How AI is Fixing Logistics and Freight Procurement’s Data Blindspots
Originally posted on Food Logistics on 9/24/2021.
Hurricane Ida had far-reaching impacts on several business sectors -- particularly on energy. Energy shippers, carriers, and freight procurement teams are no strangers to having to navigate several unique nuances and challenges given the sensitivity of what they are shipping. But the destruction of Hurricane Ida threw a whole new level of complication into the mix.
AI is becoming a power player in logistics just as it has been in many other industries. The ability to automate the freight procurement process to positively impact key performance metrics, such as on-time delivery (OTD) is helping ease the strain for many shippers-- especially when capacity is tight!
Just a few weeks ago, the Senate approved a massive $1.2 trillion infrastructure package that will help rebuild the worn-out bridges and roads throughout the country, as well as fund new climate resilience and broadband initiatives. With the bill now clearing its first hurdle, a wide range of industries are paying close attention to additional developments and what will and will not ultimately be included. Chief among them is logistics, which relies heavily on these areas to help transport goods.
Originally posted on Food Logistics on 7/12/21.
Originally posted on SupplyChainBrain on 7/5/21
As the technology revolution has taken hold, AI and data science have become indispensable tools for freight procurement and logistics management businesses when it comes to boosting their efficiency and revenue. Yet, while many “old school” procurement and logistics processes have been overhauled using modern computing tools, one process continues to be stuck in yesteryear: freight tendering!
Much like in many business verticals, understanding supply and demand in logistics is pivotal to making sound, profitable decisions. Unfortunately, thanks to the data gaps and other hurdles that exist in the logistics space today, understanding supply and demand in freight procurement is incredibly tough to nail down. But this doesn’t have to be the case. Here are a few ways to crack the supply and demand code:
Although data continues to help proliferate the supply chain, making companies more agile and boosting revenue, there’s one critical area that has lagged behind: logistics. Shippers use data to measure when products are picked up and dropped off and where a shipment is along a given route. But this data only tells half the story, which means logistics managers are missing key insights in order to drive efficiency across freight procurement operations.
Freight procurement and logistics professionals are increasingly relying on data to drive their business decisions today. However, given these spaces are traditionally slow-moving when it comes to adopting technology, organizations within these fields find themselves at varying levels of maturity when assessing their data infrastructure. Yet, whether you are a company that is already well-versed in integrating data into business intelligence decisions or a company just starting to get a data infrastructure in place, data interchange is one of the foremost data-related challenges throughout freight procurement and the broader logistics space. Luckily, however, advanced computing such as artificial intelligence (AI) and machine learning (ML), along with other sophisticated data science tools, are helping shippers become much more agile.