SP Technologies successfully designed and implemented an AI-powered Inventory Management System for one of Switzerland’s largest pharmaceutical distributors. This 4-month project focused on optimizing inventory levels, reducing waste, and improving supply chain efficiency.
Key achievements:
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Data Science: We developed advanced predictive models using machine learning algorithms to forecast demand for various pharmaceutical products across different regions and seasons.
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System Integration: Our team integrated the new AI models with the client’s existing Enterprise Resource Planning (ERP) system, ensuring seamless data flow and real-time inventory optimization.
The project leveraged historical sales data, market trends, and external factors such as disease outbreaks and weather patterns to make accurate inventory predictions. We used Python for data processing and TensorFlow for building and training the machine learning models.
The implementation resulted in a 25% reduction in overstocking, a 15% decrease in stockouts, and an overall 10% improvement in inventory turnover. This intelligent system not only optimized the client’s supply chain operations but also significantly reduced pharmaceutical waste, aligning with sustainability goals.
The success of this project opened doors for further AI applications in the client’s supply chain management, positioning them at the forefront of data-driven pharmaceutical distribution in Switzerland.