Smart solutions for inventory challenges in Indian fashion retail

India's fashion retail landscape is undergoing a seismic shift. Growing at a CAGR of 11%, the sector is outpacing other retail segments, with significant growth driven by tier 2 and 3 cities. These markets, shaped by rising disposable incomes and urbanisation, present exciting opportunities. In addition to the expanding market, the range of offerings has also evolved, with new materials and an increasing variety of categories now trending, diversifying the customer purchase mix.

Exciting growth opportunities lie ahead for retail chains and apparel brands, but they also bring significant risks. Inventory for sale is pre-bought or produced for the Spring-Summer and Autumn-Winter seasons 6–9 months in advance and refreshed every season. If the inventory is not sold within the season, it is often cleared at massive discounts to make room for new collections. Leading brands sell only about 50%–60% of their merchandise at total price. With the growing complexity of seasonal forecasting, driven by an ever-expanding variety of styles and categories, maintaining the current sell-through rates has become increasingly difficult. This challenge is exerting substantial pressure on profit margins. To address these hurdles, technology has emerged as an indispensable tool, offering innovative solutions to streamline inventory management and enhance forecasting accuracy.

The Retail Planning Roadblocks

Each new store, catering to specific localised consumer preferences, requires meticulous assortment planning to ensure the right products are available. However, as store networks expand, the combinations of stores, styles, sizes, and colours multiply, significantly complicating the process. To simplify, merchandisers often aggregate stores based on shared attributes or group product categories, deriving forecasts at an aggregated level. While reducing complexity, this approach increases forecasting errors, necessitating quicker, real-time adjustments during the sales season.

Manual methods like spreadsheet-based allocation fail to meet this demand, reducing the agility of retail chains. These inefficiencies result in missed sales opportunities due to stockouts or force retailers into markdowns for excess inventory at season's end, perpetuating a harmful discount culture. Poor-performing stores, burdened with outdated inventory, struggle to attract customers and often face the risk of closure, exacerbating the issue.

Technology as the Game Changer

Introducing Business Intelligence (BI) tools revolutionises how fashion brands manage inventory. These tools offer real-time insights, enabling brands to replace static, manual processes with dynamic, data-driven planning. By integrating predictive analytics and automation, BI systems address both pre-season planning and in-season adjustments.

Pre-Season Precision

1. Localised Assortment Planning:

BI tools analyse historical data, market trends, and regional preferences to create a tailored product assortment for each store for every season.

2. Data-Driven Merchandise Planning:

Advanced analytics calculate optimal purchase quantities, incorporating depth, width, and size ratios.

In-Season Agility

1. Dynamic Stock Allocation:

Real-time updates of sales trends  allow planners to rapidly adjust assortment and deployment quantiles to stores,  enabling them to prevent overloading of stores and hold it at a central stock reserve.

2. Automated Replenishment:

By monitoring sales patterns, the system triggers restocking of fast-moving items, ensuring availability throughout the season.

3. Inter-Store Transfers:

Unsold inventory at low-performing stores' can be redistributed to high-performing locations, ensuring better stock utilisation.

4. Dud Identification:

AI-powered analytics identify slow-moving styles early, enabling corrective action such as targeted promotions or reallocation.

Seamless Workflow Integration 

BI tools must integrate with existing ERP and POS systems for maximum impact. This linkage automates crucial tasks like stock adjustments, inter-store transfers, and markdown implementations, reducing manual workload and ensuring quick and faultless execution of decisions. Such integration also standardises workflows, enabling planners to focus on strategy rather than operational minutiae.

Retailers leveraging such tools can meet consumer expectations more accurately, ensuring profitability and sustained growth.

Conclusion

In an industry where trends shift overnight, agility and precision in inventory management are paramount. Store and category expansions present immense potential and operational challenges, but technology offers a way forward. BI systems, like VectorFLOW, can harmonise planning, execution, and real-time adjustments, providing the tools needed to navigate the complexities of modern fashion retail. By embracing such solutions, brands can optimise inventory and build agility in how they respond to changing consumer preferences—thus expanding profitably in a highly competitive landscape.

 By Nisarga Vichare, Product Manager, Vector Digital Labs, Vector Consulting Group

                  Murali Krishna B, Consultant, Vector Consulting Group