TMW has released a new white paper authored by Tim Leonard called “Measuring and Maximizing Utilization in Trucking”, part of their series Stepping Cases for Blockchain. This paper does not directly address a use case for blockchain, but is meant to educate readers about new utilization metrics, which will presumably be tied to blockchain later in the series.
In “Measuring and Maximizing Utilization in Trucking,” Leonard argues that the concept of ‘throughput’ is a more powerful asset utilization metric than the traditional measures used by trucking companies (top line revenue, miles per truck, hours, tonnage, and deliveries).
Each of the traditional metrics used by trucking companies to measure asset utilization has advantages and disadvantages: top line revenue is easy to calculate, but only includes one side of the balance sheet; miles per truck per unit of time is also easy to calculate and is generally positively correlated with revenue, but doesn’t allow decision-makers to compare the relative attractiveness of different lanes; hours are also easy to calculate for local delivery networks but ‘more isn’t always better’; tonnage or volume is also easy to calculate, but the correlation to money is not linear or precise.
But throughput, writes Leonard, “aligns the measurement of resource utilization with the goal of the organization. Throughput is defined as Revenue minus Total Variable Cost. It is a monetary metric, putting it in the same unit of measure as the goal of the organization [cash].”
Revenue is fairly simple: it’s the money generated by the sale of a service to move cargo from point A to point B. Total Variable Cost (TVC) is more complicated, and represents the cost incurred by the activity of a production resource (importantly, it doesn’t include fixed or semi-fixed costs like lease payments, maintenance, registration and license fees, insurance payments of various kinds, etc). For trucking, TVC covers driver pay and fuel, but, importantly, “segments of the trucking industry that do not pay drivers based solely on their productive activity would not include driver pay in the TVC calculation,” Leonard points out.
The benefits of using throughput as a resource utilization metric are numerous: firstly, throughput is a monetary metric automatically aligned with the goal of the company, where a higher number is always better in a linear way (unlike, for instance, raw miles, which don’t indicate the differences in profitability between lanes). Secondly, unlike top line revenue, throughput includes a significant cost component, but it avoids confusion by excluding fixed and semi-fixed costs to variable transaction activity. Finally, the math is fairly simple.
Leonard identifies some cautions to keep in mind when using throughput: “throughput includes a total variable cost component, and different modes within a transportation segment may have different cost models,” he writes. In other words, settlement models for owner operators, independent contractors, or outside carriers are different for company drivers and company vehicles. In addition, there’s another consideration to keep in mind, no matter what utilization measure your company is using: “because trucking involves the physical movement of the production resource from the domicile to more-or-less distant locations, and some activity must be identified to move that production resource back to the domicile location (eventually), the evaluation of individual trips using a utilization measure should be done with caution. It is entirely possible to perform a trip that provides excellent utilization results but leaves the resource in an awkward and costly location for the next move,” Leonard acknowledges.
The white paper goes on to discuss the business management theory of constraints, which seeks to identify bottlenecks in production that can dictate the final outcome of processes. In trucking, the constraint is typically seated trucks, and that’s what TMW uses in the white paper to generate its models in the section “A Detour into Constraint Analysis and Management.”
“As noted above,” Leonard concludes, “one of the key challenges in transportation is that the productive resource is mobile. In transportation, the machine goes to the raw materials instead of the raw materials coming to the machine. Therefore, what we do next has a profound impact on the success of our prior actions. A single lane that looks great in our simplified model might be a disaster for your business because it puts your productive resource in a geographical “black hole” from which it cannot emerge. Alternatively, a lane that looks poor in the simplified model might actually be a good choice because it positions your resource to take advantage of excellent opportunities located at the real-world destination.”