Integrating Holt-Winters Exponential Smoothing and SAW for Distribution Priority Optimization

Muhammad Rohman Irsyadi; Mohammad Idhom; Afina Lina Nurlaili

doi:10.59395/ijadis.v7i2.1561

Authors

Muhammad Rohman Irsyadi Department of Informatics, University of Pembangunan Nasional Veteran Jawa Timur, Indonesia
Mohammad Idhom Department of Data Science, University of Pembangunan Nasional Veteran Jawa Timur, Indonesia
Afina Lina Nurlaili Department of Informatics, University of Pembangunan Nasional Veteran Jawa Timur, Indonesia

DOI:

https://doi.org/10.59395/ijadis.v7i2.1561

Keywords:

Holt-Winter, SAW, Demand Forecasting, Distribution Priority, Decision Support System

Abstract

Effective distribution planning requires both accurate demand forecasting and objective prioritization mechanisms to ensure operational efficiency and service reliability. At CV. Citra Nalar Teknologi, distribution scheduling was previously conducted manually without incorporating predictive demand analysis, resulting in inconsistent and subjective prioritization decisions. This study proposes an integrated decision support framework that combines Holt–Winters Exponential Smoothing (HWES) for demand forecasting and Simple Additive Weighting (SAW) for multi-criteria distribution ranking. Monthly sales data from January 2024 to December 2025 (24 periods) were analyzed using the additive HWES model to capture level, trend, and seasonal components. The forecasting results achieved MAPE values ranging from 3.82% to 7.47%, supported by low RMSE, MAE, and sMAPE, indicating high predictive accuracy. Comparative evaluation further shows that HWES outperforms baseline methods, including Moving Average and Seasonal Naive. The forecasted demand was then incorporated as a primary criterion in the SAW model, alongside delivery distance, customer priority level, and stock availability. The proposed framework was evaluated using two sets of actual customer orders (January 4–5 and January 6–7, 2026). The results demonstrate that the integration produces consistent, data-driven distribution priorities and improves decision objectivity and transparency compared to manual scheduling. This study contributes by integrating time series forecasting with multi-criteria decision-making into a unified framework for practical distribution optimization.

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References

[1] A. T. C. Onstein, L. A. Tavasszy, and D. A. Van Damme, Factors determining distribution structure decisions in logistics: a literature review and research agenda, Transport Reviews, vol. 39, no. 2, pp. 243260, Mar. 2019, doi: 10.1080/01441647.2018.1459929. DOI: https://doi.org/10.1080/01441647.2018.1459929

[2] T. Bmer and A. Meyer, Freight Costs Versus Service Level: Optimizing the Distribution of a Materials Trader, in Computational Logistics, vol. 13557, J. De Armas, H. Ramalhinho, and S. Vo, Eds., in Lecture Notes in Computer Science, vol. 13557. , Cham: Springer International Publishing, 2022, pp. 397411. doi: 10.1007/978-3-031-16579-5_27. DOI: https://doi.org/10.1007/978-3-031-16579-5_27

[3] A. M. El-Araby, I. Sabry, and A. El-Assal, Multi-Criteria Decision Making Approaches for Facilities Planning Problem, in 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES), Giza, Egypt: IEEE, Oct. 2021, pp. 4750. doi: 10.1109/NILES53778.2021.9600538. DOI: https://doi.org/10.1109/NILES53778.2021.9600538

[4] A. Kovari, AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors, Information, vol. 15, no. 11, p. 725, Nov. 2024, doi: 10.3390/info15110725. DOI: https://doi.org/10.3390/info15110725

[5] I. Svetunkov, H. Chen, and J. E. Boylan, A new taxonomy for vector exponential smoothing and its application to seasonal time series, European Journal of Operational Research, vol. 304, no. 3, pp. 964980, Feb. 2023, doi: 10.1016/j.ejor.2022.04.040. DOI: https://doi.org/10.1016/j.ejor.2022.04.040

[6] I. Svetunkov, N. Kourentzes, and J. K. Ord, Complex exponential smoothing, Naval Research Logistics, vol. 69, no. 8, pp. 11081123, Dec. 2022, doi: 10.1002/nav.22074. DOI: https://doi.org/10.1002/nav.22074

[7] M. Udenio, E. Vatamidou, and J. C. Fransoo, Exponential smoothing forecasts: taming the bullwhip effect when demand is seasonal, International Journal of Production Research, vol. 61, no. 6, pp. 17961813, Mar. 2023, doi: 10.1080/00207543.2022.2048114. DOI: https://doi.org/10.1080/00207543.2022.2048114

[8] J. Wickowski and W. Saabun, Sensitivity analysis approaches in multi-criteria decision analysis: A systematic review, Applied Soft Computing, vol. 148, p. 110915, Nov. 2023, doi: 10.1016/j.asoc.2023.110915. DOI: https://doi.org/10.1016/j.asoc.2023.110915

[9] F. Ciardiello and A. Genovese, A comparison between TOPSIS and SAW methods, Ann Oper Res, vol. 325, no. 2, pp. 967994, Jun. 2023, doi: 10.1007/s10479-023-05339-w. DOI: https://doi.org/10.1007/s10479-023-05339-w

[10] S. Islam, S. H. Amin, and L. J. Wardley, Machine learning and optimization models for supplier selection and order allocation planning, International Journal of Production Economics, vol. 242, p. 108315, Dec. 2021, doi: 10.1016/j.ijpe.2021.108315. DOI: https://doi.org/10.1016/j.ijpe.2021.108315

[11] T. E. Goltsos, A. A. Syntetos, C. H. Glock, and G. Ioannou, Inventory forecasting: Mind the gap, European Journal of Operational Research, vol. 299, no. 2, pp. 397419, Jun. 2022, doi: 10.1016/j.ejor.2021.07.040. DOI: https://doi.org/10.1016/j.ejor.2021.07.040

[12] E. A. Andoh and H. Yu, A two-stage decision-support approach for improving sustainable last-mile cold chain logistics operations of COVID-19 vaccines, Ann Oper Res, vol. 328, no. 1, pp. 75105, Sep. 2023, doi: 10.1007/s10479-022-04906-x. DOI: https://doi.org/10.1007/s10479-022-04906-x

[13] F. Dunke and S. Nickel, Simulation-based multi-criteria decision making: an interactive method with a case study on infectious disease epidemics, Ann Oper Res, vol. 360, no. 1, pp. 6796, May 2026, doi: 10.1007/s10479-021-04321-8. DOI: https://doi.org/10.1007/s10479-021-04321-8

[14] Y. Ensafi, S. H. Amin, G. Zhang, and B. Shah, Time-series forecasting of seasonal items sales using machine learning A comparative analysis, International Journal of Information Management Data Insights, vol. 2, no. 1, p. 100058, Apr. 2022, doi: 10.1016/j.jjimei.2022.100058. DOI: https://doi.org/10.1016/j.jjimei.2022.100058

[15] F. Petropoulos et al., Forecasting: theory and practice, International Journal of Forecasting, vol. 38, no. 3, pp. 705871, Jul. 2022, doi: 10.1016/j.ijforecast.2021.11.001. DOI: https://doi.org/10.1016/j.ijforecast.2021.11.001

[16] A. M. Khedr and S. R. S, Enhancing supply chain management with deep learning and machine learning techniques: A review, Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 4, p. 100379, Dec. 2024, doi: 10.1016/j.joitmc.2024.100379. DOI: https://doi.org/10.1016/j.joitmc.2024.100379

[17] W. Jeon, J. Kim, and J. Lee, A Data-Driven Decision-Making Support Method for Priority Determination for an Intelligent Road Problem Reporting System, Applied Sciences, vol. 14, no. 23, p. 10861, Nov. 2024, doi: 10.3390/app142310861. DOI: https://doi.org/10.3390/app142310861

[18] M. Anvari, A. Anvari, and O. Boyer, A prepositioning model for prioritized demand points considering lateral transshipment, JHLSCM, vol. 13, no. 4, pp. 433455, Nov. 2023, doi: 10.1108/JHLSCM-01-2023-0005. DOI: https://doi.org/10.1108/JHLSCM-01-2023-0005

[19] M. F. Carnero Quispe, A. S. Couto, I. De Brito Junior, L. R. A. Cunha, R. M. Siqueira, and H. T. Y. Yoshizaki, Humanitarian Logistics Prioritization Models: A Systematic Literature Review, Logistics, vol. 8, no. 2, p. 60, Jun. 2024, doi: 10.3390/logistics8020060. DOI: https://doi.org/10.3390/logistics8020060

[20] N. Vafaei, R. A. Ribeiro, and L. M. Camarinha-Matos, Assessing Normalization Techniques for Simple Additive Weighting Method, Procedia Computer Science, vol. 199, pp. 12291236, 2022, doi: 10.1016/j.procs.2022.01.156. DOI: https://doi.org/10.1016/j.procs.2022.01.156

[21] K.-H. Chang, Integrating SubjectiveObjective Weights Consideration and a Combined Compromise Solution Method for Handling Supplier Selection Issues, Systems, vol. 11, no. 2, p. 74, Feb. 2023, doi: 10.3390/systems11020074. DOI: https://doi.org/10.3390/systems11020074

[22] K. Aliyeva, A. Aliyeva, R. Aliyev, and M. zdeer, Application of Fuzzy Simple Additive Weighting Method in Group Decision-Making for Capital Investment, Axioms, vol. 12, no. 8, p. 797, Aug. 2023, doi: 10.3390/axioms12080797. DOI: https://doi.org/10.3390/axioms12080797

[23] O. akr, . Grler, and B. Gndzyeli, Analysis of a Non-Discriminating Criterion in Simple Additive Weighting Deep Hierarchy, Mathematics, vol. 10, no. 17, p. 3192, Sep. 2022, doi: 10.3390/math10173192. DOI: https://doi.org/10.3390/math10173192

[24] M. N. nce and . Tademir, Forecasting Retail Sales for Furniture and Furnishing Items through the Employment of Multiple Linear Regression and HoltWinters Models, Systems, vol. 12, no. 6, p. 219, Jun. 2024, doi: 10.3390/systems12060219. DOI: https://doi.org/10.3390/systems12060219

[25] . Tz Ebesek and . Ebesek, Optimizing Holt-Winters Exponential Smoothing Parameters for Construction Cost Index Forecasting with PSO and Walk-Forward Cross-Validation, Kent Akademisi, vol. 16, no. 4, pp. 24222439, Dec. 2023, doi: 10.35674/kent.1343590. DOI: https://doi.org/10.35674/kent.1343590

[26] W. N. Achmadin, D. A. Retnowardani, D. Mashitasari, and F. Fatimah, Forecasting Coffee Exports to the United States Using the Holt-Winters Exponential Method, EMJ, vol. 7, no. 1, pp. 4449, May 2024, doi: 10.29303/emj.v7i1.202. DOI: https://doi.org/10.29303/emj.v7i1.202

[27] G. Rumbe, M. Hamasha, and S. Al Mashaqbeh, A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industry, Results in Engineering, vol. 21, p. 101899, Mar. 2024, doi: 10.1016/j.rineng.2024.101899. DOI: https://doi.org/10.1016/j.rineng.2024.101899

[28] N. Dobrovolskien and A. Pozniak, Simple Additive Weighting versus Technique for Order Preference by Similarity to an Ideal Solution: which method is better suited for assessing the sustainability of a real estate project, JESI, vol. 8, no. 4, pp. 180196, Jun. 2021, doi: 10.9770/jesi.2021.8.4(10). DOI: https://doi.org/10.9770/jesi.2021.8.4(10)

[29] B. Ayan, S. Abacolu, and M. P. Basilio, A Comprehensive Review of the Novel Weighting Methods for Multi-Criteria Decision-Making, Information, vol. 14, no. 5, p. 285, May 2023, doi: 10.3390/info14050285. DOI: https://doi.org/10.3390/info14050285