Transportation API rate quote endpoint has slow response times during peak hours, delaying shipment planning

SCM Epicor SCM
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1

We’re experiencing significant performance issues with the transportation management rate quote API endpoint in Epicor SCM 10.2.700. Our shipment planning delays are impacting customer commitments.

When requesting carrier rate quotes through the REST API, response times average 8-12 seconds per request, sometimes spiking to 20+ seconds. We’re calling the endpoint for multi-carrier rate comparisons during order processing, and this latency is unacceptable.

We’ve noticed the slowdown correlates with the number of carriers in our TMS setup (currently 15 active carriers). The API seems to query each carrier sequentially rather than in parallel. We need guidance on API performance monitoring best practices and rate quote endpoint optimization strategies.

Has anyone successfully optimized this endpoint or implemented better TMS integration patterns? Our volume is about 500 rate requests daily during peak hours.

2

Have you considered implementing asynchronous rate requests? Instead of waiting for all carriers synchronously, submit the request and poll for results. The API supports webhook callbacks when all carrier responses are collected.

This pattern works well for non-urgent rate shopping scenarios. For time-sensitive quotes during order entry, you could request quotes from your top 3-5 carriers synchronously and get the rest asynchronously in the background.

3

Check your database connection pool settings too. We found that the rate quote endpoint opens multiple database connections per request when querying carrier configurations and rate tables. If your pool is exhausted, requests queue at the database level before even hitting the external carrier APIs.

Run a connection pool analysis during peak load. We increased maxPoolSize from 20 to 50 and saw immediate improvement in API response times across all transportation endpoints.

4

The batch endpoint is /api/v1/transportation/rate-quotes/batch and accepts an array of shipment requests. Documentation is sparse, but the payload structure mirrors the single quote endpoint, just wrapped in a shipments array.

For monitoring, implement logging at the API gateway level to track per-carrier response times. We use this data to dynamically exclude slow carriers from real-time quotes and queue them for background processing instead. You can set carrier priority flags in the TMS carrier master to control this behavior.

Another optimization: enable response caching for similar shipment profiles (same origin/destination/weight class). Cache TTL of 15-30 minutes works well for most scenarios.

5

I’ve seen similar issues with the rate quote endpoint. The sequential carrier polling is definitely a bottleneck. Have you checked your carrier connection timeout settings? In our environment, we reduced the default timeout from 30 seconds to 8 seconds per carrier, which helped prevent slow carriers from blocking the entire request queue.

Also, are you using the batch rate quote endpoint or making individual calls? The batch endpoint can process multiple shipments in one request, which reduces overhead significantly.

6

Let me provide a comprehensive solution addressing API performance monitoring, rate quote optimization, and TMS integration best practices:

API Performance Monitoring: Implement application performance monitoring (APM) at three levels:

  1. API Gateway metrics: Track request volume, response times, and error rates per endpoint
  2. Carrier-level tracking: Log individual carrier response times to identify slow performers
  3. Database query profiling: Monitor the carrier configuration and rate table queries that execute before external API calls

Set up alerting thresholds: >5 seconds for 95th percentile response time, >10% error rate. Use these metrics to establish carrier SLAs.

Rate Quote Endpoint Optimization: Switch to the batch endpoint (/api/v1/transportation/rate-quotes/batch) for processing multiple shipments. This reduces authentication overhead and connection management costs.

Implement a tiered carrier strategy:

  • Tier 1 (Primary): 3-5 carriers queried synchronously with 8-second timeout
  • Tier 2 (Secondary): Remaining carriers queried asynchronously via webhook callback
  • Tier 3 (Backup): Cached rates from previous 24 hours for fallback scenarios

Enable intelligent caching with these rules:

  • Cache key: origin ZIP, destination ZIP, weight class, service level
  • TTL: 30 minutes for LTL, 60 minutes for parcel
  • Cache invalidation: Carrier rate updates, fuel surcharge changes

Optimize database queries:

CREATE INDEX idx_carrier_rates
ON carrier_rate_table(origin_zip, dest_zip, weight_class, effective_date);

TMS Integration Best Practices: Configure carrier connection pooling to reuse HTTP connections rather than establishing new ones per request. Set keepAliveTimeout to 60 seconds and maxSockets to 10 per carrier.

Implement circuit breaker pattern for unreliable carriers:

  • After 3 consecutive failures or timeouts, exclude carrier from real-time quotes for 5 minutes
  • Continue background health checks during exclusion period
  • Automatically restore when carrier responds successfully

For your 15-carrier setup with 500 daily requests, this architecture should reduce average response time from 8-12 seconds to 2-4 seconds for primary tier carriers, with secondary tier results arriving within 15-30 seconds via webhook.

Configuration Steps:

  1. Update carrier master records with tier assignments and timeout values
  2. Enable batch endpoint in API gateway configuration
  3. Configure Redis or in-memory cache for rate results
  4. Set up webhook endpoint for asynchronous carrier responses
  5. Implement APM dashboards for ongoing monitoring

Monitor the impact over 2-3 weeks and adjust tier assignments based on actual carrier performance data. You should see immediate improvement in shipment planning workflow responsiveness.

7

Thanks for the suggestion. We’re using individual calls currently - didn’t realize there was a batch endpoint. Where can I find documentation on that?

Regarding timeouts, we haven’t modified those settings. Are they in the TMS configuration or API gateway settings? We definitely have a couple of carriers that respond slowly, so the 8-second timeout makes sense.