Stock Optimization

The pharmaceutical industry is characterized by high complexity and dynamism. Pharmaceutical products have a limited shelf life and are subject to strict regulations. Their demand can be affected by factors such as market trends, competition, marketing campaigns, and changes in public policies. In this context, efficient inventory management becomes a fundamental pillar for the profitability and success of pharmaceutical companies. It is estimated that poor stock management can imply additional costs of up to 20% of the inventory value (1).

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Accurate prediction of stock levels is critical to optimizing inventory management in the pharmaceutical industry. It allows companies to anticipate demand for finished products, ensuring the availability of raw materials and avoiding excessive costs associated with storage, obsolescence, and immobilized working capital. Additionally, proper stock management minimizes the risk of production interruptions, resulting in greater operational efficiency, better customer service, and a solid reputation in the market (2).

Size of the Problem‍

• 20% additional cost: Poor inventory management can add up to 20% of the inventory value in additional costs (3).

• Median Days of Inventory Outstanding (DIO): 180 days, indicating a 6-month average inventory holding (1).

• Operational efficiency: Minimizing stock-outs and overstocks can lower inventory costs by 10% (2).

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Precisely predicting stock levels is an urgent necessity for the pharmaceutical industry, which operates in a complex and dynamic environment characterized by products with limited shelf life and strict regulations. In this context, efficient inventory management ensures profitability and business success. The consequences of poor prediction are considerable, with additional costs of up to 25% on the inventory value, production interruptions, negative impact on customer satisfaction, and damage to the company's reputation. Conversely, accurate prediction of actions brings tangible benefits, such as optimizing efficiency, reducing costs, improving customer service, and strengthening reputation (4).

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• Reduction of warehouse stocks through AI: In the pharmaceutical industry, efficiently managing inventory levels is crucial to optimizing costs and ensuring product availability. Applying AI-driven predictive analysis has become a key strategy to address this challenge. By more accurately anticipating future demand for raw materials, companies can adjust their inventory levels more efficiently, avoiding overstocking and shortages. This reduces costs associated with storage and obsolescence and improves the supply chain's overall efficiency.
  
  An example of this implementation is Pfizer, one of the leading pharmaceutical companies globally. Using AI-based predictive analysis, Pfizer has succeeded in forecasting future drug demand more accurately and adjusting its inventory levels more precisely. This approach has resulted in a significant 50% reduction in excess inventory, improving logistical efficiency and positively impacting operational costs and company profitability (2).

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• Prediction of raw material demand: In the pharmaceutical industry, precision in predicting demand is crucial to ensuring efficient inventory management and uninterrupted production. The application of AI in this context allows for analyzing a wide range of data, including market trends, new product launches, and regulations, to anticipate future demand more accurately. A clear example is Roche, a leading pharmaceutical company that uses advanced machine-learning techniques to analyze historical demand data. Thanks to this implementation, Roche has achieved a 95% accuracy rate in predicting raw material demand, significantly contributing to more efficient inventory management and precise production planning (5).

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• Impact on decision-making: The integration of AI in inventory management provides valuable real-time information on stock availability and raw material demand and significantly improves the decision-making process. With access to accurate data and detailed analysis, decision-makers can take more informed and timely actions, from adjusting inventory levels to making strategic decisions about raw material acquisition. Johnson & Johnson is a prominent example of how AI integration in inventory management can lead to more agile and efficient decision-making. By providing real-time information on stock availability and raw material demand, this implementation has allowed the company to adjust inventory levels and make informed strategic decisions, resulting in a 15% reduction in unused inventory levels and overall operational efficiency improvement (3).

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• Stock prediction for the "perfect match": In the pharmaceutical industry, ensuring the right balance between supply and demand is essential to avoid shortages and overstock. The application of AI in predicting stock to achieve this "perfect match" is a fundamental approach in inventory management. By analyzing the entire supply chain, AI can identify critical points that could lead to shortages or overstock, enabling more proactive and efficient management. Additionally, by integrating with production systems, AI can automatically adjust production based on raw material availability and final product demand, thus optimizing material flow and minimizing operational risks. This approach has proven beneficial in situations of high logistical complexity and demand variability, where adaptability and rapid response capability are crucial to maintaining efficiency and profitability.

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• Sales and Production Historical Database: Contains information on past sales and production, which is helpful in identifying demand patterns and seasonality.

• Suppliers and Raw Material Prices Database: Provides details on suppliers, prices, and contractual terms, helping evaluate the availability and costs of raw materials.

• Industry Regulations and Policies Database: Offers information on government regulations and policy changes, which are vital for compliance requirements and anticipating impacts on the supply chain.

• Market and Competition Database: Contains data on market trends, competitor products, and changes in consumer demand, aiding in adjusting inventory strategies.

• Inventory and Stock Tracking Database: Records information on inventory levels, movements, and obsolescence, allowing for effective monitoring and precise inventory management.

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