Used across 450+ 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.
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Tanishq, CaratLane, Thangamayil, Kirtilal, Kalidas (methodology adopted
Jewellery
SENCO GOLD
Jewellery
Gold and diamond jewellery, 450+ stores, pan-India.
Problem
Senco Gold’s event-driven allocation, particularly for Akshaya Tritiya, suffered from fragmented manual planning. Multiple merchandisers managed diverse regions using inconsistent logic, leading to a misaligned retail network. This lack of standardization created a dual crisis: high-demand stores faced frequent stock-outs during peak periods, while others held stagnant, excess inventory.
These imbalances severely strained working capital, eroded revenue, and compromised customer experience during critical sales windows. Without a centralized data framework, visibility into store-level performance remained limited, rendering inventory management reactive rather than strategic. Planning cycles spanned several days, stripping the brand of the agility needed to respond to real-time demand shifts.
Ultimately, the absence of an automated, data-driven system meant Senco Gold could not consistently optimize stock distribution across its vast clusters. This operational lag resulted in significant missed opportunities and inefficient capital utilization during the most vital seasonal spikes in the jewelry retail calendar across all major Indian markets.
Intervention
Gulmohars implemented a structured, automated allocation engine to replace manual planning, utilizing granular store-level velocity and historical sales data for precision. A sophisticated 23-step allocation logic was introduced, which systematically scores individual stores and applies specific depth caps across diverse product categories. This transition shifted the entire process from fragmented regional efforts to a centralized framework, enabling faster, more consistent, and data-driven decision-making across the network.
The new system significantly enhanced operational control by allowing merchandisers to perform manual overrides and conduct rigorous scenario testing before finalizing stock distribution. By integrating real-time demand patterns into the planning cycle, Gulmohars successfully reduced the risk of stock-outs in high-performing clusters while simultaneously preventing inventory pile-up in slower locations. This balanced approach optimized working capital and ensured that inventory alignment remained responsive to seasonal shifts, ultimately driving higher sell-through rates and improving overall customer satisfaction levels throughout the entire retail organization effectively.
The new system significantly enhanced operational control by allowing merchandisers to perform manual overrides and conduct rigorous scenario testing before finalizing stock distribution. By integrating real-time demand patterns into the planning cycle, Gulmohars successfully reduced the risk of stock-outs in high-performing clusters while simultaneously preventing inventory pile-up in slower locations. This balanced approach optimized working capital and ensured that inventory alignment remained responsive to seasonal shifts, ultimately driving higher sell-through rates and improving overall customer satisfaction levels throughout the entire retail organization effectively.
Outcome
- 70% reduction in understocked stores across 450+ stores → achieved during the 2-week Akshaya Tritiya planning period
- 5 days → under 24 hours allocation cycle time across all categories within 6 weeks of implementation
- Improved sell-through by 15–20% across gold and diamond categories across 450+ stores → within 3 months
- Balanced inventory across 450+ stores → reducing stock imbalances by 30% within 6 months
“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.”
— Regional Planning Lead
“What used to take days is now handled within hours, with significantly improved accuracy and consistency.”
Implementation Timeline
- Week 1–2: Data integration
- Week 3: Model setup
- Week 4: Pilot runs
- Week 5: Testing
- Week 6: Go-live
Case-1
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Fashion
Jewellery
GIVA
Fashion Jewellery
Sterling silver jewellery, 100+ store.
Problem
GIVA faced significant operational hurdles as it scaled rapidly across physical stores and digital channels, with inventory allocation remaining largely reactive. Store replenishment decisions relied heavily on static rules and manual inputs, which inevitably triggered frequent stock imbalances across the network. Consequently, high-performing stores suffered from recurring stock-outs, while slower locations accumulated costly excess inventory. The team struggled with a lack of clear visibility into granular store-level demand patterns, particularly for their most critical fast-moving SKUs.
These inefficient planning cycles were remarkably time-consuming, making it increasingly difficult for the brand to respond with agility to rapidly changing market trends and promotional shifts. Without a proactive, data-driven approach, the disconnect between supply and actual demand hindered GIVA’s ability to maximize its growth potential. This operational lag impacted both the bottom line and the overall customer experience during a period of aggressive expansion across all diverse retail platforms nationwide today.
These inefficient planning cycles were remarkably time-consuming, making it increasingly difficult for the brand to respond with agility to rapidly changing market trends and promotional shifts. Without a proactive, data-driven approach, the disconnect between supply and actual demand hindered GIVA’s ability to maximize its growth potential. This operational lag impacted both the bottom line and the overall customer experience during a period of aggressive expansion across all diverse retail platforms nationwide today.
Intervention
Gulmohars implemented a highly sophisticated, velocity-based allocation system specifically tailored to support GIVA’s complex omnichannel model. By integrating granular store-level sales, individual SKU performance metrics, and broader category trends, the platform now drives significantly smarter replenishment decisions. The system automates allocation recommendations, ensuring that every store receives a precise inventory mix that remains perfectly aligned with its unique local demand.
Furthermore, dynamic rules were introduced for fast-moving products, while planners retained the ability to adjust decisions through built-in controls. This transformation effectively replaced slow, manual planning with a centralized, data-driven workflow. Consequently, GIVA can now respond with incredible speed to emerging trends, ensuring optimal stock availability across its entire network while simultaneously minimizing waste. This strategic shift has empowered the brand to maintain its rapid scaling trajectory without the previous operational bottlenecks, ultimately delivering a more consistent and satisfying experience for customers across every single touchpoint and region.
Furthermore, dynamic rules were introduced for fast-moving products, while planners retained the ability to adjust decisions through built-in controls. This transformation effectively replaced slow, manual planning with a centralized, data-driven workflow. Consequently, GIVA can now respond with incredible speed to emerging trends, ensuring optimal stock availability across its entire network while simultaneously minimizing waste. This strategic shift has empowered the brand to maintain its rapid scaling trajectory without the previous operational bottlenecks, ultimately delivering a more consistent and satisfying experience for customers across every single touchpoint and region.
Outcome
- 40% reduction in stock-outs across high-performing stores → achieved across 100+ stores within 8 weeks
- 2x faster replenishment planning cycles → across all locations within 6 weeks of implementation
- Improved availability of fast-moving SKUs across locations → increased by 20% across 100+ stores within 3 months
- Better inventory balance across 100+ stores → reducing stock imbalances by 25% within 4 months
“We now allocate inventory based on actual demand instead of assumptions.”
— Head of Retail Operations, GIVA
“The visibility across stores has significantly improved our decision-making speed.”
— Supply Chain Manager, GIVA
“Planning that once required multiple iterations is now streamlined into a single, accurate cycle.”
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
Case-1
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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. This manual approach created significant operational strain, as merchandisers struggled to balance inventory levels across a growing network of stores. Without real-time data, high-potential sales were lost to stock-outs, while capital remained tied up in underperforming regions.
The absence of a standardized framework meant that replenishment was often reactive rather than strategic, failing to account for localized demand nuances or seasonal shifts. Consequently, the brand faced the dual challenge of markdown risks on aged inventory and missed revenue opportunities during peak periods. This lack of data-driven coordination ultimately hindered the organization’s ability to scale efficiently and maintain a consistent customer experience across its entire retail footprint.
The absence of a standardized framework meant that replenishment was often reactive rather than strategic, failing to account for localized demand nuances or seasonal shifts. Consequently, the brand faced the dual challenge of markdown risks on aged inventory and missed revenue opportunities during peak periods. This lack of data-driven coordination ultimately hindered the organization’s ability to scale efficiently and maintain a consistent customer experience across its entire retail footprint.
Intervention
Gulmohars introduced a highly sophisticated, demand-driven allocation system that leverages granular SKU-level velocity and comprehensive store performance data to optimize inventory. By automating allocation recommendations, the platform significantly accelerated decision-making processes across all product categories. This transition allowed planners to maintain agility, utilizing built-in controls to adjust allocations manually while ensuring overall network consistency.
The implementation effectively synchronized supply with localized demand, ensuring high-velocity items reached the most profitable locations. This strategic shift eliminated the inefficiencies of manual planning, reducing stock-outs and inventory pile-ups simultaneously. By providing deep visibility into real-time sales trends, Gulmohars empowered the brand to scale its operations with precision and speed. Consequently, the organization achieved a more balanced inventory distribution, which maximized sell-through rates and improved capital efficiency. This robust, data-centric workflow transformed inventory management into a competitive advantage, allowing for seamless adaptation to shifting market dynamics across the entire retail landscape effectively.
The implementation effectively synchronized supply with localized demand, ensuring high-velocity items reached the most profitable locations. This strategic shift eliminated the inefficiencies of manual planning, reducing stock-outs and inventory pile-ups simultaneously. By providing deep visibility into real-time sales trends, Gulmohars empowered the brand to scale its operations with precision and speed. Consequently, the organization achieved a more balanced inventory distribution, which maximized sell-through rates and improved capital efficiency. This robust, data-centric workflow transformed inventory management into a competitive advantage, allowing for seamless adaptation to shifting market dynamics across the entire retail landscape effectively.
Outcome
- 38% reduction in stock-outs across top-performing stores → achieved across 250+ stores within 6 weeks
- 3x faster allocation planning cycles → across all categories within 6 weeks of implementation
- Improved sell-through in key fashion categories → increased by 18–22% across 250+ stores during peak season within 3 months
- Better inventory balance across 250+ stores → reducing stock imbalances by 28% within 4 months
“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
“We no longer rely on assumptions — every allocation decision is now backed by clear data and store-level insights.”
— VP Merchandising, midtier omnichannel apparel brand, 250 stores
Implementation Timeline
- Week 1–2: Data integration
- Week 3: Model setup
- Week 4: Pilot runs
- Week 5: Testing
- Week 6: Go-live
Case-1
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Lifestyle Accessories Brand
ACCESSORIES
Lifestyle retail, 120+ stores with strong mall presence.
Problem
The brand struggled with uneven inventory distribution across diverse locations, particularly regarding its fast-moving accessories. Manual replenishment decisions led to significant delays, frequent stock gaps, and frustrating overstock in low-performing stores. Planning consistently lacked the agility required to respond effectively to rapidly changing market trends. This lack of data-driven coordination caused a mismatch between supply and localized demand, hindering overall sales growth.
Without a centralized system, merchandisers relied on intuition rather than real-time performance metrics, making it impossible to scale efficiently. High-potential revenue was lost as popular items vanished from shelves, while capital remained locked in stagnant inventory elsewhere. These operational bottlenecks prevented the brand from maintaining a competitive edge during seasonal spikes and promotional events. Ultimately, the absence of a structured framework compromised the customer experience and strained the supply chain, necessitating a fundamental shift toward an automated, responsive, and highly centralized inventory management strategy across the entire network.
Without a centralized system, merchandisers relied on intuition rather than real-time performance metrics, making it impossible to scale efficiently. High-potential revenue was lost as popular items vanished from shelves, while capital remained locked in stagnant inventory elsewhere. These operational bottlenecks prevented the brand from maintaining a competitive edge during seasonal spikes and promotional events. Ultimately, the absence of a structured framework compromised the customer experience and strained the supply chain, necessitating a fundamental shift toward an automated, responsive, and highly centralized inventory management strategy across the entire network.
Intervention
Gulmohars deployed a highly sophisticated, velocity-based replenishment system designed to dynamically adjust inventory flows across the entire store network. This platform significantly improved visibility into real-time stock health, enabling faster and more accurate transfer decisions to ensure products moved to the right locations at the right time. By replacing manual processes with automated, data-driven logic, the system effectively eliminated localized imbalances and ensured that high-demand items were always available where they were needed most.
Furthermore, the implementation allowed for seamless coordination between diverse retail clusters, reducing the risk of excess inventory in slower regions. This strategic agility empowered planners to react instantly to emerging sales trends and seasonal shifts, maximizing revenue potential while optimizing working capital efficiency. The transition to a centralized, responsive workflow transformed the brand’s supply chain into a competitive asset, consistently delivering a superior and reliable customer experience throughout every single operational branch effectively.
Furthermore, the implementation allowed for seamless coordination between diverse retail clusters, reducing the risk of excess inventory in slower regions. This strategic agility empowered planners to react instantly to emerging sales trends and seasonal shifts, maximizing revenue potential while optimizing working capital efficiency. The transition to a centralized, responsive workflow transformed the brand’s supply chain into a competitive asset, consistently delivering a superior and reliable customer experience throughout every single operational branch effectively.
Outcome
- 32% improvement in product availability across 120+ stores → achieved within 8 weeks of implementation
- 45% reduction in excess inventory value across 120+ stores → within 4 months of rollout
- Faster inter-store transfers and replenishment cycles → reduced cycle time by 2x across all locations within 6 weeks
- Improved inventory efficiency across 120+ stores → increased by 25% within 3 months
“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
“We now make faster, more confident decisions because we finally have visibility across every store.”
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
Case-1
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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.