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Artificial Intelligence for Logistics

Logistics

AI in LogisticsLogistics optimisation with AI

Decision AI for a more efficient supply chain

Optimise every link of your supply chain with Decision AI. From route planning and transportation to warehouse optimisation and reverse logistics, our solutions help reduce costs and improve operational efficiency.

value chainExplore how Decision Intelligence solves logistical challenges at every stage of the supply chain.

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Supply chain planning and management
1
Storage and inventory management
2
Transportation and cargo handling
3
Last mile and customer delivery
4
Returns management and reverse logistics
5
Sustainability and carbon footprint reduction
6

Supply chain planning and management

Suppliers and supply of raw materials

Transportation and cargo handling

Last mile and delivery to the customer

Returns management and reverse logistics

Sustainability and carbon footprint reduction

E-book: AI in LogisticsSubmit 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.

Supply Chain Summit

Access the event’s summary video and dive into the full article with all the insights and conclusions on how Decision AI is revolutionizing logistics.
Read event article

casos de usoSome use cases

Discover how our solutions have improved efficiency and optimised operations in the energy sector.

Supply chain planning

  • Industry: Logistics and Distribution
  • Challenge: Optimise supply planning, storage, and distribution to reduce costs and ensure efficient deliveries for a multinational company.
  • Approach: Implemented a comprehensive optimisation model covering strategic planning through daily operations, improving resource allocation and significantly cutting costs.
  • Results: Increased operational efficiency, resource optimisation, and reduced logistics costs.
  • Key Metrics:
    12% reduction in operational costs.
    10% reduction in idle inventory
0 %
Reduction in operational costs

Transport route optimisation in logistics.

  • Industry: Transport and Distribution.
  • Challenge: Optimise route management in courier company, considering fluctuating demand and fleet availability.
  • Approach: Implemented a route optimisation system using a two-stage process: demand forecasting for different delivery types, followed by a weekly plan with optimised routes combining own and subcontracted fleets.
  • Results: Reduced delivery times, improved resource utilisation, and increased delivery reliability.
  • Key Metrics: +17% increase in profit margin, –14% reduction in workload.
0 %
Improve profit margin.

Strategic improvement of the warehouse network.

  • Industry: Retail and e-commerce.
  • Challenge: Optimise the opening and closing of warehouses in a globally distributed company to minimise logistical costs.
  • Approach: Application of Decision AI to select strategic warehouse locations, minimising costs and maximising efficiency.
  • Results: Achieved a significant reduction in logistical costs, along with improved sensitivity analysis and scenario planning capabilities.
  • Key metrics: -22% in logistical costs.
0 %
Reduction in logistical costs

Optimal location of warehouses

  • Industry: Supply chain and manufacturing.
  • Challenge: Optimise the warehouse network of a manufacturing company to reduce logistical costs and improve long-term operational efficiency.
  • Approach: Implementation of Decision Intelligence to analyse multiple warehouse location scenarios using predictive and optimisation models. Strategic locations were identified that minimise transportation costs and maximise distribution efficiency.
  • Results: Reduction in distances travelled, optimisation of logistics asset utilisation, and improvement in operational capacity.
  • Key metrics:
    -15% fewer kilometres travelled.
    +18% more optimised logistics assets.
0 %
Kilometres travelled.

NewsNews and trends in AI applied to logistics

Stay up-to-date on how AI is transforming logistics, from route optimization to strategic warehouse management and reducing operating costs.
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    • Power Generation

    Activa DecisionAI
    • How to optimize production to reduce costs and maximize efficiency?
    • How to anticipate infrastructure failures to avoid unexpected outages?
    • How can we improve renewable energy forecasting to stabilize supply?
    • How can infrastructure (wind farms, solar farms, production plants) be planned and optimized to obtain the most energy at the lowest cost?

    Added value of AI and analytics:

    • Production optimization: ML models adjust production based on demand and resource availability.
    • Predictive maintenance: IoT sensors and algorithms anticipate failures in generating infrastructure.
    • Renewable energy forecasting: Advanced climate models improve solar and wind energy forecasting.

    Problems solved:

    • Inefficiency in energy production, which generates additional costs.
    • Unexpected failures in generating plants, which affect the stability of supply.
    • Variability in renewable generation, improving the integration of intermittent sources.
    Contact Us

    Supply chain planning and management

    Activate DecisionAI
    • How can routes be optimized to reduce costs and avoid unnecessary journeys?
    • How can demand forecasting be improved to avoid excess inventory or shortages?
    • How can supplier performance be evaluated to optimize the supply chain?

    Added value of AI and analytics:

    • Route planning optimization: Optimization models find the best combination of transportation and times.
    • Demand forecasting in logistics: Machine learning anticipates changes in cargo volumes based on consumption patterns.
    • Supplier management with AI: Performance analysis to improve relationships with carriers and logistics partners.

    Problems solved:

    • Delivery delays due to poor planning.
    • High costs due to excess inventory.
    • Lack of visibility into suppliers.
    Contact Us

    Storage and inventory management

    Activate DecisionAI
    • How can you organize your warehouse to minimize time and reduce operating costs?
    • How can you automate picking and packing to streamline order fulfillment?
    • How can you adjust stock levels based on actual demand and avoid waste?

    Added value of AI and analytics:

    • Warehouse layout optimization: AI algorithms organize storage to minimize picking times.
    • Picking and packing automation: Robots and AI systems optimize order preparation.
    • Inventory turnover prediction: Machine learning adjusts stock levels based on consumption patterns.

    Problems solved:

    • Underutilized storage space, reducing operating costs.
    • Order preparation delays, optimizing internal routes.
    • Losses due to excess inventory, adjusting stock management.
    Contact Us

    Transportation and cargo handling

    Activate DecisionAI
    • How can transportation routes be optimized to reduce times and fuel costs?
    • How can fleet monitoring be done in real time to improve efficiency and delivery times?
    • How can vehicle failures be anticipated to avoid operational disruptions?

    Added value of AI and analytics:

    • Transportation route optimization: Prescriptive algorithms identify the most efficient routes, taking into account traffic, weather, and costs.
    • Real-time fleet monitoring: IoT sensors track transportation locations and conditions.
    • Predictive vehicle maintenance: AI anticipates mechanical failures and reduces downtime.

    Problems solved:

    • High fuel costs, optimizing routes.
    • Delivery delays, ensuring more efficient routes.
    • Unexpected vehicle failures, minimizing downtime.
    Contact Us

    Last mile and delivery to the customer

    Activa DecisionAI
    • How can we improve last-mile routes to reduce costs and ensure fast deliveries?
    • How can we predict demand for express deliveries to optimize logistics resources?
    • How can we increase customer satisfaction with more accurate delivery times?

    Added value of AI and analytics:

    • Dynamic delivery optimization: AI algorithms adjust routes in real time based on traffic conditions.
    • Demand prediction for express deliveries: ML models anticipate urgent order volumes.

    Problems solved:

    • Delays in urban deliveries, ensuring efficient routes.
    • High last-mile operating costs, reducing logistics spending.
    • Low customer satisfaction due to late deliveries, ensuring faster and more accurate deliveries.
    Contact Us

    Returns management and reverse logistics

    Activate DecisionAI
    • How can we optimize returns sorting and collection to reduce costs?
    • How can we analyze return patterns to minimize delivery errors?
    • How can we improve the recovery and reuse of returned products?

    Added value of AI and analytics:

    • Returns management automation: AI optimizes the processing and categorization of returned products.
    • Return pattern prediction: Machine learning analyzes historical data to reduce the return rate.
    • Reverse transportation optimization: Routing algorithms improve the efficiency of returned product collection.

    Problems solved:

    • High returns handling costs, optimizing reverse logistics.
    • Loss of returned products, improving inventory recovery.
    • Lack of return cause analysis, reducing delivery errors.
    Contact Us

    Sustainability and carbon footprint reduction

    Activate DecisionAI
    • How can we minimize our carbon footprint by optimizing routes and sustainable transportation?
    • How can we implement eco-friendly fleets without compromising operational efficiency?
    • How can we reduce the use of unsustainable materials in packaging and logistics?

    Added value of AI and analytics:

    • Sustainable route optimization: AI adjusts routes to minimize carbon footprint.
    • Efficient use of electric transport: Machine learning analyzes the adoption of eco-friendly fleets.
    • Reusable packaging management: Algorithms optimize the use of recyclable materials in logistics.

    Problems solved:

    • High carbon emissions in transportation, promoting cleaner routes.
    • Excessive use of unsustainable materials, improving packaging.
    • Energy waste in warehouses and fleets, optimizing consumption.
    Contact Us