What Does the Future Hold for Freight Management Companies?

 

Everyone talks about the future. You hear terms thrown around like blockchain, robotics, and machine learning. Though not unrelated, only one of the three sticks the landing in the real world. Along a ‘hype curve’ (where new trends are mapped along how relevant they are, and would they be able to sustain the ‘hype’ to eventually become productive), blockchain and robotics (in terms realistic and cost-effective application) fall off early. Machine learning, however, has reached the point of stability and scale. Let’s look into how technology such as machine learning helps the freight management industry.

 

What are the Problems Facing Freight Management?

 

Some of the key problems facing freight transport are the rising freight rates and driver shortage. Fluctuating fuel costs and increasing anxiety over the lack of quality drivers are pushing freight rates higher. For consumer goods, larger companies such as Walmart, Amazon, Home Depot, etc. are sustaining competitive pricing. This means that right now the excess costs are cutting down on margins, either of the distributors or the companies themselves.

 

According to the American Trucking Associations, freight tonnage hauled by trucks would increase by 27% (between 2016 and 2027). With global retail sales to touch $27 trillion by 2020, it just adds to the problems of high volume and restricted resources. As of now, the higher costs are being borne (in some cases) by the suppliers but they would eventually be passed on to the end-customers. Logistics and freight movement would be the playing ground, eventually, to create positive delivery experiences and cost-leadership among competitors. Most of these companies would win or lose based on how they optimize their last mile deliveries.

 

How Proper Freight Capacity Utilization Would Boost Profits

 

Transportation management would become the playing ground for the application of machine learning backed optimization. Companies would invest in scalable and reliable technology to not just save costs but better utilize the available vehicle capacity. Moreover, with predictive analytics, companies would be able to know exactly how many vehicles they require to fulfill incoming demand. This would help them balance their shipment movement without relying too much on spot-market buy-ins.

 

The existing and additional capacity would be utilized better, leading to minimal idle capacity and lost opportunity cost (such as deadheading or when a vehicle moves with idle capacity which could have been used).

 

Optimal capacity utilization would help divide the freight movement cost so that the marginal cost for each unit transported is less, and hence profit margins are higher.

 

How Tracking Driver Behavior and Hours of Service Can Benefit Operations

 

Better driver management would help allocate the right trip to the most-suited driver well-versed with the route and learned in the type of vehicle assigned. Automated allocation of shipments to vehicles and drivers would speed-up transportation, bringing down lead-time and downtime. Machine learning would be used more-and-more freight movement now has a clear and renewed focus on tracking driver behavior and hours of service. to design faster and safer routes for trips. This would bring down the turnaround time, in-turn bringing down the fuel and maintenance costs.

 

Live GPS tracking of moving resources would make transport companies agile and responsive.

 

Companies would be able to track driver behavior such as speeding, unnecessary detention, deviation from planned routes, harsh breaking, etc. with instant alerts and notifications passed on to the supervising manager or stakeholder.

 

Recommended Read: Where Are Your Drivers? Trucker Lost In Oregon Woods For Days With Wrong GPS Info

 

Real-time traffic pattern analysis would help predict the best route and accurate ETAs for reaching the in-transit hubs and destination locations. Fast scanning with in-app or connected scanners would help with fast loading and unloading at hubs, reducing the total time spent there while increasing the transparency (with error-free documentation and tracking of each unit transported). All this would help companies better manage the hours-of-service of each driver, to comply with regulatory and service level agreements. Instances, where a driver’s mandatory break-time ends up delaying critical orders, would be almost nullified as driver’s time would be well-tracked and managed right from a single dashboard giving end-to-end visibility overall moving and on-ground resources.

 

Such automation of allocation, routing, tracking, and compliance would be the primary need for most companies to run their operations sustainably and profitably.

 

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