APL Cargo quit its legacy TMS and freed six figures and half its dispatch work
The longhaul carrier left McLeod's legacy platform for Datatruck, preserving five years of data, cutting manual dispatch work by over 50% and saving $100K-plus.
What was changed
A GlobeNewswire announcement carried by Markets Insider on 9 July 2026 reported that APL Cargo, a longhaul trucking carrier, migrated off McLeod Software's transportation management system onto Datatruck, an AI-native TMS. Before the switch, dispatchers managed load updates through back-and-forth email and factoring reconciliation was hand-matched against the WEX factoring line item by item, while profitability data lagged the operation by hours or days.
President Stefan Trifan said the legacy TMS 'couldn't keep up with our operation'. The full transition preserved five years of operational data and eliminated the fragmented spreadsheet workflows that had pulled work outside the core system. After migrating, APL Cargo deployed Datatruck's AI Updater for automated load-update communication and proof-of-delivery verification, replacing the email chains, and its AI reconciliation engine to match aging reports automatically.
Per the announcement, the carrier saved more than $100,000 and cut manual dispatch work by over 50%, with POD verification now catching documentation issues before invoices reach the factoring company, reducing disputes and payment delays that manual review had missed. The figures come from the TMS vendor's own announcement.
Why it worked
The legacy platform's gaps accumulated as dispatcher labor rather than visible cost.
Margin decisions were being made on numbers hours or days old.
Preserving five years of data removed the usual switching penalty.
POD verification upstream of factoring cut the disputes that delayed payment.
What can be applied
The hidden cost of legacy systems is absorbed labor: once work leaks into email chains and spreadsheets, the software invoice was never the real price.
Aftermath
Per the announcement, APL Cargo's team now operates from one platform with real-time profitability visibility; no longer-term results are given.