Experts Archives - SPS Commerce

Tariff Refunds: The Operational Nightmare Businesses Aren’t Ready For

Melissa Clark — Thu, 06 Nov 2025 17:09:15 +0000

In this article, learn about:

Current landscape of tariff updates
Potential outcomes of tariff refunds
Data and timing problems of refunds
How to get your business refund-ready

Current Landscape of Tariff Updates

The U.S. Supreme Court is hearing arguments this week on whether Trump’s emergency tariffs were legal. Coverage focuses on constitutional questions and economic forecasts.

Yet almost no one is addressing the operational question: if tariffs get struck down, will companies be able to prove what they’re owed?

It can only be assumed that the chaos that followed the escalation of tariffs could be even messier to untangle in reverse.

Treasury Secretary Scott Bessent told NBC the government might need to refund “about half the tariffs”—potentially $750 billion to $1 trillion—if the Court delays its ruling until mid-2026. He called unwinding that amount “significant disruption.”

Two Potential Outcomes of Tariff Refunds

U.S. Customs and Border Protection (CBP) could handle refunds one of two ways:

The easy way: Use the tariff classification codes already embedded in every import entry to identify affected shipments and process automatic refunds through their ACH system.
The hard way: Require every importer to file individual refund requests for each and every affected entry. For unliquidated entries, that means Post Summary Corrections. For liquidated entries, it would mean administrative protests.

NOTE: The CBP has done “the easy way” before. When the Generalized System of Preferences program lapsed in 2018, CBP issued automatic refunds to importers who’d filed electronically with the proper codes. The process took about three months.

The CBP processes roughly 105,000 merchandise entries daily. That number climbed in 2025 when millions of de minimis packages became subject to formal entry requirements. If refunds require individual requests, large, sophisticated importers with dedicated trade compliance teams will recover their money. Smaller operators will struggle.

Furthermore, court filings have already warned that refunds would be “chaotic and administratively burdensome.”

The Data Problem Most Companies Haven’t Considered

Even if the CBP chooses the easy path, most importers face an internal challenge: they don’t have the resources to perform the investigation needed to prove what they’re owed.

Tariff duties get paid at the entry level, often by customs brokers working from commercial invoices. Those payments flow to the CBP. The corresponding costs flow into ERP systems, sometimes as separate line items, sometimes absorbed into landed cost calculations, and other times handled entirely outside the primary accounting system.

Most finance teams lack visibility into which orders paid emergency tariffs, how much was collected, and even what the refund amount should be.

The knowledge gap created by emergency tariffs is the same one that surfaced during tariff escalation when buyers asked for analytics showing margin impact by SKU. Most systems couldn’t answer because tariff costs and product costs lived in different places.

The Unpredictable Timeline of Tariff Refunds

Refunds won’t arrive uniformly. Rather,

Entries filed electronically with proper Chapter 99 classification codes would get processed first.
Entries requiring manual review would take longer.
Entries where importers missed filing deadlines or didn’t maintain proper documentation might never get refunded.

Meanwhile, companies will need to decide whether or not they should:

Wait for refunds to hit their bank account before adjusting pricing
Pass expected refunds through to customers immediately
Or absorb costs to rebuild margin that compressed during tariff implementation

Each choice has second-order effects:

If you lower prices before receiving refunds, you’re betting on a timely government processing speed and your own documentation quality.
If you wait, competitors who moved faster capture market share.
If you absorb refunds without adjusting prices, you’re making a margin decision that may or may not align with how you handled the original tariff increases.

None of these choices can be made confidently without knowing which SKUs were affected, by how much, and what the timelines were.

The Invoice Problem

When tariffs increased, many suppliers changed how they presented costs. Some added explicit tariff line items to invoices. Others built tariff costs into product pricing to keep EDI documents clean. Some used separate statements or periodic true ups.

If refunds come through, the way they chose to document and communicate these costs will determine how easily businesses can reconcile what they’re owed against what they receive.

Companies that kept tariffs as separate line items can trace costs more easily.
Companies that absorbed tariffs into base pricing will need to reconstruct cost basis by entry date and classification code.
Companies that handled tariffs outside their primary invoice flow may struggle to connect refunds back to specific products or customers.

The competitive advantage goes to whoever maintained clean data architecture when tariffs were implemented. The penalty for messy systems won’t be obvious until refund checks arrive, and, when they do arrive, there could be challenges around tying those refunds to individual SKUs and timelines.

Leveraging B2B Data Exchange for Refund-Readiness

Most companies thinking about refund readiness are asking the wrong questions. They’re wondering if they should be exploring new solutions, AI-capabilities, or specialized partners to figure this out.

The better question is: Is there a way to connect the data you’re already exchanging?

Your B2B transactions contain most of what you need to defend a refund claim and decide what to do with the recovered margin. Purchase orders and acknowledgments establish which SKUs were ordered, at what cost, and on what date. Advanced ship notices timestamp when items are moved through your supply chain. Invoices show where tariffs were embedded or separated, and how adjustments were handled.

The challenge isn’t missing data. It’s that these documents live in separate systems that don’t talk to each other.

When a broker is filing an entry with Customs, they’re working from commercial invoices.
When finance is booking costs, they’re pulling from ERP.
When operations teams are tracking inventory, they’re logging into warehouse management systems.
When you need to reconcile a refund against what you actually paid, you’re manually connecting pieces that should already be joined.

Four Key Questions to Check your Refund-Readiness

Do all purchase orders that became shipments have corresponding ASNs and invoices? Gaps mean weaker traceability when reconciling what the government sends back.
What’s the lag between order, shipment, warehouse receipt, and invoice? Large or variable gaps complicate matching entries to physical flows.
What percentage of invoices reconcile cleanly to shipments at the carton and SKU level? Low match rates signal data quality problems auditors will question.
Which items were direct import where your customer was importer of record? Refund proceeds likely flow to them, not you.

Conversations Between Trading Partners

Like all things in the supply chain, there are many wrinkles that must be ironed out between trading partners.

For buying orgs working with brands on direct import programs, there are three questions to consider with your partners:

Who receives the refund on each flow, and if it’s shared, how? Through redit memos, future cost adjustments, or allowances?
What documentation validates pass-through? Entry numbers, tariff codes, duty amounts, payment dates?
What cadence for reconciliation keeps the exchange stable while finance books cash?

For anyone working with customs brokers:

Can you provide machine-readable files showing entry number, tariff classification, duty paid, importer of record, and ACH refund dates per line item?
How will you flag corrected entries or reclassifications to avoid double-counts?

For 3PLs handling your inventory:

Can we rely on warehouse confirmation timestamps and carton details to align physical receipt with entry dates?
What’s the cleanest source of truth for carton IDs that tie back to ship notices?

The practical answer is unifying your transaction spine (purchase orders, shipments, receipts, invoices, adjustments) with effective-date pricing. That creates a connected dataset showing which entries paid tariffs, what those entries contained, what you invoiced, and what margin resulted.

From there you can identify coverage gaps by trading partner, measure timeliness, flag direct import flows where refunds accrue elsewhere, and standardize how credits or price adjustments flow through without breaking document exchange.

In Closing

The heart of the issue is not tariff refunds. Refunds, rather, are creating the opportunity for building resilience by connecting commercial decisions and operational execution, helping you understand the impact of disruptions and make smarter decisions in the face of them.

TL;DR

If tariff refunds land, the winners will be the teams that can:

Prove what they’re owed
Reconcile it quickly to items, customers, and periods
Deploy the cash without creating pricing chaos, disrupting ASN/EDI flows, or damaging relationships

Align Your Data with SPS EDI

Organizations that have good visibility into their EDI documents can stay one step ahead of tariff refunds by keeping their systems in conversation with each other. Check out our EDI solutions with SPS Fulfillment to see if joining the network is right for your business.

4 strategies to conquer lean inventory challenges

SPS Analytics Retail Intelligence Team — Thu, 21 Mar 2024 14:30:38 +0000

AT A GLANCE

Uncover how modern tools streamline supply chain processes.
Learn how automation enhances supplier collaboration.
Examine benefits like reduced costs and improved efficiency.
Master adoption of full-service EDI solutions.

Retailers are navigating the post-pandemic landscape with a shift toward lean inventory management, aiming to maximize inventory efficiency. This adjustment places suppliers in a tricky spot, walking a fine line between insufficient production and excessive stock.

Maintaining the right inventory balance requires a clear understanding of demand that relies on point-of-sale (POS) data.

Let’s take a closer look at four ways suppliers can thrive in the lean inventory era.

1. Establish routine inventory health checks

Start with a robust routine for monitoring your inventory and sell-through data—a proactive approach empowers you to align your stock levels closely with current demand trends. Regularly assess stock levels, checking on inventory by both product and location. This way, you can avoid potential stock issues by catching discrepancies early and adjusting before minor issues become significant problems.

2. Foster open dialogue with retailers

Lean inventory management depends greatly on how you communicate with retailers, and data is the common language. Proactively engage with retailers, sharing insights and analysis from point-of-sale data. This isn’t just about addressing current needs, but also anticipating future demands and challenges. By presenting data-backed suggestions, you can help your retail partners make informed decisions, optimize inventory levels, and meet consumer demand without overstocking.

3. Focus on successful end-of-season outcomes

Achieving successful end-of-season outcomes in a lean inventory environment requires keeping a close eye on the health of your product assortment across various locations. By ensuring you have the correct on-hand inventory levels, you can capture more sales while avoiding excess stock and costly markdowns at the end of the season. To start, proactively identify potential stockout risks weekly and use this opportunity to reallocate inventory from locations with surpluses. With the right data and some proactive spot-checking, you can maximize the value of your inventory investment by ensuring your products are in the right place at the right time.

4. Plan with precision for future seasons

Use the insights from your ongoing analyses and conversations to plan for future seasons with greater accuracy. By applying the lessons learned from each season’s data, you can better predict future demand, tailor your inventory more closely to retailer needs and continue to refine your strategies. Identify which products performed well, see emerging trends and adjust future production plans accordingly. The cycle of feedback and adjustment will help you remain agile and responsive in a constantly changing retail environment.

Ready to elevate your inventory strategy? With SPS Analytics, you gain direct access to our team of Retail Intelligence experts who are ready to guide you in using point-of-sale data to drive inventory decisions. For a deeper dive into how data can transform your inventory strategies, contact our team or visit our website today.

The importance of big data in retail

Nick Schwalbach — Wed, 14 Dec 2022 14:00:15 +0000

AT A GLANCE

Discover how big data improves inventory management.
Learn how analytics drive personalized customer engagement.
Understand the role of data in accurate demand forecasting.
Gain insight into big data as essential for retail competitiveness.

Here are the top things you should know when it comes to the importance of big data in retail:

What is big data?
How big data is being used in retail?
How do retailers collect data?
Real-life examples of retailers using big data
How big data is transforming retail

We all know that data-driven research and development is the key to success in an ever-growing digital age. And this is no different in retail.

Since the start of the online retail boom, brick-and-mortar stores have often found it difficult to keep pace with the speed and convenience of online deliveries.

However, big data analytics gives physical stores a unique springboard to success, opening the door to improved customer experience and upselling opportunities that even digital competitors can’t match.

Now, both business models are thriving and enjoying greater efficiency and profitability – namely because of the many retail analytics solutions now available.

What is big data?

Put simply, big data analytics is the collection and interpretation of information on a grand scale.

Computer algorithms identify patterns and trends in retail data, which can then be used in conjunction with qualitative data on typical human behavior, interactions and experiences.

This gives individuals and companies tangible data that—with the right software, resources and knowledge—can be used effectively to reveal more about the habits of their customers.

Big data can also be defined as a mass increase in the volume, variety and velocity of data coming in. This is known as “the three Vs” of big data:

Volume – Retail data is often vast and unstructured. Without relevant resources, staff can be left to draw findings from this data manual, which is often inefficient and can be inaccurate. Big data analytics solutions automate this responsibility, generating quick and accessible findings that drive actions.

Variety – With great strides in technology in recent decades in how and where we can collect information, retail data takes many more shapes and forms than ever before, so businesses must be wary.

Velocity – The speed at which data arrives is also faster than ever before. This means dedicated teams need to react quickly to extract value from that data and act on it in real time.

How big data is being used in retail

Big data analytics provides retailers with so much valuable and actionable information that it’s now critical for companies in almost every decision.

To start, big data analytics help retailers understand customers. In brick-and-mortar stores, this means everything from which POS displays are selling the best to the directional shopping habits of customers.

Online, big data analytics helps predict upcoming trends and which SKUs each regional store will need to stock to remain competitive year-round.

Whether it’s monitoring social media trends for the latest “buzz” or making sure stock matches seasonal demand, big data analytics reveals the exact stock businesses need, and how much, ahead of time.

As well as helping businesses to improve the customer experience, big data analytics in retail is used to drastically boost efficiency. Many companies use cloud data solutions to track inventory levels and sales figures in real time, and they also use these solutions to predict future demand more accurately.

Big data is increasingly used to personalize the online shopping experience, too. For example, online retailers use data-driven algorithms to provide shoppers with product recommendations – based on their purchase history – to add to their baskets pre- and post-checkout.

How do retailers collect data?

With so much data now available to retailers, it needs to be collected in many ways. Retailers can either ask for data directly – via email address and phone number forms for marketing purposes – or go through more indirect channels.

When consumers click on a website, they’ll be asked to accept tracking cookies. These are chunks of data that attach themselves to the user’s unique browsing ID, giving websites an idea of how long they’re browsing, which pages and products they’re looking at and what they buy. This information then helps companies tailor their marketing efforts.

Retailers can also tap into third-party data from suppliers. This providesinformation on consumer habits to streamline the online experience.

Brick-and-mortar stores can also collect internal data. Point-of-sale data collection is key in managing which products need to be in specific regions to match consumer demand year-round.

Big data analytics can be used to streamline the order process as well, primarily in EDI systems to provide more data points to shipping teams throughout the supply chains.

EDI software helps keep everything from orders and invoices to shipping notices in one easy-to-use hub. Big data analytics provides more data on any external factors that could interfere with anyone of these processes, letting companies respond quicker.

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Real-life examples of retailers using big data

Big data in retail underpins the growth of every successful modern business and we’re seeing it being integrated into businesses’ strategies in real-time.

For example, retail giant Walmart is developing the “world’s largest private cloud,” with algorithms built to track data on inventory, transactions, and competitor activity. This allows them to respond to market changes almost instantly.

Some companies even reap the rewards of mutual collaboration. In a seemingly confusing collaboration between Pantene, The Weather Channel, and supermarket giant Walgreens, Pantene saw its sales skyrocket over 10% in Walgreens stores through its data-driven “haircast” project.

With the help of forecast data from The Weather Channel, retailers could market selected products based on seasonal changes and the weather forecast that week, driving increased sales.

Global giant Amazon has also perfected the art of data collection and application in its user recommendations. In fact, Amazon is so successful in using big data marketing and sales tactics, that 35% of its sales are generated from its customer recommendations algorithm.

How big data is transforming retail

It’s no secret that increased data unlocks a wealth of customer insight. More than ever, retailers can plan for inventory, stock, logistics and customer expectations with greater precision.

Big data analytics in retail not only has the potential to improve the operating margins of companies by 60% but revolutionize all areas of retail.

Big data analytics also shapes inventory management and logistics and provides detailed insights into customer habits. These are being used to drive sales, streamline the sales process with product recommendations and slicker payment options and to improve customer service across the board.

The role of big data in retail is also to identify potential bottlenecks and find work-around solutions before they have a chance to evolve into more significant issues, saving retailers the costs of downtime and disruptions.

Using as many data insights as possible helps retailers and supply chains manage inventory issues and potential disruptions, thereby improving customer satisfaction, brand loyalty, and revenue generation.

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Gain the insights you need to sell the right product, in the right place, at the right time with SPS Analytics.

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The Most Eco-Friendly Shoppers in the U.S.

Alison Tribus — Wed, 17 Mar 2021 13:00:19 +0000

Social responsibility is of increasing importance to consumers. This made the research team at SPS Commerce wonder, what U.S. cities are the most eco-conscious and which eco-friendly products are trending?

Thanks to Google, we can compare consumer demand data for the most popular eco-friendly products and services in the United States:

We can also identify the top U.S. cities whose populations are searching for eco-friendly products:

Search Trends for Kindles, Solar Panels and Electric Cars

According to our research, San Jose has the most eco-friendly shoppers in the U.S. On average, San Jose searches for eco-friendly products almost seven times more than the city in last place, Dallas.

Below we’ve taken an in-depth look at the three most popular eco-friendly products, Kindles, solar panels and electric cars, and highlighted the most and least interested cities.

The most popular eco-friendly product was an Amazon Kindle. San Jose has the highest interest in Kindles, according to Google searches. There are more benefits to e-readers than the reduction of paper pollution. Although, as San Jose also ranks highly for other eco-friendly products, they likely take the Kindle’s environmental impact into consideration prior to purchasing.

It goes without saying that some cities are at a distinct advantage for solar panels. El Paso has the highest demand for solar panels and the city also ranks as the sixth sunniest city in the U.S. (achieving up to 84% average annual sunshine).

However, Austin has up to 60% average annual sunshine yet still ranks as the fifth most interested in solar panels. And, Dallas has 61% average annual sunshine but ranks in the top five least interested in solar panels. Opportunity doesn’t always mean social obligation.

Not only is San Jose the top for consumer interest in electric cars, but it is also home to some of the biggest names in the electric car business. Notably, QuantumScape, a company that produces lithium batteries for electric cars whose investors include Bill Gates and Volkswagen.

Shoppers Seek Out Eco-Friendly Retailers and Suppliers

Studies have already shown that two-thirds of consumers consider sustainability prior to making a product purchase. While some regions may be slightly more conscious than others, there’s no question that eco-friendly products are on the minds of shoppers across the U.S. This is why if you are an eco-friendly retailer or supplier, it’s important to identify which cities are best to target for these products and make sure these shelves are always fully stocked.

Eco-friendly or all-natural products sell fast when discovered, just ask a few eco-friendly suppliers such as Nanoleaf and Nutricare that have partnered with SPS Commerce. With retail analytics software you can see trends from store level to a heatmap of the U.S. Allowing you more opportunities to capitalize on trends within your market.

Methodology

We used Google Keyword Planner to identify the most popular eco-friendly products and services of the past year. The regional feature identified the number of searches for the top 25 most populated cities in the U.S.

Searches were divided by the total number of searchers in each city to get searches per capita. This was then multiplied by 100,000 for searches per 100,000.

Each product was then averaged out to find an average number of searches per 100,000.