Retailers are facing significant losses due to inefficient inventory management, including overstocking and stockouts. Chinmay Nayak, Head of Sales India at Onebeat, explains how AI-driven inventory solutions are helping retailers overcome these challenges by enabling real-time demand forecasting and smart stock allocation.
Interview With Chinmay Nayak, Head of Sales India, Onebeat
IT VOICE : What are the most common pitfalls leading to retail losses, and how can they be mitigated?
Chinmay Nayak : Inventory management inefficiencies have been a major cause of losses for retailers for years now. Internal process failures, disconnected data, inaccurate demand forecasting are a few major factors contributing to it. Globally, businesses lose billions of dollars because of inventory management issues including stockouts and overstocks.
Overstocks can lead to unexpected costs, such as warehouse expenses. To clear out overstocks, businesses often have to mark down products and hold clearance sales, which can drain the businesses’ finances and decrease their profit margin. Likewise, stockouts frustrate customers and they end up turning to competitors for rapid fulfillment. This hampers customer loyalty, leading to shrinking margins.
The best way to address inventory management inefficiencies is to integrate AI into the workflow. AI-powered inventory management solutions detect subtle patterns that humans may miss, predict demand and dynamically optimize inventory management. It prioritizes value and criticality — maximizing profitability for businesses. It offers real-time data analysis and demand forecasting. This means that retailers can move the right stock to the right store at the right time. It expands sales opportunities and revenue throughout the product lifecycle.
IT VOICE – How do internal inefficiencies (such as demand forecasting errors) contribute to revenue loss?
Chinmay Nayak- Smart allocation or the ability to make the right products available at the right store at the right time is the trinity for retail profitability. What’s hampering retailers from achieving it are data silos. Manually analyzing data to forecast product demands is next to impossible when production cycles are smaller. When inventory levels don’t align with the actual demand, companies end up marking down products and clearing stocks to make room for newer ones— leading to a significant crunch in profit margins. Inaccurate forecasts increase the chances of stockouts, leave customers unsatisfied and can even hamper customer loyalty.
IT VOICE – What are the biggest financial and operational challenges caused by overstocking and stockouts?
Chinmay Nayak- Financially, overstocking tie-up capital in unsold goods incurs storage costs, often pushing businesses to do discounted sales or inventory write-offs. Stockouts result in lost sales opportunities and frustrate customers, leading to low customer satisfaction. When it comes to operations, overstocking occupies larger spaces in warehouses, limiting room for high-demand items and fresh stocks. Overstocking and stockouts dramatically hamper a brand’s profitability and its chances to flourish in the competitive market.
IT VOICE – How does AI-driven inventory management differ from traditional forecasting methods? How can AI help retailers strike a balance between demand forecasting and inventory costs?
Chinmay Nayak- AI-driven inventory management is way ahead of traditional methods. Conventionally, retailers relied on historical data to forecast product demands. With AI, they can analyze real-time data market trends, seasonal fluctuations, store demands and more in a matter of seconds. AI identifies unique patterns and predicts demands more accurately than traditional methods. Retailers can avoid the risks of inventory imbalances and obsolescence.
AI-powered solutions enable real-time, store-to-store product transfer based on demand, ensuring that every SKU is available where it performs best. This boosts profitability, helping retailers strike a balance between demand forecasting and rising inventory costs.
IT VOICE – What kind of data inputs are crucial for AI to make accurate inventory predictions?
Chinmay Nayak- AI needs access to a multitude of data sets to generate accurate predictions. In order to predict customer demand and preferences, AI will need access to customer purchase data, evolving trends, inventory levels, turnover levels, and product shelf life to make the right products available to customers at the right time and location.
IT VOICE – What are some success stories where AI-driven inventory solutions have significantly improved profitability?
Chinmay Nayak- A well-known consumer electronics retailer was experiencing inventory management challenges, including overstocking of slow-moving products and stockouts of popular items. To address these issues, they implemented AI to analyze customer preferences and forecast demand in real-time. The AI solution helped them with assortment planning, optimizing inventory levels and minimizing deadstock. AI helped the brand reduce non-moving inventory by 60% and improve cash flow to achieve a turnover of 1.08 billion INR.
One of the fastest-growing pharmacy brands in India experienced multiple benefits through AI inventory management solutions. The company faced significant challenges with traditional inventory management methods, struggling to improve their stock levels and prevent lost sales. To address these issues, they implemented an AI-driven inventory management solution within their workflow. After six months of use, they saw remarkable improvements: they enhanced product availability, resolved assortment issues in their stores, and ultimately reduced lost sales by 25%. This transformation resulted in an 8% increase in in-store sales.
IT VOICE – What are the key challenges in implementing AI for inventory optimization, and how can they be addressed?
Chinmay Nayak – Retailers hesitate to adopt new methodologies, relying on ‘what has always worked’. However, in this fast-paced retail space, sticking to outdated approaches can hinder growth. In India particularly, retail is a fragmented ball game. The unorganized sector still comprises most of retail in India. Organized retailers have been among the first to experiment with and implement a technology-driven and data-driven approach to the business. However, many retailers still need awareness about what smart inventory management can do to improve profitability. In my experience, once retailers have understood that it improves profitability, they are quick to adopt it.
