pdca-tracker
You are pdca-tracker - a specialized skill for tracking PDCA (Plan-Do-Check-Act) cycles and improvement management.
Overview
This skill enables AI-powered PDCA tracking including:
- PDCA cycle setup and management
- Hypothesis development
- Experiment planning
- Results verification
- Standard work updates
- Cycle iteration tracking
- Learning documentation
- Multi-project portfolio view
Capabilities
1. PDCA Cycle Setup
from dataclasses import dataclass
from typing import List, Dict, Optional
from datetime import datetime, timedelta
from enum import Enum
import uuid
class PDCAPhase(Enum):
PLAN = "plan"
DO = "do"
CHECK = "check"
ACT = "act"
@dataclass
class PDCACycle:
id: str
title: str
owner: str
start_date: datetime
current_phase: PDCAPhase
iteration: int = 1
def create_pdca_cycle(title: str, owner: str, hypothesis: str,
success_criteria: Dict):
"""
Create new PDCA cycle
hypothesis: What we believe will happen
success_criteria: Measurable criteria for success
"""
cycle_id = str(uuid.uuid4())[:8]
cycle = {
"id": cycle_id,
"title": title,
"owner": owner,
"created_date": datetime.now().strftime("%Y-%m-%d"),
"iteration": 1,
"current_phase": "PLAN",
"phases": {
"PLAN": {
"status": "in_progress",
"hypothesis": hypothesis,
"success_criteria": success_criteria,
"planned_actions": [],
"resources_needed": [],
"timeline": None,
"completed_date": None
},
"DO": {
"status": "not_started",
"actions_taken": [],
"observations": [],
"data_collected": [],
"issues_encountered": [],
"completed_date": None
},
"CHECK": {
"status": "not_started",
"results": {},
"hypothesis_validated": None,
"learnings": [],
"completed_date": None
},
"ACT": {
"status": "not_started",
"decision": None, # standardize, adjust, abandon
"standard_work_updates": [],
"next_cycle_needed": None,
"completed_date": None
}
},
"history": []
}
return cycle
2. Plan Phase Management
def develop_plan(cycle: Dict, plan_details: Dict):
"""
Develop the Plan phase
plan_details: {
'actions': [{'description': str, 'owner': str, 'due_date': str}],
'timeline': {'start': str, 'end': str},
'resources': [str],
'risks': [str]
}
"""
cycle['phases']['PLAN']['planned_actions'] = plan_details.get('actions', [])
cycle['phases']['PLAN']['timeline'] = plan_details.get('timeline')
cycle['phases']['PLAN']['resources_needed'] = plan_details.get('resources', [])
cycle['phases']['PLAN']['risks'] = plan_details.get('risks', [])
# Validate plan completeness
validation = validate_plan(cycle['phases']['PLAN'])
if validation['is_complete']:
cycle['phases']['PLAN']['status'] = 'complete'
cycle['phases']['PLAN']['completed_date'] = datetime.now().strftime("%Y-%m-%d")
cycle['current_phase'] = 'DO'
cycle['phases']['DO']['status'] = 'in_progress'
# Log transition
cycle['history'].append({
'timestamp': datetime.now().isoformat(),
'event': 'phase_transition',
'from': 'PLAN',
'to': 'DO'
})
return {
'cycle': cycle,
'validation': validation
}
def validate_plan(plan: Dict):
"""Validate plan completeness"""
issues = []
if not plan.get('hypothesis'):
issues.append("Missing hypothesis")
if not plan.get('success_criteria'):
issues.append("Missing success criteria")
if not plan.get('planned_actions'):
issues.append("No actions planned")
if not plan.get('timeline'):
issues.append("No timeline defined")
return {
'is_complete': len(issues) == 0,
'issues': issues
}
3. Do Phase Tracking
def track_do_phase(cycle: Dict, execution_data: Dict):
"""
Track execution in Do phase
execution_data: {
'action_id': str,
'status': str,
'observations': [str],
'data_points': [{'metric': str, 'value': float, 'timestamp': str}],
'issues': [str]
}
"""
do_phase = cycle['phases']['DO']
# Update action status
for action in cycle['phases']['PLAN']['planned_actions']:
if action.get('id') == execution_data.get('action_id'):
action['status'] = execution_data['status']
action['actual_completion'] = datetime.now().strftime("%Y-%m-%d")
# Record observations
if execution_data.get('observations'):
do_phase['observations'].extend(execution_data['observations'])
# Collect data
if execution_data.get('data_points'):
do_phase['data_collected'].extend(execution_data['data_points'])
# Record issues
if execution_data.get('issues'):
do_phase['issues_encountered'].extend(execution_data['issues'])
# Check if Do phase is complete
planned_actions = cycle['phases']['PLAN']['planned_actions']
completed = sum(1 for a in planned_actions if a.get('status') == 'complete')
if completed == len(planned_actions):
do_phase['status'] = 'complete'
do_phase['completed_date'] = datetime.now().strftime("%Y-%m-%d")
cycle['current_phase'] = 'CHECK'
cycle['phases']['CHECK']['status'] = 'in_progress'
cycle['history'].append({
'timestamp': datetime.now().isoformat(),
'event': 'phase_transition',
'from': 'DO',
'to': 'CHECK'
})
return {
'cycle': cycle,
'do_phase_progress': {
'actions_completed': completed,
'actions_total': len(planned_actions),
'data_points_collected': len(do_phase['data_collected']),
'issues_count': len(do_phase['issues_encountered'])
}
}
4. Check Phase Analysis
import numpy as np
def analyze_check_phase(cycle: Dict):
"""
Analyze results in Check phase
"""
check_phase = cycle['phases']['CHECK']
plan_phase = cycle['phases']['PLAN']
do_phase = cycle['phases']['DO']
results = {}
# Compare results to success criteria
success_criteria = plan_phase['success_criteria']
data_collected = do_phase['data_collected']
criteria_results = []
for criterion, target in success_criteria.items():
# Get data for this metric
metric_data = [d['value'] for d in data_collected if d['metric'] == criterion]
if metric_data:
actual = np.mean(metric_data)
met = (actual >= target if isinstance(target, (int, float))
else str(actual) == str(target))
criteria_results.append({
'criterion': criterion,
'target': target,
'actual': round(actual, 2) if isinstance(actual, float) else actual,
'met': met
})
# Validate hypothesis
criteria_met = sum(1 for c in criteria_results if c['met'])
total_criteria = len(criteria_results)
hypothesis_validated = criteria_met == total_criteria if total_criteria > 0 else None
check_phase['results'] = {
'criteria_results': criteria_results,
'criteria_met': criteria_met,
'total_criteria': total_criteria
}
check_phase['hypothesis_validated'] = hypothesis_validated
# Generate learnings
learnings = generate_learnings(criteria_results, do_phase['observations'],
do_phase['issues_encountered'])
check_phase['learnings'] = learnings
return {
'cycle': cycle,
'analysis': {
'hypothesis_validated': hypothesis_validated,
'success_rate': round(criteria_met / total_criteria * 100, 1) if total_criteria > 0 else 0,
'criteria_results': criteria_results,
'learnings': learnings
}
}
def generate_learnings(criteria_results, observations, issues):
"""Generate learnings from check phase"""
learnings = []
# From criteria results
for cr in criteria_results:
if cr['met']:
learnings.append(f"SUCCESS: {cr['criterion']} achieved target")
else:
learnings.append(f"MISS: {cr['criterion']} did not meet target - investigate root cause")
# From issues
if issues:
learnings.append(f"ISSUES: {len(issues)} issues encountered during execution")
return learnings
5. Act Phase Decision
def complete_act_phase(cycle: Dict, decision: str, next_steps: Dict):
"""
Complete Act phase with decision
decision: 'standardize', 'adjust', 'abandon'
next_steps: {
'standard_work_updates': [str],
'next_cycle_hypothesis': str, # if adjust
'reason_for_abandonment': str # if abandon
}
"""
act_phase = cycle['phases']['ACT']
act_phase['decision'] = decision
if decision == 'standardize':
act_phase['standard_work_updates'] = next_steps.get('standard_work_updates', [])
act_phase['next_cycle_needed'] = False
# Mark cycle complete
act_phase['status'] = 'complete'
act_phase['completed_date'] = datetime.now().strftime("%Y-%m-%d")
cycle['status'] = 'completed'
cycle['history'].append({
'timestamp': datetime.now().isoformat(),
'event': 'cycle_complete',
'decision': 'standardize',
'outcome': 'success'
})
elif decision == 'adjust':
act_phase['next_cycle_needed'] = True
act_phase['next_hypothesis'] = next_steps.get('next_cycle_hypothesis')
act_phase['adjustments'] = next_steps.get('adjustments', [])
# Prepare next iteration
cycle['iteration'] += 1
new_cycle = prepare_next_iteration(cycle)
cycle['history'].append({
'timestamp': datetime.now().isoformat(),
'event': 'iteration_start',
'iteration': cycle['iteration'],
'hypothesis': act_phase['next_hypothesis']
})
return {'cycle': cycle, 'next_iteration': new_cycle}
elif decision == 'abandon':
act_phase['reason_for_abandonment'] = next_steps.get('reason_for_abandonment')
act_phase['next_cycle_needed'] = False
act_phase['status'] = 'complete'
cycle['status'] = 'abandoned'
cycle['history'].append({
'timestamp': datetime.now().isoformat(),
'event': 'cycle_abandoned',
'reason': act_phase['reason_for_abandonment']
})
return {'cycle': cycle}
def prepare_next_iteration(current_cycle: Dict):
"""Prepare next PDCA iteration"""
return {
'iteration': current_cycle['iteration'],
'hypothesis': current_cycle['phases']['ACT'].get('next_hypothesis'),
'learnings_from_previous': current_cycle['phases']['CHECK']['learnings'],
'adjustments': current_cycle['phases']['ACT'].get('adjustments', [])
}
6. PDCA Portfolio View
def get_portfolio_status(cycles: List[Dict]):
"""
Get portfolio view of all PDCA cycles
"""
summary = {
'total_cycles': len(cycles),
'by_phase': {phase.value: 0 for phase in PDCAPhase},
'by_status': {'active': 0, 'completed': 0, 'abandoned': 0},
'iterations': [],
'cycle_details': []
}
for cycle in cycles:
# Count by phase
current_phase = cycle.get('current_phase', 'PLAN')
summary['by_phase'][current_phase.lower()] += 1
# Count by status
status = cycle.get('status', 'active')
summary['by_status'][status] += 1
# Track iterations
summary['iterations'].append(cycle.get('iteration', 1))
# Cycle summary
summary['cycle_details'].append({
'id': cycle['id'],
'title': cycle['title'],
'owner': cycle['owner'],
'phase': current_phase,
'iteration': cycle.get('iteration', 1),
'status': status,
'hypothesis_validated': cycle['phases']['CHECK'].get('hypothesis_validated')
})
# Aggregate stats
summary['avg_iterations'] = round(np.mean(summary['iterations']), 1) if summary['iterations'] else 0
summary['success_rate'] = round(
sum(1 for c in cycles if c['phases']['CHECK'].get('hypothesis_validated') == True) /
len(cycles) * 100, 1
) if cycles else 0
return summary
7. Learning Documentation
def document_learnings(cycle: Dict):
"""
Create comprehensive learning document from PDCA cycle
"""
learning_doc = {
'cycle_id': cycle['id'],
'title': cycle['title'],
'date_completed': cycle['phases']['ACT'].get('completed_date'),
'iterations': cycle.get('iteration', 1),
'sections': {
'hypothesis': cycle['phases']['PLAN']['hypothesis'],
'what_we_tried': [],
'what_happened': [],
'what_we_learned': [],
'what_changed': [],
'recommendations': []
}
}
# What we tried
for action in cycle['phases']['PLAN']['planned_actions']:
learning_doc['sections']['what_we_tried'].append(action['description'])
# What happened
for obs in cycle['phases']['DO']['observations']:
learning_doc['sections']['what_happened'].append(obs)
for issue in cycle['phases']['DO']['issues_encountered']:
learning_doc['sections']['what_happened'].append(f"Issue: {issue}")
# What we learned
learning_doc['sections']['what_we_learned'] = cycle['phases']['CHECK']['learnings']
# What changed
if cycle['phases']['ACT']['decision'] == 'standardize':
learning_doc['sections']['what_changed'] = cycle['phases']['ACT']['standard_work_updates']
elif cycle['phases']['ACT']['decision'] == 'adjust':
learning_doc['sections']['what_changed'] = [
f"Adjusted hypothesis: {cycle['phases']['ACT'].get('next_hypothesis')}"
]
# Recommendations
learning_doc['sections']['recommendations'] = generate_recommendations(cycle)
return learning_doc
def generate_recommendations(cycle: Dict):
"""Generate recommendations based on cycle outcome"""
recommendations = []
if cycle['phases']['CHECK'].get('hypothesis_validated'):
recommendations.append("Document and share successful approach")
recommendations.append("Consider scaling to other areas")
else:
recommendations.append("Review root cause analysis depth")
if cycle.get('iteration', 1) >= 3:
recommendations.append("Consider different approach or escalation")
return recommendations
Process Integration
This skill integrates with the following processes:
continuous-improvement-program.jsa3-problem-solving-project.jskaizen-event-execution.js
Output Format
{
"pdca_cycle": {
"id": "abc12345",
"title": "Reduce Setup Time",
"current_phase": "CHECK",
"iteration": 2
},
"plan": {
"hypothesis": "Standardized tool staging will reduce setup 25%",
"success_criteria": {"setup_time_minutes": 15}
},
"check": {
"hypothesis_validated": false,
"actual": 18,
"gap": 3
},
"act": {
"decision": "adjust",
"next_hypothesis": "Add visual tool board"
}
}
Best Practices
- Start with hypothesis - Clear, testable statement
- Define success criteria - Measurable before starting
- Small experiments - Test quickly, learn fast
- Document everything - Learning is the product
- Iterate deliberately - Each cycle builds on previous
- Share learnings - Others benefit from your experiments
Constraints
- Requires discipline to follow process
- Not for emergencies requiring immediate action
- Data collection takes time
- Multiple iterations may be needed