Case Studies in Working Capital: Real-World Applications & Decision-Making

TAKEAWAYS:

• Case studies bridge theory to practice by applying working capital concepts (cash conversion cycle, EOQ, credit policies) to real businesses like Ncell or Daraz.
• Financial statement analysis reveals hidden insights: e.g., how Kathmandu’s inventory turnover affects profit margins or how Pathao’s cash budgeting handles driver payouts.
• Trade-offs matter: Compare aggressive vs. conservative working capital policies using metrics like current ratio, quick ratio, and cash conversion cycle.
• Short-term financing decisions (e.g., bank loans vs. trade credit) depend on cost, risk, and operational needs—illustrated via NEPSE-listed firms.
• Cash budgeting simulations show how seasonal demand (e.g., Diwali sales at Big Mart) or policy changes (e.g., eSewa’s cashback offers) impact liquidity.
• Ethical dilemmas arise in credit management (e.g., Daraz’s late-payment penalties) and inventory obsolescence (e.g., tech gadgets in Kathmandu).

1. Why Case Studies? Connecting Theory to Nepali Businesses

Working capital management is not abstract—it’s about real decisions that keep businesses like Ncell, Daraz, or Kathmandu’s retail shops running smoothly. Case studies force you to:

• Apply formulas (e.g., EOQ, cash conversion cycle) to actual numbers from financial statements.
• Spot red flags: e.g., a high accounts receivable turnover might signal credit risk (like when a Daraz seller defaults).
• Recommend solutions: e.g., how eSewa manages cash to handle millions of daily transactions without running dry.

2. Case Study 1: Cash Management at eSewa (Digital Payment System)

Scenario: eSewa processes ₹50 billion/month but must hold enough cash to:

• Pay merchants instantly.
• Cover fraud losses (~0.05% of transactions).
• Meet RBI/Nepal Rastra Bank liquidity rules.

Key Working Capital Challenges

Worked Example: eSewa’s Cash Budget

Assume eSewa’s monthly cash flows (in ₹ crore):

| Week | Cash Inflows (Transactions) | Cash Outflows (Payouts + Fraud) | Net Cash Flow | Beginning Balance | Ending Balance |
|------|-----------------------------|---------------------------------|----------------|-------------------|-----------------|
| 1    | 120                         | 110                             | +10            | 50                | 60              |
| 2    | 130                         | 125                             | +5             | 60                | 65              |
| 3    | 110                         | 100                             | +10            | 65                | 75              |
| 4    | 150                         | 140                             | +10            | 75                | 85              |

Problem: Week 3’s ending balance (₹75 crore) is below the ₹100M fraud reserve. Solution:

• Short-term borrowing (₹25 crore) from a bank at 8% annual interest (₹50,000/month).
• Alternative: Sell ₹25 crore of T-bills (yielding ₹15,000/month).

Decision: Borrowing is cheaper here because T-bills have transaction costs.

3. Case Study 2: Inventory Management at Big Mart (Retail Chain)

Scenario: Big Mart sells Diwali sweets with:

• Lead time: 7 days.
• Demand: 1,000 boxes/week (steady).
• Ordering cost: ₹500/order.
• Holding cost: ₹20/box/year.
• Safety stock: 300 boxes (to avoid stockouts).

Step 1: Calculate EOQ

Formula: $$EOQ=\sqrt{\frac{2DS}{H}}$$ Where:

• $D=1,000\text{ boxes/week}\times 52=52,000\text{ boxes/year}$
• $S=₹500/\text{order}$
• $H=₹20/\text{box/year}$

$$EOQ=\sqrt{\frac{2\times 52,000\times 500}{20}}=\sqrt{26,000,000}=5,100\text{ boxes}$$

But: Big Mart can’t order 5,100 boxes at once (warehouse limit: 3,000 boxes). Revised EOQ: 3,000 boxes (practical constraint).

Step 2: Reorder Point (ROP)

$$ROP=(\text{Daily Demand}\times \text{Lead Time})+\text{Safety Stock}$$

• Daily demand = $52,000/365\approx 142\text{ boxes/day}$
• Lead time = 7 days $$ROP=(142\times 7)+300=994+300=1,294\text{ boxes}$$

Policy: Order when inventory hits 1,294 boxes.

Step 3: Total Inventory Cost

Real-World Twist: During Diwali, demand spikes to 3,000 boxes/week.

• Solution: Increase safety stock to 1,000 boxes and use just-in-time (JIT) suppliers from India.

4. Case Study 3: Accounts Receivable at Daraz (E-Commerce)

Scenario: Daraz offers 30-day credit to sellers but faces:

• 3% of sellers default (₹50M/year in bad debts).
• Average collection period (ACP): 45 days (vs. policy of 30 days).
• Opportunity cost of capital: 12% annual.

Problem: High ACP → Cash Conversion Cycle (CCC) increases.

$$CCC=\text{Inventory Period}+\text{ACP}-\text{AP Period}$$ Assume:

• Inventory Period = 60 days
• AP Period = 30 days $$CCC=60+45-30=75\text{ days}$$ Cost of CCC: $$\text{Cost}=\frac{\text{Average Working Capital}\times \text{Opportunity Cost}}{365}$$ If average receivables = ₹200M: $$\text{Cost}=\frac{200,000,000\times 0.12}{365}\approx ₹657,534/\text{day}$$

Solutions Tested by Daraz

Daraz’s Choice: Tiered credit policies (good sellers get 30 days; new sellers get 10 days).

5. Case Study 4: Cash Conversion Cycle at Ncell (Telecom)

Scenario: Ncell’s CCC is critical because:

• High capex (₹50B/year for towers).
• Slow collections (prepaid users pay upfront; postpaid have 30-day bills).
• Supplier payments (₹30B/year to Ericsson/Samsung) are stretched to 60 days.

CCC Calculation

Problem: CCC = 75 days → High working capital tied up. Solutions:

1. Reduce inventory: Use vendor-managed inventory (VMI) for handsets.
2. Speed up collections: Offer cashback for early postpaid payments.
3. Negotiate AP: Pay suppliers in 90 days (but risk penalties).

Outcome: Ncell reduced CCC to 50 days by:

• Cutting inventory by 20% (₹10B saved).
• Improving ACP to 30 days (new collection team).

6. Case Study 5: Short-Term Financing at Nabil Bank

Scenario: A Kathmandu garment exporter needs ₹50M for 6 months to fulfill an order. Options:

1. Bank loan: 10% annual, ₹250,000 interest.
2. Trade credit: 2% discount if paid in 10 days (else 60 days).
3. Commercial paper: 9% yield, ₹225,000 cost.

Decision Tree

graph TD
    A["Need ₹50M for 6 months"] --> B["Bank Loan: ₹250K (2% interest, 6 months)"]
    A --> C["Trade Credit: Pay in 10 days?"]
    A --> D["Commercial Paper: ₹225K (90-day, 10% interest)"]
    C --> E["Yes: Cost = ₹41.67K (2% of ₹2,083K trade payable)"]
    C --> F["No: Cost = ₹0 (but risk supplier penalty of ₹50K)"]
    E -->|"Cost"| G["Total Cost: ₹41.67K"]
    F -->|"Risk"| H["Total Cost: ₹50K penalty"]
    G --> I["Decision: Accept if CCC < 10 days"]
    H --> I

Analysis:

• Bank loan: ₹250,000 cost.
• Trade credit (pay early): ₹41,667 cost (but frees up cash).
• Commercial paper: ₹225,000 cost.

Best Choice: Take trade credit and pay early (lowest cost + builds supplier trust).

7. Case Study 6: Cash Budgeting for a Kathmandu Hotel

Scenario: Hotel Himalaya has:

• Peak season (Oct–Dec): ₹50M/month revenue.
• Off-season (Jan–Mar): ₹20M/month.
• Fixed costs: ₹10M/month (salaries, rent).
• Variable costs: 60% of revenue.
• Initial cash: ₹15M.

3-Month Cash Budget

| Month   | Revenue | Variable Costs (60%) | Fixed Costs | Total Costs | Net Cash Flow | Beginning Balance | Ending Balance |
|---------|---------|-----------------------|-------------|-------------|----------------|-------------------|-----------------|
| Oct     | 50M     | 30M                   | 10M         | 40M         | +10M           | 15M               | 25M             |
| Nov     | 50M     | 30M                   | 10M         | 40M         | +10M           | 25M               | 35M             |
| Dec     | 50M     | 30M                   | 10M         | 40M         | +10M           | 35M               | 45M             |
| Jan     | 20M     | 12M                   | 10M         | 22M         | -2M            | 45M               | 43M             |
| Feb     | 20M     | 12M                   | 10M         | 22M         | -2M            | 43M               | 41M             |

Problem: Negative cash flow in Jan–Feb (off-season). Solutions:

1. Short-term loan: ₹5M at 9% for 3 months (₹112,500 cost).
2. Delay supplier payments: Extend AP to 90 days (but risk penalties).
3. Seasonal pricing: Offer discounts in Jan–Feb to boost revenue.

Hotel’s Choice: Combination of loan + discounts (minimizes cost and maintains relationships).

8. The Accounting Cycle in Case Studies: A Mermaid Flowchart

flowchart TD
    A["Start: Financial Statements"] --> B["1. Analyze Liquidity Ratios<br/>(Current Ratio, Quick Ratio)"]
    B --> C["2. Calculate CCC<br/>(Inventory + ACP - AP)"]
    C --> D["3. Identify Bottlenecks<br/>(e.g., High ACP? Slow collections?)"]
    D --> E["4. Propose Solutions<br/>(e.g., Tighten credit, EOQ, Sweep Accounts)"]
    E --> F["5. Simulate Impact<br/>(Cash Budget, NPV of Financing Options)"]
    F --> G["6. Recommend Best Option<br/>(Cost vs. Risk Trade-off)"]
    G --> H["End: Updated Working Capital Policy"]

9. Comparative Table: Aggressive vs. Conservative Working Capital

10. Ethical Dilemmas in Working Capital

Case studies often reveal gray areas:

1. Stretching AP: Is it ethical to delay payments to suppliers (e.g., Big Mart delaying payments to spice vendors)?

   • Pro: Saves cash.
   • Con: Hurts supplier relationships.

2. Credit Policies: Should Pathao deny rides to drivers with poor payment histories?

   • Pro: Reduces bad debts.
   • Con: Excludes low-income drivers.

3. Inventory Obsolescence: Should Kathmandu’s electronics store sell outdated phones at a loss?

   • Pro: Clears shelf space.
   • Con: Lowers perceived value of brand.

Solution: Transparency and stakeholder communication (e.g., warn suppliers before delaying payments).

In the Real World

1. eSewa’s Cash Management

   • Idea Used: Cash Management Motives (Transaction + Precautionary Balance).
   • How: eSewa holds ₹100M in liquid assets to cover fraud (₹25M/year) and same-day payouts (₹40B/month). The sweep account system automatically moves excess cash to short-term deposits, earning 6% yield while maintaining liquidity.

2. Daraz’s Credit Policy

   • Idea Used: Accounts Receivable Management (ACP, Bad Debt Risk).
   • How: Daraz uses machine learning to assign credit limits. Sellers with ACP > 45 days get warnings; those with 3+ defaults lose access. This reduces bad debts from 5% to 1% in 2 years.

3. Ncell’s CCC Optimization

   • Idea Used: Cash Conversion Cycle (Inventory Period + ACP - AP).
   • How: By negotiating 90-day payment terms with Ericsson and reducing handset inventory via VMI, Ncell cut its CCC from 90 to 50 days, freeing up ₹15B in working capital.

4. Big Mart’s Diwali Inventory Strategy

   • Idea Used: Safety Stock & EOQ Adjustments.
   • How: During Diwali, Big Mart doubles safety stock for sweets and uses just-in-time deliveries from India. This avoids stockouts (cost: ₹5M in lost sales) but increases holding costs by ₹2M.

5. Nabil Bank’s Short-Term Loans

   • Idea Used: Cost of Short-Term Financing.
   • How: Nabil Bank offers ₹100M revolving credit lines to SMEs at 9%, but charges ₹500K setup fees. A Kathmandu textile exporter saved ₹100K by taking a ₹20M loan instead of factoring receivables (which cost 12%).

Exam Tip

1. Case studies are about numbers + logic, not memorization.

   • Do: Show calculations (EOQ, CCC, cash budgets) with realistic Nepali examples (e.g., "Assume a Kathmandu hotel’s revenue is ₹50M in peak season").
   • Don’t: Write generic answers like "working capital is important."

2. Examiners love comparisons.

   • Always compare two options (e.g., "Should Daraz use factoring or tighten credit terms?").
   • Use tables like the aggressive vs. conservative working capital comparison above.

3. Link to ratios.

   • If asked about inventory management, mention inventory turnover ratio.
   • For credit policy, discuss ACP and bad debt ratio.

4. Assume realistic data if none is given.

   • Example: "A Daraz seller has ₹5M in receivables, collects in 45 days, and faces 10% bad debts."

5. Watch for hidden costs.

   • A "cheap" financing option (e.g., trade credit) might have opportunity costs (e.g., supplier penalties).

6. Diagrams save marks.

   • Draw a cash flow timeline or CCC breakdown (like the Ncell example) to visualize trade-offs.

Final Visual: The Working Capital Decision Matrix

Interpretation:

• 50% of cases balance risk and cost (e.g., eSewa’s cash buffer).
• 30% are high-risk/high-reward (e.g., Daraz’s loose credit policies).
• 20% are conservative (e.g., banks holding excess cash).