Smart supermarkets: how GenAI and Machine Learning are redefining the sector.

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AI-generated summary:

The article shows how supermarkets and wholesalers face complex challenges—such as managing perishable inventory, tight margins, fragmented customer experience, losses, and omnichannel integration—and how data, machine learning, and GenAI are already being applied to transform these pain points into operational and commercial gains. Using international examples from chains like UVESCO, Migros, Walmart, and Sainsbury's, as well as a real-world case study from BlueMetrics in digital retail, the text highlights that AI-based solutions bring personalization, efficiency, and scalability, becoming not only a competitive differentiator but a strategic requirement for the future of the supermarket and wholesale sector.

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When a consumer enters a supermarket, they carry not only a shopping list, but also unspoken expectations: finding the right product, without queues, in a well-stocked store; offers that make sense; a seamless shopping experience, whether in a physical or digital channel.

For wholesale and supermarket chains, meeting these expectations is becoming increasingly complex. Rapid changes in consumer habits, rising operating costs, labor shortages, regulatory and sustainability pressures—all of this imposes an unprecedented combination of challenges. The good news is that technologies and solutions already exist that can address all of these problems.

How data, GenAI, and machine learning improve management and customer experience.

Imagine a supermarket chain with 200 stores spread across different cities. Today, it suffers from stockouts in some locations: milk, eggs, and fresh fruit frequently run out before more deliveries arrive; in others, there are losses due to products that become non-conforming or expire. Traditional analysis via spreadsheets and monthly reports only identifies this after the damage has been done.

Using machine learning and real-time data, this network can detect patterns: a heat wave is predicted for one of the cities; this indicates that customers will buy more yogurt, cold drinks, and ice cream. The system suggests increasing the order in advance to the distribution center in that region. It identifies that in stores near the center, the turnover of these items increases by 30% during a hot week. It also detects that if a supplier has a longer lead time, the safety stock needs to increase slightly to compensate.

Simultaneously, GenAI steps in to automatically adjust communications: generating personalized offers for customers in that city for refreshing drinks, with coupons in the app or messages on WhatsApp. The suggested store layout can adjust the display of these products to make them more visible. The self-checkout or scan-&-go system can free up employees for customer service or internal logistics, reducing queues.

At the same time, the network closely monitors which promotions work best in each store, which products are sensitive to discounts, which ones have promotions to clear stock before expiration, and which ones simply have stable sales and don't justify expensive promotions. All this with intelligent dashboards, preventive alerts, and the ability to simulate scenarios before executing actions.

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Main pain points in the supermarket/wholesale sector.

Inventory management: stockouts versus surpluses

Perishable products have a short shelf life. If they are not sold in time, they are losses. On the other hand, excess inventory generates costs related to storage, deterioration, and immobilization of capital. Many retail chains still operate with forecasts based on simple historical data (moving averages, rudimentary seasonality) without incorporating external variables such as weather, holidays, local events, promotional campaigns, delayed logistics, or unstable suppliers. This leads either to empty shelves (which irritate customers) or overflowing shelves and waste.

Narrow margins and strong competition

Gas, energy, transportation, ingredients, packaging: all cost more. More demanding buyers, in terms of price and quality, also raise expectations. The challenge of setting prices that cover costs and desired profit without losing competitiveness is enormous. Poorly calibrated promotions, untimely discounts, or overly generic offers can further erode margins.

Fragmented customer experience and the expectation of personalization.

Today's consumer is familiar with digital trends: buying online, in-store pickup, apps with personalized offers, knowing if a product is available via smartphone, contactless payment, among others. In physical supermarkets, many of these conveniences are still in their infancy. If this journey is full of friction, such as queues, outdated information, and irrelevant offers, the customer loses satisfaction.

Costly physical operation and labor shortage.

Shelf restocking, fruit weighing, batch checking, cashier duties, cleaning, expiration date control: many tasks require continuous and specialized human labor. In environments where wages are high, workers are scarce, or they lack the necessary qualifications, productivity drops. And high turnover generates additional inefficiencies.

Sustainability, losses and waste

There is increasing pressure from consumers, regulators, and investors to reduce food, energy, and packaging waste, and to improve the environmental footprint. Expired products, poorly planned transportation, and failures in cold chains all weigh not only on direct costs but also on brand reputation.

Integration between channels (omnichannel)

Today, many supermarkets have physical stores, e-commerce platforms, apps, click & collect pickup, and delivery services. Making each channel work in isolation is easier; integrating them so that in-store inventory feeds into the digital world, digital offers are consistent with in-store offers, and logistics feeds back into purchasing decisions requires robust data collection, real-time visibility, and coordination between systems.

Strategic decisions based on deficient data.

Some retailers still work with outdated reports, unreliable forecasts, or even overly intuitive decisions. Without a solid data foundation, it becomes difficult to plan store expansion, product assortment, dynamic pricing, and loyalty programs that truly work.

How can GenAI, Machine Learning, and data mitigate these pain points?

This is where emerging technologies and data techniques that go beyond pretty dashboards come in: they allow you to anticipate, simulate, adapt, and customize.

More sophisticated demand forecasting

Machine learning models powered by internal data (historical sales, turnover, seasonality) and external data (weather, holidays, local events, demographics). These models learn complex patterns: for example, recognizing that unusually hot weather generates higher demand for drinks or ice cream; that a local sporting event increases demand for snacks; that holidays have a greater influence than traditional reports capture. This reduces stockouts and excess inventory.

Dynamic pricing and price sensitivity

Use models that assess the elasticity of demand for different products, in different stores, at different times. This makes it possible to dynamically adjust prices/promotions: for example, increasing the discount on items with weak demand before they expire; reducing offers on items that already sell well without promotion; testing personalized offers for customer segments.

Personalization and customer engagement

Data from purchase history, preferences, digital behavior, and GenAI techniques can enable customized offers and personalized suggestions (in apps, SMS, or via voice assistant/bot). The customer begins to see the supermarket not as a mere dispenser of products, but as a service tailored to their tastes and routines. This fosters loyalty, reduces wasted inventory from unproductive offers, and improves digital conversion.

Operational improvement through automation and operational intelligence.

AI can support or automate repetitive tasks: automatic replenishment based on empty shelf alerts, real-time inventory visibility systems, robotics or vision systems to monitor shelves, standardize checkouts (self-checkout, scan & go). Machine learning can also optimize delivery routes, employee allocation, and deploy the right team at the right time.

Seamless shopping experiences and omnichannel

Data-driven applications allow for mirroring inventory in both physical and digital time, managing deliveries with predictability, and offering quick pickup. GenAI can help with chatbots, shopping assistants that suggest lists or recipes based on what you have at home. The entire journey (app, store, website) can cease to seem like separate compartments and become a coherent flow.

Reducing losses, extending shelf life, and reducing waste.

Models that assess the risk of expired products, perishable goods, and transportation. Predictions that indicate when there will be low turnover of certain items. Systems that simulate the impact of changes (e.g., promotions, store layout) to see if they help to improve inventory turnover.

Using generative AI (GenAI) for exploratory insights and decision support.

Beyond traditional ML models, GenAI can synthesize reports, generate scenarios ("What if I run a beverage promotion next week, given the predicted warm weather?"), create catalog drafts, automate some of the offer copywriting, and personalize communication. GenAI can also serve strategic planning: helping managers envision new strategies based on historical data without requiring entire teams to simulate everything manually.

Real-World Case Studies of AI and Data Application in Supermarket and Wholesale Retail

Let's look at some concrete initiatives that show how AI and data science have moved beyond experiments and become strategic cornerstones in food retail.

UVESCO (Spain): shelves always full

The UVESCO supermarket chain in northern Spain collaborated with Neurolabs to apply synthetic computer vision to the task of automatically monitoring shelves. The idea was to detect, in real time, when certain products were out of stock or overdue for restocking, without relying exclusively on manual inspections, which are costly, less frequent, and prone to error.

In this project, the UVESCO chain used digital twins of the SKUs to train machine vision models even before many of these products physically appeared in stores. This simplified visual identification, even when there are variations in packaging or shelf placement.

Following a pilot implementation in some stores, it resulted in continuous visibility of stock levels on the shelves, allowing for faster reactions from store staff and distributors. In just a few months, UVESCO managed to reduce visible stockouts for consumers, improve shelf occupancy rates, and detect early which SKUs were most affected by lack of display or delivery delays.

Migros (Switzerland, Sweden, Türkiye): Demand management at the next level.

The Migros network operates thousands of stores with a huge variety of SKUs and distribution centers. They faced the classic dilemma: maintaining high product availability, especially of perishable and fresh goods, while simultaneously reducing capital tied up in inventory.

By adopting an AI platform that combines demand forecasting, product turnover tracking, consumption patterns by location, and channel integration (physical stores + online), the Migros network implemented automatic adjustments to its replenishment processes.

Results:

• An approximate 11% reduction in inventory days, freeing up working capital and reducing storage costs.

• Product availability on shelves increased by 1.7%, meaning fewer stockouts perceived by customers.

• A 1.3% reduction in lost sales, with fewer product shortages at the moment the customer wants to buy.

Walmart (United States) and Sainsbury's (United Kingdom): data in service of efficiency.

Walmart has been using AI/ML to forecast demand, optimize its supply chain, manage fresh produce, and monitor temperature during transport to reduce losses. The company also uses algorithms to decide on product substitutes when a SKU is out of stock and to balance inventory across stores based on local demand.

The Sainsbury's chain has formed a strategic partnership with Microsoft to use AI to generate better data insights, personalize the online shopping experience, improve the e-commerce search system, and provide employees in physical stores with real-time data for shelf restocking.

Other initiatives

The use of smart carts equipped with sensors, cameras, scales, and automatic identification can offer customers a smoother experience, reduce checkout lines, and improve traceability. An example of this is the Instacart/Caper AI, used by global retailers such as Wegmans, ALDI, and Coles.

Finally, building online order fulfillment centers that use automation, robotics, integrated inventory control systems, and AI forecasting can minimize product substitutions, ensure more accurate delivery times, and better preserve perishable goods.

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BlueMetrics Case Study: GenAI and data for large-scale personalization in digital retail.

Context

BlueMetrics' client is a well-established company in the corporate gifts market, operating three online platforms that connect suppliers and buyers. In this highly competitive sector, the variety of products and the specificities of each demand make the decision-making process complex. To differentiate itself, the company needed to offer faster and more personalized service, while also seeking to scale its operations without increasing costs proportionally.

As Gabriel Casara, CGO of BlueMetrics, points out: “We specialize in developing real solutions for real problems. This type of challenge aligns perfectly with both our work methodology and our solutions offering.”

Problem

The operation had clear bottlenecks: limited service during business hours, dependence on the individual knowledge of service representatives, and a manual process for interpreting requests that resulted in delays, errors, and dissatisfaction. Furthermore, the available data on product categories was poorly structured and lacked semantic content, hindering the adoption of intelligent recommendations.

During peak demand periods, such as Christmas, staff overload became even more evident, leading to inaccurate directives and missed opportunities. Therefore, there was a need for a scalable, impartial solution available 24/7 to reduce response time and democratize access to supplier options.

Solution

BlueMetrics has developed a GenAI-based solution supported by three main pillars:

Data enrichment: using LLM models in Amazon Bedrock to provide more context to product category descriptions, including appropriate events and purposes. This resulted in a semantically rich knowledge base.

Intelligent knowledge base: enriched information organized in a vector database, optimized for semantic search and continuously updated.

Contextual virtual assistant: a bot that understands the context of requests and suggests product categories accurately and impartially, using Information Retrieval (IRR) techniques.

According to Diórgenes Eugênio, Head of GenAI at BlueMetrics, “The project's key differentiator was creating a robust knowledge base from poorly structured data. We were able to semantically enrich the information and offer the virtual assistant much more context, making the recommendations more useful and relevant.”

Results

The implementation brought operational, technical, and customer experience gains:

Operational advantages: 24/7 service, reduced initial waiting time, standardized recommendations, scalability in service volume, and less manual workload.

Technical advantages: semantically enriched knowledge base, flexible and scalable architecture, easy maintenance, and the possibility of incorporating new LLM models.

Customer experience: instant answers, more precise and contextualized recommendations, unbiased suggestions, and greater accuracy in product selection.

The result was a more efficient digital operation, prepared to handle increased demand without sacrificing service quality. As Luciano Rocha, commercial director of BlueMetrics, summarizes: “When you offer speed, accuracy, and impartiality in the first contact, the customer perceives value immediately. And this is a competitive advantage that can be transferred to any digital retail segment, including supermarkets and wholesalers.”

Conclusion

Increasingly, the adoption of AI and structured data in supermarkets and wholesalers is ceasing to be an experimental differentiator and becoming a highly strategic competitive requirement. The key is to integrate data and AI technologies with each other and into daily operations, the customer journey, the supply chain, marketing, and internal processes. Or, as we like to say here at BlueMetrics: AI and data solutions for the real world, to solve real problems and generate measurable results in the short term.

For supermarket or wholesale chains that haven't yet fully embarked on this journey, some key lessons stand out:

Start small, with well-defined pilot projects (a group of stores, a product category, or a channel).

- Ensuring data quality and integration: without reliable, well-structured data integrated across systems (ERP, e-commerce, physical stores, logistics), AI models lose effectiveness.

- Involve multidisciplinary teams: operations, technology, finance, marketing, logistics, data science, so that insights are translated into practical actions.

- Monitor metrics beyond immediate marginal gains: customer satisfaction, delivery time, waste, sustainability footprint.

- To develop a vision of continuous improvement on the platform, as well as to be attentive to its applicability in other areas or verticals with similar problems.

The future is shaping up with a more agile supermarket and wholesale retail sector, capable of anticipating customer desires, adapting to supply chain disruptions, offering coherent experiences between physical and digital stores, and operating with less waste and lower costs.

With over 200 AI and data projects delivered to more than 90 clients in Brazil, the USA, and other Latin American countries, BlueMetrics is an ideal partner to accelerate your digital journey. Let's talk about it?

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