Manyavar uses AI to tailor store inventory and target 90% sell-through

Ethnic-wear retailer Manyavar says algorithms now guide 70-75% of merchandising decisions, using nearly 85 Cr historical data points to tailor assortments by store. The system also enables inter-store transfers, clearing a further 7% of inventory while protecting margins above 65%.

— Source published Wed, 19 Aug, 2026, 12:19 IST · First seen Wed, 19 Aug, 2026, 12:54 IST · Source Inc42 · Buzz

What happened

Manyavar says AI, automation and granular product data drive store-specific assortment, inventory allocation and transfers. The ethnic-wear retailer targets 90%

Key facts

  • Over ₹1,000 Cr revenue
  • Margins exceeding 65%
  • 35% inventory design overlap between South Delhi and Karol Bagh
  • 70-75% of decisions taken by algorithms
  • 90% target sell-through per store
  • Further 7% inventory cleared through inter-store transfers
  • Two decades of historical data
  • Almost 85 Cr data points
  • Seventh edition of Inc42 D2C & Retail Summit

Why this matters

Manyavar’s merchandising AI and inter-store transfer capability make data, allocation software, and retail-logistics partners strategically relevant targets for apparel players seeking faster inventory turns.

What to watch

  • Reported changes in full-price sell-through, gross margin and end-of-season markdown rates.
  • Inventory days, stock-turn improvement and the share of aged inventory cleared through transfers.
  • Growth in inter-store transfer volume relative to transfer cost and fulfillment lead times.
  • Evidence that algorithm-guided merchandising rises above the stated 70-75% of decisions.
  • Regional in-stock rates for high-demand wedding and festival categories.
  • Competitor announcements on localized allocation, inventory pooling or AI merchandising deployments.
  • Expand AI allocation from core apparel into accessories, occasion-specific capsules and regional wedding collections.
  • Link local demand signals such as wedding calendars, festival timing, weather, search behavior and store appointments to replenishment forecasts.
  • Increase RFID, SKU-level inventory accuracy and transfer-routing capability to make cross-store fulfillment economical.
  • Use sell-through and markdown-risk scores to trigger earlier transfers, online fulfillment or targeted customer outreach.
  • Reallocate store buying authority toward centrally governed algorithmic recommendations with merchant override controls.

Also reported by