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

Core Concepts: Capacity Planning

Capacity planning ensures an organization’s resources (machines, labor, space) match demand over time. It balances cost efficiency (avoiding idle resources) and customer service (meeting demand without overloading).

1. Types of Capacity

mindmap
  root((Capacity Types))
    Short-Term
      "Adjustments to existing resources (e.g., overtime, subcontracting)"
    Medium-Term
      "Expanding facilities or adding shifts (e.g., Daraz’s warehouse upgrades)"
    Long-Term
      "Major investments (e.g., Nabil Bank’s new ATMs)"

Key Definitions:

• Design Capacity: Maximum output under ideal conditions (e.g., a factory’s theoretical max units/day).
• Effective Capacity: Realistic output after accounting for inefficiencies (e.g., 80% of design capacity due to maintenance).
• Utilization Rate: $\text{Actual Output}/\text{Effective Capacity}\times 100\%$.

Worked Example: Daraz’s Warehouse Daraz’s Pokhara warehouse has:

• Design Capacity: 500 orders/day.
• Effective Capacity: 400 orders/day (due to 2-hour daily maintenance).
• Current Output: 350 orders/day. Utilization Rate: $350/400\times 100=87.5\%$. Question: Should Daraz expand? Use break-even analysis (see below).

2. Capacity Planning Models

A. Break-Even Analysis

Determines the volume where total revenue = total cost. Used to decide if expanding capacity is viable.

Formula: $$\text{Break-Even Point (units)}=\frac{\text{Fixed Costs}}{\text{Selling Price per Unit}-\text{Variable Cost per Unit}}$$

Worked Example: Nabil Bank’s ATM Expansion

• Fixed Costs (FC): Rs. 500,000 (ATM installation).
• Variable Cost (VC): Rs. 200 per transaction.
• Revenue (R): Rs. 300 per transaction. Break-Even Point: $$\frac{500,000}{300-200}=5,000\text{ transactions}$$ Interpretation: Nabil must serve 5,000 transactions/month to cover costs. If demand is 6,000/month, expansion is profitable.

Visual:

graph LR
  A["Total Cost (FC + VC)"] -->|"Rising Line"| B["Break-Even Point"]
  C["Total Revenue"] -->|"Rising Line"| B
  B --> D["Profit Zone"]
  B --> E["Loss Zone"]

B. Learning Curve Theory

As workers repeat a task, their efficiency improves. Used for labor-intensive processes (e.g., garment factories).

Formula: $${\text{Time per Unit}}_n={\text{Time per Unit}}_1\times n^b$$ Where:

• $n$ = unit number,
• $b$ = learning rate (e.g., 0.8 for 20% improvement per doubling).

Example: Himalayan Java’s Coffee Packaging

• First unit: 10 minutes.
• Learning rate (b): 0.7 (30% improvement per doubling). Time for 8th unit: $$10\times 8^{0.7}\approx 37.2\text{ minutes}$$ Implication: Hiring more workers may not always reduce costs if learning effects dominate.

3. Location Planning: Factors and Models

A. Key Location Factors

B. Location Decision Models

1. Factor Rating Method

Assign weights to factors (e.g., cost = 40%, labor = 30%) and score locations.

Example: Choosing a Factory Site for a Nepali Textile Firm

Decision: Choose Chitwan (higher score) despite infrastructure issues.

2. Center of Gravity Method

Minimizes transportation costs by locating near demand centers.

Formula: $$X=\frac{\sum (x_i\times d_i)}{\sum d_i},Y=\frac{\sum (y_i\times d_i)}{\sum d_i}$$ Where:

• $(x_i,y_i)$ = coordinates of demand point,
• $d_i$ = demand volume.

Example: NTC’s Data Center Location Assume 3 cities with demands:

• Kathmandu: $(x=0,y=0)$, demand = 500 units,
• Pokhara: $(x=200,y=100)$, demand = 300 units,
• Biratnagar: $(x=150,y=200)$, demand = 200 units.

Calculations: $$X=\frac{(0\times 500)+(200\times 300)+(150\times 200)}{500+300+200}=\frac{90,000}{1,000}=90$$ $$Y=\frac{(0\times 500)+(100\times 300)+(200\times 200)}{1,000}=70$$ Optimal Location: Near coordinates (90, 70) on a map—closer to Pokhara.

4. Capacity and Location in Supply Chains

A. Supply Chain Capacity Planning

• Bullwhip Effect: Demand fluctuations amplify as they move up the supply chain (e.g., Daraz’s suppliers overordering due to unpredictable demand).
• Solution: Vendor-Managed Inventory (VMI) (e.g., Nabil Bank’s ATMs restocked automatically by suppliers).

B. Outsourcing vs. Insourcing

In the Real World

1. Daraz’s Warehouse Network

   • Idea Used: Center of Gravity Method + Break-Even Analysis.
   • How: Daraz locates warehouses near major cities (Kathmandu, Pokhara) to minimize delivery costs. Their break-even analysis ensures warehouses operate at 70%+ capacity before expansion.

2. Nabil Bank’s ATM Placement

   • Idea Used: Factor Rating Method.
   • How: Nabil scores locations based on foot traffic, safety, and rent costs. For example, an ATM in Thamel scores higher than one in a remote village due to demand.

3. Himalayan Java’s Coffee Processing

   • Idea Used: Learning Curve Theory.
   • How: Workers in their Pokhara factory reduce processing time by 25% after 100 batches, lowering costs without hiring more staff.

Exam Tip

1. Break-Even Questions: Always show the formula and interpret the result (e.g., "The firm should expand if demand exceeds 5,000 units").
2. Location Models: For factor rating, show the weighted score table. For center of gravity, plot the map and mark the optimal point.
3. Real-World Links: Examiners love examples. Relate capacity to Daraz’s warehouses, Nabil Bank’s ATMs, or NTC’s data centers.
4. Shortcomings: Discuss limitations (e.g., break-even ignores inflation; factor rating is subjective).
5. Diagrams: Draw break-even graphs and location factor tables—they fetch marks!

Visual Summary:

flowchart TD
  A["Capacity Planning"] --> B["Break-Even Analysis"]
  A --> C["Learning Curve"]
  A --> D["Utilization Rate"]
  E["Location Planning"] --> F["Factor Rating"]
  E --> G["Center of Gravity"]
  E --> H["Supply Chain Impact"]
  F -->|"Example"| I["Nabil Bank ATM Sites"]
  G -->|"Example"| J["Daraz Warehouse Locations"]