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From demand forecasting to inventory ordering decisions for red blood cells through integrating machine learning,
Na Li1,2,3, Donald M Arnold2,4,5, Douglas G Down3
1Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.
Transfusion
|November 16, 2021
Summary
A new data-driven strategy for managing red blood cell (RBC) inventory significantly cut costs by 43% and reduced ordering frequency by 62.6%. This approach optimizes blood supply chains, minimizing waste and shortages for better healthcare delivery.
Area of Science:
- Healthcare Management
- Operations Research
- Biomedical Informatics
Background:
- Blood inventory management is challenged by variable demand and supply, often relying on experience-based decisions.
- This can lead to inefficiencies such as high operational costs, wastage, and blood shortages.
Purpose of the Study:
- To develop and evaluate a data-driven strategy for red blood cell (RBC) demand forecasting and inventory management.
- To address last-mile delivery challenges with a secondary semi-weekly ordering strategy.
Main Methods:
- Combined statistical modeling, machine learning, and optimization for RBC demand forecasting and inventory control.
- Developed daily and semi-weekly ordering strategies informed by the data-driven model.
- Evaluated strategies using the TRUST database from four hospitals in Hamilton, Ontario (2012-2018).
Main Results:
- The proposed daily ordering strategy reduced RBC inventory levels by 38.4% without increasing shortages.
- Achieved a 43.0% overall cost reduction compared to actual costs.
- The semi-weekly ordering strategy decreased ordering frequency by 62.6%.
Conclusions:
- A data-driven approach to blood ordering, integrating demand forecasting and inventory optimization, yields substantial cost savings.
- The proposed strategy enhances efficiency for both healthcare systems and blood suppliers.
