Data is an essential fuel for artificial intelligence (AI) to power the underlying machine learning (ML) algorithms. Missing data is common in most real-world datasets due to measurement errors, non-responses, and human errors during the data collection, which can ultimately lead to reduced...

Predicting delivery delays is crucial for companies, especially in times of increasing global uncertainty and vulnerable supply chains. Machine learning (ML) offers significant potential to improve the forecast performance and quality of delivery delay prediction. Although various prediction...

In today's dynamic technological environment, the integration of IoT into Supply Chain Management Systems (SCMS) has significantly enhanced functionality, visibility, and decision-making. However, integrating Industrial-IoT (IIoT) with Supply Chain Networks (SCN) is an equally significant...

Sustainable supply chain management plays a pivotal role in shaping corporate reputation and enhancing customer loyalty in the contemporary market. This study uniquely integrates regional, demographic, and psychographic data with advanced machine learning methodologies, including clustering,...

In the automotive sector, excess inventory increases costs and prevents progress toward Sustainable Development Goals, particularly those related to Industry, Innovation, and Infrastructure (SDG 9) and Responsible Consumption and Production (SDG 12). This study introduces an innovative approach...

Purpose - This study investigates the application of machine learning (ML) techniques in predicting firm performance, responding to the challenges posed by the large volumes of data required for accurate predictions. It aims to assess the effectiveness of various ML methods and algorithms used...

Australian bushfires are catastrophic, and their impacts span social, economic, and environmental factors. To reduce the damages experienced by bushfires, predicting Australian bushfire ignition allows for an early warning system to give first responders and disaster managers prompt and accurate...