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

TL;DR

This paper introduces a novel ARIMA-based framework combined with a proprietary formula to predict and quantify bad goods risk scores, improving supply chain quality control and risk management accuracy.

Contribution

It presents a new integrated approach using ARIMA models and a custom formula for better prediction and assessment of bad goods risk in supply chains.

Findings

Outperforms traditional models like Exponential Smoothing and Holt-Winters in accuracy.

Demonstrates effectiveness on real-world dataset from 2022-2024.

Provides a scalable solution for proactive quality risk management.

Abstract

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.