Forecasting Blood Availability in Pontianak City using ARIMA Models to Optimize Inventory Planning at UTD PMI

Nurfitri Imro'ah; Nur'ainul Miftahul Huda; Lyra Mauditia

doi:10.31764/jtam.v8i4.24789

Forecasting Blood Availability in Pontianak City using ARIMA Models to Optimize Inventory Planning at UTD PMI

Nurfitri Imro'ah, Nur'ainul Miftahul Huda, Lyra Mauditia

Abstract

It is of utmost importance to control the blood supply in UTD PMI because if there is a requirement for blood, PMI can fulfill the necessary blood needs and keep the ideal blood availability. PMI UTD may encounter a shortfall of blood supply if increases in blood demand are not supported by an increase in the number of donors contributing blood. A forecast of the number of blood requests is essential to estimate the quantity of blood that is necessary and the number of blood donors that are required to be prepared to fulfill the needed blood requests. This study is a quantitative investigation that use the Autoregressive Integrated Moving Average (ARIMA) method in order to provide an accurate prediction regarding the quantity of blood that is required for each blood type in Pontianak City. UTD PMI Pontianak City provided the information that was used in this study. The information that was used included information on the number of blood requests for blood types A, AB, B, and O. Following this, the data was subjected to three iterative steps of Box Jenkins analysis, which included order identification, parameter estimation, and diagnostic testing. The goal was to obtain the most accurate model, which was then utilised to forecast the quantity of blood demand that will occur in the subsequent periods. Furthermore, the findings of this investigation indicate that the ARIMA (2,0,0), ARIMA (3,0,3), ARIMA (1,0,2), and ARIMA (1,0,0) models are the most accurate models for predicting the availability of blood categories A, AB, B, and O. ..UTD Pontianak City is anticipated to be able to manufacture bloodstock consisting of 73 blood bags over the next five days. The bloodstock will include 19 bags of Group A, 6 bags of Group AB, 22 bags of Group B, and 6 bags of Group O specifics. In light of the forecast results, it is envisaged that UTD PMI will be able to maximize inventory planning for blood in Pontianak City to reduce the number of instances in which there are shortages of blood availability.

Keywords

Accuration; Blood Availability; Least Square; Time Series.

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References

Ben Elmir, W., Hemmak, A., & Senouci, B. (2023). Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning. Information, 14(1), 31. https://doi.org/10.3390/info14010031

Booth, C., Allard, S., & Robinson, S. (2021). Blood transfusion. Medicine, 49(4), 238–242. https://doi.org/10.1016/j.mpmed.2021.01.012

Deif, M. A., Solyman, A. A. A., & Hammam, R. E. (2021). ARIMA Model Estimation Based on Genetic Algorithm for COVID-19 Mortality Rates. International Journal of Information Technology & Decision Making, 20(06), 1775–1798. https://doi.org/10.1142/S0219622021500528

Gopu, P., Panda, R. R., & Nagwani, N. K. (2021). Time Series Analysis Using ARIMA Model for Air Pollution Prediction in Hyderabad City of India (pp. 47–56). https://doi.org/10.1007/978-981-33-6912-2_5

Gress, K. L., Charipova, K., Urits, I., Viswanath, O., & Kaye, A. D. (2021). Supply, Demand, and Quality: A Three-Pronged Approach to Blood Product Management in Developing Countries. Journal of Patient-Centered Research and Reviews, 8(2), 121–126. PMID: 33898644; PMCID: PMC8060046.

Hanifi, S., Liu, X., Lin, Z., & Lotfian, S. (2020). A Critical Review of Wind Power Forecasting Methods—Past, Present and Future. Energies, 13(15), 3764. https://doi.org/10.3390/en13153764

Hewamalage, H., Ackermann, K., & Bergmeir, C. (2023). Forecast evaluation for data scientists: common pitfalls and best practices. Data Mining and Knowledge Discovery, 37(2), 788–832. https://doi.org/10.1007/s10618-022-00894-5

Hussain, S., Madi, E. N., Khan, H., Gulzar, H., Etemad, S., Rezapour, S., & Kaabar, M. K. A. (2022). On the Stochastic Modeling of COVID-19 under the Environmental White Noise. Journal of Function Spaces, 2022, 2022(1), 1–9. https://doi.org/10.1155/2022/4320865

Imro’ah, N., & Huda, N. M. (2022). Control Chart As Verification Tools In Time Series Model. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 16(3), 995–1002. https://doi.org/10.30598/barekengvol16iss3pp995-1002

Imro’ah, N., Huda, N. M., & Pratama, A. Y. (2023). The Implementation of Control Charts as a Verification Tool in a Time Series Model for COVID-19 Vaccine Participants in Pontianak. ComTech: Computer, Mathematics and Engineering Applications, 14(1), 55–67. https://doi.org/10.21512/comtech.v14i1.8462

Imro’ah, N., Huda, N. M., Utami, D. S., Umairah, T., & Arini, N. F. (2024). Control Chart for Correcting the ARIMA Time Series Model of GDP Growth Cases. JTAM (Jurnal Teori Dan Aplikasi Matematika), 8(1), 312. https://doi.org/10.31764/jtam.v8i1.19612

Li, N., Chiang, F., Down, D. G., & Heddle, N. M. (2021). A decision integration strategy for short-term demand forecasting and ordering for red blood cell components. Operations Research for Health Care, 29, 100290. https://doi.org/10.1016/j.orhc.2021.100290

Raturi, M., & Kusum, A. (2020). The blood supply management amid the COVID-19 outbreak. Transfusion Clinique et Biologique, 27(3), 147–151. https://doi.org/10.1016/j.tracli.2020.04.002

Ravishanker, N., Chi, Z., & Dey, D. K. (2021). A First Course in Linear Model Theory. Chapman and Hall/CRC. https://doi.org/10.1201/9781315156651

Sarvestani, S. E., Hatam, N., Seif, M., Kasraian, L., Lari, F. S., & Bayati, M. (2022). Forecasting blood demand for different blood groups in Shiraz using auto regressive integrated moving average (ARIMA) and artificial neural network (ANN) and a hybrid approaches. Scientific Reports, 12(1), 22031. https://doi.org/10.1038/s41598-022-26461-y

Shashvat, K., Basu, R., & Bhondekar, A. P. (2021). Application of time series methods for dengue cases in North India (Chandigarh). Journal of Public Health, 29(2), 433–441. https://doi.org/10.1007/s10389-019-01136-7

Shih, H., & Rajendran, S. (2020). Stochastic Inventory Model for Minimizing Blood Shortage and Outdating in a Blood Supply Chain under Supply and Demand Uncertainty. Journal of Healthcare Engineering, 2020, 2020(1),1–14. https://doi.org/10.1155/2020/8881751

Shokouhifar, M., & Ranjbarimesan, M. (2022). Multivariate time-series blood donation/demand forecasting for resilient supply chain management during COVID-19 pandemic. Cleaner Logistics and Supply Chain, 5, 100078. https://doi.org/10.1016/j.clscn.2022.100078

Shokouhifar, M., Sabbaghi, M. M., & Pilevari, N. (2021). Inventory management in blood supply chain considering fuzzy supply/demand uncertainties and lateral transshipment. Transfusion and Apheresis Science, 60(3), 103103. https://doi.org/10.1016/j.transci.2021.103103

Sudtachat, K., Chanta, S., & Saengsathien, A. (2023). Periodic blood inventory system with two supplies and two priority demand classes. International Journal of Industrial Engineering Computations, 14(2), 201–220. https://doi.org/10.5267/j.ijiec.2023.2.005

Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A. (2021). Deep Learning for Time Series Forecasting: A Survey. Big Data, 9(1), 3–21. https://doi.org/10.1089/big.2020.0159

Twumasi, C., & Twumasi, J. (2022). Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana. International Journal of Forecasting, 38(3), 1258–1277. https://doi.org/10.1016/j.ijforecast.2021.10.008

Wang, S., Wecht, J. M., Legg Ditterline, B., Ugiliweneza, B., Maher, M. T., Lombard, A. T., Aslan, S. C., Ovechkin, A. V., Bethke, B., Gunter, J. T. H., & Harkema, S. J. (2020). Heart rate and blood pressure response improve the prediction of orthostatic cardiovascular dysregulation in persons with chronic spinal cord injury. Physiological Reports, 8(20), 1-16, . https://doi.org/10.14814/phy2.14617

Xie, J., & Wang, Q. (2020). Benchmarking Machine Learning Algorithms on Blood Glucose Prediction for Type I Diabetes in Comparison With Classical Time-Series Models. IEEE Transactions on Biomedical Engineering, 67(11), 3101–3124. https://doi.org/10.1109/TBME.2020.2975959

Zeroual, A., Harrou, F., Dairi, A., & Sun, Y. (2020). Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study. Chaos, Solitons & Fractals, 140, 110121. https://doi.org/10.1016/j.chaos.2020.110121

Zhao, M., Zhong, S., Fu, X., Tang, B., Dong, S., & Pecht, M. (2021). Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis. IEEE Transactions on Industrial Electronics, 68(3), 2587–2597. https://doi.org/10.1109/TIE.2020.2972458

Zhou, B., Perel, P., Mensah, G. A., & Ezzati, M. (2021). Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension. Nature Reviews Cardiology, 18(11), 785–802. https://doi.org/10.1038/s41569-021-00559-8

DOI: https://doi.org/10.31764/jtam.v8i4.24789