Rekonsiliasi Temporal dan Struktural Hierarkis untuk Meningkatkan Akurasi Peramalan Penjualan pada UKM Ritel
DOI:
https://doi.org/10.23969/infomatek.v28i1.41554Keywords:
Peramalan Hierarkis, Hierarki Temporal, Rekonsiliasi Ramalan, Peramalan Permintaan Ritel, Usaha Kecil dan Menengah (UKM)Abstract
Usaha Kecil dan Menengah terutama pada sektor retail menghadapi tantangan tingginya jumlah produk, keterbatasan sumberdaya, serta pola karakteristik permintaan produk yang fluktuatif dan intermittent. Penelitian ini menginvestigasi peran struktural rekonsiliasi dan temporal rekonsiliasi dalam meningkatkan akurasi ramalan penjualan UKM Funan Mart, sebuah ritel sembako di Kabupaten Belu, Nusa Tenggara Timur. Model dasar yang digunakan dalam penelitian ini yaitu State Space Exponential Smoothing (ETS) yang banyak digunakan karena tidak memerlukan biaya komputasi yang tinggi dan dapat menyesuaikan dengan berbagai jenis data deret waktu. Hasil ramalan dasar dari ETS kemudian direkonsiliasi menggunakan pendekatan MinTrace (OLS), MinTrace dengan batasan negatif, Weighted Least Squares structural scaling (WLS-S), dan WLS-S non-negatif. Hasil penelitian ini menunjukkan bahwa rekonsiliasi dapat meningkatkan akurasi ramalan terutama pada level hierarki bawah dan agregasi temporal bulanan. Metode WLS-S dengan batasan negatif menghasilkan kinerja terbaik melalui penurunan RMSE dari model dasar ETS 0,638 menjadi 0,626. Pada level ProdukByMonth, kesalahan ramalan berkurang sebesar 6,7% terhadap model dasar ETS, sedangkan pada KategoriByMonth terjadi peningkatan akurasi sebesar 1,4%.
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References
Athanasopoulos, G., Ahmed, R. A., & Hyndman, R. J. (2009). Hierarchical forecasts for Australian domestic tourism. International Journal of Forecasting, 25(1), 146–166. https://doi.org/10.1016/j.ijforecast.2008.07.004
Carazas, L., Barrios, M., Nuñez, V., Raymundo, C., & Dominguez, F. (2019). Management model logistic for the use of planning and inventory tools in a selling company of the automotive sector in Peru. Advances in Intelligent Systems and Computing, 971, 299–309. https://doi.org/10.1007/978-3-030-20494-5_28
Dangerfield, B. J., & Morris, J. S. (1992). Top-down or bottom-up: Aggregate versus disaggregate extrapolations. In International Journal of Forecasting (Vol. 8).
Dunn, D. M., Williams, W. H., & Dechaine, T. L. (1976). Aggregate versus subaggregate models in local area forecasting. Journal of the American Statistical Association, 71(353), 68–71. https://doi.org/10.1080/01621459.1976.10481478
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
Gross, C. W., & Sohl, J. E. (1990). Disaggregation Methods to Expedite Product Line Forecasting. In Journal of Forecasting (Vol. 9).
Hyndman, R. J., Ahmed, R. A., Athanasopoulos, G., & Shang, H. L. (2011). Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579–2589. https://doi.org/10.1016/j.csda.2011.03.006
Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002a). A state space framework for automatic forecasting using exponential smoothing methods. In International Journal of Forecasting (Vol. 18). www.elsevier.com/locate/ijforecast
Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002b). A state space framework for automatic forecasting using exponential smoothing methods. In International Journal of Forecasting (Vol. 18). www.elsevier.com/locate/ijforecast
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting, 38(4), 1346–1364. https://doi.org/10.1016/j.ijforecast.2021.11.013
Oliveira, J. M., & Ramos, P. (2019). Assessing the performance of hierarchical forecasting methods on the retail sector. Entropy, 21(4). https://doi.org/10.3390/e21040436
Orcutt, G. H., Watts, H. W., & Edwards, J. B. (1968). Data Aggregation and Information Loss (Vol. 58, Number 4).
Shlifer, E., & Wolff, R. W. (1979). Aggregation and Proration in Forecasting. Management Science, 25(6), 594–603. https://doi.org/10.1287/mnsc.25.6.594
Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. (2016). Supply chain forecasting: Theory, practice, their gap and the future. In European Journal of Operational Research (Vol. 252, Number 1, pp. 1–26). Elsevier. https://doi.org/10.1016/j.ejor.2015.11.010
Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3, 100026. https://doi.org/10.1016/j.sca.2023.100026
Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019). Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization. Journal of the American Statistical Association, 114(526), 804–819. https://doi.org/10.1080/01621459.2018.1448825
Widiarta, H., Viswanathan, S., & Piplani, R. (2009). Forecasting aggregate demand: An analytical evaluation of top-down versus bottom-up forecasting in a production planning framework. International Journal of Production Economics, 118(1), 87–94. https://doi.org/10.1016/j.ijpe.2008.08.013
WTO. (2022). RECENT EVOLUTION OF DEVELOPED-ECONOMY MSME PARTICIPATION IN INTERNATIONAL TRADE MSME Research note #1 2 Recent evolution of developed-economy MSME participation in international trade. https://www.wto.org/english/tratop_e/msmes_e/ersd_research_note1_msmes_in_developed_economies.pdf
