Here’s the complete technical implementation addressing all the key components:
1. Scheduled API Polling
We use a Java Spring Boot service running on the MES application server:
@Scheduled(cron = "0 0 */4 * * *") // Every 4 hours
public void syncForecastData() {
String lastSyncTime = getLastSyncTimestamp();
String query = "SELECT Id, SKU, Quantity, ForecastDate FROM Forecast__c WHERE LastModifiedDate > " + lastSyncTime;
SalesforceResponse response = salesforceClient.query(query);
List<ForecastRecord> records = response.getRecords();
validateAndStage(records);
}
The scheduler runs at 6am, 10am, 2pm, 6pm, 10pm, and 2am to align with our production shift changes. We skip the 2am run on weekends when there’s no production activity.
2. Data Validation Rules
Implemented as a validation pipeline with multiple stages:
-- Stage 1: Load raw forecast data
INSERT INTO forecast_staging (sku, quantity, forecast_date, source_id)
SELECT sku, quantity, forecast_date, salesforce_id FROM api_import;
-- Stage 2: Apply validation rules
UPDATE forecast_staging SET validation_status = 'FAILED',
error_message = 'Invalid SKU'
WHERE sku NOT IN (SELECT material_number FROM materials);
UPDATE forecast_staging SET validation_status = 'FAILED',
error_message = 'Quantity out of range'
WHERE quantity < 0 OR quantity > 10000;
UPDATE forecast_staging SET validation_status = 'FAILED',
error_message = 'Invalid forecast date'
WHERE forecast_date < CURRENT_DATE OR forecast_date > CURRENT_DATE + 90;
Validation rules are configurable via admin UI so business users can adjust thresholds without code changes.
3. Forecast Staging Tables
Three-layer staging architecture:
- forecast_staging: Raw import from Salesforce with validation status
- forecast_validated: Cleaned records ready for planning
- forecast_production: Active forecast used by MRP runs
This separation allows us to review and correct validation failures before they impact production planning. The staging tables also maintain full audit history with timestamps and source system IDs for traceability.
4. Plan Regeneration Triggers
Automatic MRP execution when forecast updates meet threshold:
if (validatedRecordCount > 50 || highPrioritySkuUpdated) {
PlanningApiClient client = new PlanningApiClient();
PlanRequest request = PlanRequest.builder()
.planningHorizon(90)
.constraintMode("FINITE_CAPACITY")
.optimizationLevel("BALANCED")
.build();
String jobId = client.triggerPlanGeneration(request);
monitorPlanningJob(jobId);
}
We don’t trigger MRP for every sync - only when we have meaningful forecast changes (>50 records or updates to high-priority SKUs). This prevents unnecessary planning runs that would disrupt production scheduling.
5. Sync Monitoring Dashboard
Built custom dashboard in Grafana showing:
- Last successful sync timestamp
- Records processed per sync (trend chart)
- Validation failure rate by error type
- API response times and error counts
- Planning job execution status
- Forecast accuracy metrics (actual demand vs forecast)
Alerts configured for:
- Sync failure (no successful sync in 6 hours)
- Validation failure rate >10%
- Salesforce API errors
- Planning job failures
Implementation Timeline
Week 1-2: API integration development and testing
Week 3: Validation rules configuration with business users
Week 4: Staging table setup and data migration
Week 5-6: Plan regeneration automation and testing
Week 7: Monitoring dashboard and alerting
Week 8: Parallel run (automated + manual process)
Week 9: Cutover to automated process
Results After 6 Months
- Planning cycle reduced from 5 days to 3 days (40% improvement)
- Forecast data latency reduced from 7 days to 4 hours (97% improvement)
- Manual effort eliminated: 20 hours/week
- Schedule change frequency reduced 30% (more stable plans)
- On-time delivery improved from 87% to 94%
- Expedite orders reduced by 45%
Lessons Learned
- Start with data quality - our first iteration failed because Salesforce forecasts had too many errors
- Incremental sync is essential - pulling full datasets every 4 hours would hit API limits
- Don’t auto-trigger MRP for every change - use intelligent thresholds
- Build comprehensive monitoring from day one - you need visibility when things break at 2am
- Keep staging separate from production - allows validation and correction workflow
Future Enhancements
We’re now working on:
- Machine learning model to predict forecast accuracy and flag suspicious changes
- Bi-directional sync to push MRP results back to Salesforce (ATP quantities)
- Real-time sync using Salesforce Platform Events instead of polling
- Integration with supplier portals to share forecast with key vendors