33.1 Capacity
Capacity is the maximum amount of output a system can produce over a given period.
Examples include units produced per day, customers served per hour, or orders processed per week.
Key question: Does the business have enough capacity to meet current and expected demand?
If demand exceeds capacity, the company may need to add equipment, labor, shifts, facilities, or outsourcing. If capacity greatly exceeds demand, the problem may instead be low utilization or excessive fixed cost.
33.2 Utilization
Utilization measures how much of available capacity is actually being used.
Utilization = Actual Output ÷ Maximum Capacity
A factory operating at 90% utilization is using most of its available capacity, while one operating at 50% may have substantial unused resources.
Important: Higher utilization is not always better. Running too close to maximum capacity can reduce flexibility, increase delays, and make the system vulnerable to disruptions.
33.3 Throughput
Throughput is the amount of output a process completes over a given period.
A warehouse might process 10,000 orders per day, while a call center might handle 500 calls per hour.
Key distinction: Capacity describes what the system could produce; throughput describes what it actually produces.
If throughput is well below theoretical capacity, look for bottlenecks, downtime, staffing issues, quality problems, or inefficient process design.
33.4 Bottlenecks
A bottleneck is the stage of a process that limits overall output.
Suppose a production line has three steps capable of processing 100, 60, and 90 units per hour. The second step limits total throughput to roughly 60 units per hour.
Core principle: Improving a non-bottleneck step may have little impact on total system performance.
Identify the constraint first, then determine whether it can be improved through additional equipment, staffing, process redesign, scheduling, or workload balancing.
33.5 Yield
Yield measures how much usable output results from the inputs entering a process.
Yield = Good Output ÷ Total Input
Low yield may result from defects, waste, rework, spoilage, or process variability.
Business implication: Improving yield can increase effective capacity and reduce unit costs without requiring additional equipment.
When yield falls, investigate where defects occur and what changed in materials, equipment, processes, or quality control.
33.6 Productivity
Productivity measures output relative to an input such as labor, time, or equipment.
Productivity = Output ÷ Input
Examples include units per labor hour, revenue per employee, or customers served per representative.
Low productivity may reflect poor processes, weak training, inadequate tools, downtime, bad scheduling, or inappropriate incentives.
Important: Productivity improvement should not come at the expense of unacceptable declines in quality, safety, or customer experience.
33.7 Inventory
Inventory helps companies meet demand but also ties up cash and creates storage, obsolescence, and spoilage risks.
Operations cases may involve raw materials, work in progress, or finished goods.
When evaluating inventory, consider:
- Demand variability: How predictable is customer demand?
- Lead times: How long does replenishment take?
- Service requirements: How costly is a stockout?
- Holding costs: What does excess inventory cost?
Goal: Hold enough inventory to support service levels without carrying unnecessary working capital.
33.8 Supply Chain
Supply-chain problems span the movement of materials and products from suppliers through production and distribution to customers.
Potential issues include supplier reliability, transportation cost, lead times, warehouse capacity, inventory positioning, and geographic network design.
Useful structure: Supplier → Manufacturing → Warehouse → Transportation → Customer.
At each stage, examine cost, time, capacity, reliability, and risk.
Be alert to trade-offs. Reducing inventory may lower cost but increase stockouts, while using faster transportation may improve service but reduce margins.
33.9 Service Operations
Operations cases are not limited to factories. Hospitals, banks, call centers, restaurants, airlines, and professional services all have operational processes.
Service cases often focus on:
- Demand: How many customers arrive and when?
- Capacity: How many customers can be served?
- Waiting time: Where do queues form?
- Staffing: Are employees scheduled when demand occurs?
- Quality: Are service outcomes consistent?
Key challenge: Service capacity is often perishable. An unused hotel room or empty airline seat today cannot be stored and sold tomorrow.
33.10 Process Improvement
Process-improvement cases ask how to make an operation faster, cheaper, more reliable, or higher quality.
A useful approach is:
- Map the process: Understand each major step.
- Measure performance: Identify time, cost, quality, and capacity at each stage.
- Find the constraint: Determine where performance breaks down.
- Diagnose the cause: Understand why the problem occurs.
- Develop solutions: Redesign, automate, rebalance, eliminate, or improve the relevant step.
- Test the impact: Quantify the expected benefit and implementation requirements.
Final principle: Operations cases reward disciplined diagnosis. Understand the process, identify the true constraint, quantify its impact, and improve the part of the system that actually limits performance.
