Predicting Bad Goods Risk Scores with ARIMA Time Series: A Novel Risk Assessment Approach
It addresses the need for advanced predictive methods to mitigate risks from bad goods in supply chains, offering a scalable solution for quality control, though it appears incremental as it combines existing ARIMA with a new formula.
This research tackled the problem of predicting defective goods in supply chains by developing a framework that integrates ARIMA time series models with a proprietary formula, achieving higher prediction accuracy and better risk evaluation compared to traditional statistical models like Exponential Smoothing and Holt-Winters.
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.