Capacity & Location Planning: Models, Trade-offs & Strategic Decisions

Core Concepts: Capacity Planning

Capacity planning ensures an organization’s resources (labor, machines, space) meet demand without over- or under-investment. It spans short-term (daily scheduling), medium-term (seasonal adjustments), and long-term (facility expansion).

1. Types of Capacity

mindmap
  root((Capacity Types))
    Short-term
      "Daily/Weekly adjustments (e.g., Ncell call-center staffing)"
    Medium-term
      "Seasonal demand (e.g., Daraz delivery trucks during Dashain)"
    Long-term
      "Facility expansion (e.g., Nabil Bank’s new ATMs in Kathmandu)"

2. Capacity Utilization & Efficiency

• Utilization Rate = (Actual Output / Best Operating Level) × 100% Example: If a factory runs at 75% capacity (producing 750 units/day vs. max 1000), it has 25% slack—useful for demand spikes but risky if demand drops.
• Efficiency = (Actual Output / Theoretical Capacity) × 100% Example: A Daraz warehouse with 100 workers packing 800 orders/day vs. theoretical max of 1200 orders/day → 66.7% efficiency.

Labelled diagram showing bottleneck (conveyor belt slowdown) and buffer storage. (Image: Cromemco, CC BY-SA 3.0, via Wikimedia Commons)

3. Capacity Planning Models

WORKED EXAMPLE: Daraz’s Warehouse Capacity Daraz’s Pokhara warehouse has:

• Fixed Costs: Rs. 5M/year (rent, salaries).
• Variable Cost: Rs. 200/order.
• Price: Rs. 1500/order. Question: How many orders must Daraz process to break even? Solution: $$\text{Break-even (units)}=\frac{5,000,000}{1500-200}=3,846\text{ orders/month}$$ Interpretation: Daraz must sell ≥3,846 orders/month to cover costs. If they sell 5,000 orders, profit = Rs. 6.2M/year.

Location Planning: Strategic Trade-offs

Location decisions impact costs (land, labor), demand (proximity to customers), and logistics (transportation, regulations).

1. Factors Influencing Location

flowchart TD
  A["Location Decision Factors"] --> B["Costs"]
  A --> C["Demand"]
  A --> D["Infrastructure"]
  A --> E["Government Policies"]
  A --> F["Competitors"]
  B --> B1["Land/rent (e.g., Kathmandu vs. Pokhara)"]
  B --> B2["Labor wages (e.g., Chitwan vs. Dharan)"]
  C --> C1["Market access (e.g., Daraz’s Kathmandu hub for urban demand)"]
  D --> D1["Transport links (e.g., NTC’s road vs. air routes)"]
  E --> E1["Tax incentives (e.g., Bhairahawa SEZ for manufacturing)"]
  F --> F1["Competitor clustering (e.g., Thamel for IT firms)"]

2. Location Models

Showing demand points (Kathmandu, Pokhara) and optimal warehouse location. (Image: Nithinhegde.mb, CC BY-SA 4.0, via Wikimedia Commons)

3. Location Strategies

• Single Location: Low risk, high fixed costs (e.g., Nepal Rastra Bank’s central office in Kathmandu).
• Multiple Locations: Higher flexibility (e.g., Ncell’s regional call centers in Dharan, Biratnagar, and Pokhara).
• Global Sourcing: Offshore manufacturing (e.g., Himalayan Java’s tea processing in India).

CASE STUDY: Toyota’s Nepal Plant (Bhairahawa)

• Capacity: 5,000 cars/year (2023).
• Location Factors:
  • Proximity to raw materials (steel from India, local labor).
  • Government incentives (SEZ tax breaks).
  • Demand: 80% of Nepali car buyers live within 300 km.
• Challenge: Seasonal demand (sales drop 30% in monsoon).
• Solution: Aggregate planning (adjust production + inventory).

In the Real World

1. eSewa’s Server Capacity

   • Idea: Break-even analysis for server farms.
   • How: eSewa’s data centers must handle 50,000 transactions/hour during Dashain. They use load balancing (distributing traffic across servers) to avoid overcapacity costs. If a server costs Rs. 2M/year and handles 10,000 transactions/hour, they need 5 servers to break even at peak demand.

2. Pathao’s Driver Location

   • Idea: Center of Gravity Model.
   • How: Pathao’s algorithm places drivers in high-demand zones (e.g., Thamel, Lakshmi Marg) to minimize wait times. During Tihar, they dynamically adjust driver locations based on real-time demand spikes.

3. NTC’s Substation Sites

   • Idea: Factor Rating Method.
   • How: NTC evaluates substation locations using:
     • Cost (land price: Kathmandu = high, Chitwan = low).
     • Demand (urban areas need more capacity).
     • Infrastructure (reliability of power grids).
   • Example: A substation in Bhaktapur scores 9/10 for demand but only 6/10 for cost, while Dhading scores 7/10 for both.

Exam Tip

1. Quantitative Questions:

   • Always show calculations for break-even, learning curves, or center of gravity. Partial credit is given for steps.
   • Example: If asked to find Daraz’s break-even point, label each variable (fixed cost, variable cost, price).

2. Case Studies:

   • Link theory to practice: For Ncell’s tower placement, mention proximity to demand (urban areas) and infrastructure (existing fiber networks).
   • Compare models: If two location methods are given (e.g., factor rating vs. center of gravity), contrast their use cases (qualitative vs. quantitative).

3. Diagrams:

   • Draw a flowchart for capacity planning steps (e.g., "Demand Forecast → Capacity Requirements → Gap Analysis").
   • Label a center of gravity diagram with real cities (e.g., Kathmandu, Pokhara, Biratnagar).

4. Common Pitfalls:

   • Ignoring seasonality: Always note if demand varies (e.g., Daraz in Dashain vs. monsoon).
   • Overlooking qualitative factors: Location isn’t just math—consider cultural fit (e.g., a bank in a conservative village may need female tellers).