Transportation data overload: That’s why I’m a picture guy
I’m a picture guy. In our kind of work, we have to be able to take a lot of data and make sense out of the process or processes that generated it. I used to work with a fellow named, Bill, who has a PhD in Operations Research, and is probably one of the smartest people I’ve ever met in my life. Bill is a guy who can look at six or seven big tables of numbers and then say something like, “… and the answer is 7.563.” He was usually right. I don’t have that talent to create the linkages among lots of different types of information in my head to come up with a conclusion like that. That’s why I like pictures.
Recently, one of my colleagues and I were visiting a manufacturing plant to assess their production scheduling process. The client invited us to visit the plant because they knew they had a problem. As we followed the scheduler through his day, we began to understand the root causes of the problem. So how did I choose to communicate what we’d found to the client? You guessed it; I drew a picture.
When the plant manager first opened the file containing the flowchart of their existing process, she told me she only needed to see that it took me three letter-sized pages to document to the process to know that the process was much too complex and cumbersome to be fixed with a couple of “quick hits.” Why is it that she knew without studying the details that we needed a full redesign to fix this process?
I think many of us are just built that way. I know there is a lot of clinical and academic research that shows how we human beings use our sense of sight as a first preference for observing the world, and that there are specific parts of our brains that are able to detect visual patterns or the lack thereof. However, I don’t think we need to see the results of that research to know why the phrase, “a picture is worth a thousand words,” is such an enduring statement. It rings true with all of us.
That’s why I like a software product called Tableau. It is marketed as a visual analysis tool and I think it does its job quite well. Although I don’t claim to be an expert user, I have found it quite useful when I need to understand what’s going on in a large dataset. Let me illustrate using an example from a recent transportation analysis that we did for one of our clients.
Our client had grown by acquisition and managed its transportation in a very de-centralized manner. Each of the sites contracted individually with their own set of carriers, using their own set of criteria for selecting and then awarding business to the carriers. Profit Point was called in to help the client understand the cost-savings opportunities that would result from a more centralized approach to carrier contracting and management.
Our first priority was to find out what was going on at all of the different sites so we developed a database from the client’s freight payment records to do it. Now, picture this (pun intended). We now have over 63,000 individual shipment records to analyze and we needed to do it in a way that told a story that we could understand and that we could then communicate to the client. The first thing we did was look at the spend by plant and by carrier. The spend by plant was more of a prioritization issue, to understand which of the plants had the highest freight spend, but the spend by carrier became the first part of our story as you can see in the two pictures below.
This second chart was a very powerful image to help the client quickly see that the number of carriers being employed was out of control. You don’t even need to be able to read the name of the carrier on the Y-axis to know that there are too many carriers in this picture. Many of these carriers had only a single load all year long, but were still carried in the system.
We also wanted to show the client the significant different in pricing policies across their carrier base. The following slides show how we used some more of Tableau’s functionality to make our point.
By plotting cost vs. distance for all of the shipments, we were able to see the general correlation of cost with distance that we expected, but we also saw a number of outliers that we wanted to better understand.
We then highlighted a group of very high-cost shipments and kept only those points to see what we might find out.
Using a simple stacked bar chart, it was very apparent that carrier “C-g,” the red bar in the chart at left, was the main player in this group. Once “C-g” was identified, we were able to demonstrate that their cost was always greater than the average cost for shipments with distances greater than 200 miles and by as much as 50-66% for shipments with distances greater than 1000 miles.
Again, these pictures allowed us to find one of the smoking guns inside this mass of data. Suffice it to say that we found many other opportunities through similar visual analysis.
Because of these pictures, and others like them, it was an easy sell. Using a tool that makes it easy to use the built-in “intelligence of our eyeballs,” we were able to develop a convincing call to action for our client, who went out to the market with a targeted freight bid and reduced their transportation spend dramatically.
As technology continues to penetrate more and more aspects of business and our everyday lives, it makes more and more data available for us to turn into useful information. But it’s only useful information when we can put it into a form that we understand and can communicate it to others. That’s why I’m a picture guy.