For large corporations, sustainability improvements have become increasingly urgent due to pressure from shareholders and customers. Many organizations have invested in initiatives such as replacing packaging with recyclable materials or using clean energy. These are necessary and noble initiatives, but they often operate on a much longer time horizon and they represent a serious capital investment.
Fortunately, advanced technology can now enable a nearly immediate reduction of excess CO2 emissions associated with waste in the transportation industry. Not only is this something that can be done today, but it is directly measurable, and can reduce overall transportation costs.
Industry research reveals that 30% of commercial trucks run empty because it is difficult for carriers to identify and contract loads that fill backhauls. When moving empty, trucks are unproductive in three important ways:
1. Their carbon emissions are not associated with productivity.
2. Independent operators are driving without a paid load.
3. Companies are incurring transportation costs for non-value-added services.
To address these productivity drags, industry participants need to fundamentally change how transportation is bought, sold and coordinated. This is now possible by utilizing machine learning across a much larger network than exists within shippers’ and logistics service providers’ own operations, creating increased predictability and a forward view into transportation capacity. These benefits can also be directly measured in scope 3 CO2 emissions reductions, as well as improved financial performance and increased reliability.
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