การลดความสูญเสียสำหรับการลดค่าเฉลี่ยของค่ายกกำลังสองความคลาดเคลื่อนของ          ค่าพยากรณ์ความต้องการยาที่มีลักษณะวัฏจักรของโรงพยาบาล

Chatpon Mongkalig

Authors

Chatpon Mongkalig Institute of Metropolitan Development, Navamindradhiraj University

Keywords:

Logistics Efficiency Improvement, Time Series Analysis, Pharmaceutical Demand Forecasting, Hospital, Mean Squared Deviation

Abstract

The objective of this research was to analyze the appropriate forecasting techniques for the case study hospital pharmaceutical products cyclical demand forecasting mean squared error reduction, and comparison between the proposed forecasting method and the current forecasting method of the case study hospital using Paired t-Test for the confirmation test. This research focused on the important pharmaceutical products using ABC analysis to identify class A, which the inventory value was more than 70 % with cyclical or seasonal demand (31 SKUs). Randomized complete block design (RCBD) was applied in the design of experiments in this research. According to the experimental results, the forecasting techniques had significant effects on the mean squared deviation (MSD). The most appropriate forecasting technique was Winters’ method with 12 months seasonal length. Mean squared deviation of the Winters’ method was significantly smaller than moving average with 3 months moving average length which was the forecasting method used by the case study hospital planners. The MSD of the proposed forecasting method decreased by 35.32 %.

References

Apriyanti, M. E. (2023). Government strategy to recover the tourism sector affected by the COVID-19 pandemic. International Journal of Multidisciplinary: Applied Business and Education Research, 4(1), 90–106. http://dx.doi.org/10.11594/ijmaber.04.01.11

Asilahijani, H., Steiner, S. H., & MacKay, R. J. (2010). Reducing variation in an existing process with robust parameter design. Quality Engineering, 22, 30–45. http://dx.doi.org/10.1080/08982110903381889

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (D. J. Balding, N. A. C. Cressie, G. M. Fitzmaurice, G. H. Givens, H. Goldstein, G. Molenberghs, D. W. Scott, A. F. M. Smith, R. S. Tsay, & S. Weisberg (Eds.), 5th ed.). John Wiley and Sons.

Christopher, M. (2016). Logistics & supply chain management (5th ed.). Pearson.

Dean, A., Voss, D., & Draguljic, D. (2017). Design and analysis of experiments (2nd ed.). Springer.

Goos, P., & Jones, B. (2011). Optimal design of experiments: A case study approach. John Wiley & Sons.

Gössling, S., Scott, D., & Hall, C. M. (2020). Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism, 29(1), 1–20. http://dx.doi.org/10.1080/09669582.2020.1758708

Kumar, P. & Ekka, P. (2024). Statistical analysis of the impacts of COVID-19 pandemic on the small and large-scale tourism sectors in developing countries. Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 26(4), 9625-9659. https://doi.org/10.1007/s10668-023-03112-4

Makridakis, S. G., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and applications. John Wiley & Sons.

Merkuryeva, G., Valberga, A., & Smirnov, A. (2019). Demand forecasting in pharmaceutical supply chains: A case study. Procedia Computer Science, 149, 3–10. https://doi.org/10.1016/j.procs.2019.01.100

Montgomery, D. C. (2019a). Design and analysis of experiments (10th ed.). John Wiley & Sons.

Montgomery, D. C. (2019b). Introduction to statistical quality control (6th ed.). John Wiley & Sons.

Montgomery, D. C., & Runger, G. C. (2014). Applied statistics and probability for engineers (6th ed.). John Wiley & Sons.

Rathipriya, R., Abdul Rahman, A. A., Dhamodharavadhani, S., Meero, A., & Yoganandan, G. (2023). Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model. Neural Computing & Applications, 35, 1945–1957. https://doi.org/10.1007/s00521-022-07889-9

Schlueter, A., & Geyer, P. (2018). Linking BIM and design of experiments to balance architectural and technical design factors for energy performance. Automation in Construction, 86, 33-43. https://doi.org/10.1016/j.autcon.2017.10.021

Yu, P., Low, M. Y., & Zhou, W. (2018). Design of experiments and regression modelling in food flavour and sensory analysis: A review. Trends in Food Science & Technology, 71, 202-215. http://dx.doi.org/10.1016/j.tifs.2017.11.013