Craftsman Automation Limited (CRAFTSMAN) Fair Value & Analysis
Consumer Cyclical · IN · Market cap ₹242B
Fair value as of: Jun 29, 2026
Analysis
Craftsman Automation Limited (CRAFTSMAN) currently trades at ₹9,252, while our model-based Fair Value estimate is ₹3,230 — implying the stock looks roughly 65.1% overvalued today. We read business quality at 97/100 (high quality), in the Consumer Cyclical sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: high).
About the company
Craftsman Automation Limited operates as an engineering company in India. It operates through Powertrain, Aluminium Products, and Industrial & Engineering segments. The Powertrain segment manufactures engine parts, including cylinder blocks and cylinder heads, camshafts, transmission parts, gear box housings, turbo charges, and bearing caps. The Aluminium Products segment offers crank case and cylinder blocks for two wheelers, and engine and structural parts for passenger vehicles; gear box housings for heavy commercial vehicle; and aluminium casting products for power transmission applications. The Industrial & Engineering segment provides stationary racking for warehouses, V-stores, roll form products, and automated storage and retrieval systems; and SPM and material handling products, gears and gear boxes, tool rooms, mould bases, and sheet metals. The company offers metal and metal products; electricals or electronic machinery, railway or tramway locomotives, rolling stock, rail…
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.