Adaptive Capacity Planning

What-If Scenario Planning: Strengthening Your Capacity Plan for the Unexpected In my recent post, I shared a full capacity planning outline. One of the most impactful ways to strengthen that plan is through scenario planning. Even the best capacity plan can collapse if you are not prepared for surprises. In workforce management (WFM), scenario planning helps your team stay resilient when the unexpected happens. Here is a practical outline for structuring what-if scenario planning in Excel: 1. Inputs sheet (core assumptions) Include forecasted volumes (baseline, optimistic, pessimistic), AHT assumptions (accounting for changes over time), concurrency assumptions if needed, shrinkage assumptions, handling or processing approaches that may affect staffing, hiring and attrition plans, productivity rates, and overtime or buffer assumptions. 2. Workload calculation sheet (per scenario) Calculate workload (volume × AHT ÷ 3600). Adjust for concurrency if needed. Determine required FTE using your preferred method, such as Erlang C for queue-based work, or simpler conversions for non-real-time tasks. Roll up interval requirements into daily or weekly needs. 3. Shrinkage adjustment sheet Apply shrinkage to determine net FTE need. List each shrinkage component such as PTO, sick leave, training, meetings, absenteeism, and downtime. Coaching, meetings, and training can also be levers to reclaim time for customer interactions. Show impact per scenario. 4. Capacity supply sheet Include starting headcount, planned hiring, and attrition. Build headcount over time. Show available FTE after shrinkage for each scenario. 5. Scenario comparison sheet Compare each scenario side by side. Show net FTE, available capacity, staffing gaps, and service level risk. Use color coding to highlight gaps. 6. Actions and mitigation sheet List potential actions like accelerated hiring, overtime, cross-training, or flexible staffing. Include adjustments to planned shrinkage activities that free up capacity. Show estimated staffing impact and costs. 7. Visual dashboards sheet Add summary charts like headcount vs. requirement, FTE surplus or deficit, and shrinkage breakdowns. Include simple heatmaps for interval or weekly risks. 8. Reconciliation and tracking sheet (optional) Compare actuals to modeled scenarios, track accuracy, and document notes for leadership. Additional tips Use drop-downs or scenario selectors to adjust assumptions. Apply conditional formatting to flag gaps. Add an executive summary section to each sheet. Scenario planning strengthens your capacity plan and builds stakeholder trust. It shows you are not just planning for what is likely, but prepared for what might happen. What unexpected scenarios have tested your plans, and how did your team respond? I would love to hear your stories and insights. #WorkforceManagement #CapacityPlanning #ScenarioPlanning #ContactCenter #OperationalExcellence

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

Imagine scaling from 50 to 500 servers in real time - then scaling back down by 3PM. No guesswork. No overprovisioning. Just real-time elasticity, driven by live workloads. That’s not just “cloud-native.” That’s convergence-native. The problem today? Most IT teams prepare for peak workloads the old-fashioned way: - Provision excess capacity based on last year’s spike. - Hope it’s enough. - Pay for the overage - whether you need it or not. - Deal with bottlenecks, downtime, or cost overruns if you guessed wrong. Black Friday. Product launches. Global sales events. Moments like these make or break systems—and reputations. But what if your infrastructure could see the surge coming—and scale in advance? What if it could shift resources between regions, balance latency, and obey compliance rules while the traffic was building? That’s what cloud convergence makes possible. Here’s what that looks like in practice: 1. Predictive scaling triggered by real-time signals AI observes usage patterns, detects anomalies, and forecasts demand before it hits critical mass. 2. Elastic provisioning across cloud providers Resources are added in AWS, Azure, or GCP—not based on preference, but based on real-time cost, availability, or proximity to users. 3. Intelligent scale-in after peak subsides Once the rush ends, the infrastructure shrinks automatically—no excess spend, no downtime, no manual intervention. This isn’t just automation. It’s adaptive orchestration at the workload level - driven by live data, not fixed rules. Because infrastructure that can scale up is table stakes. What matters is infrastructure that knows when to scale, where, and how much - in the moment. That’s the level of intelligence we’re building into Verge. And that’s why cloud convergence isn’t just architecture - it’s competitive advantage.

Pairing Velocity and Capacity Planning in Scrum Velocity is a common metric for sprint planning in Scrum. Teams typically use the average story points completed over the last several sprints to forecast future work. Let's set aside the "flaw of averages" (read my earlier post on using confidence intervals instead) and assume teams reading this post just use their average velocity. Relying solely on velocity can cause overcommitment when sprint durations fluctuate or team capacity changes. That's why capacity planning can complement velocity to improve planning accuracy. Pairing velocity with capacity planning creates a realistic, adaptable approach to sprint planning. Let's talk about why - and how - it works. Velocity Velocity measures the work a team delivers in a sprint, expressed in story points. It would be common for a team averaging 20 points to use that as a benchmark for future sprints. The risk is that velocity doesn’t adjust for sprint-specific factors like holidays or planned absences. That can lead to unrealistic commitments. Capacity (Availability) Capacity planning evaluates actual team availability for a specific sprint. It considers sprint length (e.g., 9 workdays instead of 10), planned absences (vacations, holidays, etc.), and working hours per developer. Team availability is converted into "developer-days." For example, a 5-person team working 8 hours daily for 10 days has 400 available hours max. Shorter sprints and absences reduce this capacity. Why Combine Velocity and Capacity? Realistic Commitments Velocity provides a stable benchmark, but capacity planning adjusts for unique sprint conditions. For example, if a team’s velocity is 20 points for a 10-day sprint, a 9-day sprint might lower this target by 10% to 18 points. Balanced Workloads Using velocity alone risks overcommitment. Using capacity alone risks underutilization. Combining them mitigates these risks and helps make commitments achievable. Adapting to Change Velocity anchors plans in proven performance (empiricism), but capacity planning accounts for variability (e.g., holidays, absences, onboarding, etc.). How to Pair Velocity with Capacity 1) Start with historical velocity as a baseline. 2) Calculate available developer-days, adjusting for holidays or absences within each sprint within the forecast timebox. 3) Scale back velocity to match capacity (e.g., if capacity is 90% of normal, reduce the velocity target by 10%). Benefits of Pairing Predictability: Commitments align with capacity for consistent delivery. Transparency: Stakeholders gain visibility into achievable goals. Flexibility: Teams adapt to sprint variations without risking outcomes. Deliver Predictably in Dynamic Conditions Velocity is a valuable metric but it doesn’t account for short-term variability. If calculating confidence intervals feels too complicated, then use capacity planning to fill the gap - creating a planning process that’s both empirical and adaptable.

Really excited to share that the U.S. Department of the Interior's new Climate Adaptation Plan, for 2024-2027, is now out! From the Plan: "The Plan builds on the Department’s 2021 Climate Action Plan by quantifying, at a high level, exposure to climate hazards—including extreme heat, extreme precipitation, flooding, wildfire, and sea level rise—that can affect the Department’s ability to meet its mission in the coming years. The impact of the projections is significant—nearly every building and employee will face hotter temperatures and more extreme precipitation events. Sea level rise will affect hundreds of Interior-managed sites, from national parks and wildlife refuges to historic sites. Uncharacteristically severe wildfire already affects millions of acres of lands managed by the Department. In addition, other climate change-influenced drivers of change, such as drought and invasive species, will also affect the natural and cultural resources the Department stewards in the years to come. ... This plan outlines steps for the Department to take through 2027, organized under three overarching themes, that will strengthen its adaptive capacity and resilience: • Understand and assess current and future impacts of climate change on Department assets, mission, operations, and services. This includes improving understanding of key vulnerabilities, pursuing research on climate hazards and stressors, and integrating findings into decision support tools and enterprise-wide planning. • Prioritize and scale adaptation and resilience efforts. This includes implementation of new Department policies, targeted investments in conservation and resilience, wider adoption of NBS, and enhancement of equitable funding opportunities for communities and partners to adapt to climate change. • Build capacity for adaptation within the Department’s workforce and through partnerships. This includes developing new guidance, training, and performance expectations for the Department’s workforce, and continued meaningful engagement and collaboration with communities, including American Indians, Alaska Natives, Native Hawaiians, and affiliated island communities." https://lnkd.in/eAgpiWcY

Is it possible to plan effectively in a world where market trends and workforce dynamics are in constant flux? Workday Adaptive Planning, latest innovations suggest a resounding yes, offering a beacon of clarity in the fog of economic and technological change. For businesses navigating the unpredictable currents of global markets and the tech revolution, staying afloat requires agile decision-making tools. Workday Adaptive Planning now boasts AI enhancements designed to simplify the complex choreography of finance and HR planning. The software's generative AI allows planners to test countless scenarios effortlessly, ensuring strategies are robust before they hit the ground. With machine learning at the helm, predictive forecasting becomes a breeze, offering a user-friendly way to anticipate demand. At the heart of these advancements is the Elastic Hypercube Technology, equipped with AI to meet intricate multi-dimensional planning needs. It's not just about the numbers; the workforce planning tool ensures you have the right people on the right projects, optimizing headcount to meet future business needs. By centralizing financial and workforce data, Workday promises not just insight but efficiency—potentially reclaiming thousands of hours for global enterprises. The Human Capital Management interface has been retooled for simplicity, seamlessly integrating updates across systems. When hiring plans need to pivot, the automated headcount reconciliation process offers real-time cost analyses, empowering leaders with the foresight needed for strategic cost management. Workday's operational planning isn't just reactive; it's predictive, fostering collaboration and adaptability across all business facets. The result? An organization that's as dynamic as the market it thrives in. The confidence in Workday's solutions is reflected in its steady customer growth and financial performance, outpacing industry averages. Behind the scenes, the company's commitment to integrating advanced AI and ML capabilities points to a future rich with innovation and efficiency. In just a year, Workday has seen its stock soar by nearly 50%, a testament to the market's belief in its vision. As they continue to harness AI for growth, the path ahead looks promising. Interested in how Workday's solutions can streamline your planning processes? Connect with me and the eCapital Advisors team, and let's turn your finance and HR planning into your strategic advantage. 🔽 🔽 🔽 👋 Hi, I'm Lisa. Thanks for checking out my Post!   Here is what you can do next ⬇️   ➕ Follow me for more FP&A insights    🔔 Hit the bell on my profile to be notified when I post   💬 Share your ideas or insights in the comments ♻ Inform others in your network via a Share or Repost #digitaltransformation #finance #business #technology #cfo