Load building challenges
While configuring loads, it is essential to pay attention to several factors. These include delivery sequence, space utilization, GVW, and handling constraints. See how each of these factors impacts load configuration and logistics KPIs:
- Delivery sequence: The last item to go in must be the first to come out. Otherwise, significant time is spent in unnecessary loading and unloading when delivering items, which delays deliveries.
- Space utilization: Ensuring maximum space utilization is crucial to achieving a low cost per delivery and a high fill rate. However, efficiently configuring irregularly shaped items manually can prove challenging.
- Handling constraints: Some items are fragile and prone to damage if kept at a lower level.
- Gross vehicle weight: Finally, exceeding gross vehicle weight can lead to fines and other legal risks.
Load configuration is more challenging in some industries
While one can efficiently load regularly shaped pallets and boxes, some objects can prove particularly challenging. For instance, space utilization becomes difficult in industries like cable manufacturing, where reels of various shapes and sizes lead to a high volume of unutilized space.
Large machinery manufacturers also face this challenge, or when truckers carry items with abnormally shaped protruding parts.
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OTM 3D Load Configurator: An intelligent approach to Load building
Oracle Transport Management (OTM) is currently the leading transport management solution (TMS) that drives global supply chain processes. 3D Load Configuration is an OTM module that supports intelligent, 3D truckload configuration plans. It accounts for numerous variables discussed above and devises an optimal load configuration for each truckload.
OTM 3D Load Configurator automatically pulls data from OTM to gain context about each item to be shipped. It enables logistics teams to configure loads based on destination data, shape and size of shipment, and other constraints like orientation preferences. The OTM 3D Load Configurator then uses this data to suggest the most optimal load configuration.