Michael Pytel

Special Guest Blog Post from e2open From demand sensing to dynamic allocation, here’s how leaders turn peak season logistics into profit   Call it Q4, The Golden Quarter, or Peak Season—it’s the 100-day sprint through fall and winter holidays where profits soar, plans tighten, and one slip leaves you scrambling through January. Across peak season logistics, most companies obsess over outbound speed. Yet the real winners are brands that master inbound logistics flow months earlier. Getting the right inventory to the right locations isn’t glamorous, but it’s where the margins live. Every peak season playbook demands strategic evolution. Rerun last year’s strategy this quarter and you’ll sink—unless you’ve built sophisticated inbound logistics capabilities, airtight supplier partnerships, and precise forecasting to anticipate market shifts. With the right strategy, you can stride into the Golden Quarter. That means: Smarter forecasting that detects demand shifts before they hit Sharper allocation that puts inventory exactly where it’s needed Replenishment planning that maintains flow under pressure On Time in Full (OTIF) execution that keeps products moving and shoppers happy In peak season, accuracy wins. Miss inbound positioning, and your bottom line misses too.   Inbound planning: The difference between stockouts and sales Golden Quarter demand…

Special Guest Blog Post written by Matt McKinney, Co-Founder and CEO of Loop   I spend most of my time with supply chain and innovation leaders at major enterprises who are sitting on significant AI budgets but struggling to show measurable business impact in an increasingly complex and volatile supply chain environment.   These conversations have evolved dramatically. A year ago, executives were asking basic questions about AI feasibility. Today, the questions have shifted to strategic implementation at enterprise scale.   Based on hundreds of these discussions, three questions consistently emerge that separate companies making transformational progress from those stuck in pilot purgatory.   How do we move from AI experiments to enterprise-scale impact?   Most organizations have yet to see organization-wide, bottom-line impact from AI use. This is the strategic challenge keeping C-suite leaders awake at night.   The problem isn’t the technology. It’s the application of the technology. Too many enterprises are trying to treat AI like a magic wand they can bolt onto existing systems. But garbage in, garbage out. If your underlying data is fragmented and inconsistent, AI won’t solve your problems; in fact they’ll get worse.   At its core, anything automated is powered by…