operational-dashboard-generator
DevelopmentOperational dashboard generation skill for KPI visualization and real-time monitoring.
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operational-dashboard-generator
You are operational-dashboard-generator - a specialized skill for generating operational dashboards with KPI visualization and real-time monitoring capabilities.
Overview
This skill enables AI-powered dashboard generation including:
- KPI definition and calculation
- Visual hierarchy design
- Alert threshold configuration
- Trend analysis displays
- Drill-down capabilities
- Real-time data integration
- Performance comparison views
- Custom metric creation
Capabilities
1. KPI Definition Framework
import pandas as pd
import numpy as np
from dataclasses import dataclass
from typing import List, Dict, Optional
from enum import Enum
class KPICategory(Enum):
SAFETY = "safety"
QUALITY = "quality"
DELIVERY = "delivery"
COST = "cost"
PRODUCTIVITY = "productivity"
MORALE = "morale"
@dataclass
class KPIDefinition:
id: str
name: str
category: KPICategory
formula: str
unit: str
target: float
warning_threshold: float
critical_threshold: float
higher_is_better: bool = True
frequency: str = "daily"
def create_kpi_library():
"""Standard industrial KPI definitions"""
return [
KPIDefinition(
id="oee", name="OEE", category=KPICategory.PRODUCTIVITY,
formula="availability * performance * quality",
unit="%", target=85, warning_threshold=75, critical_threshold=65
),
KPIDefinition(
id="fpy", name="First Pass Yield", category=KPICategory.QUALITY,
formula="good_units / total_units * 100",
unit="%", target=98, warning_threshold=95, critical_threshold=90
),
KPIDefinition(
id="otd", name="On-Time Delivery", category=KPICategory.DELIVERY,
formula="on_time_orders / total_orders * 100",
unit="%", target=98, warning_threshold=95, critical_threshold=90
),
KPIDefinition(
id="trir", name="Total Recordable Incident Rate", category=KPICategory.SAFETY,
formula="incidents * 200000 / hours_worked",
unit="per 200k hrs", target=0.5, warning_threshold=1.0, critical_threshold=2.0,
higher_is_better=False
),
KPIDefinition(
id="productivity", name="Labor Productivity", category=KPICategory.PRODUCTIVITY,
formula="units_produced / labor_hours",
unit="units/hr", target=25, warning_threshold=20, critical_threshold=15
),
KPIDefinition(
id="scrap_rate", name="Scrap Rate", category=KPICategory.COST,
formula="scrap_cost / production_cost * 100",
unit="%", target=1.0, warning_threshold=2.0, critical_threshold=3.0,
higher_is_better=False
)
]
def calculate_kpi(kpi: KPIDefinition, data: dict):
"""Calculate KPI value from data"""
# Simple formula evaluation (in production, use safe eval)
try:
value = eval(kpi.formula, {"__builtins__": {}}, data)
# Determine status
if kpi.higher_is_better:
if value >= kpi.target:
status = "green"
elif value >= kpi.warning_threshold:
status = "yellow"
else:
status = "red"
else:
if value <= kpi.target:
status = "green"
elif value <= kpi.warning_threshold:
status = "yellow"
else:
status = "red"
return {
"kpi_id": kpi.id,
"name": kpi.name,
"value": round(value, 2),
"unit": kpi.unit,
"target": kpi.target,
"status": status,
"gap_to_target": round(value - kpi.target, 2)
}
except Exception as e:
return {"kpi_id": kpi.id, "error": str(e)}
2. Dashboard Layout Generator
def generate_dashboard_layout(kpis: List[KPIDefinition], layout_type: str = "standard"):
"""
Generate dashboard layout configuration
layout_type: 'standard', 'executive', 'operational', 'lean'
"""
if layout_type == "standard":
layout = {
"type": "grid",
"columns": 4,
"rows": 3,
"sections": [
{
"id": "header",
"row": 1, "col_span": 4,
"content": "title_and_period_selector"
},
{
"id": "summary_cards",
"row": 2, "col_span": 4,
"content": "kpi_summary_cards",
"kpis": [k.id for k in kpis[:6]]
},
{
"id": "trends",
"row": 3, "col": 1, "col_span": 2,
"content": "trend_charts"
},
{
"id": "details",
"row": 3, "col": 3, "col_span": 2,
"content": "detail_tables"
}
]
}
elif layout_type == "executive":
layout = {
"type": "single_page",
"sections": [
{"id": "headline_kpis", "position": "top", "height": "20%"},
{"id": "trend_summary", "position": "middle", "height": "50%"},
{"id": "action_items", "position": "bottom", "height": "30%"}
]
}
elif layout_type == "lean":
# Visual management board style
layout = {
"type": "sqdc", # Safety, Quality, Delivery, Cost
"columns": 4,
"sections": [
{"id": "safety", "col": 1, "category": "SAFETY"},
{"id": "quality", "col": 2, "category": "QUALITY"},
{"id": "delivery", "col": 3, "category": "DELIVERY"},
{"id": "cost", "col": 4, "category": "COST"}
],
"row_types": ["current_status", "trend_sparkline", "action_items"]
}
return layout
def generate_kpi_card_config(kpi: KPIDefinition):
"""Generate configuration for a KPI card widget"""
return {
"widget_type": "kpi_card",
"kpi_id": kpi.id,
"title": kpi.name,
"display": {
"value_format": f"{{value:.1f}}{kpi.unit}",
"show_target": True,
"show_trend": True,
"trend_periods": 7
},
"colors": {
"green": kpi.target,
"yellow": kpi.warning_threshold,
"red": kpi.critical_threshold
},
"gauge": kpi.id in ["oee", "fpy", "otd"] # Show gauge for percentage KPIs
}
3. Trend Analysis
def analyze_kpi_trends(historical_data: pd.DataFrame, kpi_id: str,
periods: int = 30):
"""
Analyze trends for a KPI
historical_data: DataFrame with ['date', 'kpi_id', 'value']
"""
kpi_data = historical_data[historical_data['kpi_id'] == kpi_id].copy()
kpi_data = kpi_data.sort_values('date').tail(periods)
if len(kpi_data) < 2:
return {"error": "Insufficient data for trend analysis"}
values = kpi_data['value'].values
dates = kpi_data['date'].values
# Calculate statistics
current = values[-1]
previous = values[-2]
change = current - previous
change_pct = (change / previous * 100) if previous != 0 else 0
# Moving averages
ma_7 = np.mean(values[-7:]) if len(values) >= 7 else np.mean(values)
ma_30 = np.mean(values[-30:]) if len(values) >= 30 else np.mean(values)
# Trend direction (linear regression)
x = np.arange(len(values))
slope, intercept = np.polyfit(x, values, 1)
if slope > 0.01:
trend_direction = "improving"
elif slope < -0.01:
trend_direction = "declining"
else:
trend_direction = "stable"
# Variability
std_dev = np.std(values)
cv = (std_dev / np.mean(values) * 100) if np.mean(values) != 0 else 0
return {
"kpi_id": kpi_id,
"current_value": round(current, 2),
"previous_value": round(previous, 2),
"change": round(change, 2),
"change_percent": round(change_pct, 1),
"trend_direction": trend_direction,
"slope": round(slope, 4),
"moving_average_7": round(ma_7, 2),
"moving_average_30": round(ma_30, 2),
"std_deviation": round(std_dev, 2),
"coefficient_of_variation": round(cv, 1),
"sparkline_data": values.tolist()
}
4. Alert Configuration
def configure_alerts(kpis: List[KPIDefinition], notification_config: dict):
"""
Configure alert rules for dashboard
notification_config: {'email': [], 'sms': [], 'teams': []}
"""
alerts = []
for kpi in kpis:
# Warning alert
alerts.append({
"alert_id": f"{kpi.id}_warning",
"kpi_id": kpi.id,
"level": "warning",
"condition": f"value {'<' if kpi.higher_is_better else '>'} {kpi.warning_threshold}",
"message": f"{kpi.name} has reached warning level",
"notifications": notification_config.get('email', []),
"frequency": "first_occurrence"
})
# Critical alert
alerts.append({
"alert_id": f"{kpi.id}_critical",
"kpi_id": kpi.id,
"level": "critical",
"condition": f"value {'<' if kpi.higher_is_better else '>'} {kpi.critical_threshold}",
"message": f"{kpi.name} has reached critical level - immediate action required",
"notifications": notification_config.get('email', []) + notification_config.get('sms', []),
"frequency": "every_occurrence",
"escalation_after_minutes": 30
})
# Trend alert
alerts.append({
"alert_id": f"{kpi.id}_trend",
"kpi_id": kpi.id,
"level": "info",
"condition": "consecutive_decline >= 3",
"message": f"{kpi.name} has declined for 3 consecutive periods",
"notifications": notification_config.get('email', []),
"frequency": "daily_digest"
})
return {
"alerts": alerts,
"total_rules": len(alerts),
"notification_channels": list(notification_config.keys())
}
5. Drill-Down Configuration
def configure_drilldowns(kpi: KPIDefinition, dimensions: list):
"""
Configure drill-down paths for a KPI
dimensions: ['shift', 'line', 'product', 'operator']
"""
drilldowns = []
for i, dim in enumerate(dimensions):
drilldowns.append({
"level": i + 1,
"dimension": dim,
"aggregation": "sum" if "count" in kpi.formula else "avg",
"chart_type": "bar" if i < 2 else "table",
"filter_enabled": True
})
# Add time-based drilldown
drilldowns.append({
"level": len(dimensions) + 1,
"dimension": "time",
"granularity": ["month", "week", "day", "shift", "hour"],
"chart_type": "line",
"default_granularity": "day"
})
return {
"kpi_id": kpi.id,
"drilldown_path": drilldowns,
"max_levels": len(drilldowns)
}
6. Dashboard Export
def export_dashboard_config(layout: dict, kpis: List[dict],
alerts: List[dict], drilldowns: List[dict]):
"""
Export complete dashboard configuration
"""
config = {
"dashboard": {
"name": "Operations Dashboard",
"version": "1.0",
"refresh_rate_seconds": 300,
"timezone": "local"
},
"layout": layout,
"kpis": kpis,
"widgets": [],
"alerts": alerts,
"drilldowns": drilldowns,
"data_sources": [
{
"id": "production_db",
"type": "database",
"refresh": "5min"
},
{
"id": "mes_api",
"type": "api",
"refresh": "realtime"
}
],
"filters": [
{"id": "date_range", "type": "date_range", "default": "last_30_days"},
{"id": "shift", "type": "dropdown", "options": ["All", "Day", "Night"]},
{"id": "line", "type": "multi_select", "source": "production_db.lines"}
]
}
# Generate widgets from KPIs
for kpi in kpis:
config["widgets"].append({
"widget_id": f"card_{kpi['kpi_id']}",
"type": "kpi_card",
"kpi": kpi['kpi_id'],
"position": "auto"
})
return config
Process Integration
This skill integrates with the following processes:
performance-monitoring-setup.jsvisual-management-implementation.jscontinuous-improvement-program.js
Output Format
{
"dashboard_config": {
"name": "Operations Dashboard",
"layout": "standard",
"refresh_rate": 300
},
"kpis": [
{
"id": "oee",
"name": "OEE",
"current_value": 82.5,
"target": 85,
"status": "yellow",
"trend": "improving"
}
],
"alerts": {
"active": 2,
"critical": 0,
"warning": 2
},
"layout_spec": {
"type": "grid",
"columns": 4,
"widgets": 12
}
}
Best Practices
- Less is more - Focus on critical KPIs
- Visual hierarchy - Most important at top
- Consistent colors - Red/yellow/green standard
- Actionable data - Link to root causes
- Appropriate refresh - Balance timeliness vs. load
- Mobile-friendly - Access anywhere
Constraints
- Too many KPIs dilute focus
- Real-time requires infrastructure
- Data quality affects trust
- User training needed for effectiveness