Batch Production Scheduling Trends

Transforming FMCG Supply Chains In most FMCG businesses, planners are stuck in a loop: 1. Forecasts drive MRP 2. MRP drives planned orders 3. Planners schedule & expedite 4. Factory fights fires The problem? 80% of item-level forecasts are 40%+ wrong. Even with great mix accuracy, MRP recommendations still drive chaos. Expediting becomes a full-time job. And despite all that effort—stockouts still happen. Let’s flip the model. What if you stopped scheduling... and started sequencing? Instead of chasing every forecast fluctuation, you: ✅ Create a fixed, efficient production sequence (like a playlist) ✅ Replenish based on actual demand (not forecast noise) ✅ Adjust using aggregate forecasts, not item-level guesswork How It Works: Demand-Driven Sequencing 🔹 Step 1: Choose Your Stock Points Manage inventory at Raw Materials, WIP, Finished Goods 🔹 Step 2: Set Reorder Points (ROP) Formula: (Avg Daily Demand × Lead Time) + Safety Stock Example: Daily Demand = 100 units Lead Time = 5 days Safety Stock = 300 ROP = 800 units 🔹 Step 3: Define Target Stock Formula: ROP + MOQ/Cycle Batch If MOQ = 500 → Target = 1300 units 🔹 Step 4: Build Your Sequence Example: Mon – Cola Tue – Juice Wed – Lemonade Thu – Tonic Fri – Maintenance/Buffer 🔹 Step 5: Only Replenish When Needed If stock > ROP → Skip If under → Produce to Target 🔹 Step 6: Adjust the Sequence Based on Aggregate Demand Smooth factory flow No last-minute scrambling Reduced inventory (~40%) Real-World Example (Laundry Factory) Before: Schedule changed daily Frequent changeovers (Powder → Liquid) Expediting every week Stockouts still happened After: Fixed weekly sequence Only replenish when below ROP Fewer changeovers Planners focus on CI, not firefighting The Shift in Planner Role 🧠 From: Scheduling, expediting, reacting 🚀 To: Designing sequences Tuning inventory targets Managing capacity proactively The Results ✅ 40% inventory reduction ✅ Higher service levels ✅ More predictable production ✅ Factory OEE improves ✅ Planners focus on strategy, not survival Ready to make the shift? Sequence, don’t schedule. Empower your factory. Free your planners. Serve your customers better. #SupplyChain #FMCG #DemandDriven #SupplyPlanning #InventoryOptimization #ManufacturingExcellence #SOP #SCM #Planners #LeanManufacturing #ContinuousImprovement #ProductionPlanning #MRP #LinkedInLearning #OperationalExcellence #ERP #FactoryFlow #NoMoreExpediting #OEE #SequencingNotScheduling --- Let me know if you'd like this turned into a carousel post, PDF one-pager, or an Excel calculator to support it!

What happens when you aim industrial AI at production scheduling but treat it like every other engineering problem? We built a multi-agent AI system that achieved a 21% increase in profit. Here’s how: 1. Make the goals explicit Production scheduling is a complex process with numerous trade-offs. Highest demand or most efficient run? Overtime or on-time delivery? We spelled out the real goals and KPIs so the agent system knew exactly which knot it had to untangle. 2. Capture expertise through machine teaching Machine teaching breaks the job into bite-size skills. An engineer shows the system why a decision works, not just what happened in the data. Rather than rely purely on data, machine teaching transfers deep human expertise into the system - digitizing decades of experience and knowledge, crucial as expert operators retire. 3. Structuring the Multi-Agent System The multi-agent system was designed to mimic human decision-making: Sensors: Gather real-time data on production status, resources, and external market conditions. Skills: Modular units responsible for specific actions, such as forecasting demand, optimizing scheduling, or adapting to sudden changes. Each skill can evolve on its own, giving the plant the same modular flexibility you expect from any well-engineered system. 4. Establishing a Performance Benchmark Good engineering demands clear benchmarks. We ran a standard optimization-based system as our baseline. This allowed us to objectively measure whether our AI agents delivered measurable improvements. 5. Rigorous Testing & Iteration Engineering thrives on iteration. We created and tested 13 agent system designs, continuously iterating based on performance data. Each iteration leveraged insights from the previous, systematically improving performance until we identified the optimal solution. --- By treating AI as an engineered system (modular, explainable, and configurable) it demonstrates significant potential results: ✅ 21% higher profit margins ✅ Improved adaptability to rapidly changing market conditions ✅ Preservation and amplification of valuable human expertise Full breakdown of the build and tests is below.👇 #ProductionScheduling #IndustrialAI #MachineTeaching #SmartManufacturing