What specific data is crucial to gather and analyze to optimize shovel-truck match factor?

To optimize the shovel-truck match factor, the crucial data to gather and analyze includes shovel loading time, truck payload capacity, truck cycle time, and the number of trucks assigned to each shovel. The shovel-truck match factor refers to the ratio between the shovel's digging capacity and the truck fleet's hauling capacity. An optimized match factor ensures that the shovel is efficiently loading trucks without excessive waiting times for either the shovel or the trucks. Shovel loading time is the average time it takes for a shovel to load a single truck. This data should be collected for each shovel, considering variations in material type, digging conditions, and operator skill. Truck payload capacity is the maximum weight of material that a truck can legally and safely carry. This information is usually provided by the truck manufacturer and should be verified periodically. Truck cycle time is the total time it takes for a truck to complete a full cycle, including loading, hauling to the dump point, dumping, and returning to the shovel. This data should be collected for each truck, considering variations in haul road distance, road conditions, traffic, and dumping time. The number of trucks assigned to each shovel is the number of trucks dedicated to serving a particular shovel. Analyzing this data involves calculating the shovel's digging capacity (tons per hour) and the truck fleet's hauling capacity (tons per hour). The optimal number of trucks assigned to each shovel is the number that minimizes the combined cost of shovel idle time and truck waiting time. If the shovel is consistently waiting for trucks, it indicates that too few trucks are assigned. If the trucks are consistently waiting for the shovel, it indicates that too many trucks are assigned. Therefore, accurate and continuous data collection and analysis are essential for optimizing the shovel-truck match factor and maximizing overall productivity.