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Artificial Intelligence in Retail

Retail

AI in RetailRetail Optimisation with AI

Decision AI tailored to your business:
Make better decisions and maximise profitability

The retail sector faces the challenge of managing large volumes of data to make strategic decisions, optimise inventories, and accurately anticipate demand. Consumers expect personalised and seamless shopping experiences, which raises the bar for service standards across all channels. In addition, cybersecurity is crucial for protecting data and building customer trust.

We address these challenges with Decision AI, a suite of tools based on advanced mathematics that enhance operational efficiency, drive sustainability, and transform your ability to adapt to today’s market.

value chainExplore how Decision AI enhances strategic decision-making at every stage of your value chain.

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Raw Materials
1
Suppliers
2
Factories
3
Warehouses
4
Store and e-commerce
5
Customer
6
Returns
7

Raw Materials

Suppliers

Factories

Warehouses

Store and e-commerce

Customer

Returns

E-book: AI in RetailSubmit your download request

This e-book edition is limited. Each request will be reviewed individually, and one copy will be sent per verified profile until all available units have been distributed.

Submit your download request.

This ebook edition is limited. Each request will be reviewed individually, and one copy will be sent per verified profile until the available units run out.

casos de usoSome use cases

Explore how our solutions have optimised operations in the pharmaceutical sector.

Product transfers between stores

  • Industry: Retail.
  • Challenge: Optimise product transfers between stores of the same brand to maximise sales and margins, taking into account factors such as seasonality, demand, and inventory constraints.
  • Approach: Implementation of Decision Intelligence models to forecast sales and optimise product transfers between stores based on demand.
  • Results: Streamlined transportation costs for inter-store transfers, improved sales through better inventory availability, and optimised global stock levels, resulting in increased margin per product sold.
  • Key Metrics: 14% increase in profit per transfer.
0 %
Increase in profit per shipment

Point of sale location

  • Industry: Retail.
  • Challenge: Identify strategic locations for new points of sale, taking into account demand, competition, and operating costs.
  • Approach: Development and implementation of predictive models, clustering algorithms, and mathematical optimisation to identify the most profitable locations for new stores, using the company’s strategic data.
  • Results: Design of an optimal retail network that minimises total costs while accounting for the future growth of the business.
  • Key Metrics: 22% reduction in store opening costs.
0 %
Reduction in store opening costs.

Staff shift planning

  • Industry: Retail.
  • Challenge: Improve staff allocation to coordinate tasks and maximise operational efficiency in-store.
  • Approach: Use of Decision AI and simulations (with digital twins) to define the optimal staff allocation in stores.
  • Results: Reduced staffing costs and increased team efficiency.
  • Key Metrics: 9% reduction in staffing costs.
0 %
Reduction in personnel costs.

Supply planning

  • Industry: Retail.
  • Challenge: Adjust inventory levels for each store based on forecasted demand, avoiding stockouts and excess logistics costs.
  • Approach: Application of Decision AI tools to forecast demand per SKU and optimise store replenishment planning.
  • Results: Improved conversion rates, faster product turnover, increased average margin, and reduced quantity and frequency of shipments.
    Key Metrics:
    – 15% reduction in transportation costs
    – 5% increase in profit margin
    – 3% increase in sales
0 %
Increase in total revenue

NewsNews and trends in AI applied to retail, operations, logistics, and inventory management

Stay up-to-date with the latest trends and advancements in artificial intelligence applied to retail. Discover how other industry leaders are adopting AI to make better decisions.

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    Raw Materials

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    Suppliers

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    • How to select and evaluate suppliers to reduce risks and ensure on-time deliveries?
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    Problems solved:

    • Delivery delays and supply problems.
    • Lack of supplier diversification.
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    Factories

    Activate DecisionAI
    • How to design or update production plants to improve efficiency and reduce waste?
    • How to anticipate machinery failures to avoid production downtime?
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    Added value of AI and analytics:

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    • Time and cost optimization: production planning algorithms.
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    Warehouses

    Activate DecisionAI
    • How can you manage inventory to avoid overstocks or shortages?
    • How can you optimize warehouse space and distribution to streamline order fulfillment?
    • How can you automate picking and stock tracking to reduce time and errors?

    Added value of AI and analytics:

    • Inventory optimization: AI-powered demand prediction to reduce overstocks and shortages.
    • Internal route management: algorithms for efficient distribution within the warehouse.
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    Customer

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    Returns

    Activate DecisionAI
    • How can you optimize returns collection and sorting to reduce operating costs?
    • How can you identify patterns in returns to improve product quality and reduce incidents?
    • How can you streamline refunds and replacements to improve the customer experience?

    Added value of AI and analytics:

    • Collection optimization: efficient return routes with optimization algorithms.
    • Intelligent classification: AI to determine whether a returned product can be refurbished or discarded.
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    Problems solved:

    • High logistics costs for returns.
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