Our customer, a leading Pharma Manufacturer, faced significant challenges in accurately predicting product demand with inaccurate forecasts, leading to stockouts, overstocking, and revenue loss. They sought a modern solution to leverage Machine Learning (ML) for improved demand forecasting and Data foundation for timely Regulatory compliance reporting.

• Data Quality and Integration: Pharmaceutical manufacturing often involves complex data sets from various sources (e.g., clinical trials, sales data, manufacturing data). Inaccurate or incomplete data can lead to unreliable forecasts and hinder regulatory compliance reporting.
• External Factors Affecting Demand: Demand for pharmaceuticals can be influenced by external factors beyond traditional forecasting models. These include disease outbreaks, new drug approvals, competitor activity, and economic fluctuations. Machine learning models need to account for these external factors to be truly accurate.
• Regulatory Compliance Requirements: The pharmaceutical industry has stringent regulations regarding data security, traceability, and auditability. Implementing a new ML solution needs to comply with these regulations, potentially requiring additional steps for data governance and model validation.

Airo provided a comprehensive Data for AI solution designed to address these challenges and modernize the healthcare organization’s data platform:

• Airo implemented a data-driven demand forecasting solution utilizing Databricks and Azure technologies.
• Data Engineering & Management: Built a robust data pipeline using Azure Data Factory (ADF) to ingest sales data, historical trends, and external factors (e.g., weather, marketing campaigns) from various sources. Data was cleansed, transformed, and integrated into a unified data lake on Azure Data Lake Storage (ADLS)
• ML with Databricks: Airo leveraged Databricks with the customer’s data science team to develop and deploy various ML models for demand forecasting. Unity Catalog within Databricks facilitated seamless data discovery and management for model training.
• Deployment & Monitoring: Airo deployed the chosen ML model as a web service within Databricks, enabling real-time demand predictions. Azure Databricks Auto Loader was configured to automatically ingest new data into the data lake
• Live forecasting visualizations in Power BI

The implementation of Airo’s Data for AI solutions led to significant improvements and benefits for our customer:

• Improved Demand Forecasting Accuracy by 11%: Machine learning models significantly outperformed traditional methods, leading to more accurate demand predictions. This translated to fewer stockouts and overstocking situations, minimizing lost sales and unnecessary inventory holding costs.
• Reduced Stockouts by 7% and Overstocking by 4%: By achieving more accurate forecasts, the manufacturer experienced a 7% reduction in stockouts, ensuring product availability for customers. Additionally, 4% less overstocking occurred, optimizing storage space and reducing waste within the first 6 months.
• Data-Driven Decisions & Improved Strategic Planning: Real-time insights from AI-powered forecasts empowered data-driven business decisions and improved strategic planning. This can translate to optimized production runs, minimized production line disruptions, and potentially lead to a 18% increase in overall production efficiency.

A leading pharma manufacturer struggling with inaccurate demand forecasts leveraged Airo’s Data for AI solution. By integrating data sources and deploying Machine Learning models, Airo achieved an impressive 11% improvement in demand forecasting accuracy. This translated to a 7% reduction in stockouts and a 4% reduction in overstocking within just 6 months, potentially leading to an 18% increase in production efficiency. This case demonstrates the power of AI to optimize operations, minimize waste, and ensure timely product availability for patients.