Click to learn more about author Oleg Yanchyk.
While data has been proliferating throughout the supply chain, helping companies drive efficiency and boost revenue, there’s one notable area that’s lagging behind: logistics. Of course, businesses have general data available to measure when products are picked up and dropped off and provide some general insight into where a shipment is along a given route. But this top-level view provides only a fraction of the insights that logistics managers need to drive efficiency and success from their trucking operations.
USE ANALYTICS AND MACHINE LEARNING TO SOLVE BUSINESS PROBLEMS
Learn new analytics and machine learning skills you can put into immediate action with our online training program.
From pricing to on-time delivery (OTD) measurement, there are huge blind spots that continue to hold the freighting logistics space back from what it could be. However, with the right strategy and tools in place, this doesn’t have to be the case.
Here are a few areas where data performance issues exist in the logistics space, and how shippers and carriers can fill this logistics data gap.
Procurement Supply and Demand
Over the past couple of decades, very little has changed for shippers and carriers regarding alternate freight procurement: When a primary carrier rejects a shipment, turn to the broker market. Unfortunately, because of the way these middlemen operate, very little data is available in terms of what the current supply and demand landscape looks like, as well as what the true market cost to haul freight is.
As a result, shippers and carriers are turning to Data Science tools – particularly AI – as a way to dynamically track supply and demand for themselves as opposed to relying on the word of brokers. This allows them to get a better sense of market conditions both in an immediate and historical sense, and also helps them better manage costs when it comes to alternate procurement.
Market Rate Pricing
Similar to the issues shippers and carriers have with brokers around supply and demand, understanding the true market rate of a load is incredibly challenging. For example, due to opaque brokers’ fees and charges, shippers and carriers operate blindly when it comes to what a load should cost in the current market and what the “true cost” from a carrier actually is.
Fortunately, AI and Data Science are once again enabling shippers and carriers to take matters into their own hands. Through AI technology, not only can shippers and carriers connect directly with each other to cut out the middlemen, but they can also see pricing for similar loads on the marketplace so that they can be sure that the right price point is being hit.
Overcoming Data Interchange Issues
Logistics and supply chain management are traditionally very slow-moving categories in terms of technology adoption. However, as more and more logistics companies begin to prioritize data-driven insights, there is still a significant gap that exists between their existing data infrastructure and decision-making agility. And AI is obviously proving to be a key solution to this problem.
Even today, many companies have data that exists in silos, or is buried to the extent that it is incredibly challenging to find. This dramatically undercuts a company’s ability to both make decisions quickly and uncover insights that could allow it to gain a competitive advantage. AI allows companies to break down these barriers, so that they can tweak strategies and react to market developments in a fraction of the time they previously were able to.
The big data market is set to be worth nearly $125 billion by 2025. Moreover, as digital transformation efforts have been expedited due to the COVID-19 pandemic, more companies are embracing sophisticated data technology and strategies to help tackle their goals. Therefore, now is the time for freighting and logistics companies to look at the areas where their data performance is subpar, and adopt a more forward-thinking approach that better positions them for the future.