Tactical Capacity Planning Spreadsheet Model

To bridge the gap between long-range corporate forecasts and plant-level production realities, I developed an Excel-based model integrating demand, mold and machine constraints, and logistics, enhancing operational efficiency. This tool transformed 18-month forecasts into actionable, constraint-aware plans and provided a dynamic interface for users to visualize various scenarios. By using visual waterfall charts and scenario toggles, the model enabled cross-functional teams to explore production strategies and assess their impact on output and resource allocation, leading to improved performance and profitability.

Situation

A global manufacturing company operating three major facilities was experiencing severe disconnects between strategic demand forecasts and what was operationally feasible on the plant floor. While corporate supply chain teams generated 18-month projections, local plant planners were left scrambling to reconcile these forecasts against constraints like mold availability, machine schedules, and regional logistics.

As a result, production plans often collapsed within the frozen 4-week window—leading to last-minute escalations, idle resources, and missed customer shipments. There was no standardized way to visualize long-term feasibility at the plant level, and no clear method for aligning strategic aspirations with capacity realities.

Task

I was tasked with building a dynamic, tactical planning tool that would:

Bridge the gap between corporate strategy and shop-floor execution
Translate long-range forecasts into plant-level production feasibility
Incorporate mold, machine, and logistic constraints across regions
Enable cross-functional collaboration across planning, engineering, and logistics teams
Serve as a living, scenario-driven model that could adapt to forecast changes in real-time

The ultimate goal: transform tactical planning from reactive guesswork into proactive, constraint-aware decision-making—all within a tool accessible to cross-functional teams.

Action

To build a truly tactical planning solution, I first had to uncover and consolidate scattered operational inputs buried across departments. Nothing about the data or processes was centralized; so, I initiated direct engagements with every key stakeholder: mold engineers, machine planners, S&OP leads, schedulers, and logistics teams. I joined monthly S&OP calls, conducted one-on-one interviews, and even partnered with the mold manager to extract messy, hard-to-read capacity and tool utilization data.

Once the landscape was mapped, I generated an Excel-based planning model that pulls in corporate forecasts, machine availability, mold readiness, logistics constraints, and historical attainment metrics. Using waterfall logic, I visualized how forecasted demand flowed through each constraint layer: mold availability, machine hours, regional capacity, and dispatch feasibility. This clarified where demand was being lost and where we could unlock capacity.

To simulate planning flexibility, I introduced adjustable toggles allowing planners to test the impact of second shifts, overtime, alternate molds, and rerouted fulfillment. These scenarios instantly recalculated fulfillment feasibility, offering real-time insights for best and worst case. The model also incorporated a constraint-based fulfillment algorithm to quantify the “demand lost” when plant bottlenecks couldn’t meet forecast targets.

To drive continuous improvement, I layered in historical benchmarking tools that allowed users to compare actual attainment against prior forecast periods—exposing trends like under-commitment or last-minute volume inflation. Finally, I enabled cross-functional rollout by training plant schedulers, industrial engineers, and logistics leads on how to use the model.

Results

This model reshaped the way the organization approached planning transforming it from top-down directives to bottom-up feasibility-informed strategy. Notable outcomes included:

Strategic Realignment Across Corporate and Plant Levels
Provided corporate forecasting with a reality-based lens into plant-level feasibility, improving planning accuracy and reducing forecast blind spots. This resulted in fewer overpromises to customers and better internal alignment between supply and demand signals.
Proactive Decision-Making Months in Advance
Enabled planners to simulate capacity constraints and staffing options as early as 3–6 months ahead of execution. This foresight allowed mold managers and site leads to reallocate resources (e.g., initiate second shifts, rebalance mold availability) early.
Improved Machine Utilization and Reduced Downtime
Bottleneck visualization directly contributed to better mold scheduling and machine uptime, minimizing idle time and enabling production to run closer to planned capacity thresholds.
Strengthened Cross-Functional Trust and Collaboration
The model served as a common ground between corporate strategy teams, plant operations, and support functions like logistics and industrial engineering. This alignment improved response times, decision accountability, and cross-site transparency.
Embedded KPIs for Long-Term Planning Advocacy
The tool included year-end visibility into stock gaps and risk thresholds, arming planning leaders with concrete data to support investment in new molds, additional shifts, or regional rerouting. This data helped drive budget decisions and capacity justification for 2026.
Created a Repeatable, Auditable Process
Shifted the organization from anecdotal escalation to a standardized, repeatable monthly planning process. This improved not only tactical execution but also created an audit trail of why and when key decisions were made—strengthening governance.

Key Skills

Tactical Forecasting & Capacity Modeling
Cross-Site Production Planning
Constraint-Based Scheduling Logic
Excel Waterfall Visualization & Interface Design
Scenario Simulation & Sensitivity Analysis
Cross-Functional Stakeholder Engagement
Demand-Supply Reconciliation
Manufacturing Strategy Alignment

Disclaimer: The company name and proprietary business information have been intentionally omitted from this case study. All details are presented strictly for academic, professional, and educational purposes only. The solutions, data representations, and outcomes described are shared to illustrate process improvement methodologies, technical skills, and strategic problem-solving, not to disclose or represent any confidential or proprietary information.