What happens when a Navy nuclear power alum and finance PhD turns his focus to trucking? Sam Tibbs joins us to make a bold case: freight doesn’t have a tech problem, it has an incentive problem. He traces the unlikely route from teaching and data science to building the tools that exposed lane-level profit and drove the most-used variables in Sonar, then explains why today’s rate products miss the mark. If your pricing relies on paperwork extracted after delivery and guesses that fill in missing details, you’re driving with fogged windows.

We dig into the real bottleneck: timely, detailed data at the moment of booking. Sam argues that brokers create the signal, yet pay to buy back their own data days later, stripped of nuance like stops, hazmat, or lead time. His answer is refreshingly practical and familiar to anyone in finance: build a neutral, broker-owned data layer, much like how MasterCard aligned competing banks, where contributors are rewarded for high-quality inputs. With ownership and governance in place, the incentives flip, participation rises, and the rate product can finally show distributions that move with the market instead of stale averages.

Along the way, we contrast “great data plus a good model” with “good data plus a great model,” explore why LLMs can’t rescue missing truth, and outline where competitors should cooperate versus where they should battle. The near-term payoff is a cleaner rate signal and fewer pricing mismatches; the long-term vision is brokers turning a cost center into a profit center while shippers and carriers benefit from sharper, faster decisions. If you care about freight pricing, data quality, or building markets that actually work, this conversation maps a credible path forward.

If this resonates, follow the show, share it with a colleague, and leave a quick review so more folks in freight can find it. What's your take: should brokers own the data layer or keep buying it back?

Orders don’t move without clean data. We go deep with Jonathan Kish, SVP at Orderful, to reveal why EDI still runs global retail, where it quietly breaks omnichannel performance, and how modern teams can turn a tangle of maps and testing into a fast, reliable growth engine. From empty shelves to OTIF deductions, the symptoms are familiar; the root causes live in outdated mapping, slow retailer testing cycles, and inventory that never syncs across stores, marketplaces, and drop ship networks.

Jonathan explains how retailers standardize on EDI specs and why that foundation won’t vanish just because APIs are popular. He breaks down the documents that matter: 850s for POs, 856s for ASNs, 204/210 for freight, 810 for invoices, and 997 acknowledgments, and shows where small errors become big penalties. We explore the onboarding bottleneck that delays first purchase orders by months, plus the ripple effects on cash flow, vendor scorecards, and customer promise dates. Real case studies highlight how a single JSON-style integration and automated validations can migrate hundreds to thousands of partners in weeks, not quarters, without rewriting code per retailer.

We also look ahead. As agentic shopping grows and real-time inventory promises are made in chat, search, and social, the margin for data errors shrinks. Jonathan shares how AI-driven validation and captured testing “tribal knowledge” can compress EDI cycles from months to days, and eventually hours, while web-based EDI opens a simple path for smaller suppliers to trade without engineers or per-transaction fees. If you care about omnichannel reliability, faster vendor onboarding, and fewer chargebacks, this conversation is a practical blueprint for turning EDI from a drag into an advantage.

If this helped clarify your EDI strategy, follow the show, share it with your ops and IT teams, and leave a quick review so others can find it.

Ever wonder why a “standard” return policy can feel so unfair at the counter? We unpack the psychology and operations behind returns with Dr. Travis Tokar, Professor of Supply Chain Management at TCU, and explore how loss aversion, fairness, and frontline interactions shape customer reactions far more than a PDF policy ever will.

We walk through the five policy levers: money, exchange, time, scope, effort, and show which ones trigger loss perceptions that spark anger, and which ones customers accept when the rules are clearly justified. You’ll hear why framing a 15% fee as an “85% refund” doesn’t help, how justice theory explains real-world reactions, and why linking stricter terms to item-specific costs (like data wiping for electronics) earns legitimacy. We also compare mail-in versus store returns, highlighting where centralization saves money, when a fast shelf restock wins, and how to nudge shoppers to lower-cost channels with faster refunds, nearby drop-offs, or small incentives.

Beyond costs, we dig into what matters for loyalty: measuring more than return rates, using returnless returns strategically to boost goodwill, and training frontline teams to balance consistency with humane exceptions. Travis shares experiments, real policy patterns across retailers, and practical tests worth trying, like adaptive leniency for high-value customers and firm guardrails for serial returners. It’s a candid playbook for designing return policies that reduce friction, respect customers, and protect margins.

If this conversation gave you new ideas for your returns strategy, follow the show, share it with a teammate, and leave a quick review telling us which policy change you’ll test next.