TITLE:
Improving Demand Forecasting for Haemodialysis Consumables in Zambia: A Comparative Analysis of Time-Series Models
AUTHORS:
Racheal Samudata, Chipasha Mbuzi, Siphiwe Makowane, Lahaye Malembeka Kapobe, Vianney Neene, Webrod Mufwambi, Steward Mudenda
KEYWORDS:
Demand Forecasting, Language Models, Renal Consumables, SARIMA, Predictive Models, Supply Chain, Time Series Models, Zambia
JOURNAL NAME:
Open Journal of Business and Management,
Vol.14 No.4,
July
10,
2026
ABSTRACT: Background: Reliable forecasting of renal consumables is essential to prevent stock-outs and ensure uninterrupted dialysis services in Zambia’s public health facilities. Recurrent shortages occur partly due to reliance on basic methods, such as the simple moving average (SMA), which struggles to capture trends and variability amid a rising burden of chronic kidney disease. This study aimed to evaluate and compare the accuracy of advanced time series predictive models against traditional forecasting methods for renal consumables at the University Teaching Hospital (UTH) in Lusaka, thereby establishing a data-driven framework for preventing stock-outs in specialised tertiary care. Methods: This retrospective, quantitative study analysed historical monthly consumption records (2023-2025) of key renal consumables at the University Teaching Hospital (UTH) Adult Hospital. Time series models, Weighted Moving Average (WMA), Exponential Smoothing (ES), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Dynamic Regression, were compared against the baseline SMA using rolling-origin cross-validation and error metrics, specifically Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Data analysis was conducted using Python libraries. Results: Analysis of historical consumption patterns revealed significant demand variability across renal consumables, with the coefficient of variation ranging from a low of 9.0% for disinfectants to a high of 47.4% for sodium bicarbonate, indicating a mix of stable and highly stochastic demand profiles. Advanced time-series models consistently outperformed the simple moving average (SMA) baseline for most stock-keeping units. At 25.80% MAPE, Exponential smoothing resulted in 67% error reduction improvement over baseline for sodium bicarbonate while SARIMA achieved 32% error reduction improvement over baseline for disinfectants at 0.60% MAPE. Notably, no significant seasonality was detected, as demand was primarily characterised by long-term consumption trends and irregular consumption fluctuations rather than cyclical consumption patterns. Conclusion: The findings of this study showed that time-series models significantly outperformed the traditional simple moving average forecasting method which is currently used in the Zambian public health system. Exponential smoothing performed well for bloodline giving sets and heparin anticoagulant. SARIMA performed well for dialysis machine disinfectants, WMA for renal concentrate dialysate and sodium bicarbonate buffer. Implementing time-series models can improve forecasting accuracy and can improve prediction of demand for renal consumables. These improvements have critical implications for hospital supply chain management. Accurate prediction can improve inventory planning and would ensure the continuity of life-saving dialysis services.