A model for predicting demand in pharmacies
Article Sidebar
Main Article Content
Abstract
Inventory management of medicines is one of the most complex tasks for a pharmacy. Accurate purchase estimation allows pharmacies to balance the need to meet user demand and minimise inventory maintenance and storage costs by reliably predicting how much of a drug should be purchased. This paper proposes two methods for predicting the demand for medicines from the Pharmacy of the Ecuadorian Social Security Institute (Ibarra): one based on time series, and one based on neural networks. The models were tested on medicines with a seasonal, cyclical demand, and assessed using mean square error and mean absolute error measurements. The model based on neural networks was found to have a lower error rate.
Keywords:
Downloads
Article Details
References
Ahmed, N., and Farzana, F. (2020). Forecasting supply chain sporadic demand using support vector machine approaches. Fuzzy Sets and Systems, 10, 87-102.
Álvarez-Rodríguez, D. A., Normey-Rico, J. E., and Flesch, R. C. C. (2017). Model predictive control for inventory management in biomass manufacturing supply chains. International Journal of Production Research, 55(12), 3596-3608. https://doi.org/10.1080/00207543.2017.1315191
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27-34.
Frank, R. J., Davey, N., and Hunt, S. P. (2001). Time series prediction and neural networks. Journal of intelligent and robotic systems, 31(1), 91-103.
Fernández, M. I., Chanfreut, P., Jurado, I., and Maestre, J. M. (2020). A Data-Based Model Predictive Decision Support System for Inventory Management in Hospitals. IEEE Journal of Biomedical and Health Informatics, 25(6), 2227-2236. https://doi.org/10.1109/JBHI.2020.3039692
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. Hazen, B., and Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317. https://doi.org/10.1016/j.jbusres.2016.08.004
Gutiérrez-Alcoba, A., Rossi, R., Martin-Barragan, B., and Hendrix, E. M. (2017). A simple heuristic for perishable item inventory control under non-stationary stochastic demand. International Journal of Production Research, 55(7), 1885-1897. https://doi.org/10.1080/00207543.2016.1193248
Hakim, I. M., and Ulfah, W. M. (2019, September). Model Development to Determine Optimal Drugs Inventory in Indonesia Public Health Services. In Proceedings of the 2019 5th International Conference on Industrial and Business Engineering (pp. 28-32). https://doi.org/10.1145/3364335.3364368.
Hamilton, J. D. (2020). Time series analysis. Princeton University Press.
Janssen, L., Sauer, J., Claus, T., and Nehls, U. (2018). Development and simulation analysis of a new perishable inventory model with a closing days constraint under non-stationary stochastic demand. Computers & Industrial Engineering, 118, 9-22. https://doi.org/10.1016/j.cie.2018.02.016
Jurado, I., Maestre, J. M., Velarde, P., Ocampo-Martínez, C., Fernández, I., Tejera, B. I., and del Prado, J. R. (2016). Stock management in hospital pharmacy using chance-constrained model predictive control. Computers in biology and medicine, 72, 248-255. https://doi.org/10.1016/j.compbiomed.2015.11.011
Landeta, J. M. I., and Lango, H. M. (2013). Estudio comparativo de la aplicación de 6 modelos de inventarios para decidir la cantidad y el punto de reorden de un artículo. Ciencia y tecnología, (13), 217-232.
Liu, I., Colmenares, E., Tak, C., Vest, M. H., Clark, H., Oertel, M., and Pappas, A. (2021). Development and validation of a predictive model to predict and manage drug shortages. American Journal of Health-System Pharmacy, 78(14), 1309-1316. https://doi.org/10.1093/ajhp/zxab152
Maestre, J. M., Fernández, M. I., and Jurado, I. J. C. E. P. (2018). An application of economic model predictive control to inventory management in hospitals. Control Engineering Practice, (71), 120-128. https://doi.org/10.1016/j.conengprac.2017.10.012
Miller, S., El-Bahrawy, A., Dittus, M., Graham, M., and Wright, J. (2020, April). In Proceedings of the web conference 2020 (pp. 2669-2675). https://doi.org/10.1145/3366423.3380022
Nasr, W. W., and Elshar, I. J. (2018). Continuous inventory control with stochastic and non-stationary Markovian demand. European Journal of Operational Research, 270(1), 198-217.
Pauls-Worm, K. G., Hendrix, E. M., Alcoba, A. G., and Haijema, R. (2016). Order quantities for perishable inventory control with non-stationary demand and a fill rate constraint. International Journal of Production Economics, 181, 238-246. https://doi.org/10.1016/j.ijpe.2015.10.009
Pavlyshenko, B. M. (2019). Machine-learning models for sales time series forecasting. Data, 4(1), 15. https://doi.org/10.3390/data4010015
Pulido-Rojano, A., Pizarro-Rada, A., Padilla-Polanco, M., Sánchez-Jiménez, M., and De-la-Rosa, L. (2020). Un enfoque de optimización para costos de inventario en modelos de inventario probabilísticos: Un caso de estudio. Ingeniare. Revista chilena de ingeniería, 28(3), 383-395. https://doi.org/10.4067/S0718-33052020000300383
Purohit, A. K., Choudhary, D., and Shankar, R. (2016). Inventory lot-sizing with supplier selection under non-stationary stochastic demand. International Journal of Production Research, 54(8), 2459-2469. https://doi.org/10.1080/00207543.2015.1102354
Sinaga, S., Pertiwi, L. S., and Ardian, T. (2016). Inventory simulation optimization under non stationary demand. International Journal of Applied Engineering Research, 11(1), 524-529.
Susarla, N., and Karimi, I. A. (2018). Integrated production planning and inventory management in a multinational pharmaceutical supply chain. In Computer aided chemical engineering, 41, 551-567). https://doi.org/10.1016/B978-0-444-63963-9.00022-1Getr
Tunc, H., Kilic, O. A., Tarim, S. A., and Eksioglu, B. (2011). The cost of using stationary inventory policies when demand is non-stationary. Omega, 39(4), 410-415. https://doi.org/10.1016/j.omega.2010.09.005
Zhang, Y., Hua, G., Wang, S., Zhang, J., and Fernandez, V. (2018). Managing demand uncertainty: Probabilistic selling versus inventory substitution. International Journal of Production Economics, 196, 56-67. https://doi.org/10.1016/j.ijpe.2017.10.001
Zhou, Q., Han, R., Li, T., and Xia, B. (2019). Joint prediction of time series data in inventory management. Knowledge and Information Systems, 61(2), 905-929. https://doi.org/10.1007/s10115-018-1302-y