Capacity Planning Technologies

I have spent nearly twenty years building energy system models. Continental-scale at granular spatial scales. Hourly (or finer) temporal resolution. Co-optimising generation, storage, transmission, distributed energy resources (DERs), and demand simultaneously. Thousands of scenarios. I have published in Nature Climate Change, Science and PNAS. My work has over 4,300 academic citations. Here is what I have learned: the tools most organisations still use to plan energy systems are not fit for the decisions ahead. Most capacity expansion models optimise generation only. They bolt on storage as an afterthought. They treat the transmission network as a copper plate or a simplified transport model. They run on annual energy balances, missing the hourly dynamics that determine whether the system actually works. They assume stable, predictable fuel prices. The last four weeks have demonstrated why every one of those assumptions is dangerous. When gas was £30/MWh, a model that ignored fuel price volatility produced a plausible answer. At £67/MWh and rising, with Ras Laffan physically destroyed, with the BoE pricing rate hikes instead of cuts, with the Ofgem cap headed for £2,000+, the same model produces an answer that could lead to billions in misallocated capital. What we actually need: models that co-optimise across the whole system (generation, storage, transmission, DERs, demand) at nodal or zonal resolution with sub-hourly dispatch, weather-synchronised across wind, solar, and demand, with stochastic fuel prices that reflect the world we actually live in. Where you build matters as much as what you build. A wind farm in northern Scotland connected to a constrained transmission corridor produces curtailed energy and consumer costs. The same wind farm sited where the grid has capacity produces revenue and system value. The UK is making decisions right now about grid investment, generation siting, storage deployment, and demand connections that will lock in infrastructure for decades. The grid queue reform, the Clean Power 2030 target, the SSEP, the data centre surge, the Hormuz shock. These are not separate problems. They are one system. The planning tools need to catch up with the reality. #EnergyModelling #EnergyTransition #UKEnergy #PowerSystems #CleanEnergy #RenewableEnergy #GridReform #EnergyPolicy #NetZero #EnergyStorage #CapacityExpansion #SystemPlanning

🔧 Exploring Capacity Planning in SAP Plant Maintenance (PM) 🚀 Ever faced a situation where planned maintenance work exceeds available resources,leading to backlogs and delays? Or worse, inefficient scheduling that results in idle technicians and wasted capacity? That’s where Capacity Planning in SAP PM comes in! 👉 What is Capacity Planning in SAP PM? Capacity planning ensures that maintenance work is planned realistically by aligning the required work hours with the available workforce and machine capacity at a Work Center. 🔍 Key Aspects of Capacity Planning in SAP PM ✅ Work Centers as Capacity Holders ▶️ In SAP PM, maintenance activities are assigned to work centers, representing maintenance teams, workshops, or machines. ▶️ Work centers hold capacity data (e.g., number of technicians, available work hours, shift schedules). ✅ Standard Value & Formula in Task Lists/Orders 👉 Every operation in a maintenance order (IW31/IW32) or task list (IA01/IA02) contains: 📌 Work center – Defines available capacity 📌 Activity Type – Links to cost rates for labor 📌 Standard Values – Defines execution time for an operation 📌 Formula – Calculates required capacity (work = duration × number of people) ✅ Capacity Load Analysis & Leveling SAP provides tools to analyze and adjust workloads: 📌 CM01 (Work Center Load Report) – Shows available vs. required capacity. 📌 CM21 (Capacity Leveling) – Helps reschedule orders to balance workloads. ✅ Integration with Preventive Maintenance (PM Plans) IP30 (Deadline Monitoring) generates maintenance orders based on schedules. Without capacity checks, workloads may exceed availability, causing scheduling conflicts. 🛠️ Managing Capacity in SAP PM – Step by Step 1️⃣ Define Work Centers & Capacities Use CR01/CR02 to set available hours, shifts, and technicians. 2️⃣ Assign Work Centers in Task Lists & Orders ▶️ Standard values & formulas in task lists (IA01) ensure accurate workload estimation. ▶️ When creating work orders (IW31), SAP calculates required capacity. 3️⃣ Monitor Work Center Loads ▶️ Use CM01 to check if maintenance teams are overloaded or underutilized. ▶️ Identify potential scheduling issues before execution. 4️⃣ Level Capacity (CM21) ▶️ Reschedule overloaded orders by adjusting start dates or shifting work. ▶️ Use dispatching functions to prioritize urgent tasks. 5️⃣ Optimize Preventive & Breakdown Workload ▶️ Ensure preventive maintenance orders align with available resources. ▶️ Adjust unplanned (corrective) work orders without overloading technicians. 🚀 Why Capacity Planning Matters? ✅ Prevents last-minute scheduling conflicts ✅ Optimizes workforce utilization & efficiency ✅ Reduces work order backlogs & delays ✅ Ensures smooth execution of preventive & corrective maintenance 👉 Pro Tip: Always review capacity before releasing large maintenance orders to avoid unexpected bottlenecks! How does your team handle maintenance capacity planning? Let’s discuss in the comments! 👇 #SAPPM #PM

A hospital deployed a brand-new Wi-Fi network. 150 APs. Solid coverage everywhere. The design looked textbook. Then 2,000 medical devices, 800 staff phones, 500 laptops, and guest devices all came online. Average of 45 devices per AP in clinical areas, many running real-time and critical applications. Infusion pumps started losing connectivity. Nurses couldn’t access patient records. Voice badges stopped working. The coverage was perfect. The capacity was never planned for. Capacity planning means answering these questions BEFORE you deploy: -> How many devices will connect to each AP at peak? -> What applications are they running, and what throughput/airtime does each need? -> What happens when a single AP goes down or becomes overloaded, and its clients roam to neighbours? Because Wi-Fi is a shared medium, every additional client increases contention and reduces airtime available per device. Here’s a rough rule: once you exceed 25–30 clients on a single AP running mixed applications, performance often starts to degrade noticeably. For voice and real-time, keep it under 15. Coverage gets the attention. Capacity wins or loses the network. Are you calculating device density per AP in your designs?

The Fab Whisperer: Capacity Planning - From Spreadsheets to Self-Learning Models. Last week we looked at the widening gap between silicon demand and fab capacity — the classic setup for another boom-and-bust cycle. Imbalance is inherent in the market. We try to balance it in the way we plan capacity. For an industry that spends hundreds of billions on CAPEX, capacity planning should be science. Yet it often I see frozen spreadsheets, heroic assumptions, and “best-guess” throughput models that quietly drift from reality. Are we building fabs based on models that no longer represent how fabs actually run? Using the wrong model for the wrong purpose? CAPEX Planning ≠ Fab Daily Operations Planning Capacity — deciding what, when, and where to build. Running Capacity — managing flow, bottlenecks, and daily WIP. CAPEX models are strategic: they test economics, demand scenarios, and sensitivity to capacity detractors. Operational models are tactical: they simulate variability, queueing, and dispatch logic. When fabs try to use the same model for both, they end up with bad investments and bad daily decisions. It’s like using a telescope to check your pulse. Most Common Methods of How We Plan Capacity 1. Static Models (Spreadsheet Economics) Quick and transparent — perfect for early CAPEX justifications. But fixed throughput and yield assumptions age fast. Once products, recipes, or WPH shift, the model collapses. 2. Dynamic Simulations (Discrete-Event or Digital Twins based) Capture queues, PM downtime, and rework loops — essential for operational decision-making. Great for optimizing how to run a fab, not what to build next. Powerful but maintenance-heavy; too often abandoned after the big study. The Next Frontier Not mainstream yet but they point to the future: AI-Driven and Hybrid Models. These models will learn from real time fab data, adapt to product mix, and continuously recalibrate effective capacity. They will bridge the gap between planning and operations — a single living model that never goes stale. The barrier isn’t technology — it’s data discipline and trust. The Real Challenge The biggest risk isn’t model complexity — it’s model decay. Assumptions age. Routings evolve. PM cycles shift. By the time the next CAPEX round starts, you’re planning the future based on a fab that no longer exists. What can we do meanwhile Match the model type to the decision horizon. CAPEX → financial sensitivity and long-term. Operations → flow dynamics, variability control, short term. Treat models as living systems, not one-off projects. Assign ownership for keeping assumptions, routings, and rates current. Benchmark quarterly — compare modeled vs. actual effective capacity. Start building the bridge: integrate AI and fab data into planning cycles today. Are your capacity models describing reality — or nostalgia? #TheFabWhisperer #Semiconductor #FabOperations #CapacityPlanning #DigitalTwin #AI #ManufacturingExcellence #FabModeling

Ever hit a scale-out failure even when your quota looked fine? It happens more often than people realize. Because in Azure, quota ≠ capacity. I’ve just published a new article on the Microsoft Tech Community breaking down how to plan for real, physical capacity, not just the soft limits shown in the portal. 👉 It walks through practical strategies using: - Quotas (to detect hidden constraints early) - Capacity Reservations (ODCR) (to lock in baseline compute) - VMSS Instance Mix (to stay flexible during scale-outs) - Compute Fleet (to orchestrate availability across SKUs and zones) This approach was shaped by working with digital natives and AI workloads that needed to scale instantly, but reliably, across tight regions. If you’ve ever hit “SkuNotAvailable,” this guide might save you hours of troubleshooting. 🔗 Read it here: https://lnkd.in/e-KH3gmQ #Azure #CloudEngineering #CapacityPlanning #AKS #DigitalNatives #Microsoft #CloudArchitecture

⚙️ Planning without constraints is just theory. Advanced Planning & Scheduling (APS) brings planning closer to real-world execution. Traditional systems often assume unlimited capacity — but factories, machines, labor, and transport all have limits. APS is designed to plan with reality in mind. Here’s what APS really does 👇 🧠 What is APS? A constraint-based optimization and simulation system that enables real-time, finite-capacity planning and decision support across the supply chain. 🔍 Core Capabilities ⚖️ Optimization Balances cost, service levels, capacity, inventory, and profitability. 🔮 Simulation (What-If Analysis) Tests scenarios like demand spikes, supplier delays, or capacity reductions before decisions are made. 🏭 Finite Capacity Scheduling Plans using actual machine, labor, and shift constraints. 🧩 Multi-Constraint Planning Simultaneously considers materials, lead times, transport, and operational rules. 📊 APS vs Traditional MRP MRP → Assumes infinite capacity & reacts after issues occur APS → Considers real constraints & enables proactive decisions 🧠 Simple Memory Framework MRP plans materials → ERP executes transactions → APS optimizes decisions Modern supply chains don’t just plan orders — they simulate outcomes before execution. Is your planning system reactive or predictive? #SupplyChain #APS #ProductionPlanning #SupplyChainPlanning #MRP #ERP #Manufacturing #OperationsManagement #DigitalSupplyChain #Industry40 #DecisionSupport #SupplyChainManagement

Overview of Capacity Planning SAP Capacity Planning ensures that production resources, such as machines and labor, are effectively utilized to meet production demands. It involves calculating the available capacity, analyzing the required capacity, and aligning them to optimize production efficiency. Key Components of Capacity Planning 1. Work Centers: • Work centers are organizational units where production operations occur. They have defined capacities based on factors like machine availability, operating hours, and workforce. • Each work center can handle specific tasks or operations, defined by the routing of the products. 2. Routings: • Routings describe the sequence of operations needed to manufacture a product. They include details such as operation times, work centers involved, and setup times. • Accurate routings are crucial for precise capacity planning. 3. Capacity Requirements Planning (CRP): • CRP calculates the load on each work center by assessing the planned and production orders against available capacity. • It helps identify whether the current resources can meet the production schedule or if adjustments are needed. 4. Capacity Evaluation: • Capacity evaluation provides tools to compare the load with available capacity. • It highlights potential bottlenecks or periods of underutilization, allowing planners to take corrective actions. 5. Capacity Leveling: • Capacity leveling involves adjusting production schedules to balance the load across work centers. • This process can include shifting production orders, extending work hours, or reallocating resources to ensure smooth operations. Methods of Capacity Planning 1. Finite Capacity Planning: • Takes actual capacity constraints into account, ensuring that work centers are not overloaded beyond their capacity. • Useful for detailed scheduling and ensuring realistic production plans. 2. Infinite Capacity Planning: • Assumes unlimited capacity, providing a rough-cut plan to highlight potential capacity issues. • Useful for initial planning stages and strategic decision-making. Metrics and Analysis 1. Capacity Utilization: • Measures the efficiency of resource usage. High utilization indicates optimal use, while low utilization may suggest inefficiencies or potential improvements. 2. Bottleneck Analysis: • Identifies work centers that are likely to be overloaded, helping prioritize resource adjustments or schedule changes. 3. What-If Scenarios: • Allows planners to simulate different scenarios, such as changes in demand or resource availability, to understand their impact on capacity.

Master planning in Dynamics 365 Finance & Operations (D365 F&O) is a crucial feature that helps organizations optimize their supply chain processes by generating detailed plans for production, procurement, and inventory management. Fine-tuning master planning in D365 F&O involves adjusting various parameters and settings to align the planning process with the specific needs and goals of the business. Here's a summary of key aspects of fine-tuning master planning in D365 F&O: - Parameters and Settings: Fine-tuning involves adjusting parameters such as safety stock levels, lead times, lot-sizing rules, and coverage groups to ensure that the master plan reflects the organization's operational strategies and constraints. - Forecast Models: Accurate demand forecasting is critical for effective master planning. Fine-tuning may involve selecting and configuring the appropriate forecast models, considering factors such as seasonality, trends, and historical data. - Item Coverage: Setting up item coverage correctly is essential for determining how individual items are planned. This includes defining reorder points, minimum and maximum inventory levels, and order quantities for each item. - Planning Algorithms: D365 F&O offers different planning algorithms, such as regenerative and net change planning. Fine-tuning involves choosing the right algorithm based on the frequency of planning runs and the level of detail required. - Capacity Planning: Fine-tuning also encompasses capacity planning settings, which ensure that the master plan is feasible in terms of production capacity, labor, and machine availability. - Supply Chain Constraints: The master planning process should consider supply chain constraints, such as supplier lead times, transportation times, and warehouse capacities, to create realistic and achievable plans. - Performance Optimization: Master planning can be resource-intensive. Fine-tuning involves optimizing performance by adjusting batch job settings, reducing the scope of planning runs, and using appropriate filters to focus on critical areas. - Continuous Monitoring and Adjustment: Fine-tuning master planning is an ongoing process. Regularly reviewing and adjusting the settings and parameters based on changing business conditions and performance

PP tables 4. Production Orders Production orders are crucial for tracking the manufacturing process. The main tables include: AUFK: Stores production order headers. AFIH: Contains maintenance order header data. AUFM: Tracks goods movement related to production orders. AFKO: Holds order header data for PP orders. AFPO: Stores production order item data. RESB: Manages order components. AFVC: Stores information about production order operations. AFVV: Holds data about quantities, dates, and values for operations. AFVU: Stores custom user fields related to operations. AFFL: Holds information about work order sequences. AFFH: PRT (Production Resource/Tool) assignment data for production orders. JSTO: Manages the status profile for orders. JEST: Tracks the object status. AFRU: Stores order completion confirmations. 5. Production Resources/Tools (PRT) PRTs are tools or resources used in production orders. The related tables include: AFFH: Manages the assignment of PRTs to work orders. CRVD_A: Links PRTs with documents. DRAW: Document info record, storing metadata of the PRT-related documents. TDWA: Stores different types of documents. TDWD: Represents data carriers or network nodes. TDWE: Stores the type of data carrier used. 6. Planned Orders Planned orders are future production orders. The table: PLAF: Holds planned order data, containing information about quantities, dates, and materials. 7. Kanban Kanban is a method for managing inventory and production flow. The important tables include: PKPS: Stores identification and control cycle data for Kanban containers. PKHD: Holds Kanban control cycle header data. PKER: Stores error logs for Kanban containers. 8. Reservations Reservations ensure that materials are available for production. The key tables are: RESB: Stores reservation details for materials. RKPF: Manages reservation header data. 9. Capacity Planning Capacity planning ensures that production has the necessary resources available. Important tables are: KBKO: Stores the header record for capacity requirements. KBED: Manages individual capacity requirements. KBEZ: Stores additional data for KBED, focusing on individual capacities or splits. 10. Planned Independent Requirements This part deals with independent demand forecasting. The tables include: PBIM: Manages independent requirements for materials. PBED: Stores data for independent requirements. PBHI: Keeps a history of independent requirements. PBIV: Maintains an index of independent requirements. PBIC: Stores independent requirement indexes for customer-specific requirements. These tables cover the different areas of production planning, routing, bill of materials, capacity planning, and other processes involved in ensuring smooth production operations in SAP PP. Each category plays a vital role in automating and optimizing manufacturing processes

What if you could predict capacity issues before they happen? One of my Linkedin connection DM'ed me asking to talk about impact of PMTS on Capacity planning. Predetermined Motion Time Systems (PMTS) can enhance capacity planning for any manufacturing setup, enabling you to avoid delays, overtime, and delivery issues. Here’s how PMTS drives proactive capacity planning and operational efficiency: 1) Precision in Time Calculation – PMTS allows you to accurately calculate time standards for tasks before production begins, creating a solid foundation for effective capacity planning. With this reliability: - You have a clear baseline for estimating production times. - Managers can forecast task durations with confidence. - Production schedules can be created with accuracy and trust. 2) Early Capacity Planning – PMTS empowers proactive capacity planning, often well before the sampling or initial production stages. The benefits are significant: - Production capacity can be estimated earlier in the development cycle. - Bottlenecks are spotted early and managed proactively. - Resources are allocated more effectively from the start. 3) Refined Production Scheduling – With PMTS, production scheduling is no longer a guessing game: - Line balancing becomes easier with accurate time data. - Workloads are evenly distributed across production lines. - Daily targets are realistic, setting up teams for success. 4) Reducing Overtime and Delivery Delays – By streamlining capacity planning, PMTS cuts down on overtime and reduces risks of late deliveries: - Reliable schedules reduce the chance of overbooking production. - Last-minute rush orders are minimized. - Potential delays are flagged early, allowing for timely mitigation. 5) Optimized Resource Allocation – PMTS gives clarity in resource planning and allocation: - Managers can accurately determine the optimal workforce for each task. - Equipment and machinery requirements are forecasted with precision. - Cross-training needs are identified to add flexibility and resilience. 6) Foundation for Continuous Improvement – PMTS doesn’t just set standards; it creates a launchpad for ongoing improvement: - Time standards are reviewed and updated regularly. - Inefficiencies in methods are pinpointed and resolved. - New production techniques are evaluated for time-saving potential. 7) Enhanced Decision-Making – PMTS provides data-driven insights, enabling strategic decisions with confidence: - Accurate costing improves pricing and profitability analysis. - Make-or-buy decisions are made with reliable data. - Capacity expansion needs are identified long before they become urgent. Leveraging PMTS for capacity planning allows manufacturers to set realistic production schedules, maximize resource use, and drastically cut overtime and delivery risks. This proactive approach boosts efficiency and ensures customer satisfaction with dependable delivery performance. - Insightful? ♻️ Repost and empower your network!