Capacity Planning – Chapter 5 (Sections 5.1–5.3)

Capacity: Core Definitions & Concepts

Capacity
- Definition: Maximum rate of output that a process or system can achieve.
- Think of it as the system’s "speed limit"—the fastest sustainable pace under ideal or specified conditions.
Capacity Management
- Concerned with ensuring the right kind, amount, and timing of capacity.
- Involves strategic, tactical, and operational decisions that balance cost, responsiveness, and risk.

Key Questions in Capacity Planning

What kind of capacity is needed?
- E.g.
- Labor hours, machine hours, beds in a hospital, seats in a classroom.
How much capacity is needed?
- Determined by demand forecasts, desired service levels, and strategic positioning.
When is the capacity needed?
- Early vs. late expansion decisions can shape competitive advantage.
Two general types
- Output capacity (finished units per period) — easier when products are standardized.
- Input capacity (machine hours, available labor hrs) — preferred for low-volume or highly customized settings.

Measures of Capacity & Utilization

Output Measures
- Best for individual processes or standardized products/services.
- Examples: cars per day, meals per hour.
Input Measures
- Better in flexible, low-volume operations.
- Examples: labor hours available, machine hours available.
Utilization
- Indicates how intensively the system is used.
- Formula: \text{Utilization} = \frac{\text{Average Output Rate}}{\text{Maximum (Design) Capacity}} \times 100\%
- High utilization ≠ efficiency; it only reflects how much of the design limit is being tapped.

Capacity Measurement: Design vs. Effective

Maximum (Design) Capacity
- Theoretical maximum output under ideal conditions.
- No downtime, perfect mix, no variability.
Effective Capacity
- Design Capacity minus allowances for breaks, maintenance, setups, etc.
- Reflects realistic operating expectations.
Formulas
- \text{Capacity Efficiency} = \frac{\text{Average Output Rate}}{\text{Effective Capacity}} \times 100\%
- \text{Capacity Utilization} = \frac{\text{Average Output Rate}}{\text{Design Capacity}} \times 100\%
Important insight: A system can look “efficient” while its utilization is low if effective capacity was set conservatively.

Worked Example ▸ Champion Kia Service Center

Given:
- Design Capacity = 60 repairs/day.
- Effective Capacity = 40 repairs/day.
- Actual Output = 36 repairs/day.
Calculations
- \text{Capacity Efficiency} = \frac{36}{40} \times 100\% = 90\%
- \text{Capacity Utilization} = \frac{36}{60} \times 100\% = 60\%
Interpretation
- Workers perform well relative to effective expectations (90 %), yet 40 % of the design capability is idle.
- Signals either overly conservative effective capacity or latent opportunity to redesign processes.

Economies & Diseconomies of Scale

Economies of Scale (unit cost ↓ as output ↑)
- Spreading fixed costs: equipment, rent, management.
- Reducing construction costs via larger facilities.
- Volume discounts on purchased materials.
- Process advantages: specialized equipment, automation, learning curves.
Diseconomies of Scale (unit cost ↑ beyond optimal size)
- Complexity: coordination, scheduling, information overload.
- Loss of focus: diluted managerial attention, mixed priorities.
- Inefficiencies: bureaucracy, longer lines of communication.
Visual Insight: The cost-vs-capacity curve first slopes downward (economies) then upward (diseconomies), yielding an optimal capacity range.

Capacity Timing & Sizing Strategies

Decisions must position the firm on that cost curve while meeting strategic goals.
Three intertwined dimensions
1. Sizing Capacity Cushions
2. Timing & Sizing of Expansion
3. Linking Capacity with Other Decisions (location, technology, workforce)

Capacity Cushions

Definition: Extra capacity above expected demand to absorb variability (demand surges, downtime).
Formula: \text{Capacity Cushion} = 100\% - \text{Average Utilization}
Industry norms
- Capital-intensive (e.g., power plants): cushions < 10 % (capacity is expensive—run it hard).
- Service/high variability (e.g., hotels): cushions 30–40 % (demand uncertain; high cost of lost sales).
Strategic role
- Larger cushions improve responsiveness and reliability but raise unit cost.

Expansion Strategies: When & How Much

Expansionist Strategy
- Add capacity ahead of demand growth.
- Pros: Market leadership, high service levels, deterrent to competitors.
- Cons: Risk of excess capacity, higher upfront cost.
Wait-and-See Strategy
- Lag demand; expand only after existing capacity is highly utilized.
- Pros: Lower risk, better information about actual demand.
- Cons: Risk of lost sales, potential for bottlenecks, may concede market share.

Systematic Approach to Long-Term Capacity Decisions

Estimate future capacity requirements (quantitative forecast).
Identify capacity gaps (positive or negative).
Develop alternative plans to close gaps.
Evaluate alternatives qualitatively (risk, strategic fit) & quantitatively (NPV, ROI) → choose.

Estimating Capacity Requirements

Single product/service, one operation, 1-year horizon
- M = \frac{D \times p}{N \times (1 - C)}
- Where:
- D = annual demand forecast (units or customers).
- p = processing time per unit (hours).
- N = total available hours per year.
- C = desired capacity cushion (expressed as decimal).
- M = required number of input units (e.g., machines).
Multiple products & setups
- Include setup time s and lot size Q per product.
- Effective processing time per unit: p + \frac{s}{Q}.

Worked Example ▸ Office-Building Copy Center

Operating context
- 250 workdays/year; one 8-hour shift → N = 250 \times 8 = 2{,}000 \text{ hr/yr} per machine.
- Desired cushion C = 15\% = 0.15.
Data table
- Client X: D=2{,}000 copies, p=0.5 hr, Q=20, s=0.25 hr.
- Client Y: D=6{,}000 copies, p=0.7 hr, Q=30, s=0.40 hr.
Effective processing times
- Client X: p + \frac{s}{Q} = 0.5 + \frac{0.25}{20} = 0.5125\text{ hr/copy}.
- Client Y: 0.7 + \frac{0.40}{30} = 0.7133\text{ hr/copy}.
Total hours required
- H_{X} = 2{,}000 \times 0.5125 = 1{,}025\text{ hr}.
- H_{Y} = 6{,}000 \times 0.7133 \approx 4{,}280\text{ hr}.
- H_{\text{total}} = 5{,}305\text{ hr}.
Capacity requirement
- M = \frac{5{,}305}{2{,}000 \times (1 - 0.15)} \approx 3.46 machines.
- Round up → 4 machines.
Insight: Despite currently owning 3 machines, the copy center risks backlogs; one additional machine maintains the 15 % cushion.

Identifying Capacity Gaps

Capacity Gap = Projected demand − Current capacity.
- Positive → need expansion.
- Negative → excess capacity (downsizing, marketing push, off-loading contracts).

Developing & Evaluating Alternatives

Base Case: Do nothing; accept backlog, overtime, or lost sales.
Alternatives: Add equipment, subcontract, add shifts, process redesign, technology upgrades, facility relocation.
Qualitative factors
- Demand uncertainty, tech obsolescence, competitor reactions, labor relations.
Quantitative factors
- Cash flows, NPV, break-even analysis, payback period.
Balanced scorecard approach links financial, customer, process, and learning perspectives.

Why Capacity Decisions Matter

Availability: Ability to meet current & future demand shapes revenue and market share.
Operating Cost: Least when capacity ≈ demand (but watch variability).
Initial Cost: Larger facilities require higher capital; however, economies of scale may offset.
Long-Term Commitment: Facilities are hard to reconfigure or divest; mistakes linger.
Competitiveness: Rapid expansion capability can deter entrants and enhance delivery speed.
Globalization: Markets and suppliers span borders; capacity choices must consider location, regulation, and logistics.
Time Lag: Building capacity consumes time → forecasts & flexibility become critical.
Iterative Nature: Capacity planning is not one-and-done; it recurs as strategy, technology, and markets evolve.