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EDI for First-Time Suppliers: A Plain-English Guide to the Documents You'll Send 

EDI for First-Time Suppliers

Landing a deal with a large retailer like Walmart, Target, or Home Depot is the ultimate emotional high for a supplier. It represents brand validation, a massive scale-up in volume, and a significant milestone in your company’s growth. However, for many executives, that celebration is cut short by the sudden mention of a term they may have never heard of: 

For brands that have been primarily Direct-to-Consumer (DTC), the sudden requirement for EDI often arrives as a shock as it shifts the business from a flexible, customer-focused model to a rigid, computer-controlled environment governed by strict retailer rules. Often, what was meant to be a celebratory time of business expansion transforms into a period of intense operational friction.  

Most suppliers are caught off guard by the technical complexity of these requirements and the aggressive timelines for implementation and strict mandates for compliance. Not to mention, EDI can be very confusing to understand in the first place. It often feels like it’s in a different language, and in some ways, it is. Next, we’ll dive into how EDI functions as the common language of documents in the retail sector.  

Understanding EDI as the Language of Retail 

EDI, or Electronic Data Interchange, is a way for businesses to send important documents directly from one computer system to another. Instead of employees emailing, faxing, or mailing paperwork, the information is shared automatically in a format both companies can understand. 

You can think of EDI as the common language used in retail. Just like two people need to speak the same language to communicate clearly, retailers and suppliers use EDI to make sure information like product numbers, quantities, prices, and shipping details are understood the same way by both sides. Each type of EDI document represents a different step in the business relationship, such as placing an order, sending an invoice, or confirming a shipment. 

Sending an EDI document is different from sending an email attachment or PDF. It is an automated exchange between your business system and a retailer’s system. 

First, your company creates a document, such as an invoice or purchase order. Then, EDI software converts that document into a standard format the retailer can read. After that, the document is sent securely over the internet to the retailer’s system. 

The type of EDI setup a company uses usually depends on its size: 

Once the system is set up, EDI can send information quickly, accurately, and with very little human involvement. 

Rather than thinking of EDI as a piece of software, it's more helpful to view it as a digital communication workflow between retailer and brand, where each step is critical. This workflow is the process that begins with a retailer order and eventually ends with cash in the supplying brand’s pockets. First, we’ll give an overview of the whole process, and then dive into each specific step.  

Real-World Example: A CPG Company Selling to Walmart 

Now, we’ll dive into a practical example of how EDI works in action. Imagine you're a mid-sized CPG (Consumer Packaged Goods) company that makes organic snack bars, and you've just landed a deal to supply them to Walmart stores across the region.  

Note: This example is only used for illustrative purposes, and brands should make sure to stay up to date with Walmart’s specific rules in their supplier portal. 

On Monday morning, anxious to make sure you don’t accidentally oversell, you send Walmart an Inventory Advice showing you currently have 50,000 cases of your Chocolate Chip snack bars in stock and 35,000 cases of your Berry Blast bars. This tells Walmart's system exactly what's available so their online and store ordering systems don't oversell what you can actually deliver. 

That same Monday afternoon, Walmart's ordering system automatically sends you a Purchase Order (EDI 850). It says: "Send us 10,000 cases of Chocolate Chip snack bars and 8,000 cases of Berry Blast. Unit cost is $2.50 per case. Deliver to our distribution center in Memphis by Friday." 

Your system receives this order automatically, so no human had to read an email or manually enter numbers into a spreadsheet. 

Within seconds, your system sends back two messages: 

First, a Technical Acknowledgment (EDI 997) fires back to Walmart's system saying, "Message received. No errors detected." This is the computer equivalent of saying, "I got your email and it didn't come through garbled." 

Second, your team reviews the order and sends a Purchase Order Acknowledgment (EDI 855) confirming you can fulfill it exactly as requested. You can send this because you have plenty of inventory, the price is correct, and you can hit the Friday delivery date.  

If you couldn't (say you only had 7,000 cases of Berry Blast available), you’d flag that in this acknowledgment so Walmart could source the remaining 1,000 cases from another supplier instead of finding out later when the truck arrives short. This document allows you to report issues with the order and let them know how many you 

Step 3A: The Official Revision (EDI 860 - Purchase Order Change)

Once you notify Walmart of the shortage, they may send back an EDI 860. This document officially revises the original purchase order to match the quantities you are actually able to ship. This audit trail ensures that everyone is working from the same set of numbers. 

It's now Wednesday. Your warehouse has packed everything, and the truck is about to leave your facility. Before it does, your warehouse management system generates an Advance Ship Notice (EDI 856) that details exactly what's on the truck: 

This document is critical. When the truck arrives at Walmart's dock, their receiving team scans the barcodes and matches them exactly to this ASN. If the ASN said 200 pallets but only 195 arrive, Walmart immediately knows 5 are missing. If the ASN is missing or late, Walmart's system might reject the entire shipment, costing you a 

 or deduction (a financial penalty deducted from your payment). 

Friday evening, after you've confirmed the truck has arrived and been unloaded, your accounting system generates an Invoice (EDI 810): 

Walmart's accounts payable system receives this invoice and automatically checks it against three things: 

This matching process takes seconds and happens automatically. If everything aligns perfectly, Walmart's system schedules payment for 30 days out. If there's a discrepancy (even a difference of one case) the payment gets held until someone manually investigates and resolves it. 

In this entire scenario, not a single email was sent, no one manually re-entered data, and no invoice was lost in someone's inbox. From the moment Walmart placed the order to the moment you received payment, the process was automated, accurate, and auditable. That's the power of EDI. 

At first, EDI documents can feel like a confusing list of numbers and acronyms. In reality, each document has a very specific job that create a chain of communication. The chart below breaks down the most important retail EDI documents, what they contain, and why they matter.  

 Suppliers do not need to master every EDI document immediately, but understanding the role each one plays makes retailer onboarding much easier and helps teams troubleshoot issues faster when problems occur. 

Ready to Go Live With Your First Retailer? 

SPS Commerce handles EDI technical complexities for brands. Get set up in weeks with built-in compliance checks that catch mistakes before they cost you chargebacks. 

Why More Supply Chains Are Pairing Forecasting and Demand Sensing 

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Supply chain planning has always depended on looking ahead. Forecasts help businesses prepare inventory, allocate labor, and align operations around expected demand weeks or months before products move. 

Modern supply chains are operating in shorter decision windows than they once did. A retailer promotion can accelerate sell-through within hours. Weather events, transportation disruptions, and fulfillment constraints can reshape inventory needs before organizations can complete their next planning cycle. 

That compression is changing how many supply chain leaders think about planning maturity. Forecasting remains foundational. But more often, organizations are pairing forecasting with demand sensing capabilities designed to identify demand shifts and support faster decisions. 

Large supply chain organizations have been moving in this direction. 

, for example, has publicly discussed using downstream demand signals and short-term consumer movement data to strengthen planning decisions across its network. The broader shift reflects an operational reality many businesses are experiencing firsthand: faster signals only create value when organizations can respond quickly enough for those signals to matter.  

The distinction matters because faster signals alone don't improve performance. Organizations also need the visibility, workflows, and coordination required to act on those signals before disruptions compound. 

So how do forecasting and demand sensing differ and where does each perform best? 

Demand sensing is a supply chain planning approach that uses near-real-time data to detect short-term demand shifts. It supports faster inventory replenishment and fulfillment decisions. 

The signals typically used in demand sensing environments include: 

These data signals help teams detect demand shifts far earlier than what was previously possible with forecasting alone. 

Forecasting Operates on Long-Term Planning Horizons 

Traditional forecasting was designed for 

Forecasting models help organizations answer larger supply chain questions around: 

Most forecasting systems rely heavily on historical sales patterns, seasonality, and broader business trends to estimate future demand across months or quarters. 

 remains foundational to modern supply chain planning because organizations still require long-range operational alignment. 

Demand forecasting uses historical sales data, seasonality, promotions, and trends to predict future demand over weeks or months. Demand sensing uses real-time signals such as POS data, web clicks, search trends, and social media data to detect demand changes in near real time

Demand Sensing vs  Forecasting (1293 x 929 px).png

Demand Sensing and Supply Chain Planning 

Many organizations assume demand sensing replaces forecasting. In practice, mature supply chain organizations 

 sensing capabilities on top of existing planning methods. Forecasting helps businesses prepare for expected demand. Demand sensing helps businesses respond when conditions begin changing in real time. 

That distinction matters more as planning windows continue shrinking. Retail promotions and social trends can change inventory needs faster than traditional planning cycles were designed to absorb. 

Organizations are increasingly able to detect demand changes quickly, but response capabilities do not always move at the same speed. Demand sensing may surface an inventory issue early, but visibility gaps or disconnected replenishment workflows can reduce the value of the signal before action occurs. 

In traditional planning environments, a retailer promotion that accelerates sell-through on Monday may not trigger a notification until the next planning cycle. In a demand sensing environment, planners may identify that same shift within hours. 

As these capabilities mature, supply chain conversations will continue to expand beyond forecast accuracy alone. Visibility and responsiveness shape planning performance. The advantage has now become reducing the time between signal, decision, and execution. 

One of the most overlooked aspects of demand sensing is that faster signals fundamentally change how planning teams operate day to day. 

Traditional planning environments often revolve around weekly reviews, forecast adjustments, and longer timelines. Demand sensing 

In some organizations, planners move from evaluating operational changes weekly to monitoring conditions continuously throughout the day. This shift can create organizational friction if teams aren’t prepared for it. 

Faster Signals Increase Operational Pressure 

Demand sensing may identify a potential inventory disruption immediately, but planners still need confidence that the signal is accurate enough to justify a departure from normal operations. Transportation teams may need to approve expedited movement. Retail partners may require replenishment coordination. Warehouse labor and allocation decisions may need to shift rapidly. Demand sensing changes the speed and context in which those decisions happen.  

Organizations are being asked to trust faster recommendations while still managing fragmented retailer data, inconsistent visibility, and disconnected workflows. Faster AI signals do not automatically remove those constraints. In some environments, that even creates skepticism, alert fatigue, and pressure to operationalize AI before surrounding processes are ready. 

This is one reason demand sensing relies heavily on operational trust. 

Without that alignment, organizations can generate more alerts, more noise, and more planning fatigue without improving responsiveness. 

Demand sensing tends to create the most value in environments where demand volatility moves faster than traditional planning cycles comfortably can. 

That is especially true in industries like: 

How Retail Volatility Changes Demand Planning 

Retailers increasingly expect faster replenishment responsiveness, stronger inventory positioning, and tighter operational coordination across fulfillment ecosystems. 

 to improve inventory positioning, replenishment timing, and broader fulfillment coordination across their operational networks. 

Organizations operating in fast-moving retail environments often experience demand fluctuations that traditional weekly planning cycles struggle to manage effectively. 

Demand Sensing Isn’t Universally Valuable 

AI discussions can sometimes flatten this distinction. Demand sensing is not automatically valuable simply because it uses AI. In stable-demand industrial environments with long manufacturing lead times, minute-by-minute signal detection may create only marginal operational improvement. 

If production cycles stretch across months and demand patterns remain relatively predictable, the value equation changes significantly. 

That distinction matters far more than the sophistication of the algorithm itself. 

Manufacturing teams often operate under very different planning constraints than retail or CPG networks. Long production lead times, raw material procurement cycles, factory scheduling, and capacity utilization may limit how quickly changes can realistically occur, even when demand shifts are detected earlier. 

In these environments, demand sensing may still improve short-term visibility around inventory movement, but the operational value often depends on whether production planning and supplier coordination can 

 for the signal to materially change outcomes. 

Why Data Flow and Execution Speed Matter More Than Algorithm Sophistication 

One of the most important shifts happening in supply chain planning right now is that forecasting is no longer being treated as a standalone planning tactic. 

The conversation is becoming operational: 

Companies like Procter & Gamble stand out because they forecast effectively and because they have built tighter coordination between sensing, planning, and execution across enormous networks. 

Clean retailer data across POS movement, 

, ASN timing, replenishment patterns, inventory visibility, and transportation activity is what separates meaningful signals from noise.  

When those signals are delayed or inconsistent across trading partners, organizations react to operational noise instead of real demand changes. That delay cascades: it affects inventory positioning, shelf availability, replenishment timing, transportation costs, and ultimately customer experience. 

Better algorithms are the easy part. Building clean, connected visibility across trading partners and the organizational responsiveness to act on it is typically much harder. This is why many AI planning initiatives become challenges long before they become technology challenges. 

5 Questions to Ask Before Investing in Demand Sensing 

Demand sensing can improve response speed, but it isn’t automatically the right fit for every supply chain environment. Before investing in new sensing platforms, supply chain leaders should evaluate whether their data, workflows, and teams can realistically support faster decision-making. 

1. Do We Actually Have Reliable Operational Data? 

Demand sensing systems depend heavily on downstream visibility.  

Retail-Ready Product Requirements for First-Time Suppliers 

When a brand has a strong DTC (direct-to-consumer) business, expanding into major retail often feels like the natural next step. But many new suppliers quickly run into a familiar and frustrating reality. A first purchase order comes in, and then problems start surfacing almost immediately: 

Each issue on its own may seem small, but together they lead to delays, deductions, and cascading operational problems that can feel difficult to recover from, often before the product even makes it to the shelf. 

 This is where many emerging brands hit a wall. They realize that just having a good product isn't enough to succeed. Now, it's about removing operational barriers. Having a product that moves through a retailer's operational system with accuracy, consistency, and at scale is a different beast entirely, which is why brands new to retail need to pay attention. 

Retailers like Walmart and Target have raised their requirements significantly. Programs like Walmart's Supplier Quality Excellence Program (SQEP) and stricter validation standards mean suppliers face a more structured compliance environment than they did years ago. 

The challenge? Most suppliers treat this as an operations problem when it's actually a product design problem. Decisions about UPCs, packaging, case packs, and item data shape everything downstream. Get those wrong, and no amount of EDI automation can fix it. 

What Retail Ready Packaging Actually Does 

 Unlike DTC packaging, which arrives with context from ads and reviews, retail packaging has to work alone on a shelf full of products. It needs to do several things simultaneously: 

Your package is the primary communicator. It must convey purpose, differentiation, and value the moment a shopper sees it. 

 for example, improved shelf performance simply by changing its name from "Cubs" to "Kids" and switching to high-visibility colors. 

Retail-ready packaging is designed to minimize labor costs. A well-designed package lets store associates lift the product display directly from the shipper to the shelf without restocking individual units. Big-box retailers prioritize this efficiency. 

Under programs like SQEP, packaging must match specifications for materials, labeling, barcoding, and more. Any deviation, such as a damaged carton, wrong materials, or a mislabeled barcode, counts as a defect and can trigger fines or deductions. 

Eight Critical Areas for Operational Standards 

When you're selling to Walmart or Target at their scale, you can't rely on the flexibility that works in digital commerce. You need a rigorous operational system built around "First Time Quality" (FTQ), which means the retailer receives exactly what they ordered, in perfect condition, exactly when promised. 

Getting to FTQ means meeting strict operational standards across eight critical areas: 

These standards are all interconnected, so new brands should be aware that just fixing one won't be enough to succeed. 

Packaging Often Requires Product Redesign 

 Retail success sometimes means rethinking your product itself: 

All of this shows that packaging should be a top priority when preparing for retail expansion. Many successful DTC brands who don't pay attention to these important layers find their product sales dip exponentially.  

UPCs and GTINs Are the First Major Failure Point 

For many brands entering retail, UPC and GTIN management becomes the first real operational challenge. In DTC, barcode discipline is often loose. Brands reuse identifiers, buy cheap third-party UPCs online, or maintain inconsistent product records across platforms. Those shortcuts usually don't cause problems when shipping directly to consumers, but in retail, that's

Major retailers validate every barcode against GS1 records. If a barcode is duplicated, unregistered, or tied to incorrect product information, the item can be rejected immediately. What looks like a minor data issue can quickly lead to onboarding delays, receiving errors, inventory mismatches, ASN discrepancies, and chargebacks. 

That's because a barcode is much more than a scannable label. In retail, it functions as the product's master identifier across the entire supply chain. Inventory systems, replenishment, warehouse receiving, forecasting, sales reporting, and product data all depend on that information being accurate. Retailers can only process shipments efficiently when the digital record matches the physical product exactly. The barcode, dimensions, weight, pack configuration, and product name all need to align across every system involved. Even a small discrepancy can create manual work, shipment exceptions, or costly downstream errors. 

Item Data Matters for Retail Ready Packaging  

In retail, data is just as important as trucks and warehouses. At big retailers like Walmart, accurate item data keeps everything running smoothly. When data is clean and correct, orders process fast, shipments arrive on time, and products reach shelves without problems. 

But when data is wrong, problems spread quickly. One mistake can cause a chain reaction that slows down receiving, delays inventory, and costs money across the entire network. 

Imagine millions of dollars in inventory sitting at a loading dock, unable to be sold. This happens when what arrives doesn't match what the retailer's system says should arrive. When that happens, receiving teams can't process the shipment. They pull it aside, document the problem, and wait for someone at corporate to fix it. This slows operations, ties up money, and forces people to do manual work that was supposed to be automated. 

Data mistakes come with real financial penalties. Walmart's Supplier Quality Excellence Program (SQEP) treats data errors as defects. A single data error can  trigger fines starting around $200 per order, plus extra fees depending on the type of mistake. 

A small data error in a system can quickly turn into hundreds or thousands of dollars in fines and damage to your relationship with the retailer. 

Additionally, when systems don't show the real state of inventory, stores can have too much product in one location and not enough in another. This means products go out of stock when they shouldn't and customers can't find what they want. Over time, this hurts sales and makes customers trust the brand less. 

Getting orders right the first time requires complete, accurate item data. This includes: 

It’s important to get this data correct, so your products can move efficiently through retailer systems.  

Getting a retail deal and being retail-ready are two different things. A product that performs well on Shopify or Amazon can fail every operational test on day one at Walmart, with wrong UPCs, mismatched case packs, incomplete item data, non-compliant labeling. 

The mistake most first-time suppliers make is treating these issues as operations problems when they're really product decisions. Your choice about case pack size, barcode assignment, packaging design, and item data accuracy happens before your first PO ships. Get them wrong, and no amount of EDI automation or routing guide perfection will fix it. But get those decisions right, and your supply chain has a real chance. 

For more articles helping new brands get started, check out 

From Static to Strategic: How SPS Commerce + Snowflake Turn Retail Data into Action 

If you lead sales, supply chain, or analytics for a retail brand, a lack of data is unlikely to be your issue. The real challenge is translating that data into business and decision-making value.  

Even when you’ve centralized data in a warehouse or BI tool, teams may still be left asking: How do we get ready-to-use sales and inventory data from our retailers? Why do we still have to pull manual reports? Which promotions actually drove sales lift? What data can we use to build our AI strategy? 

That’s the gap the SPS Commerce and Snowflake partnership closes.

By combining retail data and insights from SPS Commerce's intelligent supply chain with Snowflake's AI Data Cloud, you can move your business beyond static reports and siloed data into a single, trusted environment where your data is both accessible and actionable. 

SPS Commerce and Snowflake Partnership Quote 

SPS & Snowflake  (1422 x 800 px).png

The Issue: Data Outside Your Walls Is Slowing Decisions 

Many brands are heavily invested in their data. You may already have: 

In most organizations, that means multiple stakeholders are involved: business leaders who own the results, frontline users who need answers fast, and data teams who build and maintain the Snowflake environment.  

However, data integration is only half the story; the real value comes when that integrated data is structured, normalized, and enriched with business context so it’s easy to turn into decisions. 

On top of that, the data that often matters most for strategic and operational decisions sits outside your four walls: 

This challenge is especially visible for brands operating across large retail ecosystems with multiple channels, partners, and data sources. For companies like 

, understanding what is happening at the point-of-sale across retailers directly impacts promotional strategies, sell-through visibility, as well as the ability to quickly respond to changing demand patterns.  

If your team spends hours logging into portals, exporting spreadsheets, and building one-off integrations just to answer basic questions about what sold, where, and why, then 

you’re leaving time and money on the table.

These manual data practices can quickly turn into:  

That’s where SPS Commerce and Snowflake come together. 

The Fix: See Every Data Point with SPS Commerce + Snowflake 

Together, SPS Commerce and Snowflake can turn your retail data into a living decision engine. SPS delivers clean, POS and supply chain data and insights; Snowflake makes that data available alongside other enterprise sources. 

With SPS Commerce and Snowflake, your teams can move from manual reports and reactive decision-making to answering bigger, more precise questions, like:  

All of these questions, and the answers, depend on having POS and supply chain data from SPS Commerce available in Snowflake.  

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Make Better, Data-Backed Decisions: SPS Commerce + Snowflake Use Cases 

So, what does this actually look like in practice? Here are just a few of the ways brands can use SPS Commerce and Snowflake together to drive better outcomes: 

1. Identify missed revenue opportunities 

With POS and supply chain data and insights in Snowflake, you can quickly see where sales are underperforming by retailer, region, or SKU. This visibility can help you spot gaps between your true performance and benchmarks, and focus your team on the specific customers, products, or locations that require immediate attention. 

2. Improve trade spend efficiency and promotion ROI 

With sell-through data in Snowflake, you can separate real incremental lift from noise. You can compare baseline and promoted performance to understand which promotions and retailers are delivering the strongest ROI. This insight can help you confidently scale high-performing efforts and cut the ones that just shift volume around. 

3. Optimize pricing based on demand trends 

SPS and Snowflake can give you a clearer picture of how consumers actually respond to price. You can see where prices have drifted from your intended architecture, where underpriced items are driving volume without profit, and where strategic price moves could protect or grow margin. 

4. Improve demand forecasts with real sell-through data 

Build your forecasts with a complete data picture. With clear visibility into POS data, you can separate slow movers from fast movers, spot early seasonality shifts, and reduce bias in your planning models. This lets you ground your inventory and production decisions in what shoppers are actually buying, instead of relying solely on shipment data. 

Ready to use retail data insights in a more strategic and higher-value way across your business and supply chain relationships? It’s time to put SPS Commerce and Snowflake to work for your team. 

 to learn how SPS Commerce and Snowflake can drive better, faster decisions across your business. 

SPS & Snowflake  Freddy Guard.png

Join us at Snowflake Summit 2026 at Moscone Center in San Francisco from June 1–4. We’ll also be hosting a 

breakout session on June 1 at 1:00 p.m. PST

, where we’ll explore how connected retail data can drive faster, smarter decisions across the business. 

We’d love to connect in person at our co-sponsored happy hour with 

What Should Fulfillment Teams Ask MAX?

What Should Fulfillment Teams Ask Max?

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Fulfillment teams are operating under a level of complexity that would have been difficult to imagine even a few years ago. 

Retailer requirements continue to evolve. Supplier networks keep expanding. Inventory positions shift constantly across channels. Transportation delays, routing compliance issues, deductions, and service expectations all compete for attention at the same time. 

For many teams, the challenge is navigating the sheer volume of operational data flowing through. 

Teams can spend hours tracking down information before they can even begin solving the actual issue. 

That is where MAX fits naturally into the process. 

MAX Is Built for the Way Supply Chain Teams Actually Work 

MAX is an AI-powered assistant embedded directly into the 

. It helps fulfillment teams move through operational work faster by surfacing insights, identifying patterns, and helping teams evaluate issues with greater clarity. 

MAX has launched with three core capabilities designed to support fulfillment teams across the SPS Commerce network. 

MAX Chat helps teams move through day-to-day operational work faster by surfacing insights, identifying patterns, and guiding issue resolution across retailer and supplier workflows. 

MAX Monitor continuously evaluates transactions, workflow requirements, and exceptions across the SPS network, helping teams stay ahead of disruptions while reducing manual operational effort behind the scenes. 

MAX Connect extends those insights into the broader technology ecosystem, allowing teams to bring SPS network intelligence into the systems they already use every day, including ERP platforms, data environments, and operational tools. 

Because SPS Commerce supports more than 300,000 trading partners across 

, MAX operates with visibility connected to real supply chain activity across the world’s largest retail network. That scale really matters because typically operational issues are not isolated events. They emerge across trading partners, systems, workflows, and timing gaps that are often difficult to identify manually. 

Why Better Prompts Produce Better Operational Outcomes 

One of the most important things fulfillment teams can understand about MAX is that, like most AI systems, the quality of the output improves when the request includes clear operational context. 

A broad question usually produces a broad answer. A detailed operational request creates a much more useful response. 

For example, asking MAX to “summarize this report” leaves significant room for interpretation. Asking MAX to “identify the operational risks most likely to affect OTIF performance over the next month” gives the system a much clearer direction. 

The strongest prompts typically include: 

Most fulfillment teams already think this way naturally. MAX simply helps accelerate and organize that reasoning process. 

What makes MAX especially valuable is not just how quickly it returns information. It is the 

How Fulfillment Teams Are Using MAX in Real Operational Work 

The teams seeing the strongest value from MAX are using it inside the operational moments where complexity drastically slows decision-making. 

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