We’re encountering a persistent issue with our demand planning forecast imports. When importing forecasts from our external forecasting tool via REST API, the process fails with ‘Invalid JSON schema’ errors. The import worked fine in our test environment, but production consistently rejects the payload.
Here’s the error we’re seeing:
Error: JSON schema validation failed
Field 'forecastDate' format invalid
Expected: ISO-8601, Received: MM/DD/YYYY
Our external tool generates forecasts with date formats that don’t match Oracle’s expected schema. We’ve checked the forecast import mapping configuration but can’t identify where the transformation should occur. The integration was set up to handle bi-weekly forecast updates from our demand sensing platform.
Has anyone dealt with JSON schema validation issues during forecast imports? We need to understand if this requires middleware transformation or if there’s a way to configure Oracle Fusion to accept alternate date formats.
Thanks for the suggestions. We’ve enabled detailed logging and confirmed the date format is definitely the primary issue. Our external forecasting tool exports in MM/DD/YYYY by default. We’re exploring middleware transformation options now. Has anyone used Oracle Integration Cloud specifically for this type of date transformation? Wondering about performance implications for large forecast datasets.
I experienced nearly identical challenges during a recent demand planning implementation. Quick question - are you using the bulk import API or the individual forecast line API? Bulk imports have stricter schema validation but better performance for large datasets.
Oracle Integration Cloud handles date transformations very efficiently. For forecast imports, you’ll want to use the mapper component with the translate-date function. Performance is excellent even with datasets containing thousands of forecast records. We process weekly imports with 50K+ forecast lines and the transformation adds negligible overhead (typically under 2 seconds for the entire batch). Just make sure you’re using batch processing rather than individual record calls to optimize throughput.