The ACCO Brands supply chain engineer built an optimization model showing that companies are leaving millions on the table, not because of market conditions but because of how they configure the software they already own.
Ask Rajashiva Ramalingam how many times he's watched a company spend a year implementing a transportation management system, go live with fanfare, and then quietly walk away from the part that was supposed to save them money. He'll tell you he stopped counting.
"The planning engine is sitting right there," says Ramalingam, who manages SAP Transportation Management and Warehouse Applications at ACCO Brands Corporation in Illinois. "But if the time-window configurations are off, if nobody's running the optimizer on a consistent cycle, it just stops consolidating freight the way it should. The costs pile up in ways that are invisible until you actually go looking."
So, he went looking. The result is a peer-reviewed paper, "The Hidden Cost of Empty Trucks: A Freight Consolidation Model for Enterprise Transportation Management," that has been making the rounds among supply chain researchers and logistics technology practitioners since its publication. The numbers in it aren't easy to dismiss.
Using a consolidation model based on the real architecture of SAP S/4HANA Transportation Management, Ramalingam shows that companies that run systematic freight consolidation can cut transportation costs by 22.7 percent relative to a less-than-truckload baseline. Vehicle utilization climbs from 34.2 percent to 71.8 percent. Tested across problem sizes ranging from 10 to 50 freight units, the savings hold up, ranging from 14.8 to 31.2 percent depending on network conditions and rate structures.
"People hear 22 percent, and they think it's a theoretical result," Ramalingam says. "But transportation spending is typically sixty to seventy percent of total logistics cost. At enterprise scale, that improvement isn't a rounding error. It's real money sitting inside systems companies have already paid for."
Opening the Black Box
Most practitioners who publish about supply chain software write from experience: war stories about go-lives, advice on change management, lessons from implementations that didn't go as planned. Ramalingam's approach was different. He wanted to understand the TMS not as a software product but as an engineering system, with distinct layers, each solving a specific class of problems.
He mapped it into four. The first layer is where raw demand from the ERP gets converted into freight orders and consolidated for shipment. The second is carrier execution, where those orders get tendered, booked, and tracked. The third is event monitoring, the part that's supposed to catch a border delay or a missed pickup window before it cascades into something worse. The fourth is financial settlement, in which completed shipments are reconciled against carrier invoices and posted to accounting.
What Ramalingam noticed is that each of these layers maps to a well-studied class of optimization problems that academic researchers have been working on for decades. Freight consolidation is a variant of the bin-packing problem. Carrier routing is related to the vehicle routing problem. Exception monitoring is fundamentally an information-processing challenge. Yet the operations research and supply chain management literatures have largely developed in parallel, with neither paying much attention to the other.
"The academic side treats these as isolated math problems," he explains. "The practitioner side treats the system as a black box. Neither is particularly useful if you're trying to figure out why your TMS isn't working as it's supposed to. I wanted to build a bridge between those two conversations."
His consolidation model formalizes the freight assignment problem mathematically, solves it with a greedy nearest-neighbor heuristic that runs in O(n²) time, and benchmarks it against a First-Fit Decreasing alternative and a random-assignment baseline. It's the kind of methodological rigor that's rare in practitioner-authored work, and it gave the paper credibility that's helped it travel.
What Logistics Leaders Are Taking From It
For executives who've sat through TMS implementations and wondered why the promised savings never fully materialized, Ramalingam's paper offers an uncomfortable but useful diagnosis. The gap between what a transportation management system is capable of and what it actually delivers on a given day isn't usually a technology problem. It's a configuration and discipline problem, and that means it's fixable.
He's careful not to oversell it. When spot LTL rates move close to FTL contract rates, something that happens in soft freight markets, the financial case for aggressive consolidation gets thinner. Under those conditions, he argues, companies might get more return from investing in carrier integration or exception monitoring than from squeezing the planning optimizer harder. His four-layer framework is meant to help logistics leaders make that call with some analytical grounding rather than gut feel.
Ramalingam closes the paper with five research propositions connecting TMS capability dimensions to measurable outcomes: supply chain visibility, carrier execution quality, freight cost efficiency, exception resolution speed, and settlement accuracy. He's explicit that these aren't proven claims. They're structured hypotheses drawn from his observations across real deployments, written in a form that other researchers can test.
That intellectual honesty is itself something peers in the field have noticed. Practitioners who publish tend to present their experience as a conclusion. Ramalingam presents his hypothesis, which is a meaningful distinction in a research context and one that has made his propositions a starting point for follow-on academic work.
"What I saw felt consistent across a lot of different organizations," he says. "But I'm a practitioner, not a researcher with a multi-firm dataset. I wanted to put it on paper in a way that someone with that kind of access could go verify, or challenge."
For a field that has long treated enterprise logistics software as either a vendor pitch or a management consulting exercise, that kind of rigorous humility is itself a contribution. The freight savings Ramalingam documented are real and reproducible. But the more lasting impact of his work may be that he showed other practitioners what it looks like to treat the systems they work with every day as worthy of serious study.
