Perbaikan Model Peramalan dan Model Persediaan Reagen Kimia di PT. OPQ Untuk Mendapatkan Persediaan Optimum

Authors

  • Paduloh Paduloh Universitas Bhayangkara Jakarta Raya, Indonesia
  • Yunita Puspaningrum Universitas Bhayangkara Jakarta Raya, Indonesia
  • Ika Yunita Universitas Mercu Buana, Indonesia

Keywords:

ARIMA, Economic Order Quantity, EOQ, Inventory, Reagent

Abstract

Performing forecasts accurately and have optimal supplies are very urgent demands for most industries today. PT OPQ is a company engaged in the pharmaceutical industry. This industry produces medicines for both OTC (Over Counter), ethical (prescription drugs), and generics. At PT OPQ, the inventory method for chemical reagents that have been carried out is straightforward; each year only adds a certain quantity from the previous year. This method proved to be less effective and efficient because the use of chemical reagents was not certain, which resulted in the chemical reagent stock experiencing advantages and disadvantages as seen from the stock on hand. This research aims to assist companies in determining the amount of chemical reagent usage and controlling chemical reagent supplies optimally. The method used is forecasting with the ARIMA model; after obtaining the forecasting results, calculations are carried out to obtain optimal inventory results, namely the EOQ method. In this study, a differencing process was also carried out so that the data used were stationary. The study results obtained a forecasting model that is close to the actual conditions in the field. This study also produces an ideal safety stock for the company. The results of calculations using the EOQ method are proven to be far more optimal than the method previously applied by the company

Author Biographies

Paduloh Paduloh, Universitas Bhayangkara Jakarta Raya

Teknik Industri

Yunita Puspaningrum, Universitas Bhayangkara Jakarta Raya

Teknik Industri

Ika Yunita, Universitas Mercu Buana

Teknik Industri

References

Aloui, A., & Grissa, A. (2015). Computational Intelligence Applications in Modeling and Control. In Studies in Computational Intelligence (Vol. 575). https://doi.org/10.1007/978-3-319-11017-2 Chopra, S.;

Meindl, P. (2016). Supply chain management: strategy, planning, and operation - third edition (sixth). https://doi.org/10.1007/s13398-014-0173-7.2

Do, D. T. T., Lee, J., & Nguyen-Xuan, H. (2019). Fast evaluation of crack growth path using time series forecasting. Engineering Fracture Mechanics, 218(July).

Ekawati, R. (2019). Planning and Controlling Inventory of Coal Using Model Probabilistic Q Backorder With Consider of Storage Capacity. Journal of Engineering and Management in Industrial System, 6(1), 20–26. https://doi.org/10.21776/ub.jemis.2018.006.01.3

Ferdiansyah, R. (2018). Analisis Model Perencanaan dan Pengendalian Persediaan Bany Product. Operation Excelle, 10(1), 26–40.

Hasbullah, H., & Santoso, Y. (2020). Overstock Improvement by Combining Forecasting , EOQ , and ROP. Jurnal PASTI, XIV(3), 230–242.

Heizer, J., Render, B., & Munson, C. (2015). Principles of Operations Management. Jakarta: Salemba Empat.

Hidayati, L., Rochmah, T. N., Qomaruddin, M. B., & Dinama, M. (2020). Quality Improvement in Laboratory Reagent Management : a Six Sigma concept. EurAsian Journal of Biosciences, 14(April 2019), 3251–3256.

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting : Principles and Practice (2nd ed.). Monash University, Australia.

Jurado, S., Nebot, À., Mugica, F., & Avellana, N. (2015). Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques. Energy, 86(i), 276–291. ttps://doi.org/10.1016/j.energy.2015.04.039

Khalid, N., Hamidi, H. N. A., Thinagar, S., & Marwan, N. F. (2018). Crude palm oil price forecasting in Malaysia: An econometric approach. Jurnal Ekonomi Malaysia, 52(3), 263–278.

Kushartini, D., & Almahdy, I. (2016). Sistem Persediaan Bahan Baku Produk Dispersant Di Industri Kimia. Jurnal PASTI, 10(2), 217–234.

Lee, C. C., & Ou-Yang, C. (2009). A neural networks approach for forecasting the supplier’s bid prices in supplier selection negotiation process. Expert Systems with Applications, 36(2 PART 2), 2961–2970. https://doi.org/10.1016/j.eswa.2008.01.063

Paduloh, P., Djatna, T., Muslich, M., & Sukardi, S. (2020). Impact Of Reverse Supply Chain On Bullwhip Effects In Beef Supply. Ijscm, 9(5), 184–194. Retrieved from http://excelingtech.co.uk/

Paduloh, & Prasetyo, R. (2018). Bahan Baku Plat Besi Industri Karoseri Menggunakan Metode Eoq. Jim, 3(1), 37–44.

Patria, R., & Sudarto, S. (2020). Integrasi forecasting pada rantai pasok manufaktur komponen otomotif Jepang di Indonesia dengan penerapan metode classic dan regresi. Operations Excellence: Journal of Applied Industrial Engineering, 12(3), 386. https://doi.org/10.22441/oe.2020.v12.i3.011

Pratiwi, F., & Hasibuan, S. (2020). Perencanaan persediaan bahan baku amoxicillin menggunakan metode material requirement planning: studi kasus. Operations Excellence: Journal of Applied Industrial Engineering, 12(3), 344. https://doi.org/10.22441/oe.2020.v12.i3.007

Rosihan, R. I., Paduloh, P., Sulaeman, D., Industri, T., Bhayangkara, U., & Raya, J. (2021). Penerapan Collaborative Planning , Forecasting And Replenishment ( CPFR ) Guna Mengurangi Bullwhip Effect Di PT . XYZ. 1–8.

Saptaria, L. (2017). Analisis Peramalan Permintaan Produk Nata De Coco Untuk Mendukung Perencanaan Dan Pengendalian Produksi Dalam Supply Chain Dengan Model Cpfr (Collaborative Planning, Forecasting, and Replenishment). Jurnal Nusantara Aplikasi Manajemen Bisnis, 2(2), 130. https://doi.org/10.29407/nusamba.v2i2.924

Shoaib, M., Shamseldin, A. Y., Khan, S., Khan, M. M., Khan, Z. M., Sultan, T., & Melville, B. W. (2018). A Comparative Study of Various Hybrid Wavelet Feedforward Neural Network Models for Runoff

Forecasting. Water Resources Management, 32(1), 83–103. https://doi.org/10.1007/s11269-017- 1796-1

Shtub, A., & Karni, R. (2012). ERP. In Springer (Vol. 66).

Taha, H. A. (2017). Operation Reseach an Introduction (Tenth Edit). Pearson.

Downloads

Published

2021-07-01

How to Cite

[1]
P. Paduloh, Y. Puspaningrum, and I. Yunita, “Perbaikan Model Peramalan dan Model Persediaan Reagen Kimia di PT. OPQ Untuk Mendapatkan Persediaan Optimum”, MBCIE, vol. 3, no. 1, pp. 11–18, Jul. 2021.

Conference Proceedings Volume

Section

Articles