LLM) across 3 GPUs. With gang scheduling: → Volcano groups all worker pods into a PodGroup. → The scheduler reserves resources only when all 3 GPUs are available. → If a node fails, Volcano retries or releases resources instantly, avoiding idle clusters. This eliminates "resource hoarding" and ensures cost-efficient scaling for AI teams. #Kubernetes #mlops

Make vs n8n - which one should you pick for your automations? I’ve worked with both tools across multiple projects, here’s a quick comparison: Make is like the Canva of automation. You can build workflows in minutes with a simple drag-and-drop interface. No coding, no complex setups. Perfect if you’re just starting out or want to move fast. But that simplicity comes at a cost. Make charges you for every single action. One complex workflow, and your usage can spike fast. n8n, on the other hand, feels more like working with code, but in a visual way. It’s flexible, powerful, and can run locally, which is a huge win if you’re privacy-conscious or working at scale. You’ll spend more time setting it up, but once it’s running, it’s way more cost-effective. Also worth noting: n8n’s AI Agent is already more stable and customizable. Make’s agent feature is still early-stage and a bit tricky to configure. Here’s how I explain it to clients: - If you want speed and ease → Simple automations → Go with Make - If you want control, power, and scalability → Agentic workflows → Use n8n What’s been your experience so far? Would love to hear which one’s worked better for you.

Your machines and people are draining your margins. The hidden cost eating away your manufacturing profits You have the raw material. You have the machines. You even have the demand. But your production is still delayed. Because your workforce isn’t aligned to your operations. - Skilled technicians are scheduled when no high-skill tasks are running. - Maintenance teams are overworked during peak load. - Project deadlines are missed due to poor shift planning. - Plant downtime increases because human resources are reactive, not predictive. It’s a planning issue. One mid sized FMCG manufacturing unit in Gujarat was losing ₹1.2 Cr/month due to idle labor hours, rework, and unplanned overtime. They ran a 3 month pilot with predictive staffing models: 1) Workforce demand synced with production load 2) Skill mapped scheduling for critical batches 3) 24x7 visibility into shift gaps and role clashes 4) Plant uptime increased by 18% In manufacturing, efficiency comes from planning smarter. If you're running plants without syncing workforce planning to production cycles, you're building inefficiency into your business model. Sooner or later, your margins will show it. #Manufacturing #WorkforceEfficiency #PredictivePlanning

Boost Your SAP PP Skills – Must-Know T-Codes for Production Planning & Control! 1. Master Data – The Production Backbone Manage all key setups like materials, routings, BOMs, and work centers. MM01/MM02/MM03 – Material Master CR01/CR02/CR03 – Work Centers CA01/CA02/CA03 – Routings CS01/CS02/CS03 – BOM CF01/CF02/CF03 – Production Resources/Tools --- 2. Planning (MRP) Ensure material availability and simulate future demand. MD01 – MRP Run for Plant MD02 – Single-Item MRP MD04 – Stock/Requirement List MD05 – MRP List MS01 – Long-Term Planning --- 3. Production Order Management Control and track the lifecycle of production orders. CO01 – Create Order CO02 – Change Order CO03 – Display Order COHV – Mass Order Processing CO26 – Order List for Material --- 4. Execution of Production Orders Track actual manufacturing on the shop floor. MIGO / MB1A – Goods Issue MIGO / MB31 – Goods Receipt CO11N – Confirm Operations CO14 – View Confirmation CO13 – Cancel Confirmation --- 5. Capacity Planning Optimize workload across machines and people. CM01 – Capacity Overview CM05 – Evaluation CM07 – Capacity Requirements CM21 – Capacity Leveling CR10 – Available Capacity --- 6. Shop Floor Control Manage daily production operations seamlessly. CO04 – Print Papers COHV / CO02 – Release Orders CO50 – Schedule Orders CO84 – Order Progress Report --- 7. Reports & Analysis Track, analyze, and optimize production performance. CS15 – Where-used BOM CO27 – Component Overview CO28 – Order Overview MCIS – Work Center Analysis MC$G / MC$H – Info Structure Reports #SAP #SAPPPC #SAPTCode #SAPConsulting #SAPPPTraining #Manufacturing #ProductionPlanning #ERP #DigitalTransformation

Batch jobs in Microsoft Dynamics 365 Finance and Operations (D365FO) are the unsung heroes of enterprise automation. They enable long-running, complex, or repetitive tasks to run in the background without manual intervention, allowing businesses to scale operations, maintain system health, and automate daily processes efficiently. This guide walks you through everything from configuring batch servers and job scheduling to monitoring, troubleshooting, and optimizing performance.     #Microsoft #ERP #D365FO #Dynamics365 #BatchJobs #PerformanceManagement #EverGreen #Maintenance

How can process control improve comminution efficiency?    Comminution accounts for 50-70% of a mine’s total energy consumption—more than any other process in mineral extraction.    If the process isn’t optimized, you’re literally throwing money away. Process control ensures mills and crushers run at peak efficiency, minimizing wasted energy and maximizing throughput.    Better control eliminates bottlenecks, stabilizes the process, and boosts throughput, without expensive new equipment. Just by fine-tuning mill loading, feed rates, and classification efficiency, a well-optimized system can drive a 5-15% increase in production.    What Happens Without Process Control?  Erratic feed sizes and fluctuating mill loads put extra stress on crushers, SAG mills, and ball mills, causing frequent breakdowns, shorter liner life, and rising maintenance costs.  Manual adjustments shift-to-shift, creating unstable recovery rates, fluctuating product sizes, and inefficiencies that ripple downstream.   Overgrinding wastes water, grinding media, and power—without adding value.    Step 1: Measure Everything!  The first step in optimizing comminution is knowing what’s actually happening inside the mill. You can’t control what you don’t measure. The best operations leverage real-time data on:    Mill power draw – Energy use in real time. Throughput rates – Tons per hour, ensuring consistent flow. Particle size distribution – Ensure the product meet its specification/liberation.  Cyclone performance – The right amount to circulating load avoid inefficiencies.   Step 2: Control.  Once you have real-time measurements, the next step is to stabilize the process. Better process control smooths out variability, ensuring predictable performance, higher throughput, and less energy waste. This is where operations shift from firefighting problems to running a system that self-corrects in real time.    Step 3: Optimize.  The biggest gains come when control moves beyond just reducing variation and starts pushing the process to its limits – without tipping into inefficiency. A well-optimized circuit runs leaner, faster, and more cost-effectively, reducing waste and maximizing output.    🔹 56% reduction in performance deviations 🔹 Elimination of operator bias 🔹 Higher operational efficiency and throughput   At its core, process control transforms a reactive operation into a proactive one. But there’s still one missing piece – real-time ore hardness data at the mill feed. With continuous ore characterization, operations can take process control even further, ensuring mills are operating with full knowledge of feed conditions. That’s where Geopyörä makes a difference. 

End-to-End Process in a Manufacturing Plant Using SAP Modules 🌍 In a manufacturing plant, numerous processes come together to create a seamless production flow. SAP integrates all these processes to ensure that every step —from planning to execution—is optimized. 1. 𝗗𝗲𝗺𝗮𝗻𝗱 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 (𝗦𝗔𝗣 𝗣𝗣 - 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴) It all begins with forecasting demand for the finished product. In SAP PP (Production Planning), demand planning helps predict how much product is needed in the future. 💡 Example: A company that manufactures cars uses Material Requirements Planning (MRP) to forecast the number of vehicles they need to produce in the next quarter. T-Code: 𝗠𝗗𝟲𝟭 (Create Planned Independent Requirements) 𝟮. 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 (𝗦𝗔𝗣 𝗠𝗠 - 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁) Once the production demand is clear, the materials required for manufacturing need to be procured. This is where SAP MM comes into play. Purchase requisitions are created, and purchase orders are sent to vendors. 💡 Example: To produce a car, materials like steel, engine parts, and tires need to be ordered from suppliers. T-Code: 𝗠𝗘𝟮𝟭𝗡 (Create Purchase Order) 𝟯. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 (𝗦𝗔𝗣 𝗣𝗣) After procurement, the actual production process begins in the plant. SAP PP handles the creation of production orders, scheduling, and tracking of the production process. 💡 Example: The car production process is broken down into operations like body assembly, engine installation, and painting. SAP PP ensures that every operation is planned and executed in sequence. T-Code: CO01 (Create Production Order) 𝟰. 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (𝗦𝗔𝗣 𝗤𝗠) As products are manufactured, they undergo quality inspections. SAP QM ensures that each product meets quality standards before moving forward. 💡 Example: In the car manufacturing process, after engine installation, a quality check is performed to ensure it’s functioning correctly. This is recorded in SAP QM. T-Code: QA32 (Results Recording for Inspection Lot) 𝟱. 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 (𝗦𝗔𝗣 𝗣𝗠 - 𝗣𝗹𝗮𝗻𝘁 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲) To keep the machinery running smoothly, regular maintenance is critical. SAP PM (Plant Maintenance) manages preventive and corrective maintenance to avoid downtime in the production line. 💡 Example: A conveyor belt in the plant needs regular lubrication. SAP PM schedules this preventive maintenance, ensuring that the production line operates efficiently. T-Code: IW31 (Create Maintenance Order) 𝟲. 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝗮𝗻𝗱 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (𝗦𝗔𝗣 𝗪𝗠/𝗘𝗪𝗠) Once the finished products are ready, they are stored in the warehouse. 𝟳. 𝗦𝗮𝗹𝗲𝘀 𝗮𝗻𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 (𝗦𝗔𝗣 𝗦𝗗) The final step is delivering the finished products to customers. SAP SD (Sales and Distribution) handles customer orders, shipping, and billing. T-Code: VA01 (Create Sales Order) #sap #process

Finding the Right Balance in Cement Mill Operations: Circulation Factor vs. Reject Rate Imagine you're standing in front of a cement mill, its hum a constant reminder of the delicate balance between energy efficiency and cement quality. Behind the scenes, two key factors—circulation factor and reject rate—are shaping your operations. Getting them right can transform your mill’s performance. So, how do you find the sweet spot? 1. Circulation Factor: The Key to Efficiency The circulation factor reflects how much material stays inside the mill versus how much exits. A high factor means more grinding time, improving quality—but at the cost of higher energy use and wear on the equipment. Too low, and the material isn’t ground enough, resulting in poor-quality cement. What you can do: Monitor material flow: Excessive recirculation wastes energy. Adjust mill speed: Slowing it down can reduce energy consumption. Use the right grinding media: A good mix of ball sizes optimizes grinding. 2. Reject Rate: The Hidden Cost The reject rate tells you how much material doesn't meet quality standards and must be discarded or reprocessed. High reject rates often indicate problems with raw materials, grinding, or classification. What you can do: Ensure raw material quality: Consistent quality leads to better cement. Optimize classifier settings: Fine-tune to improve separation and reduce rejects. Balance mill load: An overloaded or underloaded mill increases rejects. 3. Real-Time Adjustments: Stay Agile Adjusting the circulation factor and reject rate isn’t a one-time task. Continuous, real-time adjustments are necessary to keep the mill running at its best. What you can do: Use sensors and monitoring systems: Track everything from material flow to temperature for quick adjustments. Automate settings: Real-time automation of mill speed, load, and classifier settings reduces errors. 4. Energy Efficiency: Small Changes, Big Impact Both factors influence energy consumption. A high circulation factor leads to excessive grinding, while a high reject rate forces more regrinding—both increase energy usage. What you can do: Find the right balance: Optimize circulation factor and reduce reject rates to minimize energy waste. Maintain equipment: Well-maintained machines use less energy. 5. Continuous Improvement: Never Stop Refining Optimizing these factors is an ongoing effort. Equipment wear, changing raw materials, and evolving conditions mean you need to keep monitoring and adjusting. What you can do: Monitor regularly: Keep track of mill performance and adjust quickly. Train operators: Empower your team to make informed adjustments. Adopt new technology: Stay updated on tools that improve performance and reduce energy consumption. finally, how do you manage these factors in your cement mill? What challenges have you faced? #CementProduction #MillOptimization #EnergyEfficiency #SustainableManufacturing #CementQuality #ProcessImprovement

SMED – How to Cut Changeover Time and Boost Efficiency Is changeover time slowing down your production? Every minute spent switching from one task, product, or machine setup is lost productivity. That’s where SMED (Single-Minute Exchange of Die) comes in. SMED is a Lean method used to reduce changeover time—turning lengthy setups into fast, efficient transitions. The goal? Get changeovers down to single-digit minutes (less than 10). ⸻ Why is SMED Important? ✅ Reduces downtime – Faster changeovers mean more production time. ✅ Increases flexibility – Smaller batch sizes and quicker adjustments to demand. ✅ Boosts efficiency – More output with the same resources. ✅ Lowers costs – Reduces inventory, scrap, and excess labor. ⸻ The SMED Process – 3 Key Steps 1️⃣ Separate Internal vs. External Tasks • Internal = Tasks that can only be done when the machine is stopped. • External = Tasks that can be done while the machine is running (e.g., preparing tools, materials). Goal: Convert as many internal tasks as possible into external ones to reduce stoppage time. 2️⃣ Streamline Internal Setup • Use quick-release mechanisms and standardized settings to minimize adjustments. • Keep tools and materials organized and within reach. 3️⃣ Eliminate Waste & Standardize the Process • Remove unnecessary steps. • Use visual guides, checklists, and dedicated setup stations. • Train employees on best practices to ensure consistency. ⸻ Example in Action A manufacturing plant used SMED to reduce a 90-minute machine changeover to 12 minutes by: 🔹 Pre-staging tools and materials before the machine stopped. 🔹 Replacing bolts with quick-clamp fixtures. 🔹 Using standardized settings instead of manual adjustments. The result? More production time, lower costs, and higher output. ⸻ The Power of SMED SMED isn’t just for manufacturing—it applies to any process with setup time, from hospital procedures to office work (think switching between tasks efficiently). Video by Nilson Rodrigues da Silva and Lean Institute Brasil