Overcoming Key Challenges in Health Commodity Forecasting

Inadequate Infrastructure and Information Systems

Insufficient infrastructure limits accurate forecasting

Data Quality and Availability

Poor data quality hampers effective forecasting

Fragmented Coordination and Collaboration

Poor stakeholder coordination disrupts forecasting

Insufficient Financial and Human Resources

Lack of resources affects forecasting capacity

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Responsive Commodity Quantification (RCQ) for Improved Health Commodity Forecasting

RCQ focuses on facility-based decentralized forecasting, advanced data analytics, stakeholder collaboration, and continuous improvement. This systematic approach enhances data accuracy, promotes efficient resource use, and improves the overall reliability of health commodity forecasts
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How We Help Implement RCQ: A High-Level Overview

Step 1: Stakeholder Engagement and Capacity Building

Engage key stakeholders and enhance technical skills.

  • Conduct workshops and meetings to align stakeholders on objectives.
  • Develop and deliver training programs to build local capacity for forecasting and supply planning.

 

Step 2: Data Quality Improvement and Advanced Forecasting Models

Enhance data quality and apply advanced forecasting techniques.

  • Assess and improve data collection infrastructure.
  • Develop and integrate advanced forecasting models tailored to local needs.

3. Step 3: Continuous Monitoring and Improvement

Ensure ongoing evaluation and refinement.

  • Establish monitoring and evaluation mechanisms to track performance.
  • Regularly update forecasting models and processes based on feedback and data analysis.
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