Our distribution center processes 15,000+ inventory transactions daily across multiple warehouses in Infor SCM IS 2023.1. We’re evaluating two approaches for pushing inventory updates from our WMS system:
Option 1: Batch import utility running every 15 minutes, processing accumulated transactions from staging tables
Option 2: Real-time REST API calls for each transaction as it occurs in the WMS
The batch approach seems more stable and easier to monitor, but we’re concerned about the 15-minute delay impacting inventory accuracy. The real-time API would give us immediate updates, but I’m worried about API rate limiting, network latency, and what happens when the API is temporarily unavailable.
Has anyone implemented either approach at this scale? What were the trade-offs you experienced? I’m particularly interested in hearing about API reliability and whether hybrid patterns (real-time for critical items, batch for others) make sense.
The hybrid approach is actually what most large operations end up implementing. Use real-time API for high-value or fast-moving items where inventory accuracy is critical (A-class items in ABC analysis), and batch processing for everything else. This gives you the benefits of immediate updates where it matters most while keeping the overall API load manageable. You can also use the API for critical transaction types (like cycle count adjustments or emergency stock moves) and batch for routine transactions. The key is having good logic in your WMS to route transactions appropriately based on business rules.
Thanks for all the insights. It sounds like batch with shorter intervals (maybe 5-10 minutes instead of 15) might be the sweet spot for us, with API reserved for truly critical scenarios. The performance and stability arguments are compelling.
From a pure performance standpoint, batch processing is more efficient for Infor SCM. The database can optimize bulk operations better than individual inserts/updates. We measured 3-4x better throughput with batch imports versus API calls for the same transaction volume. However, batch processing requires more sophisticated error handling - if one transaction in a batch fails, you need logic to handle partial success. API calls give you transaction-level control but at the cost of overhead. Consider your database load too - 15K daily transactions is manageable either way, but real-time APIs create constant database connections versus periodic batch loads.
We went with real-time API initially but switched to batch after six months. At 15K transactions daily, you’re looking at roughly 10 API calls per minute if evenly distributed, but warehouse operations are bursty - you might hit 50-100 calls per minute during peak receiving or shipping periods. The API rate limiting became problematic, and we had to implement complex retry logic. Network blips that would be invisible with batch processing caused transaction failures that required manual intervention. Batch processing gave us better control over error handling and recovery.
That’s a valid concern. The way we addressed it was by using the same underlying integration framework for both paths - essentially the API calls and batch imports both go through the same validation and transformation layer. The only difference is timing and error handling. API calls fail fast with immediate notification, while batch imports collect errors for review. We also built a fallback mechanism where failed API calls automatically queue to the next batch run, so you get the best of both worlds. Documentation and monitoring are critical though - your ops team needs clear visibility into which path each transaction took.
Based on the discussion, let me synthesize the key considerations for your decision:
API Rate Limiting and Retries:
Infor SCM’s REST API typically has rate limits around 100-200 requests per minute (varies by license tier). At 15K daily transactions, you’re averaging 10/minute, but warehouse operations are bursty. During peak periods (receiving shipments, end-of-shift processing), you could easily hit 50-100 transactions in minutes, triggering rate limits. Implementing exponential backoff retry logic is essential, but adds complexity. Failed API calls need queuing mechanisms, dead letter queues for persistent failures, and monitoring to alert on retry exhaustion. This infrastructure overhead is significant.
Batch Processing Stability:
Batch imports are inherently more stable because they’re designed for bulk operations and have built-in transaction management. The import utility handles partial failures gracefully - you can configure it to skip bad records and continue processing, logging errors for review. Database performance is better with batch operations due to bulk insert optimization and reduced connection overhead. Network issues during a 2-minute batch window are less impactful than issues during 8 hours of continuous API calls. However, batch processing requires staging infrastructure (database tables or file systems) and scheduled job management.
Hybrid Integration Patterns:
The hybrid approach works well when you have clear business rules for classification. Consider implementing:
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Transaction-Type Routing: Critical transactions (cycle counts, stock adjustments, high-value receipts) → Real-time API. Routine transactions (putaway, replenishment moves) → Batch processing.
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Item Classification: A-class items or items with safety stock concerns → Real-time API. B/C-class items → Batch with 5-10 minute intervals.
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Time-Based Routing: During peak periods (8 AM - 4 PM) → Batch every 5 minutes. Off-peak → Real-time API with lower risk of rate limiting.
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Fallback Architecture: API calls that fail after retries automatically queue to next batch run. This provides resilience without losing transactions.
For your 15K daily volume, I’d recommend starting with optimized batch processing (5-10 minute intervals) as the primary method. This gives you near-real-time updates (acceptable for most inventory accuracy needs) with maximum stability. Reserve the API for exception handling and truly time-critical scenarios (emergency stock moves, critical shortage situations). As you gain experience, you can selectively move high-priority transaction types to real-time API.
The 5-10 minute batch interval provides 95% of the benefit of real-time updates while maintaining the stability and performance advantages of batch processing. Most inventory discrepancies that matter (order fulfillment, allocation decisions) can tolerate a 5-minute lag. Monitor your actual inventory accuracy metrics to validate this assumption for your specific operation.