Demand now swings in sharper, less predictable cycles. Transportation costs fluctuate week to week, and labor availability changes month to month. Yet retailers and suppliers are still expected to deliver precise, reliable execution with almost no margin for error.

Teams aren’t just trying to stay ahead of trends anymore; they’re trying to stay ahead of volatility. In that environment, predictability becomes a strategic asset. And across the market, retailers are already making the shift:

• They’re building more resilient and transparent supply chains designed to withstand disruption — not react to it.
• They’re tightening control and increasing item-level visibility as demand becomes harder to forecast.
• They’re using digital planning tools to stabilize inventory instead of responding to every fluctuation in real time.

In short, retailers aren’t chasing chaos anymore — they’re engineering calm. The shift toward steadier, data-driven operations is clear. Yet even as supply chains become more resilient, one quiet destabilizer remains: constant order changes.

The mid-cycle adjustments, delayed acknowledgments, quantity edits and shipment updates that happen after a truck is already rolling may seem minor on their own, but together they’re costly. They disrupt planning, confuse systems, slow execution and quietly drain cash, undermining the very stability retailers are working to build. And they happen every day, often unnoticed and almost always underestimated.

The data tells the story

When SPS Network Intelligence examined the real movement of orders across the retail network, the pattern became impossible to ignore. The analysis covered 1.2 million purchase orders, 4.8 million associated documents and $9.7 billion in merchandise volume — a scale large enough to reveal where volatility starts, how it spreads and what it ultimately costs.

Across that dataset, one signal cut through: order volatility is far more common, and far more expensive, than most teams realize.

The findings were consistent across categories and order types:

• 6.5% of total merchandise value, or $634 million, was exposed to volatility-related risk. That exposure represents inventory sitting longer, shipments moving inconsistently and cash tied up for days or weeks longer than planned.
• Every 1% reduction in volatility returned $9–10 million in cash flow back into the operation.
• Even categories known for stable, predictable demand — including grocery — showed meaningful volatility, underscoring that this isn’t an issue limited to seasonal or trend-driven businesses.

The operational consequences were equally clear.

Every small change — whether a timing update or a line-level edit — creates a series of downstream ripple effects: delayed shipments, mismatched inventory, extended dwell times and lost sales opportunities. A single mid-cycle adjustment can trigger rework across ERP, WMS, transportation and store systems.

As one retail operations leader put it: “A single delayed PO can freeze a week’s worth of sales and tie up millions in inventory.”

Volatility doesn’t just slow down one order. It compounds across the network, introducing friction at every handoff and silently pulling performance, liquidity and customer experience in the wrong direction.

Explore the complete findings in our on-demand webinar:
Pulling back the curtain on network-level volatility

What order volatility is and how it drives cost

Volatility isn’t just a process issue. It’s a financial one. At its core, order volatility is the variation or fluctuation that occurs throughout a purchase order’s lifecycle: timing updates, quantity edits, line-level changes or shipment corrections. These shifts often seem routine, but they introduce uncertainty at every step. And uncertainty creates rework, delays and added cost.

Volatility can originate from multiple touchpoints:

• Timing updates that adjust acknowledgment or ship-by dates
• Quantity changes made after the PO is issued
• Line-level edits affecting individual SKUs
• Shipment corrections made after goods have already left the warehouse

Each update triggers a downstream chain reaction. A single header-level change may force multiple revisions across ERP, WMS, carrier or 3PL systems. That rework absorbs time, slows decision-making and reduces the predictability of both shipments and inventory placement. SPS Network Intelligence found that even minor fluctuations compound as they move through the network, lengthening cycle times and increasing operational strain.

How volatility impacts financial performance

The financial impact is equally clear. When orders change mid-cycle, inventory spends more time in transition, tying up cash and extending the cash conversion cycle. Carrying costs — often 20–30% of inventory value — rise as goods sit idle or move inconsistently. Teams lean on expedited transportation to recover lost time. Missed windows introduce penalties, deductions and lost sales opportunities. The ripple effect touches every KPI:

• Delayed orders
• Higher fees
• Reduced operational efficiency
• Lower OTIF
• Narrower margins and tighter cash flow

Across $9.7 billion in analyzed order volume, SPS identified more than $600 million in merchandise value exposed to volatility risk. The pattern is direct: the more an order changes, the more value is put at risk.

This is why predictability matters more now than ever. In a market defined by thin margins, tight schedules and unpredictable demand, stability isn’t just operational efficiency — it’s liquidity, profitability and resilience.

How leaders are fighting back

Volatility has become a direct threat to profitability, especially as margins shrink and demand grows more erratic. What used to be minor exceptions now trigger downstream delays, stranded inventory and costly last-minute workarounds.

The retailers making progress have one advantage: earlier visibility into when, where and why orders are changing. With that clarity, they plan better, react faster and prevent issues before they spread.

Predictability strengthens execution by improving visibility across the PO lifecycle, reducing shipment mismatches, sharpening cash-flow accuracy and enabling clearer retailer–supplier communication. Unpredictable orders do the opposite — slowing goods midstream, weakening forecasts and compounding cost across the network.

Leading organizations counter volatility by focusing on a few core habits:

• They benchmark how often orders change and what it costs.
• They connect POs, acknowledgements, ASNs and shipments into a clear real-time view.
• They create shared accountability with suppliers to address issues early rather than react late.

These behaviors are part of a broader journey toward operational maturity. Most retailers progress through four stages of predictability, moving from reactive processes to more stable, data-driven execution. Each stage builds greater visibility, reduces variance and strengthens financial performance — especially in high-volatility environments.

Organizations that reach predictive maturity consistently see 12–18% better on-time delivery and 20–30% faster acknowledgment cycles, leading to fewer surprises, faster turns and stronger liquidity.

Predictability doesn’t remove problems — it reveals them early enough to stay in control.

The bottom line

Order volatility is the quiet force undermining even the best-built supply chains. Small changes ripple into delays, deductions and trapped cash — costing far more than most teams realize.

Leaders who take volatility seriously are already seeing the difference: fewer surprises, stronger turns, higher OTIF and more confident planning.

In a market defined by tight margins and unstable demand, predictability pays off — every single time.

Learn more with the predictability pays off analysis

Explore the research, data, and frameworks behind these findings in our new analysis,
Predictability pays off: How retail and supply chain leaders turn order volatility into a competitive advantage.

Companies like LVMH are embedding AI across their entire operations, while fast fashion players use it to accelerate everything from forecasting to logistics. Every day more brands are turning to AI to optimize production planning, predict equipment maintenance and streamline fulfillment processes.

As we’re seeing across thousands of supply chains in our network, AI isn’t just about gaining a competitive advantage anymore; today it’s table stakes.

According to Gartner’s 2025 Supply Chain Symposium, 74% of CEOs believe AI will have the most significant impact on their businesses over the next three years. But it’s critical to understand: your AI is only as good as the data you’re feeding it.

What’s working with AI today?

Every day we’re hearing about new uses of AI in the marketplace:

• Generative AI for scenario planning: Cutting edge retailers are using AI to run “what if” scenarios—simulating demand spikes, supplier issues and logistics constraints through digital twins. Instead of being stuck in reactive mode, they’re testing their responses before disruptions hit.
• Turning waste into wins: Companies are applying AI to optimize returns, cut packaging waste and forecast more precisely to avoid overproduction. Traditional cost centers of waste management and sustainability are becoming margin protection tools.
• Connected automation: From smart warehouses to exception automation, retailers are using AI to reduce manual work and simplify operations. Gartner calls out “connected supply chains” and workflow automation as must-haves for 2025.

How’s your data? A reality check

When it comes to where we see AI working in supply chains, the companies winning with AI aren’t the ones with the fanciest algorithms—they’re the ones with the cleanest, most standardized trading partner data.

And here’s why:

• Your AI may build beautiful supplier disruption models, but if the lead time data is inconsistent, its recommendations are worthless when real problems hit.
• Optimizing returns with AI should work, but without accurate item data from trading partners, AI can’t tell the difference between defects and customer preferences.
• While your customers expect flawless execution, your AI can only deliver if your partner data is consistently accurate across every single relationship.

What do you need for a better data foundation?

Across our retail supply chain network, we see that the companies who successfully apply AI are using standardized, real-time partner data. Without it, AI can’t deliver.

The foundations required for AI implementations include:

• Clean EDI data: AI systems need consistent product info, order acknowledgments and shipment notifications. When this varies across trading partners, your AI models produce unreliable outputs.
• Standardized communications: Exception automation requires partners to communicate disruptions in standard formats. Manual, inconsistent communications break AI workflows every time.
• Real-time visibility: AI lives on current information. You need up-to-the-minute partner feeds, but across diverse trading relationships, most companies can’t maintain them.

When AI ideals meet reality

While new technology is always part of the discussion in modern business, what we’re hearing about AI usage across customers is consistent: Companies start excited about the possibilities of what AI can do for them but quickly realize it won’t work without first standardizing their data.

The dilemma:

• The most sophisticated AI fails if trading partners can’t feed it accurate, timely information.
• Manual exception handling is getting replaced by automated workflows, but only when the underlying data triggers actually work.

Where are we heading?

The future of supply chains may actually be written by AI.

We’re moving toward autonomous systems that respond to disruptions without human intervention: connected ecosystems, with AI orchestrating workflows across all trading partners, and sustainable applications optimizing resource usage.

But how well this works (or not) will depend on if there’s standardized, reliable partner data.

Build your foundation now

The companies who’ll win with AI understand that AI transformation begins with better data. They’re investing in standardized trading partner data formats, real-time partner performance visibility and automated workflows that eliminate manual errors.

AI has incredible potential to transform retail supply chains. But you must have a foundation of clean, standardized and real-time supplier data.

Want to see how leading retailers are preparing their supply chains for the future? Explore our latest insights.

Beyond traditional seasonal peaks

While cornerstone holidays like Thanksgiving and Christmas continue to drive major shopping patterns, today’s grocery landscape is evolving. The familiar rhythm of turkey and stuffing in November, or corned beef and cabbage for St. Patrick’s Day, now shares the calendar with hundreds of micro-seasonal opportunities that reflect our changing relationship with food and community.

The rise of micro-seasons

Unlike the fashion industry’s micro-seasons, which are primarily driven by design trends, grocery micro-seasons tap into consumers’ emotional connection to food and shared experiences. Take Whole Foods’ embrace of #Veganuary. What began as a social media trend has become a full-fledged shopping season, complete with plant-based product launches and cooking demonstrations.

These micro-seasons aren’t just marketing inventions. They reflect how we live and celebrate today. National Ice Cream Day becomes a summer highlight on Instagram, while National Pizza Day turns a regular Friday into a reason for family gathering. Even Dry January has evolved from a post-holiday health kick into a significant retail opportunity for non-alcoholic beverages and wellness products.

What’s driving the change?

Several factors fuel this transformation:

• Social media influence: The #foodie culture has turned every meal into a potential celebration.
• Cultural diversity: Growing multicultural communities bring new traditions and food holidays to mainstream retail.
• FOMO: Social media creates awareness and excitement around food trends.
• Comfort seeking: In uncertain times, food holidays offer something to look forward to and celebrate.
• Exploration: Consumers increasingly seek new flavors and cooking experiences.

Impact on grocery operations

This micro-seasonal transformation requires retailers to fundamentally rethink their operations and supplier relationships. Traditional seasonal planning no longer suffices when consumers expect everything from heart-shaped chocolates to pumpkin spice products earlier each year. Success now depends on maintaining an ever-expanding product assortment while timing each micro-seasonal launch perfectly.

Must-haves in this new landscape include:

• Real-time data sharing with suppliers to anticipate and meet emerging trends
• Agile supplier partnerships that enable same-season response
• Streamlined processes to set up and launch new products quickly
• Strategic inventory optimization across micro-seasonal peaks

The heart of celebration

At its core, this evolution in grocery retail reflects our changing relationship with food and celebration. Success means more than just stocking shelves with products consumers want. It requires the right mix of cultural awareness, technological capabilities and strong supplier partnerships to turn food holidays into an opportunity for connection and growth.
Ready to strengthen supplier collaboration and stay ahead of the micro-seasonal calendar? Partner with a team that equips you with the insights, tools and support to make every season a success.