This is one of the most common architectural debates in Manhattan implementations, and there’s no universal right answer - it depends on your organization’s capabilities and priorities. Let me break down the key considerations:
Workflow Automation vs Scripting - Complexity Analysis:
Your 47 allocation scenarios sound complex, but complexity can be managed through decomposition. Most ‘complex’ allocation logic can be broken down into reusable components. For example, instead of 47 monolithic scenarios, you likely have 8-10 core allocation patterns (customer tier-based, region-based, product category-based, promotional) that combine in different ways.
Workflow automation excels when you can model allocation as a series of decision points and actions. The visual workflow designer makes logic transparent and maintainable by business users. However, workflows struggle with:
- Deep conditional nesting (more than 3-4 levels)
- Complex mathematical calculations
- Dynamic rule evaluation based on runtime data
- Iterative logic that requires loops
Custom scripting (Groovy/Java) handles these scenarios naturally but introduces technical debt and upgrade risk.
Allocation Rule Complexity - The Hybrid Architecture:
Implement a three-layer architecture:
Layer 1 - Workflow Orchestration: Use workflows to define the high-level allocation sequence and decision points. This handles scenario selection (which of your 47 scenarios applies), data validation, and process flow. Workflows call Layer 2 for actual allocation calculations.
Layer 2 - Allocation Rule Engine: Implement core allocation patterns as configurable rule templates. These can be either standard Manhattan allocation engines (for standard patterns) or custom services (for unique patterns). The key is making these parameterized - same code handles multiple scenarios with different parameters.
Layer 3 - Business Rules Configuration: Store allocation parameters (thresholds, weights, priorities) in configuration tables rather than hard-coding. This allows business users to tune allocation behavior without code changes.
Example architecture:
// Workflow calls allocation service
AllocationService.allocate(orders, ruleSetId);
// Service loads rules from config
RuleSet rules = RuleConfig.load(ruleSetId);
for (Rule rule : rules.getPrioritizedRules()) {
rule.execute(orders, context);
}
Upgrade Impact - Real-World Experience:
Having managed multiple Manhattan upgrades with custom code, here’s the actual impact:
-
Standard Workflows: Minimal upgrade impact (2-5% require changes). Manhattan maintains backward compatibility well for workflow definitions. Typical effort: 10-20 hours of regression testing.
-
Custom Scripts with Poor Practices: High impact (30-50% require changes). Direct API calls, tight coupling to internal classes, no abstraction layer. Typical effort: 60-100 hours of remediation per major version.
-
Custom Scripts with Good Practices: Moderate impact (10-15% require changes). Abstraction layer, dependency injection, comprehensive test coverage. Typical effort: 20-40 hours of targeted fixes.
The difference is architectural discipline. If you go the custom script route, enforce these practices:
- Abstract all Manhattan API calls through a service layer
- Use dependency injection, never direct instantiation
- Maintain 80%+ unit test coverage
- Version control everything with automated regression tests
- Document API dependencies explicitly
Maintainability and Flexibility Trade-offs:
Workflow automation provides better long-term maintainability IF your allocation logic is relatively stable. If requirements change monthly, the deployment overhead of workflow changes becomes a bottleneck.
Custom scripting provides better flexibility IF you have the technical capability to maintain it properly. Without strong development practices, you’ll create unmaintainable technical debt.
My Recommendation for Your Situation:
With 47 allocation scenarios, start by categorizing them:
- How many are truly unique vs. variations of common patterns?
- Which scenarios change frequently vs. remain stable?
- Which scenarios have complex conditional logic vs. straightforward rules?
For scenarios that are stable and straightforward (probably 60-70% of your 47), use workflow automation. For scenarios with complex logic or frequent changes (probably 30-40%), implement custom allocation services with the three-layer architecture I described.
This gives you maintainability where it matters (stable scenarios) and flexibility where you need it (complex/dynamic scenarios), while minimizing upgrade impact through proper abstraction.