International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Demand Forecasting Analysis of Body Scrub Product at PT. XYZ Using Autoregressive Integrated Moving Average (ARIMA) Method

Abstract

PT. XYZ is one of the private companies engaged in the cosmetics industry, especially as a manufacturer of SPA products with natural raw materials. The SPA products produced by PT. XYZ are various kinds of scrubs, body scrubs, masks, essential oils, soaps and other products. Body scrub is the most popular product or best seller in this company. The purpose of this study is to find the best ARIMA model in calculating demand forecasting of body scrub products at PT. XYZ for the period January to December 2024. The method used in this research is the Autoregressive Moving Average (ARIMA) method. There are two ARIMA models that can be used in this study, namely ARIMA (1,1,0) and ARIMA (0,1,1). The selected ARIMA model used to calculate body scrub forecasting at PT. XYZ is the ARIMA (1,1,0) model because it has a smaller error value, namely MSE = 745.46 and MAPE = 7.40%. The results of forecasting the demand for body scrub products at PT. XYZ for the period January to December 2024 using the ARIMA (1,1,0) model show that demand has increased every month with an average demand of 343.4 kg. The error obtained by comparing the forecasting results with the actual sales data for 6 months, namely January - June 2024 is 2.45%, which means that the accuracy of forecasting in 6 months reaches 97.55%.

Country : Indonesia

1 Ni Wayan Mutriani2 I Ketut Satriawan3 I Made Mahaputra Wijaya

  1. Department of Agroindustrial Technology, Faculty of Agricultural Technology, Udayana University, Indonesia
  2. Department of Agroindustrial Technology, Faculty of Agricultural Technology, Udayana University, Indonesia
  3. Department of Agroindustrial Technology, Faculty of Agricultural Technology, Udayana University, Indonesia

IRJIET, Volume 8, Issue 8, August 2024 pp. 17-25

doi.org/10.47001/IRJIET/2024.808003

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References

  1. U. Baser, M. Bozoglu, N. A. Eroglu, andB. K. Topuz, “Forecasting chestnut production and export of Turkey using ARIMA,”Turkish Journal of Forecasting,vol. 02, no. 2, pp. 27-23, 2018.
  2. C. Ingle, D. Bakliwal, J. Jain, P. Singh, P. Kale and V. Chhajed, "Demand Forecasting: Literature Review on Various Methodologies,” 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, pp. 1-7, 2021.
  3. S. Deviana, Nusyirwan, D. Azis,  andP. Ferdias,“Analisis model autoregressive integrated moving average data deret waktu dengan metode momen sebagai estimasi parameter,”Jurnal Siger Matematika, vol. 02, no. 02, pp. 57-67, 2021.
  4. B. Dhyani, M. Kumar, P. Verma, and A. Jain, “Stock market forecasting technique using ARIMA Model,”International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 6, pp. 2694-2697, 2020.
  5. C. Dritsaki, D. Niklis, andP. Stamatiou, “Oil consumption forecasting using ARIMA models: an empirical study for Greek,”International Journal of Energy Economics and Policy, vol. 11, no. 4, pp. 214-224, 2021.
  6. J. Fattah, E. Latifa, Z. Aman, H. E. Moussami, andA. Lachhab, “Forecasting of demand using ARIMA model,"International Journal of Engineering Business Management, vol. 10, pp. 1-9, 2018.
  7. A.Fauzi,“Peramalan menggunakan model ARIMA pada harga saham telkom dan lippo,” Skripsi, Diploma III Jurusan Statistika, Institut Teknologi Sepuluh Nopember, 2015.
  8. J. Hanke, D. Wichern,andA. Reistch,“Businessforecasting (2th ed.),”New Jersey: Prentice Hall, 2021.
  9. W. Hariadi, and Sulantari,“Application of arima model for forecasting additional positive cases of Covid-19 in Jember Regency,”Enthusiastic International Journal of Statistics and Data Science, vol. 1, no. 1, pp. 22-27, 2021.
  10. N. Iriawan, &S. P. Astuti,“Mengolah data statistik dengan mudah menggunakan minitab 14,”Yogyakarta: Penerbit Andi, 2006.
  11. B. Iskandar, J. Tarigan,Leny, and W. Hanum, “Uji sifat fisik lulur ekstrak bayam merah (Amaranthus tricolor L.) serta efektivitas kelembaban (moisture) dan kehalusan (evennes) pada kulit.”Majalah Farmasetika, vol. 9, no. 1, pp. 104-124, 2023.
  12. A.Kerkkanen, J. Korpela, and J. Huiskonen, “Demand Forecasting errors in industrial context: Measurement and impacts,” International Journal of Production Economics, vol. 118, no. 1, pp. 43-48, 2009.
  13. K. Laia, “Peramalan produksi crude palm oil (CPO) di Provinsi Riau dengan pendekatan model ARIMA (autoregresif Integrated moving average),”Skripsi, Program Studi Agribisnis, Universitas Islam Riau, 2019.
  14. A.Lusiana, & P. Yuliarty, “Penerapan metode peramalan (foecasting) pada permintaan atap di PT. X,” Industri Inovatif – Jurnal Teknik Industri ITN Malang, vol. 10, no. 1, pp. 11-20, 2020.
  15. S. Makridakis, S. Wheelwright, andV. McGee,“Forecasting: methods and applications,”New York: John Wiley & Sons, Inc, 1999.
  16. T. Mashadihasanli, “Stock market price forecasting using the ARIMA model: an application to Istanbul, Turkiye,”Journal of Economic Policy Researches, vol. 9, no. 2, pp. 439-454, 2022.
  17. N. Novyta, & L. Alzhami, “Peramalan permintaan produk nata de coco dalam supply chain management dengan model ARIMA,”Theorems, vol. 7, no. 2, pp. 152-162, 2022.
  18. R. B. Sanjaya,“Bali di tengah pandemi COVID-19: studi kualitatif terhadap perilaku pariwisata di wilayah Kuta, Bali,”JUMPA, vol. 10, no. 1, pp. 102-134, 2023.
  19. C. Tarmanini, N. Sarma, C. Gezegin, andO. Ozgonenel, “Short term load forecasting based on ARIMA and ANN approaches,”Energy Reports, vol. 9, no. 3, pp. 550-557, 2023.
  20. W. Wei, “Time series analysis univariate and multivariate methods,”Canada: Pearson Education, Inc, 2006.
  21. Zulhamidi, &R. Hardianto,“Peramalan penjualan teh hijau dengan metode ARIMA (studi kasus pada PT. MK),”Jurnal PASTI, vol. 11, no. 3, pp. 231-244, 2017.

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