用时间序列模型预测商品缺陷风险，提前防范损失。
Predicting Bad Goods Risk Scores with ARIMA Time Series: A Novel Risk Assessment Approach
- 结合ARIMA与自研公式，基于历史数据预测质量风险。
- 在2022-2024年有机啤酒数据上，准确率优于传统方法。
- 适合供应链质量管控与风险预警场景使用。
供应链复杂度提升及劣质商品带来的成本增加，凸显了先进预测方法的迫切需求。本文提出一种新框架，将时间序列ARIMA模型与专有公式结合，用于计算时间序列预测后的劣质商品风险评分。该模型利用销售、退货和产能等历史数据，捕捉时间趋势，通过新公式更精准量化缺陷的可能性与影响。在2022-2024年有机啤酒-G 1升数据集上的实验表明，该方法在预测准确性和风险评估方面均优于指数平滑与霍尔特-温特斯等传统统计模型。本研究推动了预测分析在供应链质量控制中的应用，实现了时间序列预测、ARIMA与风险管控的融合，提供了一种可扩展且实用的劣质商品损失防控方案。
原文摘要 · Abstract (English)
The increasing complexity of supply chains and the rising costs associated with defective or substandard goods (bad goods) highlight the urgent need for advanced predictive methodologies to mitigate risks and enhance operational efficiency. This research presents a novel framework that integrates Time Series ARIMA (AutoRegressive Integrated Moving Average) models with a proprietary formula specifically designed to calculate bad goods after time series forecasting. By leveraging historical data patterns, including sales, returns, and capacity, the model forecasts potential quality failures, enabling proactive decision-making. ARIMA is employed to capture temporal trends in time series data, while the newly developed formula quantifies the likelihood and impact of defects with greater precision. Experimental results, validated on a dataset spanning 2022-2024 for Organic Beer-G 1 Liter, demonstrate that the proposed method outperforms traditional statistical models, such as Exponential Smoothing and Holt-Winters, in both prediction accuracy and risk evaluation. This study advances the field of predictive analytics by bridging time series forecasting, ARIMA, and risk management in supply chain quality control, offering a scalable and practical solution for minimizing losses due to bad goods.
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