Ch. 5: Strategic Capacity Management

Capacity is the output a system can achieve over a specific period of time (customers served between noon and 1 p.m., cars per shift). Effective capacity depends on the product mix, and firms with an uncertain mix often state capacity in inputs (a hospital counts beds). Capacity always needs a time dimension:

• Long range (more than one year): buildings, equipment, facilities; needs top management approval. This is the focus of the chapter.
• Intermediate range (plans made per month or per quarter, covering the coming 6 to 18 months): hiring and layoffs, new tools, small equipment, subcontracting.
• Short range (less than one month): daily or weekly scheduling, overtime, personnel transfers, alternative routings.

Strategic capacity planning sets the total capacity of capital-intensive resources (facilities, equipment, size of the workforce) at the level that supports long-term strategy best. With too little capacity, slow service drives customers away or lets competitors in; with too much, the firm ends up cutting prices, paying idle workers, holding surplus inventory or making less profitable products.

Best operating level and utilization

A stated capacity says nothing about how long a rate can be sustained. The best operating level is the capacity level the process was designed for, where average unit cost is minimized.

Economies and diseconomies of scale, focus and flexibility

Economies of scale: as a plant grows and volume increases, average cost per unit falls, because equipment with twice the capacity does not cost twice as much and dedicated support resources are better used. Beyond some size, diseconomies of scale appear: heavy discounting needed to keep the plant busy, high maintenance for a few large machines, or transport costs (a cement plant can only serve nearby customers).

A focused factory concentrates on a limited set of production objectives (a product or product group) instead of trying to excel on every dimension. It can be realized with a plant within a plant (PWP): separate areas under one roof with their own organization, equipment, workforce policies and control, for example a high-volume line for a standard product and a separate machine shop and assembly area for a custom product.

Capacity flexibility is the ability to raise or lower output quickly or shift capacity between products. It comes from flexible plants (zero-changeover-time plants with movable equipment), flexible processes (flexible manufacturing systems and easily set up equipment, enabling economies of scope: producing several products together more cheaply than separately) and flexible workers with multiple skills.

Considerations when changing capacity

• Maintaining system balance: stages rarely have identical best operating levels, and variability creates imbalance. Remedies: add capacity at bottlenecks (overtime, leasing, subcontracting), buffer inventory before the bottleneck, or duplicate the facilities another department depends on.
• Frequency of capacity additions: expanding too often is costly (removal, training, purchase, idle time during changeover); expanding too rarely means buying large chunks and carrying excess capacity as overhead.
• External sources of capacity: outsourcing (Dell using a Chinese assembler) or sharing capacity (airlines exchanging aircraft across seasons, code sharing).
• Decreasing capacity: temporarily with fewer hours or shutdowns; permanently by selling equipment or closing facilities.

Determining capacity requirements

1. Forecast sales for individual products within each product line.
2. Calculate equipment and labor requirements to meet the forecasts.
3. Project labor and equipment availability over the planning horizon.

Example 5.1: three bottling machines of 150,000 bottles/year each give 450,000 capacity; year-1 demand of 135,000 bottles uses 30 percent, or 0.9 machine. Five bag machines of 250,000 each give 1,250,000; demand of 300,000 needs 1.2 machines. Labor follows from machines needed times operators per machine (2 per bottling machine, 3 per bag machine).

Decision trees

A decision tree is a diagram that maps the order of decisions and chance events. Decision nodes are drawn as squares and chance nodes as circles, with probabilities on the branches leaving them. Work backwards from right to left: find the value of every outcome, calculate the expected value at each chance node (probability times value, summed), and at every decision node retain only the best branch and prune the others. Use present values when the time horizon is long.

Example 5.2 (Hackers Computer Store, 5 years, strong growth probability 0.55): move = 0.55 x 765,000 + 0.45 x 365,000 = $585,000; expand = 0.55 x 863,000 + 0.45 x 413,000 = $660,500; do nothing = 0.55 x 850,000 + 0.45 x 525,000 = $703,750 (at the second-year decision, not expanding, $850,000, beats expanding, $843,000). Best decision: do nothing now and next year.

Capacity planning in services

Service capacity differs from manufacturing capacity in three ways:

• Time: services cannot be stored; capacity must be available when needed (an empty seat on yesterday's flight is lost).
• Location: capacity must be near the customer; it is distributed first, then the service is produced.
• Volatility of demand: higher because there is no inventory to smooth demand, customers have different needs, and demand reacts directly to consumer behavior (weather, events, lunch-hour rush). Capacity is therefore often planned in 10 to 30 minute increments.

The optimal utilization is context specific: low for high uncertainty and high stakes (emergency rooms, fire departments), close to 100 percent for predictable services without customer contact (commuter trains, postal sorting), and deliberately high where a full house improves the experience (sports teams, theaters, bars).