I wanted to share our successful implementation of an automated order validation system that dramatically reduced manual review effort. Before this project, our order fulfillment team manually reviewed every customer order for pricing accuracy, credit limits, inventory availability, and shipping constraints. This process consumed 40+ hours per week and caused order processing delays.
We implemented a configurable rules engine integrated with our order entry system that automatically validates orders against multiple criteria. The rules engine evaluates credit status, validates pricing against contracts, checks inventory allocation, and verifies shipping address completeness. Orders passing all validation rules proceed directly to fulfillment, while exceptions are flagged for manual review with specific reasons.
The configurable rule sets allow us to adjust validation logic without code changes. We can modify credit limit thresholds, add new pricing validation rules, or update shipping constraint logic through a configuration interface. This flexibility has been crucial as our business rules evolve.
The results have been transformative - we’ve reduced manual order review time by 75%, decreased order processing cycle time by 40%, and nearly eliminated pricing errors in fulfilled orders.
Let me provide comprehensive details on our implementation covering all the key aspects:
Configurable Rule Sets Architecture:
We designed a flexible rule configuration system with these components:
-
Rule Definition Framework:
- Each rule has: Name, Description, Priority, Validation Logic, Action (Block/Warn/Log)
- Rules are stored in a configuration table, not hardcoded
- Business users can modify rule parameters through an admin interface
- Rule versioning tracks changes with effective dates
-
Rule Categories and Examples:
Credit Rules:
- Customer credit limit vs order value
- Outstanding AR balance thresholds
- Payment terms compliance
- Credit hold status check
Inventory Rules:
- ATP (Available to Promise) validation
- Lead time feasibility for requested ship dates
- Minimum order quantity compliance
- Allocation priority for constrained items
Pricing Rules:
- Contract price validation (±2% tolerance)
- Discount authorization level checks
- Volume tier pricing accuracy
- Special pricing approval requirements
Shipping Rules:
- Address completeness (all required fields)
- Shipping method vs delivery date feasibility
- Hazmat compliance for regulated products
- International export documentation requirements
-
Rule Conflict Resolution:
- Priority-based evaluation (1-100 scale)
- Same-priority rules use AND logic (all must pass)
- Blocking rules override warning rules
- Exception rules can bypass standard rules with proper authorization
Order Entry Integration Details:
We implemented the validation layer as a loosely-coupled service:
- Trigger Point: Post-save event in Order Entry module
- Execution: Asynchronous validation (doesn’t block UI)
- Status Updates: Real-time via order status field and validation log
- User Experience: Order saves immediately, validation results appear within 2-3 seconds
- Exception Handling: Failed validations update order status to “Validation Required” and send alert to fulfillment team
This approach minimizes customization to core Epicor screens while providing seamless validation integration. Upgrades don’t affect the validation logic since it’s event-driven, not screen-embedded.
Reduced Manual Review Achievement:
To clarify the 75% reduction metric:
- Baseline: 100% of orders (avg 850/week) required manual review
- Post-Implementation: Only 25% of orders (avg 210/week) require manual review
- Automatic Pass Rate: 75% of orders pass all validation rules automatically
- Manual Review Reasons: Credit exceptions (40%), special pricing (35%), shipping constraints (15%), other (10%)
The 210 orders requiring manual review are genuine exceptions that need human judgment - the automation successfully eliminated the routine validation work that was consuming 40+ hours weekly.
Change Management and Training:
We took a phased approach to minimize disruption:
Phase 1 - Parallel Operation (Weeks 1-4):
- Validation engine ran in “shadow mode” - logging results without blocking orders
- Order entry team continued manual reviews
- We compared automated vs manual validation results to tune rules
- Identified and corrected 15 rule logic issues during this phase
Phase 2 - Assisted Automation (Weeks 5-8):
- Enabled blocking for high-confidence rules (credit, inventory)
- Kept pricing and shipping rules in warning mode
- Team reviewed validation results and provided feedback
- Refined rule thresholds based on real-world patterns
Phase 3 - Full Automation (Week 9+):
- All rules activated in blocking mode
- Manual review only for flagged exceptions
- Continuous monitoring and rule optimization
Training focused on:
- Understanding validation status indicators
- Interpreting validation failure messages
- Exception resolution workflows
- When to override validations vs modify rules
- Rule configuration basics for power users
Resistance was minimal because:
- Team saw immediate reduction in tedious validation work
- Exception handling was clearer than reviewing every order
- Validation accuracy improved (fewer pricing errors reached fulfillment)
- Time savings allowed focus on complex customer issues
Business Impact Summary:
- Manual Review Time: Reduced from 40 hours/week to 10 hours/week (75% reduction)
- Order Processing Cycle Time: Reduced from 24 hours average to 14 hours average (40% reduction)
- Pricing Errors: Reduced from 3-4 per week to less than 1 per month (95% reduction)
- Credit Hold Violations: Eliminated entirely (was 1-2 per month)
- Customer Satisfaction: Order accuracy complaints decreased by 60%
- Team Satisfaction: Order entry team reports higher job satisfaction focusing on complex issues
Technical Implementation Notes:
- Rules engine built using Epicor’s Business Process Management (BPM) framework
- Configuration interface uses standard Epicor dashboard technology
- Integration points: Order Entry post-save event, Customer Credit service, Inventory ATP service, Price List service
- Performance: Average validation execution time 1.8 seconds per order
- Scalability: Currently processing 850 orders/week, tested up to 3000 orders/week
Lessons Learned:
- Start with high-confidence rules - build trust before expanding
- Shadow mode is essential - don’t go live without parallel validation
- Business user configuration is critical - IT shouldn’t own rule changes
- Exception reporting must be specific - “validation failed” isn’t helpful, “credit limit exceeded by $5,000” is actionable
- Regular rule review prevents rule bloat - we audit quarterly and retire unused rules
Future Enhancements:
We’re now working on:
- Machine learning to predict which orders need manual review based on historical patterns
- Customer-specific rule profiles for major accounts
- Integration with supplier lead time data for more accurate ATP validation
- Mobile app for exception approval by managers
This implementation has fundamentally changed how our order fulfillment team operates, shifting from routine validation work to strategic exception management and customer relationship building. The configurable rules approach ensures the system evolves with our business without requiring constant IT involvement.
This is impressive! Can you share more details about how you structured your configurable rule sets? We’re considering a similar project and I’m curious about the rule hierarchy and how you handle rule conflicts when multiple rules apply to the same order.
I’m interested in the reduced manual review metric. Does the 75% reduction mean you’re catching 75% of issues automatically, or that you’re reviewing 75% fewer orders overall? What percentage of orders still require manual intervention?
How did you handle the change management aspect? I imagine your order entry team needed training on the new validation messages and exception handling process. Did you face resistance to the automation?