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Artificial Intelligence in Pharmacy

Pharma

AI in the Pharmaceutical sectorOptimisation and innovation for strategic decision-making in a single Artificial Intelligence solution.

Strategic decisions in your value chain

At Baobab Soluciones, we apply Decision AI to optimise decision-making in the pharmaceutical sector. From improving clinical trials and managing R&D resources to efficient supply chain planning, our solutions analyse data and evaluate scenarios to find the best decision.

Value chainExplore our interactive value chain.

Discover how Decision AI addresses key challenges at every stage of the process, facilitating strategic decision-making.

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Research and development (R&D)
1
Production and manufacturing
2
Supply chain
3
Distribution and logistics
4
Marketing and acces
5
Safety and pharmacovigilance
6

Drug R&D

Production and Manufacturing

Supply chain management

Distribution and Logistics

Marketing and access to medicines

Safety and Pharmacovigilance

e-book ia en farmaciaSubmit your download request

The edition of this ebook is limited; each request will be reviewed individually, and a copy will be sent to verified profiles until the available units are exhausted.

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.

Use casesSome use cases

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

Pharmaceutical production optimised with Decision AI

  • Industry: Pharmaceutical.
  • Challenge: Optimise pharmaceutical production and logistics planning by integrating strategic, tactical, and operational decisions.
  • Approach: Use of Decision AI to model optimal production and logistics scenarios in real time.
  • Results: Greater accuracy in planning, reduction in deviations, and improved responsiveness to market changes.
  • Key metrics: 20% reduction in total costs.
0 %
Reduction in total costs.

Supply chain planning

  • Industry: Pharmaceutical.
  • Challenge: Improve planning of procurement, production, storage, and distribution to reduce costs and enhance service quality.
  • Approach: Implementation of a multi-level optimisation model, covering from strategic planning to daily operations.
  • Results: Greater operational efficiency, resource optimisation, and improved adaptability to market demand.
  • Key metrics:
    12% reduction in total operational costs.
    10% reduction in inactive inventory.
0 %
Operational costs

Hydroelectric production optimisation

  • Industry: Energy.
  • Challenge: Maximise profitability of hydroelectric generation by adjusting reservoir water flow based on demand and market prices.
  • Approach: Application of Decision Intelligence to forecast price fluctuations in the electricity market and optimise the opening and closing of gates for optimal financial performance.
  • Results: Efficient use of water resources, reduction of waste, and increased revenue from energy sales.
  • Key metrics: 7% increase in total revenue.
0 %
Increase in total revenue

NoticiasNews and trends in AI applied to decision-making in the pharmaceutical industry

Stay informed about how artificial intelligence is optimizing decisions in the pharmaceutical industry. Discover how other leaders are leveraging AI to maximize results and save costs. Since 2011, we’ve saved our clients over €400 million.
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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

    Drug R&D

    Activa DecisionAI
    • How can we accelerate the discovery of new drugs to reduce costs and development times?
    • How can we improve patient and molecule selection to increase the success of clinical trials?
    • How can we predict side effects and optimize formulations before extensive testing?

    Added value of AI and analytics:

    • AI-assisted drug discovery: Machine learning algorithms analyze biomedical databases to identify promising molecules.
    • Clinical trial optimization: AI models select optimal patients to reduce trial times and increase success rates.
    • Drug interaction simulation: AI predicts side effects and efficacy without extensive testing.

    Problems solved:

    • High research costs, reducing the time and resources needed to discover new compounds.
    • Low failure rates in clinical trials, improving molecule and patient selection.
    • Long development times, accelerating the preclinical and clinical phases with AI-based simulations.
    Contact Us

    Production and Manufacturing

    Activate DecisionAI
    • How can production processes be optimized to reduce waste and improve efficiency?
    • How can machinery failures be anticipated to avoid unscheduled downtime?
    • How can the quality of each batch of medication be ensured without increasing operating costs?

    Added value of AI and analytics:

    • Manufacturing process optimization: Prescriptive models adjust production in real time to maximize efficiency.
    • Predictive maintenance in machinery: AI identifies failures before they occur, avoiding unscheduled downtime.
    • Automated quality control: Computer vision detects deviations in formulation or packaging.

    Problems solved:

    • Variability in drug quality, ensuring consistency in production.
    • Machine failures, reducing plant downtime.
    • High operating costs, optimizing resources and materials.
    Contact Us

    Supply chain management

    Activate DecisionAI
    • How can we predict demand for medicines to avoid shortages or excess stock?
    • How can we optimize inventory levels to reduce costs and ensure availability?
    • How can we manage supply chain risks to avoid disruptions?

    Added value of AI and analytics:

    • Demand Prediction: ML models anticipate drug consumption based on historical data and public health trends.
    • Inventory Optimization: Operational research algorithms adjust stock levels based on actual demand.
    • Supply Chain Risk Management: AI identifies potential disruptions and recommends contingency plans.

    Problems solved:

    • Shortages of essential medicines, ensuring balanced production and distribution.
    • Excess inventory and expiration of drugs, reducing storage costs.
    • Dependence on single suppliers, diversifying supply strategies.
    Contact Us

    Distribution and Logistics

    Activa DecisionAI
    • How can we ensure the thermal stability of medicines throughout the entire cold chain?
    • How can we optimize delivery routes to reduce logistics times and costs?
    • How can we automate shipment tracking to improve distribution efficiency?

    Added value of AI and analytics:

    • Delivery route optimization: Optimization algorithms reduce times and costs in the transportation of medicines.
    • Real-time condition monitoring: IoT sensors ensure adequate temperatures throughout the cold chain.
    • Shipment tracking automation: AI analyzes distribution patterns and improves logistics planning.

    Problems solved:

    • Lack of control over the cold chain, ensuring thermal stability throughout the entire journey.
    • Distribution delays, improving logistics planning.
    • High transportation costs, optimizing routes and resources.
    Contact Us

    Marketing and access to medicines

    Activate DecisionAI
    • How can you adjust dynamic pricing to balance profitability and accessibility?
    • How can you anticipate market trends and optimize marketing strategies?
    • How can you customize sales and marketing strategies for each patient segment?

    Added value of AI and analytics:

    • Price optimization: Dynamic models adjust prices based on costs, demand, and regulations.
    • Market trend analysis: Machine learning predicts the impact of new drugs in different regions.
    • Sales strategy customization: AI recommends marketing and sales approaches based on patient segmentation.

    Problems solved:

    • Inefficient pricing, ensuring access and profitability.
    • Lack of expansion strategies, optimizing entry into new markets.
    • Inadequately anticipating demand, ensuring availability in each region.
    Contact Us

    Safety and Pharmacovigilance

    Activate DecisionAI
    • How can adverse effects be detected in real time to improve patient safety?
    • How can treatments be optimized with personalized doses based on patient profiles?
    • How can regulatory reporting be automated to ensure compliance?

    Added value of AI and analytics:

    • Real-time side effect analysis: AI analyzes medical databases and social media to identify adverse reactions.
    • Personalized dose optimization: AI models adjust treatments based on patient profiles.
    • Regulatory reporting automation: Machine learning streamlines the detection and reporting of safety issues.

    Problems solved:

    • Delays in detecting adverse effects, ensuring faster responses.
    • Difficulty in personalizing treatments, optimizing the dose for each patient.
    • Poor management of incident reporting, ensuring regulatory compliance.
    Contact Us