Browse past Chain Reaction newsletters archive to explore past editions, with the newest issue listed first. Each edition highlights the latest retail, supply chain, CPG, and AI insights from The Supply Chain Source.
CHAIN REACTION: July Edition
Welcome to the July edition of Chain Reaction, your source for staying ahead of the news and insights shaping modern supply chains.
Supply chain volatility is not new. But the pace of disruption in 2026 has made reactive operations genuinely unsustainable. Tariff policy has shifted repeatedly across major trading relationships, port volumes are hitting record highs as brands pull shipments forward to stay ahead of the next change, and the average retail order now changes six times before it ships. The organizations absorbing this without breaking are not the ones with the most sophisticated tools. They are the ones that built clean, connected foundations before the disruption arrived and are now using AI to move faster on top of those foundations.
The Industry Landscape
The industry's message on volatility is hardening into something practical: move from reactive to predictive, or keep paying the price. As JAGGAER documents, supply chain disruptions cost $1.5M per day on average for companies that are not watching until something breaks. The shift procurement teams are making is from periodic audits to real-time risk signal monitoring — catch the exposure early, not after the damage is done.
Retailers are being pushed in the same direction. ITWire's analysis of modern category management shows that agility and resilience are no longer separate goals. The organizations outperforming are the ones acting on shared data quickly, adjusting assortments, and keeping supplier alignment intact rather than chasing problems after they surface.
Meanwhile, the macro backdrop keeps moving. Supply Chain Dive's running tracker of US tariff actions captures how frequently landed cost assumptions have been upended across Canada, Mexico, China, and the EU. And Modern Retail reports that July is on pace for record-high imports as retailers and brands front-load shipments to get ahead of the next potential shift. Peak season arrived early this year, whether teams were ready or not.
The SPS Take
From inside hundreds of thousands of trading relationships, the pattern SPS Commerce sees is consistent: the teams navigating this environment with the least disruption are the ones whose data was structured enough to act on when things moved. For CPG brands, that means using AI demand forecasting to model tariff exposure and protect margins before a policy shift forces a reaction. Our piece on how leading CPG brands are using AI to get ahead of tariff volatility breaks down what separates the brands getting results from those still reacting after the fact: a clean, connected picture of landed cost, sell-through signals, and supplier capacity before the model is asked to reason about risk.
Volatility is not just a macro story, either. SPS Commerce data shows order changes have doubled year over year, with 1 in 5 orders now coming back with acknowledgement changes. Our piece on overlooked strategies for managing volatility in modern retail supply chains, drawn from a session with Sprouts Farmers Market and Sunkist Growers, shows what proactive coordination looks like in practice: weekly data-driven conversations, scenario planning, and onboarding practices that align on item data from day one. For midmarket retailers, promotional stockouts remain one of the most visible and costly places volatility shows up, and the root cause traces back to supplier data accuracy problems that existed weeks before launch, not the demand planning failures that get blamed in post-mortems.
For teams looking to stay current as conditions keep shifting, The Supply Chain Source, the evolution of SupplierWiki, now offers more than 2,800 resources across every part of the trading ecosystem, an AI retail tool built on official industry rules for instant compliance and routing answers, and Expert Exchange, a peer community of 60,000 members where practitioners share what actually works in the field. In a volatile environment, fast access to reliable answers is its own form of resilience.
We want to hear from you
This month's newsletter has been all about what it takes to survive volatility — and if this month's theme resonated, we want to know where you actually stand. We're sending a short survey focused on AI readiness and data foundation: the bedrock that determines whether your team can respond to disruption or just absorb it. The questions cover data quality, system connectivity, and where your organization is in its AI journey right now.
We'll surface the results in August's newsletter, the same way we did with last month's AI usage findings. The more responses we get, the sharper the picture — and the more useful it will be to everyone in the network. Take a few minutes and tell us where things really stand.
Where to Find SPS In August
The events and webinars below are ideal places to connect with SPS and continue the conversation with our team. Are you attending any of the below? We'd love to connect.
Tue, Aug 5 at 12PM EDT: Build the Supply Chain That Holds When Disruption Hits Webinar
CHAIN REACTION: June Edition
Welcome to the June edition of Chain Reaction, your source of staying ahead of the news and insights shaping modern supply chains.
Growing your business with AI feels like a no-brainer. AI tools allow us to stay agile, focused, and increase our productivity more than we’ve ever seen. The most important piece, though, is building a solid foundation for your tech stack and ensuring you know what specific agentic tools are built to do, and what they’re not intended for. Ensure your bedrock is solid and set your business up for real growth.
The Industry Landscape
The industry's message on AI is getting sharper: it doesn't fix broken foundations, it amplifies them. As SupplyChainBrain notes, companies deploying AI on top of duplicate supplier records and siloed inventory data aren't fixing those problems; they're accelerating them. The prescription is simple: fix the foundation first, then layer AI on top.
The Q1 earnings season showed what that divide looks like in practice. Best Buy, Gap, and Dick's Sporting Goods each reported meaningful returns from AI because their investments were built on structured data. Starbucks told the opposite story, pulling its AI inventory system after nine months because employees had to manually verify every automated count, doubling the work instead of cutting it.
The race to deploy is accelerating regardless. Amazon is now licensing its Alexa for Shopping technology as an AI shopping assistant for other retailers. Alternative carriers are using AI to close the gap with FedEx and UPS. And with over half of consumers now comfortable filtering brand communications through AI, suppliers without clean, connected product data risk going invisible before the sale even starts.
The SPS Take
From inside hundreds of thousands of trading partner relationships, the view is consistent: data quality is where AI investments succeed or stall. The supply chain teams getting real results from AI aren't starting with the most sophisticated tools. They're starting with clean data. Our guide on what agentic AI actually means day to day shows how these agents work best as high-volume task runners using guardrails. They are effective only when the data underneath them is structured and trustworthy.
That's especially true when breaking into a new channel. Forecasting for a retailer you've never sold to before means starting without historical sell-through data, which makes demand planning models unreliable by default. The result, when the foundation isn't there, is a chain of bad decisions: wrong replenishment, missed compliance requirements, and deductions that were entirely preventable.
Speaking of which, the best operators catch preventable chargebacks early, not through heroic effort, but through accurate EDI, automated validation, and visibility from order to payment. Most deductions come from data gaps, not bad intent.
And for manufacturers, supplier data quality is the hidden cost limiting AI and ERP investments. When specs, attributes, and compliance docs live in disconnected spreadsheets, no AI tool can reason accurately about that inventory. The answer isn't a better model. It's better data at the source.
What You Told Us
Last month, we asked how AI is actually showing up in your day-to-day work and the results reveal a telling gap. Comfort with AI tools is moderate and clustered tightly: most respondents rated themselves a 6 or 7 out of 10, with no one at either extreme. People aren't intimidated by the technology, but they aren't confident power users yet either. Actual usage, however, tells a more cautious story. The majority of respondents said AI accounts for only 20-30% of their work. Familiarity has arrived. Deep integration hasn't.
That gap may explain the split on impact. Half of the respondents said AI has made their work easier, a meaningful signal that real value is being realized for those who have put the tools to use. But a third said it hasn't, and another sixth were still unsure. Taken together, the majority of the network is still waiting for AI to deliver on its promise. That's not skepticism, it's an honest reflection of where most organizations are: tools in hand, foundation still being built.
The through-line from this month's industry conversation is hard to miss. Moderate comfort, limited usage, and uneven results are exactly what you'd expect when AI is layered onto workflows that aren't yet ready for it. The teams reporting real gains are likely the ones who invested in clean data and clear processes first. The rest are still doing the foundational work, which, it turns out, is the right place to start.