Practice cases: India
Four full cases set in India: a biscuit maker's price point, digital lending to small shops, a new hospital in a smaller city, and rooftop solar.
Key takeaways
- Worked case: Desi Crunch: profit on INR 10 biscuit packs has collapsed.
- Worked case: Nilgiri Credit: loans to small shops using digital payment data.
- Worked case: A 150-bed hospital in a smaller Indian city.
- Worked case: SunRoof Homes: rooftop solar in a western Indian state.
Four full cases set in India: a biscuit maker's price point, digital lending to small shops, a new hospital in a smaller city, and rooftop solar.
Cover the solution and run each case out loud, ideally with a partner playing the interviewer. Ask your own clarifying questions, state a hypothesis, build a structure, and do the math on paper before you look. Then compare your synthesis with the one given, and read the strong and weak candidate notes. Each case is labeled Starter, Standard, or Stretch.
Case 1: Desi Crunch: profit on INR 10 biscuit packs has collapsed
Worked case
Starter: Desi Crunch: profit on INR 10 biscuit packs has collapsed
The prompt
Desi Crunch, an Indian biscuit maker, sells most of its volume in INR 10 packs. Profit fell from about INR 100 crore to about INR 20 crore in a year. The exhibit shows the economics of one pack. What happened, and what should it do?
Difficulty: Starter. Format: interviewer-led, with an exhibit. Industry: Consumer goods. Region: India. Interview length: about 25 minutes. The company is fictional and all figures are illustrative.
Clarifying questions, with the interviewer's answers
- Can the INR 10 price change?Answer: Shoppers expect INR 10 for small packs; changing it risks large volume losses.
- What volume do we sell?Answer: About 100 crore packs (1 billion) a year, flat.
- What are fixed costs?Answer: About INR 100 crore a year, flat.
A hypothesis to say out loud: Wheat, sugar, and oil prices have been volatile. My hypothesis is that ingredient costs rose while the INR 10 price stayed fixed, squeezing contribution per pack.
The structure
- Profit = packs x contribution per pack - fixed costs
- Price per pack (fixed at INR 10)
- Key: Variable cost per pack: ingredients, packaging, trade margins, other
- Fixed costs
The exhibit
| INR per pack | Last year | This year |
|---|---|---|
| Price | 10 | 10 |
| Ingredients (wheat, sugar, oil) | 4 | 4.8 |
| Packaging | 1 | 1 |
| Distributor and retailer margins | 2.5 | 2.5 |
| Other variable costs | 0.5 | 0.5 |
Working it through
1. Contribution per pack, last year
INR 10 minus all variable costs.
Contribution last year (INR):10 - 4 - 1 - 2.5 - 0.5 = 22. Contribution per pack, this year
Ingredients rose to INR 4.8.
Contribution this year (INR):10 - 4.8 - 1 - 2.5 - 0.5 = 1.23. Profit last year
100 crore packs x INR 2, minus INR 100 crore of fixed costs (in INR crore).
Profit last year (INR crore):100 × 2 - 100 = 1004. Profit this year
Same volume at INR 1.2 per pack.
Profit this year (INR crore):100 × 1.2 - 100 = 205. Size of the ingredient rise
Ingredient cost per pack rose by:
Ingredient cost rise (%):(4.8 - 4) ÷ 4 × 100 = 206. Keep INR 10 by using fewer grams
To bring ingredient cost back to INR 4.0 at today's prices, the pack must hold this much less.
Weight reduction needed (%):(1 - 4 ÷ 4.8) × 100 = 16.677. Curveball: a rival advertises "more biscuits for INR 10"
Interviewer: "A rival keeps its pack size and advertises it. Suppose we lose 10 percent of volume after reducing grams." Profit with contribution back at INR 2:
Profit after the change (INR crore):100 × 0.9 × 2 - 100 = 80
What the exhibit shows
Only ingredients moved, up INR 0.8 per pack, which cut contribution from INR 2.0 to INR 1.2 on a billion packs.
The recommendation
The fall comes entirely from ingredient costs, up 20 percent while the INR 10 price stayed fixed. First, contribution per pack fell from INR 2.0 to INR 1.2, which on a billion packs cuts profit by INR 80 crore. Second, raising the price is risky at a price point shoppers treat as fixed, so reduce grams per pack by about 17 percent to restore contribution. Third, even if a rival's advertising costs 10 percent of volume, profit recovers to about INR 80 crore. Also buy key ingredients further ahead to smooth price swings, and raise prices on larger packs, where shoppers are less sensitive.
Risks: Shoppers may notice smaller packs and switch; Ingredient prices may rise again.
Next steps: Test a smaller pack in two states and track volume; Agree six-month supply contracts for wheat and oil.
A strong candidate
Found the one line that moved, respected the INR 10 price point, sized the grams change, and tested the rival's response.
A weak candidate
Recommended raising the price to INR 12 without asking whether the price point could move.
Case 2: Nilgiri Credit: loans to small shops using digital payment data
Worked case
Standard: Nilgiri Credit: loans to small shops using digital payment data
The prompt
Nilgiri Credit, a mid-sized Indian non-bank lender (NBFC), wants to lend to small shops, judging their creditworthiness from their digital payment history. Should it launch, and how?
Difficulty: Standard. Format: candidate-led, with interviewer dialogue. Industry: Lending (non-bank finance). Region: India. Interview length: about 30 minutes. The company is fictional and all figures are illustrative.
Clarifying questions, with the interviewer's answers
- How many shops and what loan size?Answer: About 200,000 shops by year three, borrowing about INR 2 lakh (200,000) each on average.
- What rate, funding cost, and loss rate?Answer: 18 percent interest, 8 percent funding cost, and expected credit losses of 4 percent of loans a year.
- What does it cost to run each loan?Answer: About INR 3,000 a year per loan, because checks are digital.
A hypothesis to say out loud: Small-business loans earn a high rate but carry high losses. My hypothesis is that the product is profitable at expected losses, and the key question is how much losses can rise before it stops paying.
The structure
- Profit = interest margin - credit losses - operating costs
- Loan book size
- Net interest income
- Key: Credit losses
- Operating costs
Working it through
1. Loan book
Candidate: "200,000 loans of INR 200,000 is INR 4,000 crore."
Loan book (INR):200,000 × 200,000 = 40,000,000,0002. Net interest income
The 10-point gap between the 18 percent rate and the 8 percent funding cost.
NII (INR a year):40,000,000,000 × (0.18 - 0.08) = 4,000,000,0003. Credit losses
4 percent of loans a year.
Credit losses (INR a year):40,000,000,000 × 0.04 = 1,600,000,0004. Operating costs
INR 3,000 per loan.
Operating costs (INR a year):200,000 × 3,000 = 600,000,0005. Profit before tax
Candidate: "That is INR 180 crore a year."
Profit (INR a year):4,000,000,000 - 1,600,000,000 - 600,000,000 = 1,800,000,0006. Break-even loss rate
Candidate: "How high can losses go before profit is zero?"
Break-even loss rate (%):(4,000,000,000 - 600,000,000) ÷ 40,000,000,000 × 100 = 8.57. Curveball: a downturn
Interviewer: "In a downturn, losses on similar loans have doubled." Candidate: "At 8 percent, profit is:"
Profit at 8 percent losses (INR a year):4,000,000,000 - 40,000,000,000 × 0.08 - 600,000,000 = 200,000,000
The recommendation
Launch, but grow carefully and price for risk. First, at expected losses the product earns about INR 180 crore a year on a INR 4,000 crore book. Second, profit disappears only if losses pass 8.5 percent, about twice the expected level, but a downturn could bring losses close to that, leaving only about INR 20 crore. Third, digital payment data is the edge: start with shops that have at least 12 months of steady payment history, lend smaller amounts first, and raise limits for shops that repay. Set a loss-rate trigger at 6 percent to slow lending if losses rise.
Risks: Payment data may not predict repayment in a downturn; Rules on digital lending may tighten.
Next steps: Test the scoring model on past data from 10,000 shops; Launch in three cities with small loan limits.
A strong candidate
Built the lender's profit from the right lines, found the break-even loss rate, and designed a cautious launch with a trigger.
A weak candidate
Focused on the size of the market of small shops and never calculated what credit losses would do to profit.
Case 3: A 150-bed hospital in a smaller Indian city
Worked case
Stretch: A 150-bed hospital in a smaller Indian city
The prompt
A private hospital group plans a 150-bed hospital in a fast-growing smaller city in central India. The exhibit shows the expected ramp-up. Does the investment meet the group's seven-year payback, and what would you change?
Difficulty: Stretch. Format: interviewer-led, with an exhibit. Industry: Healthcare. Region: India. Interview length: about 40 minutes. The company is fictional and all figures are illustrative.
Clarifying questions, with the interviewer's answers
- What payback does the group require?Answer: Within seven years.
- What revenue per occupied bed per day do current hospitals earn?Answer: About INR 30,000.
- What does the new hospital cost?Answer: About INR 1 crore per bed, INR 150 crore for 150 beds.
A hypothesis to say out loud: New hospitals take years to fill. My hypothesis is that slow ramp-up makes the seven-year payback hard to meet, and that patient mix in a smaller city could lower revenue per bed.
The structure
- Payback = capital cost / yearly EBITDA as the hospital fills
- Revenue: beds x occupancy x days x revenue per bed-day
- EBITDA margin as occupancy rises
- Key: Payback versus seven years
- Patient mix and capital cost
The exhibit
| Year | Occupancy (%) | EBITDA margin (%) |
|---|---|---|
| 1 | 40 | 0 |
| 2 | 55 | 12 |
| 3 | 70 | 20 |
Working it through
1. Year-3 revenue
150 beds, 70 percent full, 365 days, INR 30,000 per occupied bed-day.
Year-3 revenue (INR):150 × 0.7 × 365 × 30,000 = 1,149,750,0002. Year-3 EBITDA
At a 20 percent margin.
Year-3 EBITDA (INR):150 × 0.7 × 365 × 30,000 × 0.2 = 229,950,0003. Year-2 EBITDA
55 percent full at a 12 percent margin; year 1 breaks even.
Year-2 EBITDA (INR):150 × 0.55 × 365 × 30,000 × 0.12 = 108,405,0004. Payback
After three years, the rest of the INR 150 crore is recovered at the year-3 EBITDA rate.
Payback (years):(1,500,000,000 - (150 × 0.55 × 365 × 30,000 × 0.12 + 150 × 0.7 × 365 × 30,000 × 0.2)) ÷ (150 × 0.7 × 365 × 30,000 × 0.2) + 3 = 8.055. Curveball: patient mix
Interviewer: "In this city, about 40 percent of patients would come through a government insurance scheme that pays about INR 18,000 per bed-day (illustrative; actual scheme rates are often lower)." Blended revenue per bed-day:
Blended revenue per bed-day (INR):0.6 × 30,000 + 0.4 × 18,000 = 25,2006. Year-3 EBITDA with that mix
Costs stay the same, so the lost revenue comes straight off EBITDA.
Year-3 EBITDA with scheme patients (INR):150 × 0.7 × 365 × 30,000 × 0.2 - 150 × 0.7 × 365 × (30,000 - 25,200) = 45,990,0007. Option: lease the building
Interviewer: "A local developer would build and lease the building, cutting the group's own capital to about INR 60 lakh (6,000,000) per bed for equipment and fit-out." Candidate: "Then our capital is:"
Capital with a leased building (INR):150 × 6,000,000 = 900,000,000
What the exhibit shows
Profit arrives only from year 2, and full margin only from year 3, so early years recover little of the capital.
The recommendation
Do not approve the hospital as planned. First, even with all patients at INR 30,000 per bed-day, payback is about 8 years, beyond the 7-year rule, because the first two years recover little. Second, if 40 percent of patients come through the government scheme, year-3 EBITDA falls from about INR 23 crore to under INR 5 crore, and the hospital may never pay back. Third, a leased building cuts the group's capital from INR 150 crore to about INR 90 crore. Rework the plan: lease the building, focus on specialties where private insurance and self-paying patients are common, agree a cap on scheme beds, and resubmit with a payback test on the blended patient mix.
Risks: Lease costs lower EBITDA, so the lease terms must be tested; Doctors may be hard to recruit in a smaller city.
Next steps: Get lease quotes from two developers; Survey the city's patient mix and competitor hospitals.
A strong candidate
Modeled the ramp-up, tested payback against the rule, caught the patient-mix risk, and proposed a lower-capital model.
A weak candidate
Used year-3 profit for every year, found a quick payback, and approved the hospital.
Case 4: SunRoof Homes: rooftop solar in a western Indian state
Worked case
Standard: SunRoof Homes: rooftop solar in a western Indian state
The prompt
First, estimate how much rooftop solar capacity homes in a large western Indian state could install. Then: SunRoof Homes, a solar installer, asks whether homeowners will find the economics attractive, and what could change that.
Difficulty: Standard. Format: market-sizing opener, then a business question. Industry: Energy. Region: India. Interview length: about 30 minutes. The company is fictional and all figures are illustrative.
Clarifying questions, with the interviewer's answers
- Homes only?Answer: Yes, homes in one large western state with about 15 million households (illustrative).
- Which homes can install panels?Answer: Mainly independent houses with their own roof, about 40 percent of households.
- What system size?Answer: About 3 kW per home.
- For the business question, what does a system cost and save?Answer: A 3 kW system costs about INR 1,80,000 installed, before the central PM Surya Ghar subsidy of INR 78,000 (check the current rules). It runs at about 4 full-sun hours a day, and each kWh used replaces grid power at about INR 7.
- What does SunRoof earn?Answer: About INR 25,000 of gross margin per system, and it can install about 20,000 systems a year.
A hypothesis to say out loud: Rooftop solar is limited by who owns a suitable roof and can pay upfront. My hypothesis is that the realistic market is a few hundred thousand homes, and that homeowner payback decides how fast it grows.
The structure
- Size the market, then test the homeowner's payback
- Households x share with own roof x share who buy
- Capacity = homes x kW per home
- Key: Homeowner payback
- Installer profit
Working it through
1. Homes with their own roof
40 percent of 15 million households.
Independent houses:15,000,000 × 0.4 = 6,000,0002. Likely buyers in the next few years
Assume 10 percent can pay and want it.
Buyer homes:15,000,000 × 0.4 × 0.1 = 600,0003. Capacity
3 kW each, converted to gigawatts (1 GW is 1,000,000 kW).
Capacity (GW):600,000 × 3 ÷ 1,000,000 = 1.84. Yearly generation per home
3 kW for about 4 full-sun hours a day.
Generation (kWh a year):3 × 4 × 365 = 4,3805. Yearly saving
Each kWh replaces grid power at about INR 7.
Saving (INR a year):3 × 4 × 365 × 7 = 30,6606. Homeowner payback
A system costs about INR 1,80,000. After the central PM Surya Ghar subsidy of INR 78,000 for a 3 kW system (check the current rules), the homeowner pays INR 1,02,000.
Payback (years):102,000 ÷ (3 × 4 × 365 × 7) = 3.337. Curveball: export rules change
Interviewer: "The state regulator proposes moving homes from net metering to net billing, paying INR 3.5 per kWh exported. About half of generation is exported." Payback becomes:
Payback under net billing (years):102,000 ÷ (3 × 4 × 365 × (0.5 × 7 + 0.5 × 3.5)) = 4.448. Installer contribution
SunRoof earns about INR 25,000 of gross margin per system and can install about 20,000 a year.
Gross margin (INR a year):20,000 × 25,000 = 500,000,000
The recommendation
The state has room for roughly 1.8 GW of home rooftop solar, and the economics are attractive but sensitive to export rules. First, after the central subsidy a homeowner earns back the cost in about 3.3 years, which should sell well. Second, if the state moves to net billing and pays half the retail rate for exported power, payback stretches to about 4.4 years, which will slow sales. Third, SunRoof can protect demand by sizing systems so more power is used at home (for example with timers for water pumps and air conditioning) and by offering monthly-payment plans. At 20,000 installs a year it earns about INR 50 crore of gross margin.
Risks: Export-rule changes; Subsidy amounts and rules may change; Installation quality problems damage the brand.
Next steps: Model payback for different system sizes and home-use shares; Partner with a lender for monthly-payment plans.
A strong candidate
Sized the market in clear steps, then shifted to the homeowner's payback and tested the rule change.
A weak candidate
Gave a capacity number without steps and never looked at what the buyer earns.
Sources for this lesson (1)
- Recognized public explanations of case-interview concepts and frameworks
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