Skill: datadrivenconstruction/erp-integration-analysis (version 2.1.0)

--- name: "erp-integration-analysis" description: "Analyze ERP system integration for construction data flows. Map and optimize data flows between ERP modules" homepage: "https://datadrivenconstruction.io" metadata: {"openclaw": {"emoji": "🔗", "os": ["win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}} --- # ERP Integration Analysis ## Overview Based on DDC methodology (Chapter 1.2), this skill analyzes ERP system integration patterns in construction organizations, mapping data flows between modules and identifying optimization opportunities. **Book Reference:** "Технологии и системы управления в современном строительстве" / "Technologies and Management Systems in Modern Construction" ## Quick Start ```python from dataclasses import dataclass, field from enum import Enum from typing import List, Dict, Optional, Set, Tuple from datetime import datetime import json class ERPModule(Enum): """Common ERP modules in construction""" FINANCE = "finance" PROJECT_MANAGEMENT = "project_management" PROCUREMENT = "procurement" INVENTORY = "inventory" HR = "human_resources" PAYROLL = "payroll" EQUIPMENT = "equipment" SUBCONTRACTS = "subcontracts" BILLING = "billing" COST_CONTROL = "cost_control" DOCUMENT_MANAGEMENT = "document_management" REPORTING = "reporting" class IntegrationMethod(Enum): """Types of integration methods""" API = "api" DATABASE = "database" FILE_EXPORT = "file_export" MANUAL = "manual" WEBHOOK = "webhook" MESSAGE_QUEUE = "message_queue" ETL = "etl" class DataFlowDirection(Enum): """Direction of data flow""" INBOUND = "inbound" OUTBOUND = "outbound" BIDIRECTIONAL = "bidirectional" @dataclass class DataFlow: """Represents a data flow between systems/modules""" source_module: str target_module: str data_type: str frequency: str # real-time, hourly, daily, weekly, manual method: IntegrationMethod direction: DataFlowDirection volume: str # low, medium, high critical: bool = False issues: List[str] = field(default_factory=list) @dataclass class ERPSystem: """ERP system definition""" name: str vendor: str version: str modules: List[ERPModule] database: str has_api: bool api_type: Optional[str] = None # REST, SOAP, GraphQL custom_modules: List[str] = field(default_factory=list) @dataclass class IntegrationPoint: """Integration point between systems""" id: str source_system: str target_system: str method: IntegrationMethod endpoint: Optional[str] = None authentication: Optional[str] = None data_format: str = "json" status: str = "active" reliability_score: float = 1.0 last_sync: Optional[datetime] = None @dataclass class IntegrationAnalysis: """Complete integration analysis results""" erp_system: ERPSystem external_systems: List[str] data_flows: List[DataFlow] integration_points: List[IntegrationPoint] integration_score: float bottlenecks: List[str] recommendations: List[str] data_flow_diagram: Dict class ERPIntegrationAnalyzer: """ Analyze ERP system integration for construction data flows. Based on DDC methodology Chapter 1.2. """ def __init__(self): self.module_dependencies = self._define_module_dependencies() self.critical_flows = self._define_critical_flows() def _define_module_dependencies(self) -> Dict[ERPModule, List[ERPModule]]: """Define typical module dependencies""" return { ERPModule.PROJECT_MANAGEMENT: [ ERPModule.COST_CONTROL, ERPModule.PROCUREMENT, ERPModule.HR, ERPModule.DOCUMENT_MANAGEMENT ], ERPModule.COST_CONTROL: [ ERPModule.FINANCE, ERPModule.PROJECT_MANAGEMENT, ERPModule.BILLING ], ERPModule.PROCUREMENT: [ ERPModule.INVENTORY, ERPModule.FINANCE, ERPModule.SUBCONTRACTS ], ERPModule.BILLING: [ ERPModule.FINANCE, ERPModule.PROJECT_MANAGEMENT, ERPModule.COST_CONTROL ], ERPModule.PAYROLL: [ ERPModule.HR, ERPModule.FINANCE, ERPModule.PROJECT_MANAGEMENT ], ERPModule.INVENTORY: [ ERPModule.PROCUREMENT, ERPModule.PROJECT_MANAGEMENT, ERPModule.FINANCE ], ERPModule.EQUIPMENT: [ ERPModule.PROJECT_MANAGEMENT, ERPModule.FINANCE, ERPModule.INVENTORY ], ERPModule.SUBCONTRACTS: [ ERPModule.PROCUREMENT, ERPModule.FINANCE, ERPModule.PROJECT_MANAGEMENT ] } def _define_critical_flows(self) -> List[Tuple[str, str]]: """Define business-critical data flows""" return [ ("project_management", "cost_control"), ("cost_control", "finance"), ("procurement", "inventory"), ("billing", "finance"), ("hr", "payroll"), ("project_management", "billing") ] def analyze_erp_integration( self, erp_system: ERPSystem, external_systems: List[Dict], integration_points: List[IntegrationPoint], transaction_logs: Optional[List[Dict]] = None ) -> IntegrationAnalysis: """ Perform comprehensive ERP integration analysis. Args: erp_system: The ERP system to analyze external_systems: List of external systems integration_points: Defined integration points transaction_logs: Optional transaction logs for analysis Returns: Complete integration analysis """ # Map all data flows data_flows = self._map_data_flows( erp_system, integration_points, transaction_logs ) # Calculate integration score integration_score = self._calculate_integration_score( erp_system, data_flows, integration_points ) # Identify bottlenecks bottlenecks = self._identify_bottlenecks( data_flows, integration_points ) # Generate recommendations recommendations = self._generate_recommendations( erp_system, data_flows, bottlenecks ) # Create data flow diagram diagram = self._create_flow_diagram( erp_system, external_systems, data_flows ) return IntegrationAnalysis( erp_system=erp_system, external_systems=[s["name"] for s in external_systems], data_flows=data_flows, integration_points=integration_points, integration_score=integration_score, bottlenecks=bottlenecks, recommendations=recommendations, data_flow_diagram=diagram ) def _map_data_flows( self, erp: ERPSystem, integration_points: List[IntegrationPoint], logs: Optional[List[Dict]] ) -> List[DataFlow]: """Map all data flows in the system""" flows = [] # Internal module flows for module in erp.modules: dependencies = self.module_dependencies.get(module, []) for dep in dependencies: if dep in erp.modules: is_critical = (module.value, dep.value) in self.critical_flows flows.append(DataFlow( source_module=module.value, target_module=dep.value, data_type=self._get_data_type(module, dep), frequency="real-time", method=IntegrationMethod.DATABASE, direction=DataFlowDirection.BIDIRECTIONAL, volume="high" if is_critical else "medium", critical=is_critical )) # External integration flows for point in integration_points: if point.source_system == erp.name or point.target_system == erp.name: flows.append(DataFlow( source_module=point.source_system, target_module=point.target_system, data_type="mixed", frequency=self._infer_frequency(point), method=point.method, direction=DataFlowDirection.BIDIRECTIONAL, volume="medium", critical=False )) # Analyze logs if available if logs: flows = self._enhance_flows_from_logs(flows, logs) return flows def _get_data_type( self, source: ERPModule, target: ERPModule ) -> str: """Determine data type for module pair""" data_types = { (ERPModule.PROJECT_MANAGEMENT, ERPModule.COST_CONTROL): "costs_budgets", (ERPModule.COST_CONTROL, ERPModule.FINANCE): "financial_transactions", (ERPModule.PROCUREMENT, ERPModule.INVENTORY): "purchase_orders", (ERPModule.HR, ERPModule.PAYROLL): "employee_time", (ERPModule.BILLING, ERPModule.FINANCE): "invoices" } return data_types.get((source, target), "general_data") def _infer_frequency(self, point: IntegrationPoint) -> str: """Infer integration frequency from method""" if point.method == IntegrationMethod.WEBHOOK: return "real-time" elif point.method == IntegrationMethod.API: return "hourly" elif point.method == IntegrationMethod.ETL: return "daily" elif point.method == IntegrationMethod.FILE_EXPORT: return "daily" else: return "manual" def _enhance_flows_from_logs( self, flows: List[DataFlow], logs: List[Dict] ) -> List[DataFlow]: """Enhance flow information from transaction logs""" # Analyze log patterns flow_stats = {} for log in logs: key = (log.get("source"), log.get("target")) if key not in flow_stats: flow_stats[key] = {"count": 0, "errors": 0} flow_stats[key]["count"] += 1 if log.get("status") == "error": flow_stats[key]["errors"] += 1 # Update flows with statistics for flow in flows: key = (flow.source_module, flow.target_module) if key in flow_stats: stats = flow_stats[key] error_rate = stats["errors"] / stats["count"] if stats["count"] > 0 else 0 if error_rate > 0.1: flow.issues.append(f"High error rate: {error_rate:.1%}") if stats["count"] < 10: flow.issues.append("Low transaction volume") return flows def _calculate_integration_score( self, erp: ERPSystem, flows: List[DataFlow], points: List[IntegrationPoint] ) -> float: """Calculate overall integration score (0-1)""" scores = [] # API availability if erp.has_api: scores.append(1.0) else: scores.append(0.3) # Integration method quality method_scores = { IntegrationMethod.API: 1.0, IntegrationMethod.WEBHOOK: 1.0, IntegrationMethod.MESSAGE_QUEUE: 0.9, IntegrationMethod.ETL: 0.8, IntegrationMethod.DATABASE: 0.7, IntegrationMethod.FILE_EXPORT: 0.5, IntegrationMethod.MANUAL: 0.2 } if points: avg_method_score = sum( method_scores.get(p.method, 0.5) for p in points ) / len(points) scores.append(avg_method_score) # Critical flow coverage critical_covered = sum(1 for f in flows if f.critical) / len(self.critical_flows) scores.append(critical_covered) # Flow health (issues) flows_with_issues = sum(1 for f in flows if f.issues) flow_health = 1 - (flows_with_issues / len(flows)) if flows else 1 scores.append(flow_health) return sum(scores) / len(scores) def _identify_bottlenecks( self, flows: List[DataFlow], points: List[IntegrationPoint] ) -> List[str]: """Identify integration bottlenecks""" bottlenecks = [] # Manual integrations manual_flows = [f for f in flows if f.method == IntegrationMethod.MANUAL] if manual_flows: bottlenecks.append( f"{len(manual_flows)} manual data flows requiring automation" ) # File-based integrations file_flows = [f for f in flows if f.method == IntegrationMethod.FILE_EXPORT] if file_flows: bottlenecks.append( f"{len(file_flows)} file-based integrations causing delays" ) # Low reliability points low_reliability = [p for p in points if p.reliability_score < 0.8] if low_reliability: bottlenecks.append( f"{len(low_reliability)} integration points with low reliability" ) # Flows with issues problem_flows = [f for f in flows if f.issues] for flow in problem_flows: for issue in flow.issues: bottlenecks.append( f"{flow.source_module} → {flow.target_module}: {issue}" ) # Missing critical flows existing_critical = { (f.source_module, f.target_module) for f in flows if f.critical } for critical in self.critical_flows: if critical not in existing_critical: bottlenecks.append( f"Missing critical flow: {critical[0]} → {critical[1]}" ) return bottlenecks def _generate_recommendations( self, erp: ERPSystem, flows: List[DataFlow], bottlenecks: List[str] ) -> List[str]: """Generate integration improvement recommendations""" recommendations = [] # API recommendations if not erp.has_api: recommendations.append( "Enable API access for the ERP system to improve integration capabilities" ) # Method upgrades manual_count = sum(1 for f in flows if f.method == IntegrationMethod.MANUAL) if manual_count > 0: recommendations.append( f"Automate {manual_count} manual data flows using API or ETL" ) file_count = sum(1 for f in flows if f.method == IntegrationMethod.FILE_EXPORT) if file_count > 2: recommendations.append( "Replace file-based integrations with real-time API connections" ) # Real-time integration non_realtime = sum( 1 for f in flows if f.critical and f.frequency not in ["real-time", "hourly"] ) if non_realtime > 0: recommendations.append( f"Upgrade {non_realtime} critical flows to real-time synchronization" ) # Data quality if any("error rate" in b.lower() for b in bottlenecks): recommendations.append( "Implement data validation at integration points to reduce errors" ) # Monitoring recommendations.append( "Implement integration monitoring dashboard for proactive issue detection" ) return recommendations def _create_flow_diagram( self, erp: ERPSystem, external_systems: List[Dict], flows: List[DataFlow] ) -> Dict: """Create data flow diagram structure""" nodes = [] edges = [] # Add ERP modules as nodes for module in erp.modules: nodes.append({ "id": module.value, "type": "erp_module", "label": module.value.replace("_", " ").title(), "system": erp.name }) # Add external systems as nodes for system in external_systems: nodes.append({ "id": system["name"], "type": "external", "label": system["name"], "system": "external" }) # Add flows as edges for flow in flows: edges.append({ "source": flow.source_module, "target": flow.target_module, "method": flow.method.value, "frequency": flow.frequency, "critical": flow.critical, "data_type": flow.data_type }) return { "nodes": nodes, "edges": edges, "legend": { "node_types": ["erp_module", "external"], "edge_methods": [m.value for m in IntegrationMethod] } } def compare_integration_options( self, options: List[Dict] ) -> Dict: """Compare different integration approaches""" comparison = [] for option in options: score = self._score_integration_option(option) comparison.append({ "name": option["name"], "method": option.get("method", "unknown"), "cost": option.get("cost", "unknown"), "implementation_time": option.get("time", "unknown"), "reliability": score["reliability"], "scalability": score["scalability"], "maintenance": score["maintenance"], "total_score": score["total"] }) # Sort by total score comparison.sort(key=lambda x: x["total_score"], reverse=True) return { "options": comparison, "recommendation": comparison[0]["name"] if comparison else None } def _score_integration_option(self, option: Dict) -> Dict: """Score an integration option""" method = option.get("method", "") # Base scores by method method_scores = { "api": {"reliability": 0.9, "scalability": 0.9, "maintenance": 0.8}, "etl": {"reliability": 0.8, "scalability": 0.8, "maintenance": 0.7}, "file": {"reliability": 0.6, "scalability": 0.5, "maintenance": 0.6}, "manual": {"reliability": 0.4, "scalability": 0.2, "maintenance": 0.3} } scores = method_scores.get(method, {"reliability": 0.5, "scalability": 0.5, "maintenance": 0.5}) scores["total"] = sum(scores.values()) / 3 return scores class IntegrationHealthMonitor: """Monitor ERP integration health""" def __init__(self, integration_points: List[IntegrationPoint]): self.points = integration_points self.history: List[Dict] = [] def check_health(self) -> Dict: """Check current integration health""" results = { "timestamp": datetime.now(), "overall_status": "healthy", "points_checked": len(self.points), "issues": [] } for point in self.points: status = self._check_point(point) if status["status"] != "healthy": results["issues"].append({ "point": point.id, "status": status["status"], "message": status["message"] }) if len(results["issues"]) > 0: results["overall_status"] = "degraded" if len(results["issues"]) > len(self.points) * 0.5: results["overall_status"] = "critical" self.history.append(results) return results def _check_point(self, point: IntegrationPoint) -> Dict: """Check individual integration point""" if point.status != "active": return {"status": "inactive", "message": "Integration point disabled"} if point.reliability_score < 0.5: return {"status": "degraded", "message": "Low reliability score"} if point.last_sync: hours_since_sync = (datetime.now() - point.last_sync).total_seconds() / 3600 if hours_since_sync > 24: return {"status": "stale", "message": f"No sync for {hours_since_sync:.0f} hours"} return {"status": "healthy", "message": "OK"} def get_health_report(self) -> str: """Generate health report""" current = self.check_health() report = f""" # ERP Integration Health Report Generated: {current['timestamp'].strftime('%Y-%m-%d %H:%M')} ## Overall Status: {current['overall_status'].upper()} ### Integration Points: {current['points_checked']} ### Active Issues: {len(current['issues'])} """ if current['issues']: report += "\n### Issues:\n" for issue in current['issues']: report += f"- **{issue['point']}**: {issue['status']} - {issue['message']}\n" return report ``` ## Common Use Cases ### Analyze ERP Integration ```python analyzer = ERPIntegrationAnalyzer() # Define ERP system erp = ERPSystem( name="SAP S/4HANA", vendor="SAP", version="2023", modules=[ ERPModule.FINANCE, ERPModule.PROJECT_MANAGEMENT, ERPModule.PROCUREMENT, ERPModule.COST_CONTROL, ERPModule.HR, ERPModule.BILLING ], database="HANA", has_api=True, api_type="REST" ) # Define external systems external = [ {"name": "Procore", "type": "project_management"}, {"name": "Revit", "type": "bim"}, {"name": "Primavera", "type": "scheduling"} ] # Define integration points points = [ IntegrationPoint( id="erp-procore", source_system="SAP S/4HANA", target_system="Procore", method=IntegrationMethod.API ), IntegrationPoint( id="erp-primavera", source_system="SAP S/4HANA", target_system="Primavera", method=IntegrationMethod.FILE_EXPORT ) ] analysis = analyzer.analyze_erp_integration( erp_system=erp, external_systems=external, integration_points=points ) print(f"Integration Score: {analysis.integration_score:.0%}") print(f"Bottlenecks: {len(analysis.bottlenecks)}") ``` ### Monitor Integration Health ```python monitor = IntegrationHealthMonitor(integration_points) health = monitor.check_health() print(f"Status: {health['overall_status']}") if health['issues']: for issue in health['issues']: print(f" - {issue['point']}: {issue['message']}") # Generate report report = monitor.get_health_report() print(report) ``` ### Compare Integration Options ```python options = [ {"name": "REST API Integration", "method": "api", "cost": 50000, "time": "3 months"}, {"name": "ETL Pipeline", "method": "etl", "cost": 30000, "time": "2 months"}, {"name": "File-based Export", "method": "file", "cost": 10000, "time": "1 month"} ] comparison = analyzer.compare_integration_options(options) print(f"Recommended: {comparison['recommendation']}") ``` ## Quick Reference | Component | Purpose | |-----------|---------| | `ERPIntegrationAnalyzer` | Main analysis engine | | `ERPSystem` | ERP system definition | | `ERPModule` | Standard ERP modules | | `IntegrationPoint` | Integration connection | | `DataFlow` | Data flow mapping | | `IntegrationHealthMonitor` | Health monitoring | ## Resources - **Book**: "Data-Driven Construction" by Artem Boiko, Chapter 1.2 - **Website**: https://datadrivenconstruction.io ## Next Steps - Use [data-silo-detection](../data-silo-detection/SKILL.md) to identify isolated systems - Use [etl-pipeline](../../Chapter-4.2/etl-pipeline/SKILL.md) for data integration - Use [interoperability-analyzer](../../Chapter-3.5/interoperability-analyzer/SKILL.md) for standards compliance

path: claw.json
content: {
  "name": "erp-integration-analysis",
  "version": "2.0.0",
  "description": "Analyze ERP system integration for construction data flows. Map and optimize data flows between ERP modules",
  "author": "datadrivenconstruction",
  "license": "MIT",
  "permissions": [
    "filesystem",
    "network"
  ],
  "entry": "instructions.md",
  "tags": [
    "construction",
    "data-processing"
  ],
  "models": [
    "claude-*",
    "gpt-*"
  ],
  "minOpenClawVersion": "0.8.0"
}

path: instructions.md
content: You are a construction industry assistant specializing in construction project management.

Analyze ERP system integration for construction data flows. Map and optimize data flows between ERP modules

When the user asks to analyze data and generate insights:
1. Gather the required input data from the user
2. Process the data using the methods described in SKILL.md
3. Present results in a clear, structured format
4. Offer follow-up analysis or export options

## Input Format
- The user provides project data, file paths, or parameters as described in SKILL.md
- Accept data in common formats: CSV, Excel, JSON, or direct input

## Output Format
- Present results in structured tables when applicable
- Include summary statistics and key findings
- Offer export to Excel/CSV/JSON when relevant

## Key Reference
- See SKILL.md for detailed implementation code, classes, and methods
- Follow the patterns and APIs defined in the skill documentation

## Constraints
- Only use data provided by the user or referenced in the skill
- Validate inputs before processing
- Report errors clearly with suggested fixes
- Follow construction industry standards and best practices


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)