Shippers experience a broad range of logistics problems ranging from shipping costs that are too high to logistics strategies that never quite seem to work. Interestingly, I find the answer to any logistics problem is always found in the data.
For example, noncompliance with a strategy will cause a shipper to have unnecessarily high shipping costs and the illusion that the strategy isn’t working. This happens because the shipper doesn’t have data that is providing intelligence—not that the strategy is necessarily wrong.
By using Intelligent Invoice Management℠ to collect and integrate the four main data points: 1) freight invoice, 2) track & trace, 3) shipment and 4) order & item level data, a shipper now has data that can provide intelligence. Then by using the ShipLinx TMS rating engine as an analytics engine, the shipper has the operational context to turn the data into the intelligence needed to reduce noncompliance or evaluate the cost of a business rule, or solve any number of specific problems.
Like A Data Scientist
Intelligent Invoice Management℠ (IIM) combined with the ShipLinx TMS analytics will do the work of a data scientist. IIM cleanses and standardizes the data, and ShipLinx TMS provides the missing “what if” data that will show the shipper that one of their routing rules is not being followed on a given shipment, the carrier that should have been used, and the price they should have paid compared to what they did pay. Or ShipLinx TMS will show that by breaking their current routing rule on a given shipment would provide the same service results with one of their other carriers (or modes) at a less expensive price. The data scientist role is important to ensure that decisions can be made based on facts so that the problems are solved quickly and effectively.
Bringing Visibility to Noncompliance
A good example of how quickly a problem can be solved with RateLinx involves one of our customers who uses 3PL warehouses to receive all of their ocean freight. One warehouse is on the east coast and the other is on the west coast. The freight is then deconsolidated and distributed to either their own DCs or directly to their customers. This customer worked with our Rate Analyst team to perform an LTL RFP. This allowed them to collaborate with their carrier to create strategies that really lowered their freight spend while lowering their carriers OR (increasing the carrier’s profit). The new LTL rates and routing rules were deployed at our customer’s locations using ShipLinx TMS, and a routing guide was created for the 3PL locations for the static lanes that they shipped to.
About a week after the new rates were in place our customer could see that the Lost Savings KPI displayed on the Dashboard was starting to increase. When they clicked on the KPI to drill into the details, they could see their west coast 3PL wasn’t following the new routing instructions. Instead, they were still following the old routing instructions. Our customer quickly called the 3PL and in less than a week after noticing that they weren’t following the new routing instructions, they had the problem solved.
Our customer was able to monitor this in near-real-time with the Lost Savings KPI on the Dashboard. Our customer thought this was the best thing ever, which confused me because I thought it was bad that the routing instructions weren’t being followed. But our customer said that without our system—that creates a data set with operational context to provide actionable intelligence— it would have taken 6 months for them to realize there was a problem, then another 3 months to figure out what it was, and another month to fix it. So, the problem would have lasted for at least 10 months and it would have cost them millions of dollars in freight instead of thousands.
Evaluating When A Business Rule Costs too Much
Using data to evaluate business rules is another example of how shippers can solve or avoid problems to lower costs. One of our customers had a rule where they could only use two carriers at a particular location because they only used 2 out of their 3 dock doors. The third dock was used for storage. The routing rule that was deployed in ShipLinx TMS was to “block” a third carrier from being selected out of this location. They also worked with our Rate Analyst team to model their historical data and put in place the two best carriers that would provide the service they needed at the price that made sense for them and the carrier. During this process, our Rate Analyst team was able to show the customer how much this “block” rule would cost them and it wasn’t that much.
After deploying the most optimal two carriers, the customer monitored this rule using the Lost Savings with Constraints KPI on the Dashboard. This showed how much each of their constraints (routing rules) were costing them. After 3 years, our customer’s shipping volume out of this location had grown to a level where the “block” rule was costing them significantly more than it would to add a storage location to their warehouse. So, they broke ground, added the necessary square footage to their warehouse, and opened up the third dock door. All of this was paid for based on about 3 months of freight savings. A great example of how things can change over time and it’s important to know how much each of your rules are costing you. This type of intelligence is created through a data set with operational context, bringing insight to information that otherwise would not be available.
If you would like to learn how you can turn your data into valuable shipping intelligence to solve your logistics problems, send me an email at [email protected].