AI in Action: How a Personalized Proof-of-Value Process Led L.J. Rogers to Decision Automation

For nearly 40 years, L.J. Rogers has operated as a family-owned transportation company with a diverse network that spans solo dry van operations, temperature-controlled team service, and a growing brokerage division. As the freight market tightened and operational complexity increased, the L.J. Rogers team knew they needed deeper clarity into how their network performed — and, more importantly, how to turn those insights into better decisions.

That need led L.J. Rogers to explore decision automation and ultimately to partner with Optimal Dynamics for a Proof-of-Value (POV) exercise — a transparent, personalized, and data-driven evaluation of their real network.

Below, we break down the insights L.J. Rogers discovered during the POV journey, including early discoveries, mindset-shifting iterations, and the moment when the L.J. Rogers team decided to move forward with decision automation.

Why L.J. Rogers Needed a Better Way to Run Their Network

Before exploring decision automation, the L.J. Rogers team had already invested in business intelligence tools that surfaced areas of concern: underperforming lanes, inconsistent utilization, and fleets that weren’t delivering expected results.

“We had some intercompany fleets that we felt were underperforming,” said Chris Partain, VP of Administration at L.J. Rogers. “We also knew our utilization wasn’t where we wanted it to be, but the difficulty was being able to actually make executable decisions based off that information.”

Like many carriers operating in a volatile market, L.J. Rogers faced pressure to use assets as efficiently as possible. But without a clear way to quantify lane performance, test different planning strategies, or understand how decisions in one part of the network impacted another, the team could only rely on experience, instinct, and fragmented insights.

They could see problems, but they couldn’t translate those problems into network-level decisions.

That gap between knowing and executing is what led them to explore the POV exercise with Optimal Dynamics. The POV process offered a path to validate internal instincts with hard numbers, uncover hidden inefficiencies, and evaluate entirely new strategies inside a safe, simulated version of their real operation. 

For the first time, L.J. Rogers could see what was happening in the network and what to do about it.

How the Optimal Dynamics POV Works

To launch the POV process, L.J. Rogers collected its historical McLeod TMS data so Optimal Dynamics could build an accurate picture of their operation without creating extra work for their team.

Using 90 days of network activity, Optimal Dynamics ran what’s called a historic simulation — a baseline model of the decisions L.J. Rogers actually made during that period. This first step allows Optimal Dynamics to mirror the network exactly as it operated in the real world, before introducing any optimization or alternative strategies. For L.J. Rogers, it validated long-held assumptions while also surfacing inefficiencies that were invisible in day-to-day operations.

The POV process followed these steps:

1. Data Gathering

Three months of TMS and ELD data are collected to accurately reflect the carrier’s operational landscape — including drivers, loads, timing, constraints, and freight mix.

2. Data Mapping & Calibration

Optimal Dynamics configures the engine to reflect the carrier’s real business rules: driver types, customer commitments, operating patterns, fleet characteristics, and dozens of operational parameters. This calibration ensures the simulation behaves as the carrier’s network does.

3. System Outputs

With the network modeled properly, the Optimal Dynamics engine begins running simulations to show how decisions could have been made. These outputs highlight missed opportunities, profitability gaps, and potential gains in utilization, loaded miles, and revenue per truck.

4. Clear, Quantifiable Results

Carriers receive a personalized, detailed readout of where optimization would have improved bottom-line performance — backed entirely by their own historical data rather than projections or assumptions.

Iterating Toward a Realistic, True-to-Life Model

One of the most important aspects of Optimal Dynamics’ POV process is that it isn’t a single static output. The first simulation establishes a baseline, but the real value emerges through an iterative process — one where the model is refined over multiple rounds until it accurately reflects a carrier’s true operational constraints, business rules, and edge cases.

For L.J. Rogers, this iterative approach surfaced opportunities they hadn’t previously been able to quantify, while also challenging long-held assumptions about what “good” planning should look like.

For example, like many carriers, L.J. Rogers operated under the traditional belief that minimizing empty miles was essential to running a profitable network. The POV process revealed a different truth: In certain situations, intentional repositioning (even if it adds empty miles) can significantly increase revenue per truck and mile.

“I’ve been in transportation for 20 years, and there’s this old-school thought process that says, ‘You’ve got to keep empty miles down,’” Partain said. “The POV allowed us to say, ‘Hey, we can actually increase empty miles to some degree, but we’re going to deliver more revenue per loaded mile,’ and I don’t know who wouldn’t take that trade-off.”

This was one of the first mindset shifts for the L.J. Rogers team. The engine showed them where repositioning would unlock better-paying freight, improve asset productivity, and create higher-value paths across the network — outcomes that are difficult, if not impossible, to identify manually.

As Optimal Dynamics and L.J. Rogers moved through more iterations, the model became increasingly more accurate and aligned with real-world operations. Optimal Dynamics incorporated constraints and nuances like:

• High-value freight requiring asset-only movement
• Dedicated customer commitments
• The removal of underperforming groups
• Fuel handling differences across divisions
• Unique patterns in the reefer and expedited teams

Each iteration allowed the L.J. Rogers team to see the effects of specific operational adjustments in isolation. Ultimately, this combination revealed the true drivers of profitability in their network.

“Every time we ran an iteration, we expected at some point that we were going to get something going the wrong direction,” Partain said, “but it was always a positive deliverable back to our organization.”

The Final Readout: What the Numbers Revealed

After several rounds of calibration and refinement, the POV process culminated in the final readout mentioned above, the one that shares a side-by-side comparison of how an organization performed during the 90-day evaluation period versus how the Optimal Dynamics engine would have performed under the same conditions. While the exact dollar values are confidential, the directional outcomes were unmistakable.

Here’s a look at the findings for L.J. Rogers:

1. A More Profitable, More Productive Network

The engine consistently demonstrated that L.J. Rogers could generate:

• More revenue per truck: By prioritizing higher-value freight and optimizing repositioning, Optimal Dynamics surfaced paths that unlocked more profitable load combinations.
• Higher revenue per loaded mile: Even when empty miles increased slightly, total profitability improved. This validated that the old “empty miles = bad” mindset doesn’t always hold.

2. Fewer Loads, But Better Loads on Assets

One of the most counterintuitive findings was that the engine chose fewer total loads than L.J. Rogers actually moved. Instead of maximizing volume, the engine maximized contribution:

• Low-value loads shifted to brokerage, where they belonged
• High-value, high-contribution loads stayed on assets
• The mix became more intentional — and more profitable

This confirmed a suspicion the L.J. Rogers team had for years: Not all freight deserves an asset. They simply lacked the mechanism to evaluate those decisions at scale. The POV process made those distinctions clear.

3. Brokerage as a Strategic Lever (Not a Safety Net)

Rather than using brokerage reactively to “fill holes,” the engine demonstrated how brokerage could play a strategic role in shaping a healthier network. By pushing certain loads to brokerage, Optimal Dynamics freed up asset capacity for freight that delivered stronger returns.

4. Validation of Internal Instincts With Hard Numbers

Finally, the readout quantified what L.J. Rogers had long suspected:

• Some lanes were dragging down network performance
• Certain fleets weren’t contributing enough to justify their complexity
• Dedicated and high-value freight constraints needed to be modeled differently
• Underutilization was costing more than anyone realized

The POV confirmed these insights and assigned real numbers to them. That clarity paved the way for the decision that came next.

Clarity, Confidence, and a New Path Forward

By the end of the POV process, L.J. Rogers had gained something they’d been seeking for years: clear, quantifiable insight into how their network really performed, plus a roadmap for how to improve it.

“What this POV process did was actually tie a number to every lane,” Partain said. “And so it was easy to give support and say, ‘Hey, we know we need to make a change.’”

For the first time, the team could:

• Distinguish profitable lanes from those quietly eroding margins
• Understand which lanes subsidized others
• Make confident decisions about which freight to keep and which to transition away from

What had long been a matter of instinct became a matter of math.

In a prolonged down-market, the L.J. Rogers leadership agreed that efficiency was the only lever fully within their control. And across every initiative they evaluated, Optimal Dynamics delivered the clearest, most measurable ROI. The internal alignment was strong, and the team felt energized rather than threatened by the change — a sign that the organization was ready for the next step.

If you’d like to see what decision automation could reveal inside your own operation, schedule a demo to learn more about our POV process and how you can experience your data in action.