Study on demand forecasting techniques for drugs with sporadic demand in a community hospital
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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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References
Alves, C. M. F. (2018). Demand forecasting in a multi-specialty hospital setting: a comparative study of machine learning and classical statistical methods. Master’s thesis. University of Porto, Portugal.
Babai, M. Z., Dallery, Y., Boubaker, S., and Kalai, R. (2019). A new method to forecast intermittent demand in the presence of inventory obsolescence. International Journal of Production Economics, 209, 30-41.
Babai, M. Z., Syntetos, A., and Teunter, R. (2014). Intermittent demand forecasting: an empirical study on accuracy and the risk of obsolescence. International Journal of Production Economics, 157, 212-219.
Candan, G., Taşkin, M. F., and Yazgan, H. R. (2014). Demand forecasting in pharmaceutical industry using artificial intelligence: neuro-fuzzy approach. Journal of Military and Information Science, 2(2), 41-49.
Cavalieri, S., Garetti, M., Macchi, M., and Pinto, R. (2008). A decision-making framework for managing maintenance spare parts. Production Planning & Control, 19(4), 379-396.
Chaiveerathai, S., and Liampreecha, W. (2015). Optimization and cost reduction of pharmaceutical inventory management: a case study of a government hospital in Phitsanulok. Journal of Community Development Research (Humanities and Social Sciences), 8(3), 139-153. [in Thai]
Chungsiwapornpong, W. (2007). Survey of drug inventory control process and performance among hospital pharmacy departments in Thailand. Master’s thesis. Mahidol University, Thailand.
Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23(3), 289-303.
Dias, V., Quick, J. D., and Rankin, J. R. (2012). Inventory management. In MDS-3: Managing access to medicines and health technologies (Embrey, M., ed.), pp. 1-24. Arlington, Virginia: Management Science for Health.
Doszyń, M. (2018). New forecasting technique for intermittent demand, based on stochastic simulation. An alternative to Croston’s method. Acta Universitatis Lodziensis, Folia Oeconomica, 5(338), 41-55.
Doszyń, M. (2019). Intermittent demand forecasting in the enterprise: empirical verification. Journal of Forecasting, 38(5), 459-469.
Eaves, A. H. C., and Kingsman, B. G. (2004). Forecasting for ordering and stock holding of spare parts. Journal of the Operational Research Society, 55(4), 431-437.
Hemeimat, R., Al-Qatawneh, L., Arafeh, M., and Masoud, S. (2016). Forecasting spare parts demand using statistical analysis. American Journal of Operations Research, 6(2), 113-120.
Hyndman, R. J., and Koehler A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.
Kalaya, P., Termsuksawad, P., and Wasusri, T. (2019). Lumpy demand forecasting for slow-moving medicines: a case study of community hospital Thailand. Isan Journal of Pharmaceutical Science, 15(1), 82-92. [in Thai]
Kritchanchai, D., and Meesamut, W. (2015). Developing inventory management in hospital. International Journal of Supply Chain Management, 4(2), 11-19.
Merkuryeva, G., Valberga, A., and Smirnov, A. (2019). Demand forecasting in pharmaceutical supply chains: a case study. Procedia Computer Science, 149(C), 3-10.
Petropoulos, F., Kourentzes, N., and Nikolopoulos, K. (2016). Another look at estimators for intermittent demand. International Journal of Production Economics, 181(A), 154-161.
Pungsak, A. (2015). Precision and effectiveness of demand forecasting for drug inventory management. Master’s thesis. Silpakorn University, Thailand.
Rachmania, I. N., and Basri, M. H. (2013). Pharmaceutical inventory management issues in hospital supply chains. Management, 3(1), 1-5.
Reid, R. D., and Sanders, N. R. (2005). Operations Management: an Integrated Approach, 2nd, New Jersey: John Wiley & Sons, Inc., pp. 280-330.
Sayed, H. E., Gabbar, H. A., and Miyazaki, S. (2010). Design of demand forecasting expert system for dynamic supply chains. In Expert Systems (Vizureanu, P., ed.), pp. 137-160. Vukovar, Croatia: IntechOpen Ltd.
Syntetos, A. A., and Boylan, J. E. (2005). The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303-314.
Syntetos, A. A., Boylan, J. E., and Croston, J. D. (2005). On the categorization of demand patterns. Journal of the Operational Research Society, 56(5), 495-503.
Teunter, R., and Sani, B. (2009). On the bias of Croston's forecasting method. European Journal of Operational Research, 194(1), 177-183.
Teunter, R. H., Syntetos, A. A., and Babai, M. Z. (2011). Intermittent demand: linking forecasting to inventory obsolescence. European Journal of Operational Research, 214(3), 606-615.
Thomopoulos, N. T. (2015). Demand Forecasting for Inventory Control. Switzerland: Springer Cham, pp. 1-10.
Uthayakumar, R., and Priyan, S. (2013). Pharmaceutical supply chain and inventory management strategies: optimization for a pharmaceutical company and a hospital. Operations Research for Health Care, 2(3), 52-64.
Wallström, P., and Segerstedt, A. (2010). Evaluation of forecasting error measurements and techniques for intermittent demand. International Journal of Production Economics, 128(2), 625-636.
Wild, T. (2002). Best Practice in Inventory Management, 2nd, Oxford: Butterworth-Heinemann, pp. 33-186.
Yang, Y., Ding, C., Lee, S., Yu, L., and Ma, F. (2021). A modified Teunter-Syntetos-Babai method for intermittent demand forecasting. Journal of Management Science and Engineering, 6(1), 53-63.