“Effective inventory volatility management in hyperlocal fulfillment is no longer about reacting to demand—it’s about engineering a predictive, adaptive, and fully integrated ecosystem. Organizations that invest in the right technology stack, cultivate strategic supply chain partnerships, and embed data intelligence at the core of their operations are best positioned to outperform in this high-velocity fulfillment environment,” emphasizes Shonik Goyal, President & Head – Supply Chain Management, Sheela Foam Ltd., during this exclusive interview…
The mattress industry was once seen as low-involvement and largely offline — but in the last few years, it has transformed with technology, personalization, and online models. What drove this shift, and how is Sheela Foam using innovation and customer insights to stay ahead in such a personal and evolving category?
Shonik Goyal
Mattresses, until recently, were considered a once-in-a-decade purchase, often decided by the retailer, not the end customer. But that changed dramatically due to three converging factors: evolving customer awareness about sleep health, a spike in digital-first brands reshaping expectations, and the pandemic accelerating online experimentation even in touch-and-feel categories like ours.
At Sheela Foam, with brands like Sleepwell and now Kurlon under our umbrella, we’ve embraced this disruption with purpose. Let me break this into three areas:
Product Science and Innovation: We’ve invested deeply in foam science, not just to sound “techy,” but to solve real customer problems — posture support, temperature regulation, motion isolation. Products like Sleepwell Nexa or the Fit Series are engineered for specific needs — from orthopedic alignment to athletic recovery. Our R&D teams in Australia and Spain contribute to global-grade tech that’s localized for Indian consumers. Memory foam is no longer the benchmark — we’re working on multi-zone support structures, AI influenced material mixes, and antimicrobial treatments as sleep needs evolve.
Channel Evolution: Offline + Online + Experience: Sleep is deeply personal, and consumers still want to “feel” before they buy. So, while the online share has grown from under 5% to over 10%, we haven’t abandoned retail — we’ve transformed it. Our Sleepwell Experience Centers blend physical and digital — they use pressure-mapping tech to help buyers find the right firmness or support. And yes, we’ve also cracked the logistics of bed-in-a-box, where compression and smart packaging allow high-rise delivery — though returns and sizing errors still remain a challenge we’re streamlining.
Customer Trust and Value Creation: What used to be ‘a piece of foam wrapped in nice fabric’ is now a health product. So, we’ve had to rethink the entire value equation — from trial periods and warranties to educating users about replacement cycles and posture science. Our newer platforms allow custom mattress sizing, subscription models for mattress protectors, and even sleep consultations in some pilot cities.
So, yes, this category has evolved — and brands that don’t evolve with it will lose relevance. For us, the goal isn’t just disruption — it’s sustainable innovation that helps consumers sleep better, live better, and stay connected with the brand beyond just one transaction every 10 years.
How do you see AI driving efficiency in quick commerce, especially across order management, delivery, and tech? What key opportunities or challenges stand out to you?
AI is also transforming how we predict demand. With dynamic replenishment systems in place, we can now forecast demand at a SKU-level, which helps us segregate stock into ‘made-To-Order’ and ‘made-To-Stock’ categories. This has been a gamechanger for our inventory management and fulfillment speed, especially when it comes to meeting quick commerce expectations. For example, made-to-stock items are guaranteed to be delivered within 24 hours, while made-to-order products are allocated different delivery windows depending on the location. AI’s role in demand prediction allows us to manage inventory more efficiently, resulting in better service levels and reduced stockouts.
Quick commerce is scaling fast, but profitability remains a challenge. Can it deliver long-term value, or is the model fundamentally unsustainable? What will it take to build a more viable ecosystem?
For us, it was indeed a tricky decision. As the leading offline mattress brand, we had to choose whether to stay focused on our dominant offline presence or step into the online space, where the dynamics were very different. Ultimately, we decided to enter online because we noticed the competition scaling aggressively in that channel—and we didn’t want to be left behind or allow others to gain an unchallenged foothold online and eventually encroach on our offline turf.
Yes, in the initial phase, our online journey involved burning cash. There were growing pains—logistics inefficiencies, high return rates, and challenges around size customization. But over time, we’ve taken several structural steps to bring this under control.
First, we optimized our manufacturing footprint. We now operate multiple decentralized facilities across the country. This not only reduces shipping costs but also shortens delivery times significantly, which is key for both cost efficiency and customer satisfaction.
Second, we’ve invested in roll-pack technology at a national scale, allowing us to ship bulkier products like mattresses more cost-effectively, with lower chances of damage or returns. That has been a game-changer for improving margins.
Third, we are actively refining our return management process. We’re leveraging better sizing guides, AI-driven personalization tools, and in some cases, even deploying service staff to customer homes to verify dimensions and suitability before dispatch. This human touch has helped reduce costly errors and improve customer trust.
We’ve also begun using predictive analytics to flag high-return-prone orders and proactively offer assistance or restrict COD options. This is helping lower our reverse logistics cost. While the journey hasn’t been easy, our aim is clear: to move toward sustainable profitability while staying relevant in a fast-evolving channel. E-commerce will never be a perfect mirror of offline retail—but it doesn’t have to be. It has its own strengths, and we’re now in a better position to harness them.
How can companies balance speed versus cost-efficiency in Q-commerce logistics?
Q-commerce companies can balance speed and cost-efficiency by strategically placing warehouses close to customers, leveraging technology for route optimization, and partnering with local retailers. This approach minimizes delivery times and associated costs while ensuring efficient inventory management and faster, more reliable service.
Strategic Warehouse Placement: Establishing micro-warehouses, also known as dark stores, in urban areas significantly reduces delivery times. These stores are designed for rapid order fulfillment, enabling quicker delivery to customers within a short radius.
Technology Integration: Advanced technologies like AI and machine learning are used to optimize inventory management, route planning, and predictive analytics for demand forecasting. This ensures smooth operations, consistent inventory levels, and high fill rates.
Route Optimization: Route optimization algorithms, powered by AI and machine learning, strategically plan delivery routes to minimize delays, reduce costs, and optimize fuel consumption. These algorithms consider various factors like traffic patterns, vehicle capacity, and delivery time windows.
Local Partnerships: Collaborating with local retailers and suppliers enhances inventory availability and distribution efficiency. This can involve partnering with local grocery stores or Kirana shops to ensure popular items are readily available.
Sustainability: Adopting eco-friendly practices, such as using electric vehicles for deliveries and sustainable packaging, can mitigate environmental concerns and appeal to eco-conscious consumers, potentially leading to lower long-term costs.
What strategies are proving most effective in managing inventory volatility in hyperlocal fulfillment models?
Managing inventory volatility in hyperlocal fulfillment models demands a sophisticated, multi-pronged approach rooted in real-time intelligence, predictive analytics, and operational agility. These models, characterized by their emphasis on rapid delivery within tightly defined geographies, necessitate inventory systems that are not just responsive, but anticipatory in nature.
At the core is real-time inventory visibility. Hyperlocal fulfillment leaves no room for latency in inventory updates— every transaction must be instantly reflected across all nodes in the network. Advanced Inventory Management Systems (IMS) are indispensable in this context. These systems must support dynamic synchronization across multiple fulfillment centers, enabling precise stock tracking, immediate replenishment alerts, and seamless integration with e-commerce platforms and point-of-sale systems. Without this level of granularity and automation, the risk of stock discrepancies and fulfillment errors escalates sharply.
Equally critical is predictive demand forecasting. Leading organizations are leveraging AI and machine learning algorithms to process vast datasets encompassing historical sales, promotional calendars, local market behavior, and external factors such as weather or regional events. This allows for the generation of highly granular forecasts, tailored down to the SKU level and hyperlocal geography. Such forecasts underpin robust demand planning, enabling businesses to optimize reorder points, set dynamic safety stock thresholds, and adjust procurement strategies in near real time.
Warehouse and fulfillment network optimization is another strategic imperative. Hyperlocal efficiency hinges on micro-fulfillment strategies that bring inventory closer to the end consumer. This includes deploying multi-node warehousing models and leveraging dark stores or urban fulfillment hubs. Just-in-Time (JIT) inventory strategies can further reduce holding costs while maintaining responsiveness, but must be underpinned by highly reliable supply chain coordination. Fulfillment processes themselves must be engineered for speed and accuracy, incorporating technologies such as automated picking systems, robotics, and real-time order routing to accelerate throughput and reduce error rates.
Collaboration across the supply chain is also evolving into a key differentiator. Strategic partnerships with Third Party Logistics (3PL) providers offer scalability, geographic reach, and fulfillment expertise, especially in last mile delivery. Deep supplier integration is equally vital. Advanced supplier relationship management ensures agile replenishment, while Vendor-Managed Inventory (VMI) frameworks enable suppliers to take proactive control of inventory levels based on real-time consumption data—reducing latency and minimizing inventory risk.
Data analytics serves as the intelligence layer across all these strategies. Mature hyperlocal operators are investing in advanced analytics platforms that provide end-to-end visibility and decision support. These systems can surface actionable insights, identify anomalies, forecast disruption risks, and simulate inventory scenarios. The most advanced use cases incorporate prescriptive analytics and digital twins, allowing for continuous optimization of fulfillment and inventory strategies.
In sum, effective inventory volatility management in hyperlocal fulfillment is no longer about reacting to demand—it’s about engineering a predictive, adaptive, and fully integrated ecosystem. Organizations that invest in the right technology stack, cultivate strategic supply chain partnerships, and embed data intelligence at the core of their operations are best positioned to outperform in this high velocity fulfillment environment.
How do you see the convergence of Q-commerce and traditional e-commerce models playing out in the next 3–5 years?
In my view, in the next 3-5 years, Q-commerce and traditional e-commerce will likely converge, with Q-commerce focusing on convenience and speed for essential goods, and traditional e-commerce handling broader product categories and bulk purchases. Both will leverage technology for optimization and customer experience and may even integrate operations for greater efficiency.
THE FOLLOWING IS MY TAKE ON HOW THE CONVERGENCE WILL HAPPEN:
Hybrid Models: We might see the emergence of hybrid models where Q-commerce platforms expand their product range and offer longer-lead-time deliveries, while traditional e-commerce platforms introduce faster delivery options for certain items.
Integration of Technology: Both Q-commerce and traditional e-commerce will rely heavily on technology to improve customer experience, optimize operations, and personalize the shopping journey.
Customer-Centric Approach: The focus will be on delivering a seamless and convenient experience, with companies adapting their business models to meet the evolving needs of consumers.
Competition and Innovation: The competition between Q-commerce and traditional e-commerce will drive innovation and lead to better services and lower prices for consumers.
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