About the Author: Rafa Santiago was the leader of the Purchasing and Logistics department for one of the largest food distribution companies in the Caribbean, where he managed more than 400 suppliers and 600-plus containers monthly around the world. This experience exposed critical inefficiencies caused by manual processes and fragmented data systems, sparking his drive to innovate. Motivated by these challenges, he co-founded Nauta, a B2B SaaS platform powered by AI that automates up to 75% of operational tasks. Nauta addresses the pain points of logistics by optimizing processes, increasing efficiency, and reducing risks for importers. The company’s mission? To empower businesses with smarter, faster, and more resilient trade operations.
Imagine being responsible for managing 10,000 individual SKUs, spanning from frozen shrimp to mops, that need to be delivered to restaurants, hotels, cruises, hospitals and more. This is the foodservice distribution business. It runs on a massive, complex global cold chain, and it’s one of the most unforgiving flow of goods in the world. At the mid-size to enterprise level in the foodservice segment, complexity compounds. Logistics, procurement, demand planning, and finance are all making thousands of interdependent decisions every day, often with partial information and shrinking margins for error.
Tribal knowledge used to be a competitive advantage for supply chain leaders. I went into supply chain because my father was in it; I learned about managing shipments, who the players were, why we liked this carrier better than the other and how to wrangle a client at the dinner table every night. He worked and led the same company for 50 years. But what worked for my father’s generation won’t work for mine, as it’s predicated on the belief that new employees will happily do the same process as you, for 25 years.
That model does not scale and will not meet the needs of today’s global food supply chain.
Across large foodservice distributors, a consistent pattern emerges. A technology stack looks modern on paper as day-to-day execution still depends heavily on emails, spreadsheets and tribal knowledge. Goods continue to flow, and the organization’s reliance on tribal knowledge shows up as financial leakage in stockouts, excess inventory, missed free time, tariff misclassification, and cash tied up in the wrong products at the wrong time.
There’s been an incredible flood of venture capital funding into the supply chain technology market. I’d argue many of these companies are riding a hype cycle and see global supply chain as a market to conquer; when in reality, after nearly twenty years running operations for some of the biggest food shippers in the world, I believe AI is the first technology capable of understanding messy, unstructured supply chain data, predicting outcomes instead of reporting history, and triggering actions instead of alerts.
For logistics, procurement, demand planning, and finance leaders, the question is no longer whether AI belongs in the supply chain. It’s my personal mission to help supply chain leaders answer two critical questions: how do we get comfortable with it, and what can we reasonably expect? Here are a few concrete, real-world examples for how AI is measurably improving logistics, procurement, and finance for food and beverage companies, and what leadership should expect of any new solution they bring on.
Logistics teams in foodservice organizations manage thousands of inbound containers and domestic shipments across a fragile cold chain, yet bills of lading, arrival notices, packing lists, and free time terms still arrive through unstructured channels (emails, PDFs) and disconnected portals that teams have resentfully already sunk time and money into.
Unstructured, dirty data obscures visibility and compounds supply chain risk, especially in risk intolerant cold chain operations. For example, according to the Federal Maritime Commission, demurrage and detention charges increased by more than 40% between 2020 and 2022, with food and agriculture among the most affected sectors. Industry estimates suggest that 15-25% of containers incur detention or demurrage risk, often because teams discover problems too late. Fragmented, unstructured data means teams learn about delays after free time has expired or after the product has already missed downstream demand windows.
For one U.S. shipper in the foodservice segment, it was paying hundreds of thousands annually in demurrage fees because containers arrived in bulk. By using predictive analytics, they were able to cut those costs by 80%, renegotiate their commercial terms, and gain visibility.
This gave them control into how shipping delays would impact cash flow weeks earlier. For supply chain leaders debating the value of structured data, they should expect any modern system to automatically extract shipment data from emails and documents, generate realistic ETAs using historical carrier and port performance, and predict which containers are likely to incur detention days in advance. What this means in practice: teams can rely on AI systems to prioritize only the shipments that truly matter.
Procurement leaders are expected to maintain high service levels while controlling cost, inventory, and supplier risk. In foodservice, the largest constraint I see is not access to supply, it is forecast accuracy. There’s a huge opportunity here for AI to improve demand planning so it becomes dynamic instead of static.
Forecasting errors is the perfect example of a real, organization-wide problem that AI can tangibly improve. Over-forecasting leads to excess inventory, spoilage, and working capital drag, while under-forecasting results in stockouts, emergency buys, and lost revenue.
Research consistently shows that forecast accuracy in consumer facing supply chains averages between 60-80% of the SKU location. In foodservice, volatility from promotions, seasonality, and weather often pushes accuracy even lower.
At enterprise scale, the financial impact is significant. A 1% improvement in fill rate can generate $10-15 million dollars in incremental revenue. Excess safety stock can inflate inventory levels by 10-20%, and emergency replenishment frequently erodes margin by 3-7% per SKU.
Yet, forecasting remains disconnected from execution. Why are lead times assumed instead of measured, inbound delays reflected after the fact, and we’re accepted demand plans that outright ignore what is actually in transit?
By continuously synchronizing real sales signals, on hand inventory, in transit supply, and supplier performance, AI can predict stockout risk weeks in advance at the SKU-level. For leaders managing a food and beverage supply chain, AI-native predictive analytics can increase fill rates and make a material difference. It can adjust forecasts based on real ETAs rather than planned ones, identify cannibalization during product transitions, and recommend when not to reorder even if the forecast suggests otherwise.
Finance typically operates on delayed information at many foodservice distributors. It’s not uncommon for finance teams to calculate final landed costs after invoices arrive, or to reconcile duties post-clearance, or to even identify inventory exposure until weeks later.
For example, a manufacturer was relying on disjointed data, which made every delay a double hit – over $2 million in capital tied up overseas and production lines idled at home. By structuring their data across logistics, procurement and finance teams, they gained visibility into both the shipment and the cash impact, which let them forecast liquidity needs weeks earlier and cut reliance on costly short-term credit by 30%.
This is a clear-use case where AI gives finance forward-looking control instead of backward-looking reconciliation. For CFOs evaluating whether investing in a new “supply chain solution” is worth it, I’d argue fixing structural issues in supply chain operations will always end up finance’s problem. With clean, structured data, finance can forecast cash exposure across open purchase orders and in transit inventory, detect freight and cost anomalies before payment, model tariff exposure scenarios ahead of sourcing decisions, and quantify the financial impact of delays before they hit the income statement.
While tribal knowledge and technology systems from the 80s have gotten us this far, global shippers under pressure to efficiently scale demand with fewer resources will find themselves woefully unprepared if they don’t start investing now.
The team at Nauta understand the most effective organizations use AI to reduce cognitive load. They surface only the exceptions that matter, recommend the next best action, and automate routine follow ups across suppliers, carriers, and internal teams. This allows experienced operators to focus on judgment and relationships rather than manual coordination.
For logistics, procurement, demand planning, and finance leaders, the question is no longer whether AI belongs in the supply chain. The question is how do we get comfortable with it, and what can we reasonably expect?
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