摘要
在微型、小型和中型企业(MSMEs)中,人工库存记录和经验性订货做法仍然普遍存在,这往往导致需求估计不准确、持有成本过高以及缺货。 本研究开发并评估了一个基于网络的库存信息系统,该系统将自回归积分移动平均(ARIMA)预测与经济订货量(EOQ)模型相结合,以提高决策准确性和成本效率。 该系统采用 CodeIgniter 3 和 MySQL 构建,并集成了基于 Python 的时间序列预测引擎。历史销售数据通过 ARIMA 模型进行建模,并基于赤池信息准则(AIC)和贝叶斯信息准则(BIC)选定了最优规格。 ARIMA(1,1,1)模型实现了8.47%的平均绝对百分比误差(MAPE),表明其在运营规划中具有较高的预测精度。 将预测的年度需求整合到经济订货量(EOQ)框架中,以确定最优订货量、再订货点(ROP)和概率性安全库存。为期一年的成本模拟表明,与现有方法相比,基于EOQ的策略将总库存成本降低了22.73%。 通过黑盒测试进行的功能验证证实,该系统完全符合规定的要求。这些结果表明,将预测分析与经典库存优化相结合,可以提高运营效率并降低总库存成本。该系统为正在进行数字化转型的微型、小型和中型企业(MSMEs)提供了一个实用且以数据为驱动的库存管理框架。
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参考
31 References[1] S. J. Berman, “Digital transformation: opportunities to create new business models,” Strategy & Leadership, vol. 40, no. 2, pp. 16–24, Mar. 2012, doi: https://doi.org/10.1108/10878571211209314
[2] F. Li, “Leading digital transformation: three emerging approaches for managing the transition,” International Journal of Operations & Production Management, vol. 40, no. 6, pp. 809–817, Sep. 2020, doi: https://doi.org/10.1108/IJOPM-04-2020-0202
[3] V. Cecilia Pah, A. Sartika Pane, and P. Negeri Kupang, “Digital Transformation: A Solution for Accounting Recording at Kampoeng Tenun Alor MSME in Kupang City,” JASa (Jurnal Akuntansi, Audit dan Sistem Informasi Akuntansi), vol. 9, no. 3, pp. 546–561, Dec. 2025, doi: https://doi.org/10.36555/jasa.v9i3.2929
[4] R. Rahayu, M. Arsal, Nurhayati, and Usman, “Challenges in recording and reporting assets,” Multidisciplinary Indonesian Center Journal (MICJO), vol. 2, no. 2, pp. 964–970, Apr. 2025, doi: https://doi.org/10.62567/micjo.v2i2.519
[5] N. Chehrazi, “Inventory Systems with Record Inaccuracy: Transaction Errors vs. Unobservable Loss,” vol. 27, no. 4, pp. 1183–1204, Jun. 2025, doi: https://doi.org/10.1287/msom.2023.0274
[6] Y. Kang and S. B. Gershwin, “Information inaccuracy in inventory systems: Stock loss and stockout,” IIE Transactions (Institute of Industrial Engineers), vol. 37, no. 9, pp. 843–859, Sep. 2005, doi: https://doi.org/10.1080/07408170590969861
[7] S. Wynn and S. Wynn, “The Financial Impact of Manual Inventory Record Errors,” Doctoral Dissertations and Projects, Oct. 2021, Accessed: Feb. 19, 2026. [Online]. Available: https://digitalcommons.liberty.edu/doctoral/3208
[8] A. H. Zadeh, R. Sharda, and N. Kasiri, “Inventory record inaccuracy due to theft in production-inventory systems,” The International Journal of Advanced Manufacturing Technology 2015 83:1, vol. 83, no. 1, pp. 623–631, Jul. 2015, doi: https://doi.org/10.1007/s00170-015-7433-3
[9] S. Guercini and A. Runfola, “Heuristics in decision-making by exporting textiles SMEs,” Journal of Global Fashion Marketing, vol. 12, no. 1, pp. 1–15, Jan. 2021, doi: https://doi.org/10.1080/20932685.2020.1835521
[10] R. R. Panigrahi, A. K. Shrivastava, and S. S. Nudurupati, “Impact of inventory management on SME performance: a systematic review,” International Journal of Productivity and Performance Management, vol. 73, no. 9, pp. 2901–2925, Nov. 2024, doi: https://doi.org/10.1108/IJPPM-08-2023-0428
[11] S. Rabiu and M. K. M. Ali, “Optimizing inventory dynamics: a smart approach for non-instantaneous deteriorating items with linear time function dependent variable demands and holding costs, shortages with backlogging,” Journal of Applied Mathematics and Computing 2024 70:4, vol. 70, no. 4, pp. 3193–3217, Apr. 2024, doi: https://doi.org/10.1007/s12190-024-02089-1
[12] C. Çalışkan, “The economic order quantity model with compounding,” Omega (Westport), vol. 102, no. 15–16, p. 102307, Jul. 2021, doi: https://doi.org/10.1016/j.omega.2020.102307
[13] P. S. You and Y. C. Hsieh, “An EOQ model with stock and price sensitive demand,” Math. Comput. Model, vol. 45, no. 7–8, pp. 933–942, Apr. 2007, doi: https://doi.org/10.1016/j.mcm.2006.09.003
[14] M. Khashei, M. Bijari, and G. A. Raissi Ardali, “Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs),” Comput. Ind. Eng., vol. 63, no. 1, pp. 37–45, Aug. 2012, doi: https://doi.org/10.1016/j.cie.2012.01.017
[15] B. K. Nelson, “Statistical methodology: V. Time series analysis using autoregressive integrated moving average (ARIMA) models,” Academic Emergency Medicine, vol. 5, no. 7, pp. 739–744, Jul. 1998, doi: https://doi.org/10.1111/j.1553-2712.1998.tb02493.x
[16] Y. Lai, D. A. Dzombak, Y. Lai, and D. A. Dzombak, “Use of the Autoregressive Integrated Moving Average (ARIMA) Model to Forecast Near-Term Regional Temperature and Precipitation,” Weather Forecast., vol. 35, no. 3, pp. 959–976, Apr. 2020, doi: https://doi.org/10.1175/WAF-D-19-0158.1
[17] A. Saurav, V. Yadav, and C. Shekhar, “An inventory optimization model for reliable and sustainable supply chains under trade credit and carbon constraints,” Supply Chain Analytics, vol. 11, no. 17, p. 100132, Sep. 2025, doi: https://doi.org/10.1016/j.sca.2025.100132
[18] M. G. Huang, “Real options approach-based demand forecasting method for a range of products with highly volatile and correlated demand,” Eur. J. Oper. Res., vol. 198, no. 3, pp. 867–877, Nov. 2009, doi: https://doi.org/10.1016/j.ejor.2008.10.002
[19] M. Pudjodriyitno, R. Dyah Kusumastuti, Y. Bustaman, and M. Tangerang, “A Comparative Study of Demand Forecasting for Aftermarket Parts in Heavy Equipment Industry (PT XYZ Case Study),” Emerging Markets : Business and Management Studies Journal, vol. 9, no. 2, pp. 113–129, Mar. 2022, doi: https://doi.org/10.33555/embm.v9i2.197
[20] M. Lei, S. Li, and S. Yu, “Demand Forecasting Approaches Based on Associated Relationships for Multiple Products,” Entropy 2019, Vol. 21, vol. 21, no. 10, Oct. 2019, doi: https://doi.org/10.3390/e21100974
[21] F. H. Abanda et al., “A systematic review of the application of multi-criteria decision-making in evaluating Nationally Determined Contribution projects,” Decision Analytics Journal, vol. 5, Dec. 2022, doi: https://doi.org/10.1016/j.dajour.2022.100140
[22] J. D. Naumann and A. M. Jenkins, “Prototyping: The new paradigm for systems development,” MIS Q., vol. 6, no. 3, pp. 29–44, 1982, doi: https://doi.org/10.2307/248654
[23] B. Kang, N. Crilly, W. Ning, and P. O. Kristensson, “Prototyping to elicit user requirements for product development: Using head-mounted augmented reality when designing interactive devices,” Des. Stud., vol. 84, p. 101147, Jan. 2023, doi: https://doi.org/10.1016/j.destud.2022.101147
[24] 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: https://doi.org/10.1016/j.jjimei.2022.100058
[25] W. K. Adu, P. Appiahene, and S. Afrifa, “VAR, ARIMAX and ARIMA models for nowcasting unemployment rate in Ghana using Google trends,” Journal of Electrical Systems and Information Technology 2023 10:1, vol. 10, no. 1, pp. 12-, Feb. 2023, doi: https://doi.org/10.1186/s43067-023-00078-1
[26] N. Talkhi, N. Akhavan Fatemi, M. Jabbari Nooghabi, E. Soltani, and A. Jabbari Nooghabi, “Using meta-learning to recommend an appropriate time-series forecasting model,” BMC Public Health 2024 24:1, vol. 24, no. 1, pp. 148-, Jan. 2024, doi: https://doi.org/10.1186/s12889-023-17627-y
[27] J. H. Lopez, “The power of the ADF test,” Econ. Lett., vol. 57, no. 1, pp. 5–10, Nov. 1997, doi: https://doi.org/10.1016/s0165-1765(97)81872-1
[28] T. Peychinov, A. Karaivanova, and T. Mecheva, “Predicting Traffic Load Data: ARIMA and SARIMA Comparison,” Engineering Proceedings 2025, Vol. 100, vol. 100, no. 1, Jul. 2025, doi: https://doi.org/10.3390/engproc2025100029.
[29] L. Zhao, Z. Li, and L. Qu, “Forecasting of Beijing PM2.5 with a hybrid ARIMA model based on integrated AIC and improved GS fixed-order methods and seasonal decomposition,” Heliyon, vol. 8, no. 12, p. e12239, Dec. 2022, doi: https://doi.org/10.1016/j.heliyon.2022.e12239.
[30] M. Alnahhal, B. L. Aylak, M. Al Hazza, and A. Sakhrieh, “Economic Order Quantity: A State-of-the-Art in the Era of Uncertain Supply Chains,” Sustainability 2024, Vol. 16, vol. 16, no. 14, Jul. 2024, doi: https://doi.org/10.3390/su16145965.
[31] A. R. Al Firdausi and D. Suprayitno, “Application of the Economic Order Quantity (EOQ) Method in Soybean Raw Material Inventory Control at the Haji Maman Tofu Factory in Matraman District, East Jakarta,” Sinergi International Journal of Logistics, vol. 1, no. 2, pp. 73–84, Aug. 2023, doi: https://doi.org/10.61194/sijl.v1i2.65.
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