Capstone Project: Full Forecast & DCF – Tata Motors Ltd by Kajal Upadhyay

Capstone Project: Full Forecast & DCF – Tata Motors Ltd by Kajal Upadhyay

1. Company Overview

Company: Tata Motors Ltd
Segments:

• Passenger Vehicles (India)
• Commercial Vehicles (India)
• Jaguar Land Rover (JLR – global)

Business Model Summary: Tata Motors is a diversified automotive manufacturer with exposure to emerging and developed markets, cyclicality in CVs, and premium luxury exposure via JLR.

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2. Historical Analysis (Last 5 Years)

Data Source

• Annual Reports (Standalone + Consolidated)
• Investor Presentations

Financial Statements Imported

• Income Statement
• Balance Sheet
• Cash Flow Statement

Key Historical Trends Observed

Revenue

• Cyclical growth with strong contribution from JLR
• India PV recovery post‑COVID
• CV segment linked to infra & capex cycle

Margins

• EBITDA margin expansion due to:
• Operating leverage
• Cost optimization at JLR
• Margin volatility driven by commodity prices & FX

Profitability Metrics

• ROCE improving but still below global auto peers
• ROE volatile due to leverage and cyclicality

Leverage & Liquidity

• Historically high debt at JLR
• Gradual deleveraging trend
• Improved operating cash flows in recent years

✅ Key Takeaway: Tata Motors is transitioning from a high‑leverage turnaround phase to a normalized growth phase.

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3. Key Historical Ratios (Summary)

Metric	Observation
Revenue Growth	Cyclical, improving trend
EBITDA Margin	Expanding
Net Margin	Low but improving
Debt / Equity	Declining
ROCE	Improving, still moderate

🚩 Red Flags: Cyclicality, FX risk, EV execution risk
✅ Positives: Cash flow recovery, demand tailwinds, management discipline

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4. Forecast Assumptions (5 Years)

Revenue Forecast

• Segment‑wise approach:
• India PV: mid‑high single digit growth
• India CV: cyclical recovery based
• JLR: moderate growth with margin stability

Revenue Driver:
Units × Average Realization

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Margin Assumptions

• EBITDA margins stabilize at normalized levels
• Operating leverage benefits offset input cost inflation

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Capex Assumptions

• EV investments
• Capacity expansion
• Assumed as % of revenue (historical average)

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Working Capital

• Receivables, inventory, payables modeled as % of revenue
• Gradual efficiency improvement assumed

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Depreciation, Interest & Tax

• Depreciation linked to capex
• Interest based on average debt balance
• Tax rate normalized to statutory effective rate

✅ All assumptions kept conservative and consistent with history

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5. 3‑Statement Integrated Forecast

Your Excel model must link:

Income Statement

• Revenue → EBITDA → EBIT → PAT

Cash Flow Statement

• CFO driven by profitability + WC
• CFI driven by capex
• CFF driven by debt reduction

Balance Sheet

• Assets funded by retained earnings and debt
• Balance Sheet fully balances every year ✅

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6. Free Cash Flow to Firm (FCFF)

Formula Used

FCFF = EBIT × (1 – Tax Rate)

       + Depreciation

       – Capex

       – Change in Working Capital

Trend

• Near‑term FCFF improving
• Stable positive FCFF in outer years

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7. WACC Calculation

Cost of Equity (CAPM)

• Risk‑free rate (10Y Govt bond)
• Beta (industry adjusted)
• Market risk premium

Cost of Debt

• Average borrowing cost
• Tax‑adjusted

Capital Structure

• Based on target long‑term mix

✅ WACC kept realistic (no aggressive assumptions)

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8. DCF Valuation

Steps

1. Discount FCFF for 5 years
2. Terminal Value using perpetual growth
3. Enterprise Value = PV of FCFF + PV of TV
4. Equity Value = EV – Net Debt
5. Value per Share calculated

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9. Scenario Analysis

Base Case

• Moderate growth
• Stable margins
• Industry‑aligned assumptions

Bull Case

• Strong EV adoption
• Faster JLR recovery
• Margin expansion

Bear Case

• Demand slowdown
• Margin pressure
• FX headwinds

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10. Sensitivity Analysis

Key Sensitivities

• WACC vs Terminal Growth (2D table)
• EBITDA Margin vs Revenue Growth (optional)

✅ Shows valuation range and downside protection.

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11. Valuation Summary

Method	Value
DCF (Base Case)	₹XXX per share
Bull Case	Higher range
Bear Case	Downside range

Comparison with market price → valuation gap identified.

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12. Final Investment Recommendation

Recommendation:
✅ Buy / Hold / Sell (Based on my analysis and output from above)

Rationale

• Improving cash flows
• Deleveraging balance sheet
• Demand recovery + EV optionality

Key Risks

• Cyclicality
• Commodity prices
• EV execution at scale

 

Investment Banking Pitch Deck by Kajal Upadhyay

 

IB_PitchDeck_Kajal Upadhyay_LogiKart.pdf

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1) Title Slide

LogiKart

Tagline: Powering same‑day logistics for India’s local retailers

What we do:
A B2B logistics platform enabling neighborhood retailers to deliver orders to customers within the same day using micro‑warehouses and optimized routing.

Prepared by: Kajal Upadhyay
Course: Investment Banking Portfolio Project
Date: April 2026

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2) Problem

Who has the problem

• Small & mid‑size retailers (kirana stores, pharmacies, electronics shops) in Mumbai

Pain points

• ❌ Dependence on unreliable third‑party delivery services
• ❌ High delivery costs reduce already thin margins
• ❌ No technology to manage routing, tracking, or delivery SLAs

Why it matters

• Lost online orders
• Poor customer retention
• Inability to compete with Amazon / Blinkit / Zepto

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3) Solution

LogiKart Platform

An end‑to‑end logistics solution tailored for local retailers

Core Features

1. Micro‑warehouse network within 3–5 km radius
2. Smart routing algorithm for same‑day delivery
3. Retailer dashboard for order tracking & analytics

Before vs After

Before LogiKart	After LogiKart
Manual delivery coordination	Automated same‑day delivery
₹80–₹120 per delivery	₹35–₹45 per delivery
No tracking visibility	Real‑time tracking & SLAs

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4) Market Opportunity (TAM–SAM–SOM)

TAM (India)

• ~12 million urban retailers
• Avg logistics spend ₹40,000/year
  ✅ TAM ≈ ₹48,000 Cr

SAM (Top 8 Indian cities)

• ~2 million retailers
  ✅ SAM ≈ ₹8,000 Cr

SOM (Mumbai – 3–5 years)

• Target: 10,000 retailers
• Avg revenue ₹18,000/store/year
  ✅ SOM ≈ ₹180 Cr

Assumption: 2% penetration of Mumbai retailers in 5 years

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5) Business Model

Revenue Streams

• Per‑delivery fee: ₹40
• Monthly SaaS subscription: ₹499/store

Unit Economics (Average Retailer)

• 45 deliveries/month × ₹40 = ₹1,800
• Subscription = ₹499
• Total revenue: ₹2,299/month

Direct Costs

• Delivery partner payout: ₹28/delivery
  ✅ Gross margin ≈ 30–35%

Pricing acceptable because it is cheaper than market alternatives and improves customer retention.

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6) Traction / Validation

Customer Validation

• ✅ 18 retailer interviews across Borivali, Andheri, Dadar
• ✅ 72% said last‑mile delivery is their #1 growth bottleneck
• ✅ 11 retailers signed LOI for pilot

Early Signals

• WhatsApp waitlist: 143 retailers
• 3 local courier partners agreed to integrate

(Early-stage validation — pilot launch planned)

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7) Go‑To‑Market Strategy

Primary Acquisition Channels

1. On‑ground sales reps (local trust matters)
2. Retail association partnerships

First 90 Days

• Pilot with 50 stores
• Launch Borivali + Andheri West
• Optimize delivery density

CAC (Initial)

• ~₹2,500 per retailer
• Payback period ≈ 2 months

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8) Competitive Landscape

Player	Focus	Price	Weakness
Dunzo	Consumer	High	Retailer‑agnostic
Shadowfax	Enterprise	High	Not SMB focused
Porter	Point‑to‑point	Medium	No SaaS layer
Local couriers	Offline	Low	No reliability
Blinkit	Inventory‑led	N/A	Competes with retailers

LogiKart Advantage

✅ Retailer‑first
✅ Predictable pricing
✅ Tech + ops bundled
✅ Defensible through routing data & density

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9) Financial Snapshot (₹ Cr)

	Year 1	Year 2	Year 3
Active Retailers	300	2,000	6,000
Revenue	2.8	18.5	62.0
Operating Costs	4.2	14.0	38.0
EBITDA	(1.4)	4.5	24.0

Assumptions

• Avg ARPU: ₹18k/year
• Retention: 75% after 6 months
• Break‑even: mid Year 2

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10) The Ask

Raising: ₹6.5 Cr (~$780k)

Instrument: Equity
Runway: 18 months

Use of Funds

• Product & tech: 35%
• Operations & delivery network: 40%
• Sales & hiring: 25%

Goal: Reach 2,000 stores + profitability

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11) Team

Founder

Kajal Upadhyay — Founder & CEO

• Background in finance & operations
• Experience with market research, financial modeling & customer discovery

Planned Hires

• Head of Operations
• Full‑stack engineer
• City sales lead

(Advisors from logistics & retail tech planned)

 

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