Major Domestic Producers Announce Sharp Price Hikes Effective 1st August 2026

India's leading polymer producers—Reliance Industries (RIL), OPaL, and IndianOil (IOCL)—have announced significant price revisions across major polymer grades, effective 1st August 2026. The revisions cover PP, HDPE, LDPE, LLDPE, and PVC, indicating a broad-based upward movement in the domestic polymer market. The sharpest increase has been seen in PP, with all three producers raising prices by ₹5,000/MT. HDPE grades have witnessed increases ranging from ₹1,500 to ₹3,000/MT, depending on the application sector, while LLDPE prices have increased by ₹4,000/MT. RIL has also revised PVC prices upward by ₹3,000/MT and increased LDPE prices by ₹2,000–₹4,000/MT, depending on the grade. In addition, Deemed Export prices have been revised upward by ₹10,000–₹12,000/MT. These revisions follow rising feedstock costs, firm crude oil prices, and strengthening domestic market sentiment. Since multiple major producers have announced similar increases simultaneously, the market is expected to align quickly with the revised pricing. What This Means for Polymer Buyers Could impact on domestic polymer procurement costs. Review pending purchase requirements before further market movement. Monitor import parity, crude oil prices, and upcoming producer announcements. Key Highlights PP: ▲ ₹5,000/MT HDPE: ▲ ₹1,500–₹3,000/MT LDPE: ▲ ₹2,000–₹4,000/MT (RIL) LLDPE: ▲ ₹4,000/MT PVC: ▲ ₹3,000/MT (RIL) PolyMart Perspective The synchronized price hikes by India's major producers signal a stronger domestic pricing environment. Buyers with immediate requirements should plan procurement strategically, while those with flexible demand should closely monitor crude oil, feedstock trends, and future producer revisions before making bulk purchasing decisions. PolyMart – Procure with Purpose.Disclaimer:PolyMart does not offer guarantees regarding the accuracy, reliability, or completeness of the information provided on its platform. Users are advised to independently verify any data before relying on it for decision-making purposes.