Predicting Bad Goods Risk Scores with ARIMA Time Series: A Novel Risk Assessment Approach

--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 A ver-age) models with a proprietary formula specifically designed to calculate bad goods after time series forecasting. 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. I. INTRODUCTION In modern industrial systems, detecting and preventing defective or substandard products--termed "bad goods"--such as manufacturing flaws or spoiled items like Organic Beer-G 1 Liter, remains a critical challenge. These defects result in financial losses, reputational harm, and supply chain inefficiencies. Traditional approaches like statistical process control and manual inspections struggle to address the complexity of large-scale operations [1]. The advent of big data and advanced analytics has elevated predictive methods as a key strategy for preempting such risks [2].