Optimisation of Inventory Management Through Time Series Analysis of Inventory Data with Double Exponential Smoothing Method
DOI:
10.47709/cnahpc.v6i3.4410Keywords:
Double Exponential Smoothing, Stock Forecasting, Time Series Data AnalysisDimension Badge Record
Abstract
Stock forecasting is very useful for companies in knowing the trend of inventory needed in the next period, with time series data often forecasting can be a solution in supporting decision making. Excess or lack of stock of goods is often caused by a less than optimal record management process and often relies on personal intuition. In this study, the Double Exponential Smoothing method is applied in analyzing time series data and forecasting stock data. This method is used because it is in accordance with the company's sales data which is up and down. In addition, this forecasting calculation does not escape the error rate of forecasting calculations, therefore this system is also supported by the MAD (Mean Absolute Deviation), MSE (Mean Square Error) and MAPE (Mean Absolute Percentage) methods to calculate the error rate of the forecasting results. The forecasting results show that this method is able to provide fairly accurate predictions with a MAD value of 5.2475, MSE of 43.009, and MAPE of 26.307%. By using DES, companies can perform better stock planning, reduce the risk of over- or under-stocking, and improve inventory management efficiency. The DES method is proven to be flexible and easy to implement in computerized information systems, so it is recommended to be used more widely in corporate inventory management.
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References
Ahmad, F. (2020). PENENTUAN METODE PERAMALAN PADA PRODUKSI PART NEW GRANADA BOWL ST Di PT . X Determine the actual and actual production plan is the main thing for the organization to avoid large losses in calculating the amount of production , PT . This research is to det. 7(1), 31–39.
Ahmar, A. S., Fitmayanti, F., & Ruliana, R. (2021). Modeling of Inflation Cases in South Sulawesi Province Using Single Exponential Smoothing and Double Exponential Smoothing Methods. Quality & Quantity, 56(1), 227–237. https://doi.org/10.1007/s11135-021-01132-8
Atmaja, K. J., Pascima, I. B. N., Asana, I. M. D. P., & Sudipa, I. G. I. (2022). Implementation of Artificial Neural Network on Sales Forecasting Application. Journal of Intelligent Decision Support System (IDSS), 5(4), 124–131.
Aziz, M. F., & Sanjaya, C. B. (2023). Aplikasi Kas Berbasis Flutter untuk Meningkatkan Efisiensi Pencatatan Transaksi Keuangan. Jurnal Krisnadana, 3(1), 34–48.
Boone, T., Ganeshan, R., Jain, A., & Sanders, N. R. (2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting, 35(1), 170–180.
David Saputra. (2024). Comparison of Double Exponential Smoothing Method With Weighted Moving Average in Forecasting UD Sales. Setya Abadi D. M as Financial Literacy. Journal of Entrepreneurial and Business Diversity, 2(1), 254–263. https://doi.org/10.38142/jebd.v2i1.121
Dewantara, R., & Giovanni, J. (2023). Analisis Peramalan Item Penjualan dalam Optimalisasi Stok Menggunakan Metode Least Square. Jurnal Krisnadana, 3(1), 59–66.
Fildes, R., Ma, S., & Kolassa, S. (2022). Retail forecasting: Research and practice. International Journal of Forecasting, 38(4), 1283–1318. https://doi.org/10.1016/j.ijforecast.2019.06.004
Hodson, T. O. (2022). Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development Discussions, 2022, 1–10.
Khairina, D. M., Daniel, Y., & Widagdo, P. P. (2021). Comparison of double exponential smoothing and triple exponential smoothing methods in predicting income of local water company. Journal of Physics: Conference Series, 1943(1), 12102.
Lin, A. K. (2024). The AI Revolution in Financial Services: Emerging Methods for Fraud Detection and Prevention. Jurnal Galaksi, 1(1), 43–51.
Maulana, H., & Mulyantika, U. (2020). The prediction of export product prices with holt’s double exponential smoothing method. 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE), 372–375.
Mentari, M., & Iftadi, I. (2023). Selection of the Best Forecasting Method at PT. Indaco Warna Dunia. Teknoin, 28(01), 1–10. https://doi.org/10.20885/teknoin.vol28.iss1.art1
Mrówczy?ska, B., Cie?la, M., Król, A., & S?adkowski, A. (2017). Application of Artificial Intelligence in Prediction of Road Freight Transportation. Promet - Traffic&transportation, 29(4), 363–370. https://doi.org/10.7307/ptt.v29i4.2227
Rizki, M., Wenda, A., Pahlevi, F. D., Umam, M. I. H., Hamzah, M. L., & Sutoyo, S. (2021). Comparison of Four Time Series Forecasting Methods for Coal Material Supplies: Case Study of a Power Plant in Indonesia. 2021 International Congress of Advanced Technology and Engineering (ICOTEN), 1–5.
Saputra, I. K. D. A., Satwika, I. P., & Utami, N. W. (2022). Analisis Transaksi Penjualan Barang Menggunakan Metode Apriori pada UD. Ayu Tirta Manis. Jurnal Krisnadana, 1(2), 11–20.
Sihotang, J. (2023). Optimization of Inventory Ordering Decision in Retail Business Using Exponential Smoothing Approach and Decision Support System. International Journal of Mechanical Computational and Manufacturing Research, 12(2), 46–52. https://doi.org/10.35335/computational.v12i2.121
Sudipa, I. G. I., Sarasvananda, I. B. G., Prayitno, H., Putra, I. N. T. A., Darmawan, R., & WP, D. A. (2023). Teknik Visualisasi Data. PT. Sonpedia Publishing Indonesia.
Suryadana, K., & Sarasvananda, I. B. G. (2024). Streamlining Inventory Forecasting with Weighted Moving Average Method at Parta Trading Companies. Jurnal Galaksi, 1(1), 12–21.
Urva, G., Albanna, I., Sungkar, M. S., Gunawan, I. M. A. O., Adhicandra, I., Ramadhan, S., Rahardian, R. L., Handayanto, R. T., Ariana, A. A. G. B., & Atika, P. D. (2023). PENERAPAN DATA MINING DI BERBAGAI BIDANG: Konsep, Metode, dan Studi Kasus. PT. Sonpedia Publishing Indonesia.
Wardah, S., & Iskandar, I. (2017). ANALISIS PERAMALAN PENJUALAN PRODUK KERIPIK PISANG KEMASAN BUNGKUS (Studi Kasus?: Home Industry Arwana Food Tembilahan). J@ti Undip?: Jurnal Teknik Industri, 11(3), 135. https://doi.org/10.14710/jati.11.3.135-142
Wiguna, I. K. A. G., Utami, N. L. P. A. C., Parwita, W. G. S., Udayana, I. P. A. E. D., & Sudipa, I. G. I. (2023). Rainfall Forecasting Using the Holt-Winters Exponential Smoothing Method. Jurnal Info Sains: Informatika Dan Sains, 13(01), 15–23. https://doi.org/10.54209/infosains.v13i01
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