Economies of scale is the one big thing that every company has the need to achieve. In the freight transportation industry, it can be accomplished by aggregating similar loads in geographic areas that are close to each other. Although, as companies don’t know their future demands, it can be hard to fathom if the loads should be accepted or not, especially where new areas are involved.
In this blog, we shall see a model that will help you figure out whether historical data can be used by private fleet runners to make optimum use of their resources and make effective decisions in real time with respect to acceptance or rejection. This model would also help in eliminating unfruitful orders in a short-journey transportation environment.
Now, the key to deciding the feasibility of shipments is whether they meet the company’s benefit requirements. Also, this determination can be made rapidly as the orders are received.
There are two fundamental criteria here: the breakeven number of orders required, and the likelihood of getting that many orders during the time staying in the acknowledged time frame. Carrier availability is also taken into the account.
The above image showcases a basic decision-making tree that companies explore as they make these assessments. As it can be seen, incoming orders for new areas are reviewed as far as the expected profitability of deploying trucks in applicable geographies. If there is no service history to draw on, the model decides the profitability of getting a breakeven number of requests inside the acknowledged time frame. On the off chance that that number exceeds the predetermined limit and truck space is available, the model will accept the orders until the capacity is fully used.
Be that as it may, more research is expected to pick up a better understanding of how the model performs in real-world scenarios. For instance, the model can be refined with the addition of more nitty gritty information on order frequency, size, and revenue. Also, the cost of order rejections ought to be represented in future research.
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