Borosil Scientific Limited (BOROSCI) Fair Value & Analysis
Consumer Cyclical · IN · Market cap ₹13.9B
Fair value as of: Jun 29, 2026
Analysis
Borosil Scientific Limited (BOROSCI) currently trades at ₹154.84, while our model-based Fair Value estimate is ₹77.83 — implying the stock looks roughly 49.7% 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
Borosil Scientific Limited engages in the manufacture and sale of laboratory glassware. It offers glassware and consumables, including lab glassware, filtration products, and analytical vials; laboratory instrumentation for use in nutrition and environment, life essentials, life sciences, and liquid handling; pharmaceutical primary packaging materials, such as glass ampoules and tubular glass vials; and laboratory process system, comprising mini reactors system; jacketed reactor; parallel reaction; automated reaction; photo and specialty; and utilities and accessories system. The company also provides Industrial Process Equipment and Original Equipment Manufacturing solutions. It serves the pharma, chemical, research, agriculture, food and beverages, and environment industries. The company was formerly known as Klass Pack Limited and changed its name to Borosil Scientific Limited in December 2023. The company was founded in 1962 and is based in Mumbai, India.
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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.