With the enormous amount of data available in freight and logistics, few industries are better positioned to take advantage of machine learning (ML) in their operations. But despite the potential of dynamic pricing, capacity planning and route optimization - 87% of these initiatives fail.
In this article, we dig deeper into why that is the case, and how you can overcome them to put your business on a successful data-driven journey.
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For carriers, there are constant concerns about accurate insights regarding the real available capacity, how to optimize networks in real-time, and how to plan for contract space. For quotes and pricing - any response, including in the spot market, requires a holistic view of the customer, their global business with a carrier, plus inputs from rates in the market. Combined with market capacity and willingness to pay inputs, this makes up for complex decisions to make quickly.
These processes could be made faster, less manual and more accurate with the use of machine learning. With the enormous amounts of data generated with the movement of goods - the freight industry is in a strong position to take full advantage of it.
But, despite the potential of dynamic pricing, automated capacity planning, route optimization or other models - the real business value is yet to be realized for most. Over 80 percent of ML projects fail to deliver.
"The problem is not the models, it is the data"
The main reason behind this high failure rate is not about the models. But rather creating the foundation - the data. To illustrate, 70 percent of companies struggle to extract value from their data.
Since one shipment can involve ten’s of partners, hundreds' of interactions and data from various sources (sensors, TMS, ERP etc.) - to harmonize and unify all this fragmented non-consisted data continuously becomes a challenge.
Although using data and analytics isn’t groundbreaking in the freight industry, now is the time to break down silos, merge disparate datasets, and apply scalable data initiatives to put ML and real-time analytics to full use.
Among the ones who are successful, however, in deploying ML to use we see a couple of common traits:
Solving the challenges above results in a more productive data team that can drive faster data to value processes. Since ML/AI/Data Science talent is rare in freight forwarding and air cargo - it has a substantial effect on the return-on-investment of these teams and initiatives.
At Forloop.ai freight and logistics companies to get control of their data and make use of machine learning (ML). We’ve helped our clients with:
Our team have backgrounds from Uber Freights similar platforms, and now want to provide the benefits of data, real-time forecasting and optimization without massive IT budgets.