A new rolling forecasting framework using Microsoft Power BI for data visualization: A case study in a pharmaceutical...

Area of Science:

• Supply Chain Management
• Pharmaceutical Industry
• Data Analytics

Background:

• Accurate demand forecasting is crucial for pharmaceutical supply chain efficiency and production planning.
• Existing forecasting models lack a concrete, generic framework, particularly for the unique demands of the pharmaceutical sector.
• This study addresses the need for improved sales prediction and visualization in pharmaceutical manufacturing.

Purpose of the Study:

• To develop and validate a generic, rolling forecasting framework for pharmaceutical sales.
• To enhance supply chain managers' decision-making capabilities through accurate sales predictions.
• To integrate modern IT and business intelligence for visualizing forecast results and improving patient satisfaction.

Main Methods:

• A rolling forecasting framework was developed using Visual Studio C++ for optimal forecasting and Power BI for accuracy monitoring.
• The framework integrates three exponential smoothing methods, with the capacity for future expansion.
• The system was tested using multiple datasets from a pharmaceutical manufacturer.

Main Results:

• The proposed framework demonstrated superior performance in sales forecasting for a pharmaceutical manufacturer.
• Forecast errors were reduced by up to 75%, and stock levels decreased by 50%.
• The framework is currently in use by the company, with future integration into their Enterprise Resource Planning (ERP) system planned.

Conclusions:

• The rolling forecasting framework provides accurate future sales insights and turnover visualization.
• It enables more effective stock management and production planning, leading to improved supply chain efficiency.
• The system supports data-driven decision-making, ultimately contributing to better patient outcomes.