Used across 320+ stores.
Trusted by India's leading
jewellery brands
Gulmohar’s powers allocation and planning decisions for brands ranging from 10-store regional chains to national multi-format networks.
Powered by Gulmohars
Tanishq, CaratLane, Thangamayil, Kirtilal, Kalidas (methodology adopted
SENCO GOLD
Jewellery
Gold and diamond jewellery, 450+ stores, pan-India.
Problem
Senco Gold managed event-driven allocation, especially for Akshaya Tritiya, through manual planning across multiple merchandisers. Each region followed different logic, leading to inconsistent stock distribution. High-demand stores faced stock-outs while others carried excess inventory. Lack of store-level insights and standardized allocation made it difficult to balance categories effectively. Planning cycles took several days, limiting responsiveness during peak demand periods.
Intervention
Gulmohars implemented a structured allocation engine using store-level velocity and historical sales data. A 23-step allocation logic was introduced to score stores and apply depth caps across categories. Planning became centralized and automated, enabling faster and more consistent decisions. The system also allowed overrides and scenario testing for better control.
Outcome
- 70% reduction in understocked stores during Akshaya Tritiya
- 5 days → <24 hours allocation cycle time
- Improved sell-through across gold & diamond categories
- Balanced inventory across 450+ stores
“We moved from reactive allocation to a structured, data-driven approach across all our stores.”
— Head of Merchandising, Senco Gold
“What earlier took days is now completed in under a day with far better accuracy.”
Implementation Timeline
- Week 1–2: Data integration
- Week 3: Model setup
- Week 4: Pilot runs
- Week 5: Testing
- Week 6: Go-live
GIVA
Fashion Jewellery
Sterling silver jewellery, 320 EBO stores, pan-India.
Problem
GIVA was scaling rapidly across stores and online channels, but inventory allocation remained reactive. Store replenishment decisions were based on static rules and manual inputs, leading to frequent stock imbalances. High-performing stores faced stock-outs while slower locations accumulated excess inventory. The team lacked clear visibility into store-level demand patterns, especially for fast-moving SKUs. Planning cycles were time-consuming, making it difficult to respond quickly to changing trends and promotions.
Intervention
Gulmohars implemented a velocity-based allocation system tailored to GIVA’s omnichannel model. Store-level sales, SKU performance, and category trends were integrated to drive smarter replenishment decisions. The platform automated allocation recommendations, ensuring each store received inventory aligned with its demand. Dynamic rules were introduced for fast-moving products, and planners could adjust decisions with built-in controls. This replaced manual planning with a centralized, data-driven workflow.
Outcome
- 40% reduction in stock-outs across high-performing stores (within 8 weeks)
- 2x faster replenishment planning cycles
- Improved availability of fast-moving SKUs across locations
- Better inventory balance across 100+ stores
“We now allocate inventory based on actual demand instead of assumptions.”
“The visibility across stores has significantly improved our decision-making speed.”
Implementation Timeline
- Week 1–2: Data integration (stores + online)
- Week 3: Model configuration
- Week 4: Pilot testing
- Week 5: Full rollout
- Week 6: Optimization & stabilization
Omnichannel Fashion Brand
APPAREL
Fashion retail, 250+ stores across India.
Problem
Inventory allocation was driven by static rules and manual inputs, leading to frequent mismatches between demand and supply. Fast-moving styles ran out quickly in top-performing stores, while slower locations accumulated excess stock. Limited visibility into store-level performance made it difficult to plan effectively across categories and sizes.
Intervention
Gulmohars introduced demand-driven allocation using SKU-level velocity and store performance data. The system automated allocation recommendations and enabled faster decision-making across categories. Planners could adjust allocations with built-in controls, improving flexibility without losing consistency.
Outcome
- 38% reduction in stock-outs across top stores (within 6 weeks)
- 3x faster allocation planning cycles
- Improved sell-through in key fashion categories during peak season
- Better inventory balance across 250+ stores
“We finally have clarity on what to send where, instead of relying on guesswork.”
— VP Merchandising
“Planning cycles are now significantly faster, and decisions are far more consistent.”
— Regional Planning Lead
Implementation Timeline
- Week 1–2: Data integration
- Week 3: Model setup
- Week 4: Pilot runs
- Week 5: Testing
- Week 6: Go-live
Lifestyle Accessories Brand
ACCESSORIES
Lifestyle retail, 120+ stores with strong mall presence.
Problem
The brand struggled with uneven inventory distribution across locations, particularly for fast-moving accessories. Manual replenishment decisions led to delays, frequent stock gaps, and overstock in low-performing stores. Planning lacked agility to respond to rapidly changing trends.
Intervention
Gulmohars deployed a velocity-based replenishment system that dynamically adjusted inventory flows across stores. The platform improved visibility into stock health and enabled faster transfer decisions, ensuring products moved to the right locations at the right time.
Outcome
- 32% improvement in product availability (within 8 weeks)
- 45% reduction in excess inventory across stores
- Faster inter-store transfers and replenishment cycles
- Improved inventory efficiency across 120+ stores
“Inventory is now where it needs to be — that alone has improved our performance.”
— Head of Supply Chain
“We can respond much faster to demand changes across stores.”
— Operations Lead
Implementation Timeline
- Week 1–2: Data integration
- Week 3: Configuration
- Week 4: Pilot testing
- Week 5: Validation
- Week 6: Go-live
Gulmohars Product Clients
Brands currently using Gulmohars for inventory planning
These references reflect methodology adoption, not Gulmohars software deployment.
Founder’s Prior Methodology Clients
Brands where similar allocation and planning methodologies were applied before Gulmohars
These references reflect methodology adoption, not Gulmohars software deployment.