Welcome to our series on solving logistics problems with data. In this post we share a real life example of how data and analytics helped a customer reduce shipping costs and solve their problem of paying carriers on time.

The first step in reducing shipping costs is to understand what influences the cost. Shipping costs are always determined by the same four characteristics: distance, density, shipment size, and speed. When a shipper has a clear picture of their four characteristics, they now have a way to understand their costs. This is when they can see that there are some profit leaks, most generally due to outdated rules and the use of the incorrect carrier on some lanes.

Analytics Reveal Faulty Logic

After the profit leaks have been identified through data, how can shippers determine if they are spending too much in the first place on shipping? The answer is analytics. Analytics makes the data actionable. Analytics exposes the number of excuses or justification statements a shipper uses when explaining their shipping rules. Statements like “I have to use this carrier because they’re the only one that knows how to haul in this lane correctly” or “I’ve tried other carriers and they just didn’t get it right” oftentimes reflect rules that were put in place many years ago. As these excuses were handed down from person to person and never questioned, the faulty logic became ingrained.

Analytics Untangles Tactical Problems

Many times, a company will become a customer of RateLinx because there is a tactical problem they can’t solve. For example, one of our customers was having an issue paying their carriers on time. To the customer, it appeared that they had hundreds of problems that were preventing them from paying freight bills. Sometimes, the dollar amount on the invoice for a given carrier would match their contracted rate and other times it would not. Sometimes they would get invoices from a carrier and their system didn’t have a rate for the carrier. With all these seemingly unrelated variables, they didn’t know where to begin to solve the problems.

In this situation, we started with Intelligent Invoice Management℠ (IIM). This is how we were able to quickly and easily diagnose the issue. We contacted the carriers and had the freight invoice (EDI 210) transmitted to IIM. IIM then standardized and cleansed the data, and transmitted a clean freight invoice (EDI 210) back to the customer for payment. This allowed us to receive the data needed to diagnose the problem, with no internal IT resources needed from the customer. Once the data was flowing through IIM, we turned on the analytics which made the data actionable and allowed us to collaborate with the customer and the carrier to find the correct solution.

Define the Problem Correctly, then Solve

To find the correct solution, you must first define the problem correctly. We wrote about the consequences of solving the wrong problem here. Through analytics, we found three problems to address:

  1. Miles were calculated incorrectly by the carrier,
  2. Tender passed to the carrier was incomplete, and
  3. Out-of-date addresses were used on both the carrier and shipper side.

Once we understood the root cause of the problems, we developed and deployed strategies to solve them.

The solution to the first issue was to talk to the carriers and tell them the exact setting they had wrong on their mileage calculation.

The solution to the second issue was to update the tender record (EDI 204) that was being transmitted from the TMS to the carrier so it had complete instructions for the given shipment.

The solution to the third issue was to have the master data in the TMS, and the carrier’s system updated with the right addresses.

Analytics Reveals Unknown Problems too

With the analytics turned on, we also found a fourth issue that the customer didn’t know they were having—incorrect routings. There were certain customer locations that were using an outdated routing guide for some lanes. By having all the locations use the latest routing guide, the customer prevented freight invoices from entering their system that did not have a rate which meant they were able to leverage the right carrier with a lower cost.

Overall, the solution saved the customer about 5% on freight costs with zero internal IT resources required. Now the company has standardized and cleansed data they can leverage to diagnose the next issue, develop a strategy, and deploy it for the next round of freight savings.

We hope you enjoy this series: Solving Logistics Problems with Data. A few weeks ago, we discussed solving noncompliance and business rules logistics problems with data, another common logistics problem we solve through our analytics. If you would like to have our blogs articles delivered to your inbox, subscribe here

Or if you’d like to talk about how we might help you reduce shipping costs through actionable data, please reach out to me.

Published by Shannon Vaillancourt in Invoice Management

Shannon Vaillancourt

Shannon Vaillancourt

Shannon Vaillancourt is president and founder of RateLinx. With a vision to harness the opportunity of big data he developed the innovative integrated supply chain and logistics management software suite. Since launching in 2002, Shannon has led RateLinx software implementation initiatives with more than 300 integrations in 10,000 locations customized for individual customer needs. He designed the unique predictive modeling engine that allows businesses of all sizes to experience freight savings faster in real-time and at a much higher level than ever before through Integrated Shipping Intelligence℠.


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