Yazaki version

src/streamlit_app.py

@@ -1,412 +1,650 @@
+#Stable version for Yazaki India Ltd
 import streamlit as st
 import pandas as pd
+import numpy as np
 import plotly.express as px
 import plotly.graph_objects as go
 from datetime import datetime, timedelta
+import random
 
-# Page config
+# Page configuration
 st.set_page_config(
-    page_title="Yazaki India Supply Chain Intelligence",
+    page_title="Yazaki India Ltd - Complete Supply Chain Hub",
     page_icon="🔌",
     layout="wide",
     initial_sidebar_state="expanded"
 )
 
-# Clean, professional CSS styling (identical to Rane version)
+# Custom CSS (same as before)
 st.markdown("""
 <style>
-    /* Import modern font */
-    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
-    
-    /* Global styling */
-    .stApp {
-        font-family: 'Inter', sans-serif;
-        background-color: #f8fafc;
-    }
-    
-    /* Main container */
-    .main-container {
-        background: white;
-        border-radius: 12px;
-        padding: 2rem;
-        margin: 1rem 0;
-        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
-        border: 1px solid #e2e8f0;
-    }
-    
-    /* Clean header */
-    .modern-header {
-        background: #1e293b;
-        color: white;
-        padding: 2rem;
-        border-radius: 12px;
-        margin-bottom: 2rem;
-        border-left: 4px solid #3b82f6;
-    }
-    
-    .header-title {
-        font-size: 2rem;
-        font-weight: 600;
-        margin-bottom: 0.5rem;
-        color: white;
-    }
-    
-    .header-subtitle {
-        font-size: 1rem;
-        font-weight: 400;
-        color: #94a3b8;
-    }
-    
-    /* Clean metric cards */
-    .metric-card {
-        background: white;
-        padding: 1.5rem;
-        border-radius: 12px;
-        border: 1px solid #e2e8f0;
-        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
-        margin-bottom: 1rem;
-        transition: all 0.2s ease;
-    }
-    
-    .metric-card:hover {
-        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);
-        border-color: #cbd5e1;
-    }
-    
-    .metric-number {
-        font-size: 2.5rem;
-        font-weight: 700;
-        color: #1e293b;
-        margin-bottom: 0.5rem;
-    }
-    
-    .metric-label {
-        color: #64748b;
-        font-size: 0.875rem;
-        font-weight: 500;
-        text-transform: uppercase;
-        letter-spacing: 0.05em;
-        margin-bottom: 0.5rem;
-    }
-    
-    .metric-change {
-        font-size: 0.875rem;
-        font-weight: 600;
-        padding: 0.25rem 0.75rem;
-        border-radius: 6px;
-        display: inline-block;
-    }
-    
-    .metric-positive {
-        background-color: #dcfce7;
-        color: #166534;
-    }
-    
-    .metric-negative {
-        background-color: #fef2f2;
-        color: #dc2626;
-    }
-    
-    .metric-neutral {
-        background-color: #f1f5f9;
-        color: #475569;
-    }
-    
-    /* Clean sidebar */
-    .sidebar-content {
-        background: white;
-        color: #1e293b;
-        border-radius: 12px;
-        padding: 1.5rem;
-        margin-bottom: 1rem;
-        border: 1px solid #e2e8f0;
-        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
-    }
-    
-    /* Filter section */
-    .filter-container {
-        background: white;
-        padding: 1.5rem;
-        border-radius: 12px;
-        margin-bottom: 2rem;
-        border: 1px solid #e2e8f0;
-        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
-    }
-    
-    /* Section headers */
-    .section-header {
-        font-size: 1.25rem;
-        font-weight: 600;
-        color: #1e293b;
-        margin-bottom: 1.5rem;
-        padding-bottom: 0.75rem;
-        border-bottom: 2px solid #e2e8f0;
-    }
-    
-    /* Status indicators */
-    .status-indicator {
-        display: inline-block;
-        width: 8px;
-        height: 8px;
-        border-radius: 50%;
-        margin-right: 8px;
-    }
-    
-    .status-good { background-color: #22c55e; }
-    .status-warning { background-color: #f59e0b; }
-    .status-critical { background-color: #ef4444; }
-    
-    /* Clean table styling */
-    .dataframe table {
-        border-collapse: collapse;
-        margin: 0;
-        font-size: 0.875rem;
-        width: 100%;
-        background: white;
-        border-radius: 8px;
-        overflow: hidden;
-        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
-    }
-    
-    .dataframe th {
-        background-color: #f8fafc;
-        color: #374151;
-        font-weight: 600;
-        padding: 12px;
-        text-align: left;
-        border-bottom: 1px solid #e5e7eb;
-    }
-    
-    .dataframe td {
-        padding: 12px;
-        border-bottom: 1px solid #f3f4f6;
-    }
-    
-    .dataframe tr:hover {
-        background-color: #f9fafb;
-    }
-    
-    /* Remove default streamlit styling */
-    .stSelectbox > div > div {
-        background-color: white;
-        border: 1px solid #d1d5db;
-        border-radius: 6px;
-    }
-    
-    .stSelectbox > div > div:focus-within {
-        border-color: #3b82f6;
-        box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.1);
-    }
+/* Add your custom CSS here */
 </style>
 """, unsafe_allow_html=True)
 
-# Sample data functions - ONLY changed company/material names
+# Initialize session state
+if 'executed_mitigations' not in st.session_state:
+    st.session_state.executed_mitigations = []
+if 'external_signals' not in st.session_state:
+    st.session_state.external_signals = []
+
+# UPDATED: Generate 8-week forward-looking demand data for Yazaki
 @st.cache_data
-def get_material_data():
-    return pd.DataFrame({
-        'Material Group': ['Wire Harnesses', 'Connectors & Terminals', 'Electronic Components', 
-                          'Fuse Box Assembly', 'Junction Blocks', 'Cable Assembly', 'Power Distribution',
-                          'Smart Sensors', 'Relay Components', 'Switch Components'],
-        'Current Rate': [72, 68, 65, 73, 75, 72, 78, 77, 80, 82],
-        'Target Rate': [75, 70, 70, 75, 78, 75, 80, 80, 82, 85],
-        'Trend': ['+2.1%', '+1.8%', '+0.9%', '+2.4%', '+1.2%', '+1.8%', '+2.1%', '+1.9%', '+1.1%', '+1.2%'],
-        'Risk Level': ['Medium', 'High', 'High', 'Low', 'Low', 'Medium', 'Low', 'Low', 'Low', 'Low'],
-        'Last Updated': ['2 hrs ago', '1 hr ago', '3 hrs ago', '1 hr ago', '2 hrs ago', '1 hr ago', '30 min ago', '45 min ago', '1 hr ago', '2 hrs ago']
-    })
+def generate_8week_demand_data():
+    today = datetime(2025, 8, 4)
+    dates = [today + timedelta(days=x) for x in range(56)]  # 8 weeks = 56 days
+    
+    # Yazaki-specific materials (wire harnesses, connectors, electrical components)
+    materials = [
+        'WH001-Engine Wire Harness',
+        'WH002-Dashboard Wire Harness',
+        'CON001-Electrical Connector',
+        'TER001-Wire Terminal',
+        'FUS001-Fuse Box Assembly'
+    ]
+    
+    all_data = []
+    for material in materials:
+        np.random.seed(hash(material) % 1000)
+        
+        # Generate base demand patterns
+        base_demand = np.random.normal(150, 15, 56)
+        
+        # First 14 days: FIRM DEMAND
+        firm_demand = np.clip(base_demand[:14], 100, 200).astype(int)
+        
+        # Days 15-56: Customer shared demand (tentative)
+        customer_shared = np.clip(base_demand[14:] * (1 + 0.05 * np.sin(np.linspace(0, 3.14, 42))), 80, 220).astype(int)
+        
+        # Days 15-56: AI-corrected demand (with external signals)
+        external_factors = np.zeros(42)
+        # Weather impact (weeks 3-4)
+        external_factors[0:14] += np.random.normal(0, 5, 14)
+        # EV policy impact (weeks 5-8) - higher for wire harnesses
+        if 'WH' in material:
+            external_factors[14:] += 12  # Higher impact for wire harnesses in EVs
+        elif 'CON' in material or 'TER' in material:
+            external_factors[14:] += 8   # Moderate impact for connectors/terminals
+        # Festive season boost (weeks 6-7)
+        external_factors[28:42] += 8
+        
+        corrected_demand = np.clip(customer_shared + external_factors, 60, 250).astype(int)
+        
+        # Generate supply plan for 56 days
+        supply_capacity = np.random.normal(155, 12, 56)
+        supply_plan = np.clip(supply_capacity, 120, 220).astype(int)
+        
+        # Apply disruptions to supply (weather impact on days 15-18)
+        supply_actual = supply_plan.copy()
+        supply_actual[15:19] = (supply_actual[15:19] * 0.8).astype(int)
+        
+        for i, date in enumerate(dates):
+            # Determine which demand to use
+            if i < 14:
+                demand_used = firm_demand[i]
+                firm_val = firm_demand[i]
+                customer_val = None
+                corrected_val = None
+                demand_type = "Firm"
+            else:
+                demand_used = corrected_demand[i-14]
+                firm_val = None
+                customer_val = customer_shared[i-14]
+                corrected_val = corrected_demand[i-14]
+                demand_type = "AI-Corrected"
+            
+            # Calculate shortfall
+            shortfall = max(0, demand_used - supply_actual[i])
+            
+            all_data.append({
+                'Date': date,
+                'Week': f"Week {(i//7)+1}",
+                'Day': i + 1,
+                'Material': material,
+                'Firm_Demand': firm_val,
+                'Customer_Demand': customer_val,
+                'Corrected_Demand': corrected_val,
+                'Demand_Used': demand_used,
+                'Supply_Plan': supply_plan[i],
+                'Supply_Projected': supply_actual[i],
+                'Shortfall': shortfall,
+                'Demand_Type': demand_type,
+                'Gap': supply_actual[i] - demand_used
+            })
+    
+    return pd.DataFrame(all_data)
 
+# Yazaki-specific Tier-2 suppliers
 @st.cache_data
-def get_enhanced_metrics():
+def get_tier2_suppliers():
     return {
-        'fulfillment': 74,  # Slightly lower due to import challenges
-        'mom_change': -1.2,
-        'material_groups': 89,
-        'skus': 8945,
-        'material_groups_at_risk': 24,
-        'risk_mom_change': 2.8,
-        'skus_at_risk': 31,
-        'sku_risk_mom_change': 3.2,
-        'active_suppliers': 156,
-        'on_time_delivery': 82.4,  # Import delays impact
-        'import_dependency': 87
+        'Furukawa Electric India': {
+            'location': 'Chennai',
+            'materials': ['WH001-Engine Wire Harness', 'WH002-Dashboard Wire Harness'],
+            'capacity': 200,
+            'reliability': 95,
+            'lead_time': 2,
+            'risk_factors': ['Monsoon flooding', 'Port congestion', 'Copper price volatility']
+        },
+        'Sumitomo Wiring Systems': {
+            'location': 'Bangalore',
+            'materials': ['CON001-Electrical Connector', 'TER001-Wire Terminal'],
+            'capacity': 180,
+            'reliability': 92,
+            'lead_time': 3,
+            'risk_factors': ['Transportation delays', 'Raw material shortage', 'Equipment failure']
+        },
+        'JST India Private Limited': {
+            'location': 'Pune',
+            'materials': ['FUS001-Fuse Box Assembly', 'CON001-Electrical Connector'],
+            'capacity': 220,
+            'reliability': 88,
+            'lead_time': 1,
+            'risk_factors': ['Quality issues', 'Capacity constraints', 'Supplier disputes']
+        }
     }
 
-# Clean, professional header - ONLY changed titles
-st.markdown("""
-<div class="modern-header">
-    <div class="header-title">🔌 Yazaki India Supply Chain Intelligence Hub</div>
-    <div class="header-subtitle">Real-time Import-Focused Supply Chain Dashboard • Automotive Wiring Systems Control Tower</div>
-</div>
-""", unsafe_allow_html=True)
+# Keep existing ecosystem generation (updated with Yazaki suppliers)
+@st.cache_data
+def generate_ecosystem_data():
+    today = datetime(2025, 8, 4)
+    dates = [today + timedelta(days=x) for x in range(14)]
+    suppliers = get_tier2_suppliers()
+    
+    all_data = []
+    for supplier_name, supplier_info in suppliers.items():
+        for material in supplier_info['materials']:
+            np.random.seed(hash(supplier_name + material) % 1000)
+            
+            base_capacity = supplier_info['capacity']
+            normal_supply = np.full(14, base_capacity, dtype=int)
+            disrupted_supply = normal_supply.copy()
+            
+            if supplier_name == 'Furukawa Electric India':
+                disrupted_supply[3:7] = (disrupted_supply[3:7] * 0.3).astype(int)
+                disruption_cause = "Monsoon flooding in Chennai"
+                disruption_days = list(range(3, 7))
+            elif supplier_name == 'Sumitomo Wiring Systems':
+                disrupted_supply[5:8] = (disrupted_supply[5:8] * 0.5).astype(int)
+                disruption_cause = "Critical equipment failure"
+                disruption_days = list(range(5, 8))
+            elif supplier_name == 'JST India Private Limited':
+                disrupted_supply[8:11] = (disrupted_supply[8:11] * 0.2).astype(int)
+                disruption_cause = "Labor strike at Pune facility"
+                disruption_days = list(range(8, 11))
+            else:
+                disruption_cause = "No disruption"
+                disruption_days = []
+            
+            lead_time = supplier_info['lead_time']
+            yazaki_supply = np.full(14, base_capacity, dtype=int)
+            
+            for disruption_day in disruption_days:
+                arrival_day = disruption_day + lead_time
+                if arrival_day < 14:
+                    reduction = normal_supply[disruption_day] - disrupted_supply[disruption_day]
+                    yazaki_supply[arrival_day] = max(yazaki_supply[arrival_day] - reduction, 0)
+            
+            for i, date in enumerate(dates):
+                all_data.append({
+                    'Date': date,
+                    'Supplier': supplier_name,
+                    'Material': material,
+                    'Tier2_Normal_Supply': int(normal_supply[i]),
+                    'Tier2_Disrupted_Supply': int(disrupted_supply[i]),
+                    'Tier2_Impact': int(normal_supply[i] - disrupted_supply[i]),
+                    'Yazaki_Normal_Supply': int(normal_supply[i]),
+                    'Yazaki_Impacted_Supply': int(yazaki_supply[i]),
+                    'Yazaki_Impact': int(normal_supply[i] - yazaki_supply[i]),
+                    'Disruption_Cause': disruption_cause if i in disruption_days else "Normal Operations",
+                    'Lead_Time_Days': lead_time,
+                    'Is_Disrupted': i in disruption_days,
+                    'Is_Yazaki_Impacted': yazaki_supply[i] < normal_supply[i]
+                })
+    
+    return pd.DataFrame(all_data)
 
-# Professional sidebar - ONLY changed company context
-with st.sidebar:
-    st.markdown("""
-    <div class="sidebar-content">
-        <h3 style="margin-top: 0; color: #1e293b;">Yazaki Navigation</h3>
-        <p style="color: #64748b; font-size: 0.875rem;">Select your workspace</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    nav_options = [
-        "Dashboard Home",
-        "Import Supply Chain", 
-        "Control Tower",
-        "Wire Harness Groups",
-        "Global Supplier Analytics", 
-        "Import Planning",
-        "Port & Logistics",
-        "Import Alerts",
-        "Compliance Center"
+# Updated external signals for Yazaki
+@st.cache_data
+def get_external_signals():
+    return [
+        {'Source': 'Weather API', 'Signal': 'Heavy rains forecasted in Chennai for next 3 days', 'Impact': 'Supply Risk', 'Confidence': 95},
+        {'Source': 'Market Intelligence', 'Signal': 'EV sales up 25% this quarter - increased wire harness demand', 'Impact': 'Demand Increase', 'Confidence': 88},
+        {'Source': 'News Analytics', 'Signal': 'Upcoming festive season - historically 15% automotive demand spike', 'Impact': 'Demand Surge', 'Confidence': 92},
+        {'Source': 'Supplier Network', 'Signal': 'Tier-2 connector supplier capacity increased by 20%', 'Impact': 'Supply Boost', 'Confidence': 98},
+        {'Source': 'Social Media', 'Signal': 'Positive sentiment around new Tata EV models', 'Impact': 'Demand Growth', 'Confidence': 75},
+        {'Source': 'Government Portal', 'Signal': 'New EV subsidy policy effective next week', 'Impact': 'Market Expansion', 'Confidence': 100}
     ]
-    
-    selected_nav = st.selectbox("", nav_options, index=1)
-    
-    # Clean alerts section - ONLY changed to import context
-    st.markdown("---")
-    st.markdown("**Import Status**")
-    st.error("🚢 JNPT port congestion - 4 days delay")
-    st.warning("💱 INR depreciation impacting costs")
-    st.success("✅ 82% customs clearance on time")
-
-# Clean filters section - ONLY changed plant names and some filters
-st.markdown("""
-<div class="filter-container">
-    <div class="section-header">Import & Supply Chain Filters</div>
-</div>
-""", unsafe_allow_html=True)
-
-col1, col2, col3, col4 = st.columns(4)
-col5, col6, col7, col8 = st.columns(4)
-
-with col1:
-    plant_location = st.selectbox("Yazaki Plant", ["Chennai (Main)", "Bawal (Haryana)", "Kanchipuram", "All Plants"], index=0)
 
-with col2:
-    material_group = st.selectbox("Component Category", ["All Components", "Wire Harnesses", "Connectors", "Electronics"], index=0)
+# UPDATED: Generate alerts for 8-week data
+def generate_detailed_alerts(df):
+    alerts = []
+    for material in df['Material'].unique():
+        material_data = df[df['Material'] == material]
+        shortage_days = material_data[material_data['Shortfall'] > 5]
+        
+        if not shortage_days.empty:
+            for _, row in shortage_days.iterrows():
+                root_causes = []
+                
+                if row['Day'] > 14:
+                    if row['Corrected_Demand'] and row['Customer_Demand']:
+                        diff = row['Corrected_Demand'] - row['Customer_Demand']
+                        if diff > 10:
+                            root_causes.append(f"AI detected {diff} units additional demand from external signals")
+                    
+                    if row['Day'] >= 15 and row['Day'] <= 18:
+                        root_causes.append("Chennai plant weather disruption reducing supply")
+                else:
+                    root_causes.append("Firm demand exceeding supply capacity")
+                
+                if not root_causes:
+                    root_causes.append("Base demand exceeding current supply capacity")
+                
+                mitigation_options = [
+                    {"option": "Activate Aurangabad backup production", "impact": "+30 units/day", "cost": "High", "timeline": "24 hours"},
+                    {"option": "Expedite Tier-2 supplier shipments", "impact": "+15 units/day", "cost": "Medium", "timeline": "12 hours"},
+                    {"option": "Emergency air freight from Yazaki Philippines", "impact": "+40 units/day", "cost": "Very High", "timeline": "6 hours"},
+                    {"option": "Reallocate inventory from other Yazaki plants", "impact": "+20 units/day", "cost": "Low", "timeline": "18 hours"}
+                ]
+                
+                if row['Shortfall'] > 30:
+                    best_option = mitigation_options[2]
+                elif row['Shortfall'] > 15:
+                    best_option = mitigation_options[0]
+                else:
+                    best_option = mitigation_options[1]
+                
+                alerts.append({
+                    'material': material,
+                    'date': row['Date'].strftime('%Y-%m-%d'),
+                    'week': row['Week'],
+                    'shortage': int(row['Shortfall']),
+                    'demand_type': row['Demand_Type'],
+                    'severity': 'Critical' if row['Shortfall'] > 30 else 'High' if row['Shortfall'] > 15 else 'Medium',
+                    'root_causes': root_causes,
+                    'mitigation_options': mitigation_options,
+                    'best_option': best_option
+                })
+    
+    return alerts
 
-with col3:
-    time_period = st.selectbox("Time Period", ["Current Quarter", "FY2025", "Last 6 Months"], index=0)
-
-with col4:
-    supplier_tier = st.selectbox("Source Country", ["All Countries", "China", "Japan", "Germany", "South Korea"], index=0)
-
-with col5:
-    risk_level = st.selectbox("Import Risk", ["All Risk Levels", "High Risk Only", "Medium Risk", "Low Risk"], index=0)
-
-with col6:
-    performance = st.selectbox("Port Performance", ["All Ports", "JNPT Mumbai", "Chennai Port", "ICD Bangalore"], index=0)
-
-with col7:
-    geography = st.selectbox("Import Mode", ["All Modes", "Sea Freight", "Air Freight", "Express"], index=0)
-
-with col8:
-    update_freq = st.selectbox("Customs Status", ["All Status", "Cleared", "In Process", "Held"], index=0)
-
-# Get data - SAME structure
-material_df = get_material_data()
-metrics = get_enhanced_metrics()
-
-# Clean metrics section - ONLY changed one metric to import-focused
-st.markdown('<div class="section-header">Yazaki India Key Performance Indicators</div>', unsafe_allow_html=True)
-
-col1, col2, col3, col4 = st.columns(4)
+# Keep mitigation strategies with Yazaki-specific updates
+def generate_mitigation_strategies(supplier, material, impact_amount, impact_days):
+    base_strategies = [
+        {
+            'strategy': 'Activate Alternate Supplier',
+            'description': f'Engage backup supplier for {material}',
+            'timeline': '24-48 hours',
+            'cost': 'High (+15% unit cost)',
+            'effectiveness': '90%',
+            'capacity': f'+{impact_amount * 0.9:.0f} units/day',
+        },
+        {
+            'strategy': 'Emergency Air Freight',
+            'description': f'Air freight {material} from Yazaki global network',
+            'timeline': '6-12 hours',
+            'cost': 'Very High (+40% logistics cost)',
+            'effectiveness': '75%',
+            'capacity': f'+{impact_amount * 0.75:.0f} units/day',
+        },
+        {
+            'strategy': 'Inventory Reallocation',
+            'description': f'Reallocate {material} from other Yazaki plants',
+            'timeline': '12-24 hours',
+            'cost': 'Medium (+5% handling cost)',
+            'effectiveness': '60%',
+            'capacity': f'+{impact_amount * 0.6:.0f} units/day',
+        }
+    ]
+    
+    if impact_amount > 100:
+        recommended = [0, 1]
+    elif impact_amount > 50:
+        recommended = [0, 2]
+    else:
+        recommended = [2]
+    
+    return base_strategies, recommended
 
-with col1:
-    st.markdown(f"""
-    <div class="metric-card">
-        <div class="metric-number">{metrics['fulfillment']}%</div>
-        <div class="metric-label">Overall Fulfillment</div>
-        <div class="metric-change metric-negative">↘ {metrics['mom_change']}% MoM</div>
-    </div>
-    """, unsafe_allow_html=True)
+# Load data
+df_demand = generate_8week_demand_data()
+df_ecosystem = generate_ecosystem_data()
+external_signals = get_external_signals()
+suppliers = get_tier2_suppliers()
 
-with col2:
-    st.markdown(f"""
-    <div class="metric-card">
-        <div class="metric-number">{metrics['import_dependency']}%</div>
-        <div class="metric-label">Import Dependency</div>
-        <div class="metric-change metric-neutral">Critical Factor</div>
-    </div>
-    """, unsafe_allow_html=True)
+# Simple title
+st.title("Yazaki India Ltd - Supply Chain Command Center")
 
-with col3:
-    st.markdown(f"""
-    <div class="metric-card">
-        <div class="metric-number">{metrics['material_groups_at_risk']}%</div>
-        <div class="metric-label">At-Risk Components</div>
-        <div class="metric-change metric-negative">↗ +{metrics['risk_mom_change']}% MoM</div>
-    </div>
-    """, unsafe_allow_html=True)
+# Tab Navigation
+st.sidebar.title("🎯 Dashboard Navigation")
+dashboard_tab = st.sidebar.radio(
+    "Select Dashboard:",
+    ["📊 Demand & Supply Forecast", "🌐 Ecosystem Supplier Impact", "🛡️ Buffer Optimizer"],
+    index=0
+)
 
-with col4:
-    st.markdown(f"""
-    <div class="metric-card">
-        <div class="metric-number">{metrics['active_suppliers']:,}</div>
-        <div class="metric-label">Global Suppliers</div>
-        <div class="metric-change metric-neutral">+12 new</div>
+# UPDATED TAB 1: 8-WEEK DEMAND & SUPPLY FORECAST
+if dashboard_tab == "📊 Demand & Supply Forecast":
+    st.markdown("""
+    <div style="background: linear-gradient(135deg, #1f4e79, #2d5aa0); padding: 20px; border-radius: 15px; margin-bottom: 20px;">
+        <h2 style="color: white; margin: 0; text-align: center;">
+            🔌 8-Week Planning Horizon | Firm Demand (Days 1-14) | AI-Corrected Demand (Days 15-56)
+        </h2>
     </div>
     """, unsafe_allow_html=True)
-
-# Clean data table - SAME structure
-st.markdown('<div class="section-header">Yazaki Component Group Performance</div>', unsafe_allow_html=True)
-
-# Display clean table
-st.dataframe(material_df, use_container_width=True, hide_index=True)
-
-# Professional charts - SAME structure, ONLY changed titles
-st.markdown('<div class="section-header">Import Performance Analytics</div>', unsafe_allow_html=True)
-
-col1, col2 = st.columns(2)
-
-with col1:
-    # Clean bar chart - SAME as Rane
-    fig1 = px.bar(
-        material_df, 
-        x='Material Group', 
-        y=['Current Rate', 'Target Rate'],
-        title="Fulfillment Rates: Current vs Target",
-        color_discrete_sequence=['#3b82f6', '#64748b']
+    
+    # Material selection
+    selected_material = st.selectbox(
+        "Select Material for Analysis:",
+        df_demand['Material'].unique(),
+        key="forecast_material"
     )
     
-    fig1.update_layout(
-        plot_bgcolor='white',
-        paper_bgcolor='white',
-        font_family="Inter",
-        title_font_size=14,
-        title_font_color="#1e293b",
-        xaxis_tickangle=-45,
-        height=400,
+    # Filter data
+    material_df = df_demand[df_demand['Material'] == selected_material].copy()
+    
+    # Create visualization
+    fig = go.Figure()
+    
+    # Add firm demand (days 1-14)
+    firm_data = material_df[material_df['Demand_Type'] == 'Firm']
+    fig.add_trace(go.Scatter(
+        x=firm_data['Date'],
+        y=firm_data['Demand_Used'],
+        mode='lines+markers',
+        name='Firm Demand (Days 1-14)',
+        line=dict(color='#2E86AB', width=3),
+        marker=dict(size=8)
+    ))
+    
+    # Add AI-corrected demand (days 15-56)
+    corrected_data = material_df[material_df['Demand_Type'] == 'AI-Corrected']
+    fig.add_trace(go.Scatter(
+        x=corrected_data['Date'],
+        y=corrected_data['Demand_Used'],
+        mode='lines+markers',
+        name='AI-Corrected Demand (Days 15-56)',
+        line=dict(color='#A23B72', width=3, dash='dot'),
+        marker=dict(size=6)
+    ))
+    
+    # Add supply projection
+    fig.add_trace(go.Scatter(
+        x=material_df['Date'],
+        y=material_df['Supply_Projected'],
+        mode='lines+markers',
+        name='Supply Projection',
+        line=dict(color='#F18F01', width=2),
+        marker=dict(size=6)
+    ))
+    
+    # Highlight shortage areas
+    shortage_data = material_df[material_df['Shortfall'] > 0]
+    if not shortage_data.empty:
+        fig.add_trace(go.Scatter(
+            x=shortage_data['Date'],
+            y=shortage_data['Shortfall'],
+            mode='markers',
+            name='Shortage Alert',
+            marker=dict(color='red', size=10, symbol='triangle-up'),
+            yaxis='y2'
+        ))
+    
+    fig.update_layout(
+        title=f"8-Week Demand & Supply Forecast - {selected_material}",
+        xaxis_title="Date",
+        yaxis_title="Units",
+        yaxis2=dict(title="Shortage", overlaying='y', side='right'),
+        height=600,
         showlegend=True,
-        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
+        hovermode='x unified'
     )
     
-    st.plotly_chart(fig1, use_container_width=True)
+    st.plotly_chart(fig, use_container_width=True)
+    
+    # Generate and display alerts
+    alerts = generate_detailed_alerts(df_demand)
+    
+    if alerts:
+        st.markdown("""
+        <div style="background: #ffe6e6; padding: 15px; border-radius: 10px; border-left: 5px solid #ff4444; margin: 20px 0;">
+            <h3 style="color: #cc0000; margin-top: 0;">⚠️ Critical Supply Chain Alerts</h3>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        for alert in alerts[:3]:  # Show top 3 alerts
+            severity_color = {'Critical': '#ff4444', 'High': '#ff8800', 'Medium': '#ffcc00'}[alert['severity']]
+            
+            st.markdown(f"""
+            <div style="background: white; padding: 15px; border-radius: 10px; border-left: 5px solid {severity_color}; margin: 10px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+                <h4 style="color: {severity_color}; margin: 0 0 10px 0;">
+                    🚨 {alert['severity']} Alert: {alert['material']}
+                </h4>
+                <p style="margin: 5px 0;">
+                    <strong>Date:</strong> {alert['date']} ({alert['week']}) | 
+                    <strong>Shortage:</strong> {alert['shortage']} units | 
+                    <strong>Type:</strong> {alert['demand_type']}
+                </p>
+                <p style="margin: 5px 0;"><strong>Root Causes:</strong></p>
+                <ul style="margin: 5px 0;">
+                    {''.join([f"<li>{cause}</li>" for cause in alert['root_causes']])}
+                </ul>
+                <p style="margin: 10px 0 5px 0;"><strong>🎯 Recommended Action:</strong></p>
+                <div style="background: #f0f8ff; padding: 10px; border-radius: 5px;">
+                    <strong>{alert['best_option']['option']}</strong><br>
+                    Impact: {alert['best_option']['impact']} | Cost: {alert['best_option']['cost']} | Timeline: {alert['best_option']['timeline']}
+                </div>
+            </div>
+            """, unsafe_allow_html=True)
 
-with col2:
-    # Clean pie chart - SAME as Rane
-    risk_counts = material_df['Risk Level'].value_counts()
-    
-    fig2 = px.pie(
-        values=risk_counts.values,
-        names=risk_counts.index,
-        title="Risk Distribution",
-        color_discrete_sequence=['#22c55e', '#f59e0b', '#ef4444']
+elif dashboard_tab == "🌐 Ecosystem Supplier Impact":
+    st.markdown("""
+    <div style="background: linear-gradient(135deg, #1f4e79, #2d5aa0); padding: 20px; border-radius: 15px; margin-bottom: 20px;">
+        <h2 style="color: white; margin: 0; text-align: center;">
+            🌐 Tier 2 Supplier Disruption Analysis | Cascading Impact Modeling | Automated Mitigation Response
+        </h2>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Supplier selection
+    selected_supplier = st.selectbox(
+        "Select Tier-2 Supplier for Analysis:",
+        df_ecosystem['Supplier'].unique(),
+        key="ecosystem_supplier"
     )
     
-    fig2.update_layout(
-        plot_bgcolor='white',
-        paper_bgcolor='white',
-        font_family="Inter",
-        title_font_size=14,
-        title_font_color="#1e293b",
+    # Filter and analyze
+    supplier_df = df_ecosystem[df_ecosystem['Supplier'] == selected_supplier]
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        # Supplier disruption chart
+        fig1 = go.Figure()
+        
+        for material in supplier_df['Material'].unique():
+            material_data = supplier_df[supplier_df['Material'] == material]
+            
+            fig1.add_trace(go.Scatter(
+                x=material_data['Date'],
+                y=material_data['Tier2_Normal_Supply'],
+                mode='lines',
+                name=f'{material} - Normal',
+                line=dict(width=2)
+            ))
+            
+            fig1.add_trace(go.Scatter(
+                x=material_data['Date'],
+                y=material_data['Tier2_Disrupted_Supply'],
+                mode='lines',
+                name=f'{material} - Disrupted',
+                line=dict(dash='dot', width=2)
+            ))
+        
+        fig1.update_layout(
+            title=f"Tier-2 Supplier Impact: {selected_supplier}",
+            xaxis_title="Date",
+            yaxis_title="Supply Units",
+            height=400
+        )
+        
+        st.plotly_chart(fig1, use_container_width=True)
+    
+    with col2:
+        # Yazaki impact chart
+        fig2 = go.Figure()
+        
+        for material in supplier_df['Material'].unique():
+            material_data = supplier_df[supplier_df['Material'] == material]
+            
+            fig2.add_trace(go.Scatter(
+                x=material_data['Date'],
+                y=material_data['Yazaki_Normal_Supply'],
+                mode='lines',
+                name=f'{material} - Normal',
+                line=dict(width=2)
+            ))
+            
+            fig2.add_trace(go.Scatter(
+                x=material_data['Date'],
+                y=material_data['Yazaki_Impacted_Supply'],
+                mode='lines',
+                name=f'{material} - Impacted',
+                line=dict(dash='dot', width=2)
+            ))
+        
+        fig2.update_layout(
+            title=f"Cascading Impact on Yazaki Production",
+            xaxis_title="Date",
+            yaxis_title="Supply Units",
+            height=400
+        )
+        
+        st.plotly_chart(fig2, use_container_width=True)
+    
+    # Disruption alerts
+    disrupted_days = supplier_df[supplier_df['Is_Disrupted'] == True]
+    
+    if not disrupted_days.empty:
+        for _, alert_day in disrupted_days.iterrows():
+            if alert_day['Tier2_Impact'] > 0:
+                strategies, recommended = generate_mitigation_strategies(
+                    alert_day['Supplier'],
+                    alert_day['Material'],
+                    alert_day['Tier2_Impact'],
+                    1
+                )
+                
+                st.markdown(f"""
+                <div style="background: white; padding: 15px; border-radius: 10px; border-left: 5px solid #ff8800; margin: 10px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
+                    <h4 style="color: #ff8800; margin: 0 0 10px 0;">
+                        🏭 Supplier Disruption Alert
+                    </h4>
+                    <p style="margin: 5px 0;">
+                        <strong>Supplier:</strong> {alert_day['Supplier']} | 
+                        <strong>Material:</strong> {alert_day['Material']}
+                    </p>
+                    <p style="margin: 5px 0;">
+                        <strong>Root Cause:</strong> {alert_day['Disruption_Cause']}
+                    </p>
+                    <p style="margin: 5px 0;">
+                        <strong>Impact:</strong> -{alert_day['Tier2_Impact']} units | 
+                        <strong>Yazaki Impact:</strong> -{alert_day['Yazaki_Impact']} units (after {alert_day['Lead_Time_Days']} days)
+                    </p>
+                </div>
+                """, unsafe_allow_html=True)
+                
+                break  # Show only first alert to avoid repetition
+
+elif dashboard_tab == "🛡️ Buffer Optimizer":
+    st.markdown("""
+    <div style="background: linear-gradient(135deg, #1f4e79, #2d5aa0); padding: 20px; border-radius: 15px; margin-bottom: 20px;">
+        <h2 style="color: white; margin: 0; text-align: center;">
+            🛡️ AI-driven safety-stock recommendations across the full network
+        </h2>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Buffer optimization logic (simplified)
+    buffer_data = []
+    for material in df_demand['Material'].unique():
+        material_data = df_demand[df_demand['Material'] == material]
+        avg_demand = material_data['Demand_Used'].mean()
+        demand_volatility = material_data['Demand_Used'].std()
+        max_shortfall = material_data['Shortfall'].max()
+        
+        # Calculate recommended buffer
+        base_buffer = avg_demand * 0.15  # 15% base buffer
+        volatility_buffer = demand_volatility * 1.5
+        risk_buffer = max_shortfall * 0.8
+        
+        recommended_buffer = int(base_buffer + volatility_buffer + risk_buffer)
+        current_buffer = int(avg_demand * 0.10)  # Assume current is 10%
+        
+        buffer_data.append({
+            'Material': material,
+            'Current_Buffer': current_buffer,
+            'Recommended_Buffer': recommended_buffer,
+            'Gap': recommended_buffer - current_buffer,
+            'Cost_Impact': f"₹{(recommended_buffer - current_buffer) * 150:,}",  # Assuming ₹150 per unit
+            'Risk_Reduction': f"{min(90, max_shortfall * 2):.0f}%"
+        })
+    
+    buffer_df = pd.DataFrame(buffer_data)
+    
+    # Display buffer recommendations
+    st.subheader("📊 Safety Stock Recommendations")
+    
+    fig = go.Figure()
+    
+    fig.add_trace(go.Bar(
+        name='Current Buffer',
+        x=buffer_df['Material'],
+        y=buffer_df['Current_Buffer'],
+        marker_color='lightblue'
+    ))
+    
+    fig.add_trace(go.Bar(
+        name='Recommended Buffer',
+        x=buffer_df['Material'],
+        y=buffer_df['Recommended_Buffer'],
+        marker_color='orange'
+    ))
+    
+    fig.update_layout(
+        title="Current vs Recommended Safety Stock Levels",
+        xaxis_title="Materials",
+        yaxis_title="Buffer Stock Units",
+        barmode='group',
         height=400
     )
     
-    st.plotly_chart(fig2, use_container_width=True)
+    st.plotly_chart(fig, use_container_width=True)
+    
+    # Buffer recommendations table
+    st.subheader("📋 Detailed Buffer Analysis")
+    st.dataframe(buffer_df, use_container_width=True)
+
+# External Signals Panel
+st.sidebar.markdown("---")
+st.sidebar.subheader("🌍 External Market Signals")
+for signal in external_signals[:3]:
+    confidence_color = "green" if signal['Confidence'] > 90 else "orange" if signal['Confidence'] > 80 else "red"
+    st.sidebar.markdown(f"""
+    <div style="background: white; padding: 8px; border-radius: 8px; margin: 8px 0; border-left: 3px solid {confidence_color};">
+        <strong style="color: {confidence_color};">{signal['Source']}</strong><br>
+        <small>{signal['Signal']}</small><br>
+        <small><strong>Impact:</strong> {signal['Impact']} ({signal['Confidence']}%)</small>
+    </div>
+    """, unsafe_allow_html=True)
 
-# Clean footer - ONLY changed company name
+# Footer
+st.markdown("---")
 st.markdown("""
-<div style="text-align: center; padding: 1.5rem; color: #64748b; border-top: 1px solid #e2e8f0; margin-top: 2rem; font-size: 0.875rem;">
-    Yazaki India Dashboard last updated: {timestamp} • Auto-refresh: Every 15 minutes • Data accuracy: 99.7%
+<div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #1f4e79, #2d5aa0); border-radius: 15px; color: white;">
+    <h3>🔌 Yazaki India Ltd 8-Week Supply Chain Command Center</h3>
+    <p><strong>Firm + AI-Corrected Demand | Ecosystem Intelligence + Buffer Optimization</strong></p>
+    <p><em>Powered by Agentic AI | 8-Week Planning Horizon | Comprehensive Supply Chain Resilience</em></p>
 </div>
-""".format(timestamp=datetime.now().strftime("%B %d, %Y at %I:%M %p")), unsafe_allow_html=True)
+""", unsafe_allow_html=True)