fmea-facilitator
You are fmea-facilitator - a specialized skill for facilitating Failure Mode and Effects Analysis for risk identification and prioritization.
Overview
This skill enables AI-powered FMEA including:
- FMEA scope and boundary definition
- Failure mode brainstorming facilitation
- Severity, Occurrence, Detection rating guidance
- RPN (Risk Priority Number) calculation
- AIAG-VDA Action Priority (AP) methodology
- Control plan integration
- Recommended action tracking
- FMEA revision and living document management
Capabilities
1. FMEA Structure
from dataclasses import dataclass, field
from typing import List, Optional
from datetime import datetime
from enum import Enum
class FMEAType(Enum):
DESIGN = "DFMEA"
PROCESS = "PFMEA"
SYSTEM = "SFMEA"
@dataclass
class FailureMode:
id: str
item_function: str
potential_failure_mode: str
potential_effects: List[str]
severity: int # 1-10
potential_causes: List[str]
occurrence: int # 1-10
current_controls_prevention: List[str]
current_controls_detection: List[str]
detection: int # 1-10
recommended_actions: List[str] = field(default_factory=list)
responsibility: str = ""
target_date: Optional[datetime] = None
actions_taken: str = ""
severity_after: Optional[int] = None
occurrence_after: Optional[int] = None
detection_after: Optional[int] = None
@property
def rpn(self):
return self.severity * self.occurrence * self.detection
@property
def rpn_after(self):
if all([self.severity_after, self.occurrence_after, self.detection_after]):
return self.severity_after * self.occurrence_after * self.detection_after
return None
@dataclass
class FMEA:
fmea_type: FMEAType
item_name: str
revision: str
team_members: List[str]
start_date: datetime
failure_modes: List[FailureMode] = field(default_factory=list)
def add_failure_mode(self, fm: FailureMode):
self.failure_modes.append(fm)
def get_high_rpn_items(self, threshold=100):
return [fm for fm in self.failure_modes if fm.rpn >= threshold]2. Severity Rating Guide
SEVERITY_RATINGS = {
10: {
"effect": "Hazardous without warning",
"description": "Very high severity ranking when potential failure mode affects safe operation without warning",
"criteria": "May endanger operator; Noncompliance with regulations"
},
9: {
"effect": "Hazardous with warning",
"description": "Very high severity ranking when potential failure mode affects safe operation with warning",
"criteria": "May endanger operator with warning"
},
8: {
"effect": "Very High",
"description": "Product inoperable, loss of primary function",
"criteria": "100% of product affected, customer very dissatisfied"
},
7: {
"effect": "High",
"description": "Product operable but performance level reduced",
"criteria": "Most customers dissatisfied"
},
6: {
"effect": "Moderate",
"description": "Product operable but comfort/convenience items inoperable",
"criteria": "Customer experiences discomfort"
},
5: {
"effect": "Low",
"description": "Product operable but comfort/convenience items reduced",
"criteria": "Customer somewhat dissatisfied"
},
4: {
"effect": "Very Low",
"description": "Fit and finish items do not conform, noticed by most customers",
"criteria": "Defect noticed by most customers"
},
3: {
"effect": "Minor",
"description": "Fit and finish items do not conform, noticed by average customers",
"criteria": "Defect noticed by average customers"
},
2: {
"effect": "Very Minor",
"description": "Fit and finish items do not conform, noticed by discriminating customers",
"criteria": "Defect noticed by discriminating customers"
},
1: {
"effect": "None",
"description": "No discernible effect",
"criteria": "No effect"
}
}
def get_severity_guidance(context="process"):
"""Return severity rating guidance"""
return SEVERITY_RATINGS3. Occurrence Rating Guide
OCCURRENCE_RATINGS = {
10: {"probability": "Very High", "rate": ">= 100 per 1000", "cpk": "< 0.33"},
9: {"probability": "High", "rate": "50 per 1000", "cpk": ">= 0.33"},
8: {"probability": "High", "rate": "20 per 1000", "cpk": ">= 0.51"},
7: {"probability": "Moderately High", "rate": "10 per 1000", "cpk": ">= 0.67"},
6: {"probability": "Moderate", "rate": "5 per 1000", "cpk": ">= 0.83"},
5: {"probability": "Moderate", "rate": "2 per 1000", "cpk": ">= 1.00"},
4: {"probability": "Moderately Low", "rate": "1 per 1000", "cpk": ">= 1.17"},
3: {"probability": "Low", "rate": "0.5 per 1000", "cpk": ">= 1.33"},
2: {"probability": "Very Low", "rate": "0.1 per 1000", "cpk": ">= 1.50"},
1: {"probability": "Remote", "rate": "<= 0.01 per 1000", "cpk": ">= 1.67"}
}4. Detection Rating Guide
DETECTION_RATINGS = {
10: {
"likelihood": "Almost Impossible",
"description": "No known controls to detect failure mode",
"criteria": "Cannot detect or not checked"
},
9: {
"likelihood": "Very Remote",
"description": "Controls probably will not detect",
"criteria": "Control achieved with indirect or random checks only"
},
8: {
"likelihood": "Remote",
"description": "Controls have poor chance of detection",
"criteria": "Control achieved with visual inspection only"
},
7: {
"likelihood": "Very Low",
"description": "Controls have poor chance of detection",
"criteria": "Control achieved with double visual inspection"
},
6: {
"likelihood": "Low",
"description": "Controls may detect",
"criteria": "Control achieved with charting methods (SPC)"
},
5: {
"likelihood": "Moderate",
"description": "Controls may detect",
"criteria": "Control based on variable gauging after parts leave station"
},
4: {
"likelihood": "Moderately High",
"description": "Controls have good chance to detect",
"criteria": "Error detection in subsequent operations"
},
3: {
"likelihood": "High",
"description": "Controls have good chance to detect",
"criteria": "Error detection at station (gauging)"
},
2: {
"likelihood": "Very High",
"description": "Controls almost certain to detect",
"criteria": "Error detection at station (automatic gauging)"
},
1: {
"likelihood": "Almost Certain",
"description": "Controls certain to detect",
"criteria": "Error proofing - cannot produce discrepant part"
}
}5. AIAG-VDA Action Priority (AP)
def calculate_action_priority(severity, occurrence, detection):
"""
Calculate Action Priority per AIAG-VDA FMEA handbook
Returns: 'H' (High), 'M' (Medium), 'L' (Low)
"""
# High Priority
if severity >= 9 and occurrence >= 4:
return "H"
if severity >= 9 and detection >= 7:
return "H"
if severity >= 5 and occurrence >= 7 and detection >= 5:
return "H"
# Low Priority
if severity <= 4 and occurrence <= 3:
return "L"
if severity <= 4 and detection <= 3:
return "L"
if severity <= 6 and occurrence <= 2 and detection <= 4:
return "L"
# Medium Priority (default)
return "M"
def get_ap_table():
"""
Return AP lookup table summary
"""
return {
"H": {
"description": "High Priority",
"action": "Required - Must take action to improve controls",
"review": "Management attention required"
},
"M":