In this day and age, everybody knows that data is important. But as supply chains grow, the amount of data they generate can quickly become overwhelming. And if companies don’t know what to do with that much data or simply have too much of it to track and analyze efficiently, the data itself becomes virtually meaningless.
As a result, many companies neglect their historical shipping data and focus solely on the now. In doing so, they inadvertently ignore countless problems that the data could have flagged—if they’d analyzed it properly.
Here are a few common problem areas that shipping pattern data can shine a light on.
Lack of visibility makes accurate benchmarking and strategic rate negotiation virtually impossible. And without regularly benchmarking their shipping rates for both inbound and outbound freight, managers will struggle to say with any certainty whether they’re paying competitive prices.
Historical shipping pattern data allows companies to see how much freight they’re moving along specific shipping lines and how much it’s costing them. With insights into the market in general, they can then gain a clearer understanding of what consistutes a competitive rate—and whether they’re overpaying.
With all this information in hand, companies can go into carrier negotiations with data to back up their arguments, rather than gut feelings. They might, for example, be able to push for a lower rate along a high-volume shipping line. A reliable stream of freight from an easy-to-work-with shipper can be a powerful bargaining chip—if companies can back their claims up with hard evidence.
The occasional late shipment isn’t worth crying about. But if a high percentage of shipments along a particular shipping line are experiencing lengthy delays, companies should step in and examine whether the route itself is the problem.
Without keeping an eye on shipping pattern data, though, spotting the difference between an isolated incident and a troublesome route can be tricky. Patterns aren’t always apparent in the moment, especially when a supply chain manager has a million other things on their mind.
Big-picture visibility allows shippers to identify bad routes before they cause bigger problems—like damaged relationships and loss of trust. The shipper can then explore alternative routes that may alleviate the ongoing delays.
Historical shipping pattern data is an essential ingredient in forecasting. Without looking to the past, supply chain managers cannot reliably predict future shipping patterns because they don’t have a clear picture of their supply chain’s flow. This can lead to all manner of problems, from trouble maintaining optimal inventory levels to issues with staff capacity.
What’s more, a lack of visibility into historical shipping patterns makes risk management unnecessarily difficult. Say shipments along a particular route are frequently disrupted by extreme weather conditions from January to March. That realization could lead a company to plan back-up routes for that period in advance, meaning they won’t be left scrambling should the worst happen.
Identifying troubles and trends starts with the right tools
Without strategically leveraging data to provide a big-picture view of their shipping patterns, companies can let small issues with their supply chains multiply and big problems fester—often with no idea of the true impact.
A high level of visibility across the supply chain empowers shippers to take corrective actions early. This can lead to greater efficiency, stronger relationships with clients and partners, and lower freight spend.
But such visibility is only possible with the right tools. CTSI-Global’s industry-leading transportation management system and Business Intelligence solutions make it easy for shippers to capture and analyze their shipping pattern data. Our tools bring together all the disparate data from a company’s systems, synthesize it, and produce strategic insights—making it easy for businesses to continually identify opportunities for improvement.
Gain total visibility and control. Contact us today.