Operational Efficiency
AI-driven lead time forecasting for pharmaceutical inventory management
In the pharmaceutical sector, managing supply chain efficiency and inventory costs remains a critical challenge for operational success. Agilytic partnered with a leading pharmaceutical company to implement AI-driven lead time forecasting, enabling them to reduce capital immobilization and optimize inventory management.
To protect confidentiality, we may alter specific details while preserving the accuracy of our core contribution.
Context and objectives
Our client, a pharmaceutical company, struggled to manage supply chain lead times efficiently, resulting in high capital immobilization and substantial annual holding costs.
The company had millions of euros tied up in inventory buffer, incurring considerable avoidable costs due to lead time inefficiencies. Unpredictable factors affecting lead times, such as supplier performance and in-house processing, exacerbated their operational risks and negatively impacted customer satisfaction.
The objective of this project was to leverage AI-driven forecasting to reduce lead time capital. This would both free up working capital and lower holding costs while maintaining service continuity.
Approach
Discovery and foundation
The project began with:
mapping the business context,
identifying key stakeholders,
and conducting a deep dive into relevant datasets to understand lead time management constraints.
Collaborating with the client's IT team and business experts, the project team clarified objectives and requirements to lay the groundwork for data-driven improvements.
Data preparation and model development
We assessed the quality and completeness of historical data, focusing on inconsistencies in delivery timing records to ensure reliable input for the forecasting model.
An AI lead time forecasting model was then developed iteratively based on feedback and further data analysis, emphasizing explainable results for business validation. We tested the model in controlled conditions, simulating real-world scenarios to ensure accuracy and reliability.
Deployment and integration
Finally, the model was formatted as a data product and integrated with analytical dashboards, allowing the client's team to explore and validate results independently.
Results
The project delivered a fully functional lead time forecasting prototype that demonstrated significant benefits in controlled conditions. The company is now able to envision
a reduction in annual capital requirement (in millions of euros)
A late volume reduction compared to current practices resulting in hundreds of thousands of euros of savings
An operational forecasting engine that allows detailed analysis of lead time behaviors at SKU, supplier, and warehouse-process levels
Comprehensive dashboards showing capital requirements and key performance indicators (KPIs), empowering the client's team for independent analysis
The client expressed strong satisfaction with the results, and plans are underway for future enhancements and broader applications of the lead time forecasting model across their operations.
To safeguard confidentiality, we may modify certain details within our case studies.