/
.webp)
Most EDI systems still process documents on scheduled intervals, queuing purchase orders, ship notices, and invoices until the next batch window runs. Real-time EDI processing instead of batch windows eliminates these scheduled delays by handling each document the moment it arrives, closing the data gaps that cause overselling, chargebacks, and fulfillment errors across supply chain operations.
The shift from batch to real-time is not about replacing EDI as a protocol. EDI remains the backbone of B2B commerce, handling over 75 percent of digital trade globally. The shift is about removing the artificial delays that batch schedules introduce between when a transaction happens and when your systems reflect it.
Batch processing in EDI means collecting inbound and outbound documents over a set period and processing them all at once. A typical batch window runs every 15, 30, or 60 minutes. Some organizations still run batch cycles on hourly or daily schedules, depending on the document type and trading partner requirements.
Batch windows became standard because early EDI infrastructure was built around Value-Added Networks (VANs) that operated on store-and-forward models. Documents were deposited in a VAN mailbox and retrieved on a schedule. The translation software that converted EDI formats into ERP-compatible data also operated in batch mode, processing queued files and writing results to staging tables or flat files.
This architecture made sense when trading partners exchanged documents once or twice a day and supply chain velocity was measured in days rather than hours. The infrastructure was designed for throughput, not latency.
In a batch-based EDI architecture, the document lifecycle follows a predictable path. Inbound documents arrive at the VAN or AS2 endpoint. They accumulate in a queue. At the scheduled interval, the translation engine picks up the queue, parses each document, maps fields to the ERP schema, and loads the data into staging tables. The ERP then processes the staged records during its own batch cycle.
Each step introduces its own delay. A purchase order that arrives at 2:05 PM might not reach the ERP until the 2:30 batch run, and the ERP might not process it until its 3:00 cycle. That is nearly an hour of latency for a single document, with every system in the chain operating on its own schedule.
Inventory accuracy depends on how frequently stock levels are synchronized across systems. When EDI operates on 15-minute batch windows, there is a 15-minute period where the inventory count in your ERP does not match the actual inventory in your warehouse or the availability shown to your trading partners.
The EDI 856 Advance Ship Notice is the document most affected by batch delays. Retailers use ASN data to plan receiving labor, allocate warehouse space, and update their own inventory systems before a shipment arrives. When the ASN is generated from a batch export rather than live warehouse data, it often contains discrepancies:
EDI transactions follow a chain where each document depends on the one before it. When batch windows delay any document in this sequence, the entire chain slows down:
Real-time EDI processing eliminates scheduled batch windows by handling each document individually as it arrives. Instead of accumulating documents in a queue and processing them on a timer, the system parses, validates, and delivers each document within seconds of receipt.
In an event-driven EDI architecture, the arrival of a document is itself the trigger for processing. When a purchase order lands at your AS2 endpoint or VAN mailbox, the system immediately picks it up, translates it, and delivers it to your ERP or database. There is no queue, no timer, and no waiting for the next batch cycle.
This model mirrors how modern applications handle data. Web applications do not batch user requests. Payment systems do not queue transactions for hourly processing. Real-time EDI applies the same principle to supply chain documents.
The difference between continuous sync and scheduled exports affects every downstream process. With scheduled exports, outbound documents like ASNs and invoices are generated from periodic snapshots of your warehouse and financial data. With continuous sync, outbound documents are generated from the current state of your systems at the exact moment they are needed.
This distinction is critical for ASN accuracy. A continuously synced ASN reflects the actual contents of a shipment at the moment the truck leaves the dock. A batch-exported ASN reflects the contents at the time of the last scheduled export, which may have been 15 or 30 minutes before the shipment was finalized.
Change Data Capture (CDC) is a technique that detects and captures changes at the field level in a database without requiring full table scans or scheduled queries. Applied to EDI, CDC enables systems to detect when an order status changes, a shipment is completed, or an inventory level drops, and trigger the appropriate EDI document immediately.
Traditional batch EDI uses polling: the system checks for new data at regular intervals. CDC uses streaming: the system reacts to changes as they happen. This is the architectural difference that makes real-time EDI possible. Instead of asking "is there new data?" every 15 minutes, the system is notified the instant data changes.
Real-time EDI processing directly protects revenue during high-volume sales events. When inventory updates propagate to trading partners within seconds instead of minutes, the window for overselling collapses to near zero. Orders placed against real-time inventory data are far more likely to be fulfillable, reducing cancellations and the associated customer experience damage. Electronics retailers like Best Buy exchange inventory inquiry documents (846) alongside purchase orders and ship notices, and stale inventory data during product launches or seasonal promotions directly translates to lost sales and fulfillment failures.
Suppliers operating in retail supply chains face 1 to 3 percent of annual revenue in EDI-related chargebacks. A company shipping $50 million in goods annually could lose $500,000 to $1.5 million in penalties, with a significant portion attributable to timing issues that real-time processing eliminates.
Trading partners increasingly expect near-real-time data exchange as a compliance requirement. Retailers track supplier performance through scorecards that measure EDI accuracy, timeliness, and completeness. Suppliers operating on batch windows start with a structural disadvantage on timing metrics.
Retailers like Lowe's enforce strict EDI compliance standards across multiple divisions, requiring suppliers to support document types from purchase orders (850) through ship notices (856) and invoices (810). Real-time processing makes compliance easier because documents are always current. An ASN generated from live data does not need to be reconciled against last-minute warehouse changes. An invoice based on real-time pricing does not carry stale contract terms. The data is accurate because it is immediate.
Batch-based EDI generates a steady volume of exceptions and errors that require manual intervention. When batch runs fail, the entire queue needs to be investigated. When documents in a batch contain errors, the team has to determine which documents succeeded and which need to be reprocessed.
Real-time processing reduces this overhead by handling documents individually. An error in one document does not affect others. Failed documents can be retried immediately with specific error context, rather than requiring a full batch investigation. EDI teams spend less time on firefighting and more time on strategic work like onboarding new partners and optimizing document flows.
One of the most effective implementations of real-time EDI converts inbound documents into structured database records the moment they arrive. Instead of routing documents through legacy translation software and batch staging tables, each document is parsed directly into a database table that any system can query immediately.
An inbound EDI 850 purchase order contains segments like BEG (beginning of order), PO1 (line items), and N1 (names). Real-time parsing converts these segments into database rows with typed columns: order number, line item SKU, quantity, price, ship-to address. The data is queryable the instant parsing completes.
Stacksync takes this approach by automatically parsing incoming EDI documents directly into your database tables and converting outgoing data back into compliant EDI formats. Supply chain teams manage their EDI data with the ease of SQL rather than legacy file parsers, making transaction data accessible to every team that needs it without waiting for batch cycles to complete.
Real-time EDI is not just about receiving documents faster. It also means generating outbound documents from live data rather than scheduled snapshots. When a warehouse completes a shipment, the ASN generates immediately from the actual shipment data. When a financial close produces an invoice, the EDI 810 transmits within seconds.
This bidirectional real-time flow ensures that both inbound and outbound document accuracy reflects the current state of operations. Trading partners receive documents that match reality because the documents are generated from reality, not from a stale export.
Real-time processing does not mean flooding your ERP or trading partner systems with unlimited traffic. Modern real-time EDI platforms manage throughput intelligently, respecting API rate limits while still delivering documents as close to instantly as possible.
ERP systems like NetSuite have concurrency limits (15 concurrent requests on standard tiers). Real-time EDI platforms handle this by queuing requests intelligently and distributing load evenly, rather than concentrating all activity into batch spikes that overwhelm API limits. The result is more consistent system performance with lower peak load compared to batch processing.
The differences between batch and real-time EDI extend across every dimension of supply chain operations. The following comparison highlights where each approach delivers and where it falls short.
| Dimension | Batch Processing | Real-Time Processing |
|---|---|---|
| Document Handling | Queued on schedule, 15 to 60 min intervals | Processed on arrival, sub-second latency |
| Inventory Accuracy | Stale between batch runs, gap-prone | Continuously updated, near-zero gap |
| ASN Timing | Generated from scheduled exports | Generated from live warehouse data |
| Error Detection | Errors caught during next batch cycle | Errors flagged immediately on receipt |
| Partner Compliance | Delays increase chargeback exposure | Real-time validation reduces penalties |
| ERP Load | Concentrated spikes during batch runs | Distributed evenly across operations |
| Scalability | Hardware-bound, fixed throughput ceiling | Event-driven, scales with transaction volume |
Real-time EDI closes the data gaps that batch schedules create between your warehouse, ERP, and trading partner systems.
Batch processing concentrates ERP load into scheduled spikes and leaves transactions exposed to errors between runs.
Start by mapping your highest-volume document flows to identify where real-time processing prevents the most chargebacks.
Real-time processing is not universally necessary for every EDI document flow. Some scenarios remain well-served by batch schedules, and understanding where batch still works helps organizations prioritize their modernization efforts.
Trading partners that exchange only a few documents per day or per week do not generate the kind of latency pressure that demands real-time processing. A supplier sending 10 purchase orders per week to a small distributor is unlikely to experience the overselling or chargeback issues that high-volume retail relationships create. Batch processing at reasonable intervals is sufficient for these flows.
Documents like remittance advice (EDI 820), payment notifications, and periodic reporting do not have the same time sensitivity as purchase orders or advance ship notices. These documents inform back-office processes that operate on longer cycles, and processing them in batch windows introduces no meaningful business risk.
Moving from batch to real-time EDI does not require replacing your ERP or abandoning your existing EDI infrastructure. The transition targets the middleware and processing layers between your EDI endpoints and your core business systems.
The first step is mapping every batch schedule in your EDI processing pipeline. Document which systems run on what intervals, which document types are included in each batch, and what downstream processes depend on batch completion.
Not all documents need real-time processing. Categorize your EDI document flows by latency sensitivity:
Start the transition with the document flows that generate the most chargebacks, the most manual reconciliation, or the longest processing delays. For most retail and distribution supply chains, this means advance ship notices and purchase orders. These are the documents where batch delays have the most direct financial impact. High-volume retailers like The Home Depot exchange over a dozen EDI document types across 11 divisions, including transportation carrier status messages and motor carrier load tenders alongside standard order and fulfillment documents, making it essential to prioritize which flows move to real-time first.
The transition from batch to real-time is rarely an overnight switch. Organizations need a platform that can run both batch and real-time processing simultaneously, allowing them to migrate document flows incrementally without disrupting existing trading partner relationships.
Stacksync provides six products on one platform with zero batch windows, covering real-time sync, workflow automation, event queues, databases, EDI, and monitoring. With pre-built EDI connectors for retailers, distributors, and suppliers, teams can connect to trading partners in days and start processing documents in real time without stitching together separate tools for translation, synchronization, and monitoring.
Batch windows were built for a supply chain that moved slowly. Modern retail, logistics, and manufacturing operations require data that reflects what is happening now, not what happened during the last scheduled export. Real-time EDI processing closes the gap between when transactions occur and when your systems know about them.
The transition does not require replacing your ERP, abandoning EDI as a protocol, or overhauling your trading partner network. It requires updating the processing layer between your EDI endpoints and your core systems to handle documents as they arrive rather than on a timer.
Ready to remove batch windows from your EDI workflow? Explore how Stacksync handles EDI by parsing documents directly into your database tables with real-time sync, pre-built trading partner connectors, and zero scheduled delays.
