A Data-Driven Predictive Framework for Inventory Optimization Using Context-Augmented Machine Learning Models

TL;DR

This paper presents a machine learning-based framework that incorporates external factors like weather and holidays to improve demand forecasting accuracy for inventory management in retail and vending sectors.

Contribution

It introduces a data-driven predictive framework utilizing external variables and compares multiple ML algorithms, highlighting XGBoost as the most effective for demand prediction.

Findings

XGBoost achieved the lowest MAE of 22.7 with external factors.

External factors significantly improve demand forecast accuracy.

XGBoost outperforms ARIMA, Fb Prophet, and SVR in this context.

Abstract

Demand forecasting in supply chain management (SCM) is critical for optimizing inventory, reducing waste, and improving customer satisfaction. Conventional approaches frequently neglect external influences like weather, festivities, and equipment breakdowns, resulting in inefficiencies. This research investigates the use of machine learning (ML) algorithms to improve demand prediction in retail and vending machine sectors. Four machine learning algorithms. Extreme Gradient Boosting (XGBoost), Autoregressive Integrated Moving Average (ARIMA), Facebook Prophet (Fb Prophet), and Support Vector Regression (SVR) were used to forecast inventory requirements. Ex-ternal factors like weekdays, holidays, and sales deviation indicators were methodically incorporated to enhance precision. XGBoost surpassed other models, reaching the lowest Mean Absolute Error (MAE) of 22.7 with the inclusion of external variables. ARIMAX and Fb Prophet demonstrated noteworthy enhancements, whereas SVR fell short in performance. Incorporating external factors greatly improves the precision of demand forecasting models, and XGBoost is identified as the most efficient algorithm. This study offers a strong framework for enhancing inventory management in retail and vending machine systems.