Horizontal transport is a critical component of the production process in underground mining operations. With the ever-expanding reach of underground mines, continuous adaptation of equipment and work organisation to dynamically changing operational conditions is essential for optimal equipment usage. This includes selecting appropriate configurations for wheeled loading and haulage machinery responsible for transporting material from production faces to transfer points. Effective process optimisation requires tools that not only facilitate an understanding of current operations but also enable the adjustment of workflows to ensure the targeted tonnage is transported within a specified time frame, while minimising energy expenditure. Key challenges to efficiency, aside from random operational incidents, include equipment downtime caused by traffic bottlenecks on haul roads and machines idling while waiting for loading. Data from existing SCADA systems and loT sensors such as NGIMU proposed in the article provide essential information, from which equipment utilisation metrics and numerical tracking of material flow within the transport network can be extracted. This article presents an example of the use of data recorded from NGIMU sensors located on machines and applying various techniques, including machine learning to address these challenges. Additionally, the use of reporting tools in a GIS environment is demonstrated to further enhance process visualisation and decision-making.