Key Concepts of Capacity Planning Models

Why This Matters

Capacity planning sits at the heart of operations management because it forces you to answer one of the toughest strategic questions: how much capability should you build, and when? Get it wrong, and you're either bleeding money on idle resources or losing customers to competitors who can deliver. Every capacity decision involves trade-offs between cost efficiency, service levels, risk tolerance, and competitive positioning—and these trade-offs show up repeatedly on exams.

You're being tested on your ability to match the right planning approach to specific business contexts. Don't just memorize model names—understand when each model applies, what trade-offs it accepts, and how it connects to broader concepts like demand forecasting, cost structures, and competitive strategy. The exam will ask you to recommend approaches for given scenarios, so think like a consultant, not a textbook.

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Timing Strategies: When to Add Capacity

The fundamental question of when to expand capacity creates three distinct strategic postures. Each accepts different risks and suits different market conditions. The key mechanism is the relationship between capacity investment timing and demand realization.

Capacity Lead Strategy

• Proactive expansion before demand materializes—builds capacity in anticipation of growth to capture market share early
• Accepts risk of underutilization if forecasted demand doesn't arrive, but avoids stockouts and lost customers
• Best for high-growth or competitive markets where being first matters more than efficiency

Capacity Lag Strategy

• Reactive expansion only after demand is confirmed—waits for evidence before committing capital
• Minimizes overcapacity risk but may result in lost sales, backorders, or customers switching to competitors
• Best for stable, predictable markets where demand forecasting is reliable and customers will wait

Match Capacity Strategy

• Incremental adjustments synchronized with demand—neither anticipates nor waits, but tracks closely
• Balances both overcapacity and undercapacity risks through frequent, smaller capacity changes
• Requires excellent forecasting and flexible operations to execute effectively

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Quantitative Decision Tools

These models provide the mathematical foundation for capacity decisions. They transform subjective judgment into defensible analysis by quantifying costs, risks, and optimal solutions.

Break-Even Analysis

• Identifies the output level where total revenue equals total cost—the formula is $Q_{BE} = \frac{FC}{P - VC}$ where $FC$ is fixed costs, $P$ is price, and $VC$ is variable cost per unit
• Essential for evaluating new capacity investments—tells you the minimum volume needed to justify expansion
• Informs pricing and volume targets by showing how changes in costs or prices shift the break-even point

Decision Tree Analysis

• Visual framework for sequential decisions under uncertainty—maps out choices, chance events, and outcomes
• Calculates expected monetary value (EMV) by weighting outcomes by their probabilities: $EMV = \sum (P_i \times V_i)$
• Critical for complex capacity scenarios with multiple stages, uncertain demand, or irreversible investments

Linear Programming for Capacity Planning

• Optimization technique for resource allocation under constraints—finds the best solution mathematically
• Maximizes output or minimizes cost subject to limitations on labor, materials, machine time, or budget
• Handles multiple variables simultaneously—essential when capacity decisions involve competing objectives

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Cost and Efficiency Models

These models explain why capacity decisions affect unit economics and how to optimize the size and timing of investments. The underlying principle is that capacity choices create cost structures that persist over time.

Economies of Scale Model

• Per-unit costs decline as production volume increases—driven by spreading fixed costs and operational efficiencies
• Encourages capacity expansion to achieve cost advantages over smaller competitors
• Watch for diseconomies of scale—beyond optimal size, complexity and coordination costs can increase unit costs

Capacity Timing and Sizing Model

• Optimizes both when and how much to expand—balances holding costs of early investment against shortage costs of late investment
• Considers lead times, market trends, and capital costs to find the sweet spot for capacity additions
• Integrates with demand forecasting to align investment cycles with expected growth trajectories

Capacity Cushion Model

• Reserve capacity maintained above expected demand—expressed as a percentage: $Cushion = \frac{Capacity - Expected\ Demand}{Capacity} \times 100\%$
• Higher cushion improves service levels but increases costs—the trade-off depends on industry and competitive dynamics
• Essential for industries with demand volatility like retail, healthcare, or seasonal manufacturing

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Service Operations Models

Service capacity differs from manufacturing because you can't inventory services—capacity unused in one period is lost forever. These models address the unique challenge of matching service supply to demand in real time.

Waiting Line Models

• Analyzes queue behavior using probability theory—key metrics include average wait time, queue length, and system utilization
• Common models include M/M/1 and M/M/c—where arrival rates ($\lambda$) and service rates ($\mu$) determine performance
• Directly links capacity to customer experience—helps justify staffing levels by quantifying the cost of customer waiting

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Quick Reference Table

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Self-Check Questions

1. A company operates in a rapidly growing market with aggressive competitors entering regularly. Which timing strategy should they adopt, and what risk does it accept?

2. Compare Break-Even Analysis and Decision Tree Analysis: what type of capacity question does each answer best?

3. If a service operation has high demand variability and customer wait times directly affect revenue, which two models should the operations manager prioritize?

4. Explain how Economies of Scale and Capacity Cushion might create conflicting recommendations—when would you prioritize one over the other?

5. An FRQ describes a manufacturer facing uncertain demand with three possible expansion options and two potential market scenarios. Which quantitative tool is most appropriate, and how would you structure your analysis?