With decades of experience at the intersection of logistics, supply chain management, and technology, Rohit Laila has become a leading voice on how artificial intelligence is revolutionizing retail operations. As severe weather events become more frequent and intense, his insights into building resilient, AI-powered supply chains are more critical than ever. We sat down with him to discuss how retailers can move from a reactive to a proactive stance when facing disruptions like major winter storms. Our conversation explored the intricate dance of data-driven forecasting, dynamic inventory allocation, and intelligent routing that keeps essential goods on the shelves when communities need them most, and how these advanced strategies are becoming accessible to retailers of all sizes.
When preparing for a winter storm, how does an AI system integrate historical sales data with advanced weather forecasts to anticipate consumer demand spikes? Can you walk me through the key decisions made 5-10 days before a storm versus in the final 72 hours?
It’s a fascinating process that moves from broad strokes to fine-tipped precision. Initially, about 5 to 10 days out, the AI models are in a scenario-planning phase. They ingest a huge amount of data, looking at past storms of similar intensity and how consumers behaved—what they bought, when they bought it, and where. This historical sales data is then layered with long-range weather projections. The system flags regions at risk and begins to model potential demand spikes for specific categories. It’s less about specific trucks and more about asking, “What if the storm hits here? What if it’s worse than expected?” As we get into that final 3-day window, the forecast is much more certain. That’s when the AI locks in its recommendations. The system shifts from ‘what-if’ scenarios to concrete operational decisions, confirming which products need to be shipped, which stores have the highest priority, and what initial routes the trucks should take.
Winter storms often trigger sharp demand for specific items. How do AI models decide which products to prioritize and which stores need them most? Could you provide an example of how this prevents a store from running out of essential goods during a blizzard?
This is where AI truly shines, by solving the problem of having the right inventory in the right place at the right time. The models don’t just see a storm; they see a pattern of human behavior. They know that a blizzard forecast will trigger a rush on staple foods, water, batteries, and medicine. The AI analyzes real-time inventory levels at each store and cross-references that with the store’s location relative to the storm’s projected path. It can identify a store that might become geographically isolated and flag it for an early, prioritized shipment. Imagine a store in a rural town. The AI anticipates that the main highway will likely close. So, days before the first snowflake falls, a truck is dispatched with a full load of those high-demand essentials. When the blizzard hits and the town is cut off, that store remains a lifeline for the community because the AI made a proactive, data-driven decision instead of waiting for the shelves to go empty.
When primary transportation routes are blocked by ice, how do AI-driven routing tools evaluate variables like road status and driver safety to find a viable alternative? Could you explain the concept of using “jump” trailers to pre-position essential goods in vulnerable regions?
Think of the AI routing system as a hyper-aware co-pilot. It’s constantly processing a stream of information far beyond a simple GPS map—live weather radar, road status reports from local authorities, and even telematics data from other trucks on the road. When a primary highway ices over, the system doesn’t just look for the next shortest path. It simulates multiple alternatives, weighing the urgency of the shipment against factors like road conditions and driver safety. This is where the “jump” trailer strategy comes into play. It’s a brilliant piece of proactive logistics. Instead of waiting for a distribution center to become inaccessible, we can pre-load trailers with essential goods and move them to secure locations closer to at-risk areas before the storm hits. These trailers act as forward-staged inventory. If a store’s regular delivery from a distant distribution center is impossible, a truck can be dispatched from a much closer point to pick up that jump trailer and complete the last leg of the journey on safer, local roads.
During a storm, logistics systems must balance delivery speed with safety. Could you describe how AI models are designed to penalize risky routes while still prioritizing critical shipments like medicine over less urgent items? What metrics would define a successful outcome?
This balance is a core function of the AI’s decision-making logic. It’s not a simple choice between fast and safe. Instead, the AI scores every potential route using a complex set of weighted variables. A route with reported icy conditions or a high risk of closure receives a heavy penalty score. This makes it a deeply unattractive option, even if it’s technically shorter. The system will almost always favor a longer but safer route. At the same time, the AI understands that not all cargo is created equal. A shipment of critical medicine or perishable baby formula is assigned a much higher urgency value than, say, a load of seasonal decorations. This ensures that the limited, safe transportation capacity is allocated to the most critical goods first. A successful outcome is measured in several ways: we’d look at the in-stock rates for those priority items at the store level, the accuracy of our demand forecast, and the on-time delivery percentage for critical loads. It’s about keeping promises to the community while protecting our drivers and assets.
For smaller retailers without massive resources, what are the first practical steps to implement AI-powered forecasting for winter storms? How can they leverage third-party tools and treat each storm as a “stress test” to build a valuable dataset over time?
The beauty is that this technology is no longer the exclusive domain of giants. A smaller retailer doesn’t need to build a custom AI platform from scratch. There are fantastic third-party, cloud-based tools for demand forecasting and transportation management that are both powerful and accessible. The most important first step is a mental shift: start treating every winter storm as a valuable learning opportunity—a “stress test” for your operations. After each event, collect and analyze your data. How accurate was your forecast? Which products sold out first? Which delivery routes failed? Each storm adds another layer of rich, specific data to your set. Over time, that dataset becomes your unique asset, allowing the AI models, even off-the-shelf ones, to become progressively smarter and more tailored to your specific business, products, and locations. It’s an iterative process of learning and improving with every weather event.
Looking ahead, how will integrating hyper-local data from traffic sensors or satellite imagery change supply chain strategies during storms? In what ways can AI help retailers achieve both resilience and sustainability goals, such as reducing emissions while improving delivery reliability?
The future is all about granularity. We’re moving from a regional view of a storm to a block-by-block, road-by-road understanding. Imagine integrating live data from municipal traffic sensors or satellite imagery that can detect snow and ice accumulation in real-time. An AI could then create highly localized, store-specific playbooks, rerouting a single truck around a neighborhood-level disruption. This level of precision builds incredible resilience. At the same time, resilience and sustainability are becoming two sides of the same coin. When an AI system finds a safer, more efficient route that avoids a storm-induced traffic jam, it’s not just getting a delivery there on time. It’s also preventing a truck from idling for hours, which directly reduces fuel consumption and carbon emissions. Designing smarter, more resilient networks inherently leads to less waste, fewer unnecessary miles driven, and a more sustainable operation overall.
What is your forecast for AI in supply chain management?
My forecast is that AI will become as fundamental to supply chain management as the shipping container was in the 20th century. It will move from being a “nice-to-have” competitive advantage to an absolute necessity for survival. The systems will become increasingly predictive, not just reacting to disruptions but actively anticipating and mitigating them weeks in advance. We’ll see AI managing entire ecosystems, orchestrating actions across suppliers, carriers, and stores in a seamless, automated flow. The focus will be on creating truly autonomous, self-healing supply chains that can withstand not just winter storms, but the full spectrum of climate-related and geopolitical disruptions we’ll face in the coming years. It’s an incredibly exciting, and necessary, evolution.
