Study on demand forecasting techniques for drugs with sporadic demand in a community hospital

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Nattaphon Thaiudomsap
Namfon Sribundit

Abstract

This research studied the appropriate forecasting techniques for drugs with sporadic demand, consisting of intermittent and lumpy demands. The actual consumption of 41 drugs in a community hospital from 2014 to 2018 was analyzed to forecast the monthly demand in 2019 using the simple exponential smoothing (SES), Croston, Syntetos-Boylan approximation (SBA), and Teunter, Syntetos, and Babai (TSB) methods and compared the forecasting performance with actual demand. The results revealed that the TSB method performed the best forecast accuracy with the root mean squared error of 12.68, mean absolute deviation of 9.61, and mean absolute scaled error of 1.04. However, it produced a higher bias than others. SES demonstrated the second-best accuracy method, presenting the least bias with the cumulated forecast error of 19.04, the percentage of the number of shortages of 76.91, and the periods in stock of -185.69. Although the SBA method exhibited a lower error than the Croston method, it contributed to a higher bias. As there were no forecasting methods that demonstrated the best forecast accuracy and bias for drugs with sporadic demand, the proper forecasting technique needed to be customized for each drug.

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Health sciences

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