diff --git a/services/orchestrator/app/services/dashboard_service.py b/services/orchestrator/app/services/dashboard_service.py
index 099a1813..4d667221 100644
--- a/services/orchestrator/app/services/dashboard_service.py
+++ b/services/orchestrator/app/services/dashboard_service.py
@@ -602,6 +602,7 @@ class DashboardService:
                 "parameters": {
                     "product_name": batch.get("product_name", "Product"),
                     "predicted_demand": batch.get("planned_quantity", 0),
+                    "current_stock": 0,  # Default to 0 if not available
                     "confidence_score": 85
                 }
             }
diff --git a/services/production/app/services/production_service.py b/services/production/app/services/production_service.py
index c54b0c45..041a6907 100644
--- a/services/production/app/services/production_service.py
+++ b/services/production/app/services/production_service.py
@@ -1839,8 +1839,15 @@ class ProductionService:
                         # Generate reasoning data for JTBD dashboard
                         from shared.schemas.reasoning_types import create_batch_reasoning_forecast_demand
 
+                        # Try to get product name from forecast, stock_info, or use placeholder
+                        product_name = (
+                            forecast.get('product_name') or
+                            (stock_info.get('product_name') if stock_info else None) or
+                            f"Product {product_id}"
+                        )
+
                         reasoning_data = create_batch_reasoning_forecast_demand(
-                            product_name=f"Product {product_id}",  # TODO: Get actual product name from inventory
+                            product_name=product_name,
                             predicted_demand=predicted_demand,
                             current_stock=current_stock,
                             production_needed=production_needed,
@@ -1849,16 +1856,23 @@ class ProductionService:
                         )
 
                         # Create production batch
+                        planned_start = datetime.combine(target_date, datetime.min.time())
+                        planned_end = datetime.combine(target_date, datetime.max.time())
+                        duration_minutes = int((planned_end - planned_start).total_seconds() / 60)
+
                         batch_data = {
                             'tenant_id': tenant_id,
                             'schedule_id': schedule.id,
+                            'product_id': product_id,  # Product ID from forecast
+                            'product_name': product_name,  # Product name resolved above
                             'recipe_id': product_id,  # Assuming recipe_id matches product_id
                             'batch_number': await self._generate_batch_number(session, tenant_id, target_date, batches_created + 1),
                             'status': 'scheduled',
                             'priority': 'normal',
-                            'planned_start_time': datetime.combine(target_date, datetime.min.time()),
-                            'planned_end_time': datetime.combine(target_date, datetime.max.time()),
+                            'planned_start_time': planned_start,
+                            'planned_end_time': planned_end,
                             'planned_quantity': production_needed,
+                            'planned_duration_minutes': duration_minutes,
                             'reasoning_data': reasoning_data,  # NEW: Structured reasoning for i18n
                             'created_at': datetime.now(timezone.utc),
                             'updated_at': datetime.now(timezone.utc),