AI for Supermarkets

Clear predictions, fewer surprises
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AI for Supermarkets
Trusted by leading organisations

How We Support Supermarkets

Supermarket performance is driven by daily decisions on stock, labour, promotions, and what work can realistically be completed in stores. Too often, these are still based on historic averages and outdated models that don’t reflect volatility at the store level, creating avoidable gaps between plan and execution.

SolvedBy.Ai links demand forecasting directly to labour, inventory, pricing, task scheduling, and opening hours, modelling how each store actually trades by day, by hour, by minute. This produces plans that keep up in practice, reducing last-minute intervention and giving leaders clearer visibility into what’s coming and where to act.

Our Supermarket AI Solutions

We forecast demand at the granularity supermarkets actually need: SKU, store, channel, and time interval. The models move beyond historic averages by ingesting hundreds of internal and external signals, sales and transactions, promotions, weather, events, holidays and macro trends, so forecasts reflect real trading behaviour.

In a supermarket context, that includes forecasting basket volumes and basket size, footfall, online grocery orders and delivery-slot demand, and fresh/short-life category demand, so you can plan replenishment, availability and store execution with early visibility of what’s coming.
Inventory optimisation determines optimal inventory levels across every SKU, site and supplier by analysing sales and transactions alongside lead times, logistics constraints and external signals like events or weather. It also models uncertainty, surfacing risks early so teams can act before shortages hit.

In supermarkets, this is how you protect availability, especially in fresh and short-life, without defaulting to overstock. You get reorder recommendations that are based on demand and move the business away from historically driven data.
Task Scheduler builds the most efficient task plan for every shift by evaluating every task, every person, and every operational rule. Tasks are defined with duration, priority, sequencing and dependencies; employee inputs include skills, certifications, performance speed and manager insight; and the model accounts for your operating environment, zones, layouts and workflow patterns.

In supermarkets, that means replenishment, reductions, compliance routines, cleaning and service support are allocated to the people most equipped to complete them, cutting wasted movement and reducing the “we ran out of time” execution gap.
Price Optimisation determines the right price or promotion by learning from your data and market dynamics, balancing demand, margin and customer sensitivity. It uses a broad forecasting library and groups products/sites into model families so learning transfers across similar items rather than maintaining thousands of isolated models.

In supermarkets, this means distinguishing which promotions create incremental demand versus those that simply give margin away, while remaining commercially sound as competitor activity and input costs change.
This model creates a labour demand curve showing precisely how required hours rise and fall by site, daypart, and interval (15–30 mins, hourly, daily, weekly). It’s driven by real trading signals, sales, transactions, footfall, bank holidays, local events, so labour planning reflects demand, not habit.

In supermarkets, that means more credible coverage at peak, less overstaffing in quieter periods, and fewer reactive redeployments because leaders can plan from a view of demand that traditional static planning can’t produce.
AI Scheduling converts labour demand into fair, compliant, efficient rotas by combining labour demand forecasts with your workforce data and business rules. It supports estate-wide or site-by-site runs and accounts for extensive constraints, skills, availability, restrictions, leave, budgets and objectives, rather than producing schedules based on historical staffing.

In a supermarket environment, this reduces rota churn and stabilises execution: managers spend less time rebuilding schedules, while HR and leadership gain confidence through compliance checks built-in (including Working Time Directive checks).
Resource allocation uses bespoke multivariate modelling to optimise where capital and physical assets should be deployed for best financial outcome, factoring in demand forecasts, local demographics, competitor actions, weather and events. Crucially, it provides transparent reasoning behind recommendations, not a black box.

For supermarkets, this supports decisions like where to prioritise investment across the estate, where formats are over/under-resourced, and how to justify trade-offs with board-level credibility.
Opening Hours Optimisation predicts the most profitable opening/closing times by analysing historical performance alongside footfall, transactions, local events and competitor activity. It tests thousands of trading windows and measures the financial impact of each, while respecting your constraints and rules.
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For supermarkets, this supports site-by-site decisions that reduce cost exposure in low-return hours and capture demand where it exists without breaking compliance or operational realities.
Shift Create generates unassigned, role-ready shifts using your demand forecasts, task lists, role definitions, skill requirements, operating rules and site constraints. It’s deliberately focused on operational design, creating the shift structure the store needs before decisions about specific colleagues are made.

For supermarkets, this standardises shift design across the estate so stores start the week with demand-aligned building blocks, reducing manager-by-manager variation and making execution more consistent during volatile trading patterns.

Case Studies

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The Outcomes We Deliver

Supermarkets organisations work with SolvedBy.Ai to achieve:

More predictable store operations

Clearer expectations ahead, fewer surprises, and plans that stand up under pressure.

Higher availability with lower waste

Stock where it’s needed without excess markdowns or spoilage.

Labour is used where it makes a difference

Service is protected at peak without inefficiency elsewhere.

Stronger execution of trading plans

Promotions, replenishment, and tasks are delivered consistently at the store level.

Greater board-level confidence

Decisions backed by credible assumptions and transparent logic.

Why SolvedBy.Ai for Supermarkets

This is built for how supermarkets actually run

Supermarkets operate with thin margins, complex estates, fresh categories, and constant volatility. SolvedBy.Ai models are bespoke for each customer and designed around their reality, modelling store-level behaviour and operational constraints rather than simplifying them away.

It draws from the widest library of forecasting and optimisation models

There is no single model that works for every category, store, or decision. SolvedBy.Ai draws from a broad range of forecasting and optimisation approaches, selecting and tuning models based on the behaviour being predicted, so outputs reflect how your supermarkets actually trade rather than forcing decisions through a one-size-fits-all method.

It reflects how demand and supply really behave

Supermarket demand is shaped by pricing, promotions, customer behaviour, channel mix, and supply constraints. SolvedBy.Ai models these drivers directly, so plans reflect how the business is likely to trade, not how it averaged out in the past.

It works with the systems you already rely on

SolvedBy.Ai integrates into existing ERP, planning, workforce management and analytics platforms. Forecasts and outputs flow into current workflows, avoiding disruption or the need to replace established systems.

It plans for variability, not a single “most likely” outcome

Supermarket trading rarely follows a single path. Promotions overperform or underperform, weather shifts demand, and local conditions change week to week. SolvedBy.Ai uses probabilistic, variable-aware models to reflect this reality, showing the range of likely outcomes and where pressure is most likely to emerge, so teams can plan with awareness of uncertainty rather than false precision.

A partnership built on ROI

We partner with supermarket groups to implement AI that improves demand forecasting, labour planning, availability, waste, task scheduling and much more, designed to work with store teams and scaled responsibly across the estate.

Our pricing is simple: we target a minimum 10:1 return on investment. For every £1 you invest, we expect to deliver at least £10 in measurable value.
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FAQ

AI for supermarkets helps predict demand, plan labour, position stock, and manage execution at store level. It supports decisions like how much to buy, where to place it, how many hours to deploy, what tasks can realistically be completed, and which promotions will hold up in stores.

Supermarkets deal with fresh categories, promotions every week, local trading variation, and very thin margins. SolvedBy.Ai models demand and workload at SKU, store, day, and hour level, which is critical in grocery but less common in other retail sectors.

It’s about decision support. SolvedBy.Ai doesn’t replace store managers or planners — it gives them earlier visibility and better signals so decisions are made before issues escalate into availability gaps, queues, or waste.

AI demand forecasting incorporates promotions, weather, holidays, local events, pricing, and customer behaviour to predict what customers will actually buy, by SKU and by store — rather than relying on historic averages.

Yes. Fresh and short-life categories are one of the strongest use cases. AI helps anticipate spikes and drops earlier, allowing supermarkets to protect availability without defaulting to overstock and markdowns.

SolvedBy.Ai forecasts fresh demand at SKU and store level, incorporating weather, promotions, and local behaviour. This gives earlier warning of spikes or drops, allowing supermarkets to act before waste or gaps materialise.

Availability improves because stock is positioned where demand is real, not insured through excess. SolvedBy.Ai enables targeted decisions rather than blanket safety stock.

By forecasting demand earlier and more precisely, supermarkets can position stock correctly, align replenishment, and prepare stores operationally before demand materialises.

Waste reduces because stock is targeted rather than buffered. Supermarkets move away from blanket safety stock and toward store-specific inventory decisions based on expected demand.

AI converts demand and footfall forecasts into labour demand curves, showing when hours are genuinely needed in each store and daypart — rather than spreading hours evenly across the week.

It reduces inefficiency, not service. Peak periods are better staffed, quieter periods are not overstaffed, and total labour spend is aligned to demand rather than fixed historic budgets.

Traditional rotas are built from last week’s pattern. AI scheduling builds rotas from expected demand while respecting contracts, skills, availability, and compliance rules.

Yes. Because schedules reflect real demand and constraints, store managers spend less time rebuilding rotas and reacting to unexpected pressure.

Task scheduling plans what work can realistically be completed in each shift — replenishment, reductions, cleaning, compliance, service support — based on demand and available capacity.

Stores receive achievable task plans rather than idealised checklists, reducing missed tasks, escalations, and operational noise during busy trading periods.

SolvedBy.Ai models how customers are likely to respond to promotions by product, store, and timing, while also showing the downstream impact on availability, labour pressure, and waste. This allows trading teams to avoid promotions that drive volume on paper but fail operationally or erode margin in practice.

Yes. Pricing decisions are informed by demand response and competitor behaviour, allowing supermarkets to remain commercially sound as prices and input costs change.

AI analyses footfall, transactions, local behaviour, and cost to recommend opening and closing times by store and day, rather than relying on historic or estate-wide assumptions.

Yes. Resource allocation models help decide where to invest, scale back, or rebalance capital and space across stores and formats using forward-looking signals.

No. It integrates with existing ERP, workforce management, planning, and analytics platforms, delivering outputs into current workflows.

Supermarkets often start with one use case — such as demand forecasting or labour planning — and expand over time. Value is delivered incrementally, not through a large system replacement.

No. Recommendations are explainable, with visible drivers and assumptions, so leaders understand why decisions are being made.

Users typically include commercial teams, operations, supply chain, workforce planning, finance, and executive leadership — all working from a shared forward view.

By showing pressure points earlier and aligning capacity in advance, fewer issues escalate into urgent store-level problems.

Yes. Performance becomes less dependent on individual experience and manual overrides, improving consistency across stores and weeks.

Yes. SolvedBy.Ai is designed for multi-store supermarket operations where local variation matters and averages break down.

Supermarkets facing demand volatility, availability pressure, labour inefficiency, execution breakdowns, or margin pressure see the strongest impact.

Yes. SolvedBy.Ai highlights where local demand patterns differ from estate averages, helping supermarkets refine range decisions by store or cluster rather than relying on broad, one-size-fits-all assortments.

SolvedBy.Ai groups products and locations into intelligent model families, allowing learning to transfer across similar stores while still respecting local demand behaviour. This avoids treating every store as identical or every SKU as isolated.

SolvedBy.Ai forecasts online and in-store demand separately first, because the drivers, timing, and operational impact of each channel are different. Online demand is shaped by factors such as delivery-slot availability, cut-off times, promotional mechanics, basket size, and substitution behaviour, while in-store demand is driven more directly by footfall, time of day, and impulse purchasing.

Each channel is modelled using its own historical patterns and external drivers, then recombined at store level to show total demand and, crucially, total operational workload. This allows supermarkets to see not just how much demand is expected, but where that demand will land — for example, whether a store is likely to face peak in-store traffic at the same time as heavy picking volume for online orders.

Yes. Demand forecasts can be translated into workload and labour requirements, helping supermarkets anticipate when online fulfilment will strain store operations.

No. SolvedBy.Ai supports judgement with better information. Managers retain control and can override recommendations where local knowledge requires it.

Managers maintain discretion, but with clearer insight into consequences. Decisions become more informed rather than instinct-driven.

Yes. SolvedBy.Ai is designed to support decision-making, not enforce it and replace managers.

Typically sales, transactions, promotions, labour data, and store attributes. SolvedBy.Ai works with what exists and improves as data quality grows.

Imperfect data is common. At SolvedBy.Ai we operate in real-world conditions and have a dedicated team that will clean your data and improve outputs as data matures.

TL;DR

SolvedBy.Ai enables supermarkets to plan and operate from a forward-looking, store-level view of demand and workload.

By forecasting demand at SKU, store, channel, and time-interval level and connecting those forecasts directly to inventory, labour, pricing, task scheduling, opening hours, and capital decisions, supermarkets improve availability, reduce waste and inefficiency, and execute trading plans with greater consistency across the estate.cution, and make decisions leaders can stand behind, even in volatile trading conditions.
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AI Demand Forecasting AI Labour Demand Forecasting AI Staff Scheduling AI Opening Hours Optimisation AI Footfall and Visitor Forecasting AI Inventory Optimisation AI Predictive Maintenance AI Resource Optimisation AI Price Optimisation AI Budget Forecasting AI Shift Create AI Task Scheduler
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