Rohit Laila has spent decades navigating the complex arteries of the logistics and supply chain world, focusing on the high-stakes environment of delivery and convenience retail. His career is defined by a belief that technology is not just a tool but the very backbone of a modern, profitable enterprise. In this conversation, we explore how cutting-edge data management and automated reporting are revolutionizing the convenience store sector, particularly for regional players who cannot afford the luxury of inventory errors. We delve into the granular details of how leading chains are using item-level profitability reports and advanced loss prevention techniques to reclaim margins that were once thought lost to the “cost of doing business.”
The discussion covers the transition from manual, error-prone inventory tracking to automated, data-driven systems that empower store managers. We touch on the strategic differences in managing perishable versus center-store inventory, the critical importance of vendor accountability in maintaining traffic-driving categories, and the sophisticated ways retailers are now quantifying and controlling various forms of shrink. Finally, the interview looks toward the future of the industry, highlighting the role of predictive analytics and AI in refining the customer experience and ensuring operational precision.
How do you distinguish between top-line margin performance and post-shrink profitability at the item level?
In many retail environments, the focus remains stubbornly on top-line sales, but that approach can be incredibly deceptive when you are dealing with a small-format environment like Crosby’s, which manages more than 88 stores. We use an Item Profitability Report, or IPR, to look at a multi-period view where we can see exactly where margin is moving independently of volume. For instance, in the dairy category, specifically milk, we have seen periods where the gross margin percentage showed strong year-over-year improvement. However, once we applied the post-shrink lens, the story changed completely because the post-shrink gross margin percentage fell well below the pre-shrink numbers. This distinction matters because without it, you are effectively flying blind, thinking a category is a star performer when, in reality, waste and spoilage are eating your profits alive. By identifying that shrink is meaningfully eroding real profitability at the item level, we can take corrective action on ordering or placement that would be impossible if we only looked at total sales.
What are the primary operational challenges when managing inventory in a small-format convenience store compared to larger retail environments?
The most significant hurdle is that we simply do not have the backroom depth to absorb mistakes the way a massive big-box retailer might. In our regional districts across New York and Pennsylvania, every square inch of shelf space must earn its keep, which means our inventory decisions have to be surgically precise. We have to pair shrink management with strict planogram compliance and vendor accountability to ensure that we aren’t wasting space on items that don’t move. Managing a small footprint requires us to build order guides that are smarter, using a calculated average of sales history that intentionally excludes zero-sales weeks to prevent the data from being skewed downward. This starting-point for suggested orders allows a manager to focus on execution rather than spending hours guessing how much stock they need to survive the next delivery cycle.
How has the integration of automated reporting changed the way store managers interact with their inventory data on a daily basis?
The shift from manual spreadsheet preparation to an automated infrastructure has been a total game-changer for our teams because it eliminates the lag and error risk that used to haunt our period reviews. We built specific system tools that take raw sales data and shrink exports and transform them into formatted, color-coded Excel workbooks that are sent directly to managers. Before this, visibility might have taken weeks of manual compilation, but now that intelligence is built into the system with items flagged automatically based on our specific criteria. When a manager opens a report and sees a color-coded alert, they can trust the data and act on it immediately because the structure is consistent across the entire chain. This level of automation ensures that our store leaders are spending their time on the sales floor and managing their staff rather than getting buried under a mountain of raw data.
In your experience with large-scale operations like Loop Neighborhood Market, how do you balance high-frequency physical counts with the use of digital monitoring tools?
At Loop, which operates across 157 locations in California, we have found that a hybrid approach is the only way to maintain true operational accuracy. We utilize a combination of daily manager counts for high-priority items and comprehensive physical inventories that occur every four months across the entire chain. These scheduled tasks are handled through a task management platform that provides a financial baseline, while our security operations center monitors red flags at the stations in real-time. By coupling daily audits with state-of-the-art surveillance monitoring software, we can capture shrink early on and educate our staff on how to prevent it. This layering of physical and digital checks has been instrumental in reducing overall shrink because it creates a culture of accountability where every item is tracked from the moment it enters the store until it leaves with a customer.
Could you elaborate on the specific logic used to generate suggested orders for perishable categories like dairy to prevent out-of-stocks?
Perishables require a very different mathematical approach because the cost of a lost sale or a frustrated customer is significantly higher than the cost of a few modest outdates. We use a monthly order guidance system based on a six-week rolling sales window, which provides a much more accurate reflection of current demand than a simple historical average. The logic is designed to be “smart”—if a store had zero sales in a particular week due to a delivery issue or a temporary closure, we exclude that week so it doesn’t artificially drag down the suggested order quantity. We then apply a formula to the average to ensure we are always in stock, providing managers with a suggested quantity relative to their actual on-hand inventory. This ensures that even in small-format stores, where space is a premium, we are erring on the side of coverage for the items that our customers rely on every single day.
What strategies do you employ to manage vendor reliability, especially for traffic-driving categories that face chronic backorders?
Vendor reliability is perhaps our most persistent challenge, particularly with tobacco manufacturers where being out of stock on a top SKU can lose us a customer for good. To combat this, we built a recurring backorder tracking system that flags any item that has been unavailable for three or more consecutive weeks. Once an item crosses that threshold, the issue is automatically escalated so we can determine if it is a broader vendor problem or an internal ordering error. Having this data allows us to have firm conversations with our suppliers and explore category-level substitutions to fill the holes on the shelves. By tracking these issues week-over-week, we can address the problem before it compounds and starts to significantly impact our total store traffic and revenue.
How do you approach the “constant tension” between maintaining high stock levels and controlling the costs of slow-moving inventory?
That tension is the heart of retail management, and we handle it by being very disciplined with our center store categories while being more flexible with perishables. For the non-perishable items, we use SKU productivity metrics to flag any item that falls below our established velocity thresholds. This allows us to build a deliberate case for cutting an item rather than letting our assortment creep up over time, which only serves to dilute our facings and complicate the ordering process. If we see an item that has chronically high waste from outdates, we don’t just accept it; we look at whether it needs a different format, a new placement in the store, or if it should be removed entirely. Our goal is to ensure that slow-moving items aren’t stealing valuable space from the fast-moving products that drive our margins.
How does real-time visibility through handheld devices change the workflow for a store manager during their shift?
When a manager at one of the 157 Loop locations uses their handheld device, they have instant access to real-time inventory data that was once locked away in a back-office computer. As they scan an item to place an order or perform a spot check, the device displays the current inventory count immediately, which creates an instant feedback loop. If the count on the shelf doesn’t match what the screen says, it raises an immediate red flag that something is wrong, whether it’s a missed delivery, a scanning error, or a theft issue. This capability allows for pre-audit checks and daily counts that are much more than just a chore; they are an active way to prep the store and ensure accuracy. It empowers the staff to be proactive about their inventory rather than waiting for a quarterly audit to find out they have a problem.
What impact do you anticipate from the upcoming 2026 USDA stocking requirement changes on the dairy and staple food categories?
The U.S. Department of Agriculture’s new stocking requirements for the Supplemental Nutrition Assistance Program, which take effect in November 2026, represent a massive strategic challenge for small-format stores. These rules will significantly increase the number of distinct varieties we are required to carry across staple food categories, and dairy is by far the most difficult area to manage given its shelf-life and space requirements. We are already in the process of revisiting our entire assortment strategy to find ways to fit these new requirements without creating a massive spike in waste. It forces us to be even more analytical with our space-to-sales ratios because we are essentially being told to add complexity to a category where space is already at an absolute premium. This is a situation where having a robust data infrastructure is the only way to survive the transition without losing profitability.
Why do you believe so many retailers struggle to control shrink, and what are the most common mistakes they make in their assessments?
The biggest mistake is the tendency to manage by gut feeling rather than relying on hard data, often assuming that shrink is just an unavoidable “cost of doing business.” In reality, shrink is highly controllable if you break it down: external theft is roughly 60-70% controllable, while internal theft is 80% controllable, and administrative errors or vendor fraud are 100% controllable. Many retailers also fail because they add new SKUs much more easily than they cut the ones that aren’t performing, leading to massive over-assortment. They treat “bad merchandise” dollars as a simple expense on a P&L rather than seeing it as diagnostic information that tells them something is wrong with a specific store or item. Finally, there is often a sense of vendor passivity where retailers don’t systematically track backorders, meaning they miss the chance to hold their partners accountable for the out-of-stocks that are hurting their bottom line.
What is your forecast for the role of artificial intelligence in convenience store inventory management over the next five years?
I believe we are on the cusp of a major shift toward AI-assisted forecasting that will account for incredibly localized demand signals, such as weather patterns, neighborhood events, and even cross-category substitution behaviors. We will see the widespread adoption of intelligent camera systems that synchronize with point-of-sale data to monitor shelf levels and track items as they are removed in real-time. For the smaller operators, the biggest impact will be predictive analytics that move beyond simple historical averages to provide truly “smart” guidance that anticipates what a store needs before the manager even realizes it. However, these tools will only be effective for the retailers who invest in their data infrastructure right now; if your transaction and inventory data isn’t clean, consistent, and timely, you won’t be able to leverage the power of AI when it arrives.
