pdca-tracker

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'],