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Stellar Value Chain Solutions launches in 7 new cities across India

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Stellar Value Chain Solutions, has set up operations in five new cities namely Surat, Nagpur, Indore, Raipur, and Patna. Set up to meet the rise in demand of the festive season sales in tier 2 cities, the new BTS warehouses, will bring the footprint to 14 million sq. ft. of warehousing space.

This is in line with the company’s vision to create best-in-class warehouse infrastructure of 50 million sq. ft. across India and transportation (50,000 trucks).

According to a report by the consulting firm Redseer, this year’s festive season is expected to bring in sales of $11.8 billion in gross merchandise value (GMV), which is up by 28% from last year. Net demand for warehouse space in 2022 is likely to be higher than in 2021.

“The consumption story in India remains strong. The three sectors of our focus namely E-Commerce, Consumer and Automotive are showing robust growth. Stellar is continuously building Grade A plus Warehousing and Logistics infrastructure in its pre-defined 21 cities of production and consumption to enable corporates to expand and grow with speed and agility with better service levels and lower costs.”

Mr. Anshuman Singh, Chairman & Managing Director, Stellar Value Chain Solutions Pvt. Ltd

Stellar Value Chain Solutions already has a strategic presence across 21 centres pan India. These growth centres spread across 21 key cities in India cater to 85% of India’s consumption and production requirement.

In addition, the company’s technology wing, StellarTECH, supports and provides cutting-edge “Warehouse Management System with LMS, Transport Management System, and Vehicle Tracking System” as the core technology value proposition and an enabler for digital operations. Customers are supported through the extensive use of IoT and Big Data Analytics, and the design principle takes IoT readiness into account. Using Big Data Analytics, the company provides its customers with real-time decision-making insights based on a variety of predictive models, route optimization algorithms, storage optimization algorithms, and machine-learning techniques.

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