Inventory management and demand forecasting with AI: key applications in the food & beverage sector

In the food & beverage sector, inventory management has always depended on a complex balance between product availability, logistics costs, stock rotation, waste, service continuity and demand forecasting. This balance becomes even more critical in fresh, refrigerated and frozen categories, as well as in beverages and products with a sensitive shelf life, where inaccurate forecasts can lead to stockouts, excess inventory, spoilage or higher operating costs.

In recent years, technologies such as artificial intelligence, digital twins, RFID, cold chain monitoring, computer vision, advanced demand planning systems and decision automation tools have started to change how producers, distributors, wholesalers and retail chains make decisions across the supply chain. The aim is to improve decision-making: forecasting demand more accurately, reducing stock issues, limiting waste, optimizing production and replenishment, managing shelf life with greater precision and responding faster to unexpected events.

The The State of Grocery Retail Europe 2026 report by McKinsey and EuroCommerce confirms the strategic importance of this topic. 47% of the grocery retail CEOs surveyed identify AI and automation as key priorities, but the economic impact is still limited: around 30% report an initial effect on their profit and loss statement, below 5%, while 83% of retailers are still at an early or intermediate stage in developing their AI strategy.

For this reason, the topic should be approached from an operational perspective. AI applied to the food supply chain should not be viewed as a single technology, but as a set of tools linked to practical business needs: forecasting demand, deciding what to produce or reorder, protecting product quality and making supply chain operations more efficient.

Why the food supply chain requires more advanced tools

The food & beverage supply chain has specific characteristics that make the adoption of advanced tools particularly valuable. Demand can change according to seasonality, promotions, weather, holidays, local events, tourism, shifts in consumer behaviour and price dynamics. Companies must also manage specific constraints, including short expiry dates, controlled temperatures, batches, traceability, returns, recalls, ingredients availability and production capacity.

In many companies, these variables are still handled through fragmented tools: spreadsheets, outdated historical data, decisions based on the experience of individual buyers or planners, and systems that are not fully integrated across production, logistics and sales. This approach can work in simple settings, but it shows its limits when the number of SKUs, sales channels, target markets and demand fluctuations increase.

The technologies available today allow companies to move from reactive management to more predictive and coordinated management. To achieve real results, however, businesses need a clear roadmap: which problems to solve, which data to collect, which technology to adopt, which indicators to measure and how to connect the investment to a measurable economic return.

1. Forecasting demand: AI forecasting, demand sensing and external signals

The first area of application is demand forecasting. Traditional systems are mainly based on historical sales and predefined rules. Artificial intelligence, by contrast, makes it possible to include a much wider range of variables: POS sales, recent orders, promotions, prices, weather, calendar data, holidays, local events, store traffic, e-commerce data, customer performance and market signals.

In food & beverage, this approach is especially useful because many categories have variable demand that is difficult to predict. This applies to fresh products, dairy products, ready meals, frozen foods, seasonal beverages, holiday and occasion-based products, promotional items and products sold through different channels, such as grocery retail, wholesalers, food service and hospitality venues.

Demand sensing: from static forecasts to dynamic forecasting

Demand sensing is an evolution of demand planning. Rather than updating forecasts only on a monthly or weekly basis, it uses data closer to real time to detect rapid changes in demand. This allows companies to adjust orders earlier, revise production plans, move stock between warehouses or change delivery priorities.

For a beverage supplier, for example, weather, sporting events, holidays and promotions can have a significant impact on demand. For a distributor of fresh products, demand sensing can help reduce excess inventory on perishable items and improve availability of the products most requested by customers.

How to measure value

The main indicators to monitor are forecast accuracy, forecast bias, stockouts, days of supply, average inventory value, lost sales, supply continuity and waste. In food & beverage, however, performance should be assessed by category: highly perishable fresh products, promotional products, seasonal items, fast-moving SKUs and imported products with longer lead times.

2. Deciding what to produce and reorder: integrated planning and automatic replenishment

A more accurate forecast creates value only when it is converted into operational decisions. This is why planning systems need to communicate with ERP software, WMS systems, TMS systems, production systems, sales data, promotion management tools and customer platforms. Planning becomes more effective when it connects demand, availability, production, transport and commercial priorities.

In recent years, many food companies have been investing in more robust planning systems that can support faster inventory decisions based on more accurate, up-to-date and connected data.

Automatic replenishment and intelligent reordering

Automatic replenishment uses algorithms to suggest or generate orders based on forecast demand, available stock, lead times, minimum order quantities, warehouse capacity, rotation, target service levels and shelf life constraints. In the food sector, this is critical, because a reorder that is correct in terms of quantity may still be inefficient if it does not take expiry dates or rotation speed into account.

For distributors and grocery retailers, intelligent reordering can reduce manual errors, improve on-shelf availability and limit excess inventory. For producers, it can support better planning of raw materials, packaging, production capacity and deliveries, reducing urgent interventions and extra costs.

S&OP and collaboration between functions

The most effective planning takes place when sales, supply chain, production and finance teams work on shared scenarios. From this perspective, AI can support the S&OP process (Sales and Operations Planning), helping teams to simulate volumes, margins, production constraints, raw material availability and the impact of promotions.

The operational questions are very practical: does the system account for sales lost because products were out of stock, rather than interpreting them as lower demand? Does it account for promotions and seasonality? Can it manage variable lead times? Does it integrate customer or store data? Does it support simulations on price, volumes and service levels? Does it help decide when to produce, how much to buy and where to position stock?

3. Protecting shelf life and quality: cold chain, IoT and dynamic shelf life

In food & beverage, product quality also depends on logistics conditions. The cold chain is one of the areas where technology and operational performance are most closely connected: a break in temperature control can compromise quality, safety, remaining shelf life and customer trust.

This is why interest is growing in IoT sensors, real-time monitoring, automatic alerts and predictive analytics applied to transport and storage.

These solutions are also supported by applications of smart packaging and active cold chain technologies, such as time-temperature indicators, integrated sensors and packaging capable of signalling anomalies along the logistics route. These tools can strengthen control over the product's remaining quality and support timely decisions when there is a risk to food safety (for further information, see: "Smart Packaging and Active Technologies: Key Trends and Solutions in Food & Beverage").

From temperature to remaining shelf life

The most interesting development concerns dynamic shelf life. Instead of treating the expiry date as the only reference point, companies can estimate remaining shelf life and quality by considering the real conditions experienced by the product: temperature, exposure time, logistics route, stops, delays, openings and handling.

This information can guide practical decisions: prioritizing the most sensitive batches, changing delivery priorities, applying targeted discounts, avoiding long-distance shipments for products with reduced shelf life or redistributing goods to channels with faster rotation.

FEFO and management of perishable products

For perishable products, the FEFO logic, or first expired, first out, becomes more effective when it is supported by updated data on batches, expiry dates, transport conditions and rotation. This helps reduce the risk of having products that are technically available in the warehouse but difficult to sell because their remaining shelf life is too short.

KPIs to monitor

Useful indicators include the number and duration of temperature excursions, value of goods at risk, quality complaints, waste, returns, on-time delivery, remaining shelf life at receipt, carrier compliance and average cost per unit managed at controlled temperature.

4. Reducing waste and stockouts with AI forecasting, markdowns and dynamic pricing

Waste reduction is one of the areas where AI can have an immediate impact. Excess inventory leads to product losses and affects disposal, work organization, warehouse space, tied-up capital, transport and margins. At the same time, stockouts reduce sales, service levels and the trust of professional customers.

Several studies highlight that food waste is one of the most controllable cost items in food distribution and that optimizing forecasts, reorders and availability can help reduce both excess inventory and out-of-stock situations.

AI to reduce food waste

AI can contribute to waste reduction through more accurate forecasts, reorder recommendations, analysis of remaining shelf life, stock redistribution, identification of products at risk of remaining unsold and sales prioritization for the most sensitive batches.

For producers, this means reducing overproduction and returns. For distributors, it means better management of rotation and expiry dates. Retail stores can also improve availability without overfilling shelves and refrigerated displays.

Markdown optimization and dynamic pricing

In fresh products, ready meals, bakery products, fruit and vegetables, deli products and categories with short expiry dates, AI can suggest targeted discounts (markdown optimization, in technical terms) based on remaining shelf life, available stock, forecast demand, time of day, channel and probability of unsold inventory.

This approach can protect margins and reduce waste, but it must be used carefully. Dynamic pricing is useful when it supports freshness management, rotation and the reduction of unsold products; it becomes more sensitive when consumers perceive it as a form of price personalization that lacks transparency. For food companies, the priority should be clarity, with discounts linked to expiry date, availability or consumption occasion, communicated clearly and understandably to consumers.

On-shelf availability and lost sales

On-shelf availability, supported by optimized inventory management, has an economic impact that should not be underestimated. An unavailable product can generate immediate lost sales and, over time, push customers towards alternative suppliers.

In retail channels, on-shelf availability depends on accurate forecasts, correct reordering and effective in-store execution, in order to avoid gaps, misplaced products or outdated assortments. In B2B channels, it depends on stock levels, punctuality, substitution capacity and timely communication of alternatives.

5. More control across the supply chain: RFID, traceability, food safety and shelf analytics

Traceability across the supply chain is one of the prerequisites for improving planning, quality and risk management. In the food sector, knowing where a product is, which batch it belongs to, what remaining shelf life it has and which steps it has gone through can make a real difference in the event of a stockout, recall, quality complaint or fraud risk.

RFID and batch traceability

RFID technology makes it possible to identify products, cases, pallets or logistics assets through tags that can be read without the need for direct optical scanning. In food & beverage, it can support inventory, traceability, batch management, movement control, recall readiness and visibility across the supply chain.

GS1, the global non-profit organization that defines standards for the unique identification of products, emphasizes that traceability standards help organizations design and implement systems to track products and critical information across the supply chain, optimizing several areas. Traceability also supports food safety, recall management, waste reduction and the mitigation of economic loss risks.

For producers and distributors, RFID tags can be useful in steps where manual errors are frequent or costly: goods receipt, order preparation, pallet handling, warehouse inventory, batch control, returns management and shipment verification.

AI for food safety and recall management

Artificial intelligence can also strengthen traceability, food safety and recall management. For example, in 2026 the European Commission presented TraceMap, an AI-based tool designed to accelerate the identification of food fraud, contaminated food and outbreaks of foodborne diseases in the EU.

For companies, traceability is becoming increasingly digital, integrated and risk-oriented. This is not only about regulatory compliance, but also about the ability to react quickly, isolate the batches involved, reduce the economic impact of a recall and protect brand reputation.

Computer vision, smart shelves and shelf analytics

In retail and grocery distribution, computer vision, smart shelves and shelf analytics can help monitor availability, misplaced products, prices, correct shelf arrangement and the display space assigned to different SKUs. AI-based shelf monitoring helps distribution companies quickly detect gaps and shelf placement errors, making replenishment activities more timely.

These technologies are also relevant for producers, because they provide a clearer view of in-store execution: actual availability, compliance with display agreements, promotional effectiveness and rotation by channel. For distributors and wholesalers, tools with a high level of automation can help improve service, replenishment and collaboration with retail customers.

6. Optimizing execution and costs: warehouse, transport, procurement and AI agents

A key aspect for the food sector is the physical and operational execution of the supply chain. Even the best forecast loses value if warehouse, transport, purchasing and customer service teams are unable to turn it into timely operations that are sustainable and aligned with commercial priorities.

Warehouse intelligence: picking, slotting and rotation

In food warehouses, AI can support dynamic slotting (optimizing the position of products within the warehouse), picking route optimization (the routes followed by operators to prepare orders), workload forecasting, staff allocation, error control and priority management for fresh, refrigerated or urgent products.

Dynamic slotting makes it possible to position SKUs in the warehouse according to rotation, temperature, format, picking frequency, logistics compatibility and expiry dates. This can reduce order preparation times, errors, unnecessary movements and operating costs. In fresh and refrigerated categories, it can also improve FEFO management and order fulfillment speed.

Route optimization and transport planning

For distributors, wholesalers and logistics operators, route optimization is one of the most practical applications of AI. Systems can take into account traffic, delivery windows, vehicle capacity, customer priorities, required temperature, fuel costs, urgent deliveries, returns and cold chain constraints.

In fresh, frozen and beverage categories, even small improvements in vehicle utilization, delivery sequencing or total distance traveled can have a significant impact on costs.

AI in procurement: raw materials, packaging and critical suppliers

Procurement is often underestimated when discussing AI in the food supply chain. For producers, purchasing involves agricultural products, processed ingredients, packaging, promotional materials, logistics services and relationships with foreign suppliers. Delays, price increases or shortages can block production and deliveries.

The food & beverage producers most attentive to these issues are already using AI in MRP systems, which help plan requirements for raw materials, ingredients, packaging and other materials needed for production. The goal is to improve purchasing decisions, reduce delays caused by unavailable materials and manage critical SKUs more effectively.

AI can help analyze supplier performance, lead times, prices, alternative availability, contracts, backorder risks and supply issues. For companies dealing with seasonal ingredients, ingredients or packaging exposed to volatility, this can become an important support for production continuity.

Supplier risk management

Closely linked to procurement is the issue of supplier risk. AI can monitor external signals such as weather events, geopolitical risks, price fluctuations, customs issues, supplier news, certifications, recalls and non-compliance.

For food & beverage, this is particularly relevant because many supply chains depend on agricultural products, seasonal ingredients, packaging and international logistics. Anticipating risk allows companies to look for alternative suppliers, change production plans or review safety stocks before the issue reaches the customer.

Generative AI and AI agents for planners, buyers and sales teams

Generative AI can become an operational assistant for planners, buyers, customer service teams and B2B sales teams. It can summarize sales data, explain forecast variations, generate alerts, prepare reports by customer or category, identify anomalies, suggest product alternatives and support communication with distributors or retailers.

AI agents represent a further step: systems capable of suggesting operational actions and, within defined limits, carrying out certain activities. For example, they can flag a stockout risk, propose a change to a reorder, suggest a promotion to reduce excess inventory, indicate an alternative supplier or prepare a scenario for the supply chain manager.

Digital twins: simulating scenarios before taking action

The digital twin deserves specific attention because it can connect many of the aspects described above. It is a digital representation of a process, logistics network, plant or supply chain. It can be used to simulate production, procurement, distribution, capacity, demand changes, supplier delays or logistics disruptions.

Digital twins make it possible to simulate the end-to-end supply chain, analyze operational disruptions in real time and support strategic decisions. A review published in Frontiers in Sustainable Food Systems ("Digital Twin applications in the food industry: a review" - April 2025) also highlights that digital twin applications in the food industry are still relatively new, but relevant to production, quality, sustainability, maintenance and supply chain management.

A digital twin is particularly useful when a company needs to make complex decisions before investing or changing its operating model: opening a new warehouse, changing the distribution network, reducing lead times, reviewing stock levels, assessing alternative suppliers, optimizing production capacity or simulating the impact of a promotion.

Assessing the economic and operational impact

To make these technologies truly useful, companies should avoid overly broad projects and start with limited and measurable problems. Adoption should be linked to concrete indicators, realistic timelines and clearly defined operational responsibilities.

Identifying the economic problem

The first step is to quantify the cost of the problem: waste, stockouts, lost sales, returns, urgent logistics activities, excess stock, manual planning hours, picking errors, quality complaints or cold chain breaks. Without a baseline, it is difficult to assess the return on investment.

Choosing a pilot category

The pilot project should focus on a category that is critical enough to generate value, but not so complex that the project becomes unmanageable. Good candidates include packaged fresh products, dairy, ready meals, refrigerated and frozen ranges, seasonal beverages, promotional items and SKUs with high demand variability.

Connecting commercial and operational data

An AI project for commercial planning works only if the data is reliable. It is important to integrate sales, orders, stock, promotions, lead times, expiry dates, batches, logistics data and, where possible, information from B2B customers or points of sale. Data quality is often the real prerequisite for the project.

Defining who decides what

Automation must be supported by clear rules. Which decisions can the system make? Which ones must be approved by the planner? When should an automatic reorder suggestion be accepted? When should a forecast be modified? Which exceptions require human intervention?

Measuring the result with a few KPIs

KPIs should be linked to the initial problem. For a demand forecasting and planning project: forecast accuracy, bias, service level, stockouts and average inventory value. For a cold chain project: temperature excursions, quality returns and remaining shelf life. For RFID applications or other traceability and automatic identification systems, the main indicators are inventory accuracy, shipping errors, inventory time and batch traceability. For the implementation of a digital twin: lower logistics costs, better use of production and warehouse capacity, fewer urgent shipments and faster assessment of alternative scenarios, such as delays, demand peaks or changes in the distribution network.

The competitive advantage: faster and more integrated decisions

Demand planning, RFID, digital twins, cold chain monitoring, computer vision, route optimization, procurement intelligence and AI agents should not be treated as separate projects. Their value increases when these technologies work together: AI-based forecasts improve production or procurement, planning systems translate forecasts into orders and stock levels, traceability increases movement accuracy, cold chain sensors monitor actual quality, digital twins simulate scenarios and risks, while AI agents help teams turn data into operational actions.

For food & beverage companies, the competitive advantage comes from the ability to make better decisions, earlier and with more reliable data. This applies to producers planning capacity, distributors ensuring availability and rotation, retailers reducing waste and stockouts, and logistics operators managing products whose quality depends on temperature, handling conditions and other physical parameters.

Technology alone does not solve supply chain problems. It becomes useful when it is connected to clear objectives, measurable processes and defined operational responsibilities. In this case, AI and other digital tools become practical levers to improve margins, service, sustainability and resilience across the entire food supply chain.