Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,30 +1,30 @@
-#Stable version for Yazaki India
 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 configuration
+# Page config
 st.set_page_config(
-    page_title="Yazaki India - Complete Supply Chain Hub",
+    page_title="Yazaki India Supply Chain Intelligence",
     page_icon="🔌",
     layout="wide",
     initial_sidebar_state="expanded"
 )
 
-# Custom CSS (keeping the same professional styling)
+# Clean, professional CSS styling (identical to Rane version)
 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;
@@ -34,13 +34,14 @@ st.markdown("""
         border: 1px solid #e2e8f0;
     }
     
+    /* Clean header */
     .modern-header {
         background: #1e293b;
         color: white;
         padding: 2rem;
         border-radius: 12px;
         margin-bottom: 2rem;
-        border-left: 4px solid #dc2626; /* Yazaki red accent */
+        border-left: 4px solid #3b82f6;
     }
     
     .header-title {
@@ -56,6 +57,7 @@ st.markdown("""
         color: #94a3b8;
     }
     
+    /* Clean metric cards */
     .metric-card {
         background: white;
         padding: 1.5rem;
@@ -87,481 +89,324 @@ st.markdown("""
         margin-bottom: 0.5rem;
     }
     
-    .alert-container {
-        background: linear-gradient(135deg, #fee2e2 0%, #fecaca 100%);
-        border-left: 4px solid #dc2626;
-        padding: 1rem;
-        margin: 1rem 0;
-        border-radius: 8px;
+    .metric-change {
+        font-size: 0.875rem;
+        font-weight: 600;
+        padding: 0.25rem 0.75rem;
+        border-radius: 6px;
+        display: inline-block;
     }
     
-    .critical-alert {
-        background: linear-gradient(135deg, #fee2e2 0%, #fecaca 100%);
+    .metric-positive {
+        background-color: #dcfce7;
+        color: #166534;
     }
     
-    .high-alert {
-        background: linear-gradient(135deg, #fef3c7 0%, #fed7aa 100%);
-        border-left-color: #f59e0b;
+    .metric-negative {
+        background-color: #fef2f2;
+        color: #dc2626;
     }
     
-    .medium-alert {
-        background: linear-gradient(135deg, #ddd6fe 0%, #c7d2fe 100%);
-        border-left-color: #8b5cf6;
+    .metric-neutral {
+        background-color: #f1f5f9;
+        color: #475569;
     }
     
-    .mitigation-option {
-        background: #f8fafc;
-        padding: 0.75rem;
-        margin: 0.5rem 0;
+    /* 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;
-        border-left: 3px solid #3b82f6;
+        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);
     }
 </style>
 """, unsafe_allow_html=True)
 
-# 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 demand data for Yazaki wiring components
+# Sample data functions - ONLY changed company/material names
 @st.cache_data
-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 (wiring harnesses and electrical components)
-    materials = [
-        'WH001-Engine Wiring Harness',
-        'WH002-Dashboard Wiring',
-        'CON001-Wire Connectors',
-        'TER001-Terminal Blocks',
-        'FUS001-Fuse Box Assembly'
-    ]
-    
-    all_data = []
-    
-    for material in materials:
-        np.random.seed(hash(material) % 1000)
-        
-        # Generate base demand patterns - lower volumes due to import constraints
-        base_demand = np.random.normal(120, 20, 56)
-        
-        # First 14 days: FIRM DEMAND (from OEM confirmed orders)
-        firm_demand = np.clip(base_demand[:14], 80, 180).astype(int)
-        
-        # Days 15-56: Customer shared demand (tentative from Maruti, Tata, etc.)
-        customer_shared = np.clip(base_demand[14:] * (1 + 0.08 * np.sin(np.linspace(0, 3.14, 42))), 60, 200).astype(int)
-        
-        # Days 15-56: AI-corrected demand (with import delays and external signals)
-        external_factors = np.zeros(42)
-        
-        # Import delays impact (weeks 3-4) - port congestion at JNPT
-        external_factors[0:14] += np.random.normal(-15, 8, 14)  # Negative impact from delays
-        
-        # EV transition impact (weeks 5-8) - different wiring needs
-        if 'WH' in material:  # Wire harnesses more affected by EV transition
-            external_factors[14:] += 12
-        elif 'CON' in material or 'TER' in material:  # Connectors/terminals high demand in EV
-            external_factors[14:] += 18
-            
-        # Festive season boost (weeks 6-7) - reduced due to import constraints
-        external_factors[28:42] += 6
-        
-        corrected_demand = np.clip(customer_shared + external_factors, 40, 220).astype(int)
-        
-        # Generate supply plan - constrained by import lead times
-        supply_capacity = np.random.normal(125, 18, 56)
-        supply_plan = np.clip(supply_capacity, 90, 200).astype(int)
-        
-        # Apply import disruptions (longer lead times, customs delays)
-        supply_actual = supply_plan.copy()
-        # Import delays at Chennai port (days 15-20)
-        supply_actual[15:21] = (supply_actual[15:21] * 0.6).astype(int)
-        # Currency fluctuation impact (days 25-30)
-        supply_actual[25:31] = (supply_actual[25:31] * 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 (OEM Confirmed)"
-            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 (Import Adjusted)"
-            
-            # 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)
+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']
+    })
 
-# Yazaki Tier-2 suppliers (mostly international)
 @st.cache_data
-def get_tier2_suppliers():
+def get_enhanced_metrics():
     return {
-        'Sumitomo Electric (Japan)': {
-            'location': 'Tokyo, Japan',
-            'materials': ['WH001-Engine Wiring Harness', 'CON001-Wire Connectors'],
-            'capacity': 150,
-            'reliability': 96,
-            'lead_time': 12,  # Import lead time in days
-            'risk_factors': ['Currency fluctuation', 'Shipping delays', 'Quality certification']
-        },
-        'Yazaki Europe GmbH': {
-            'location': 'Regensburg, Germany', 
-            'materials': ['WH002-Dashboard Wiring', 'TER001-Terminal Blocks'],
-            'capacity': 130,
-            'reliability': 94,
-            'lead_time': 15,
-            'risk_factors': ['European port strikes', 'Customs clearance', 'Exchange rate volatility']
-        },
-        'Yazaki Thailand': {
-            'location': 'Bangkok, Thailand',
-            'materials': ['FUS001-Fuse Box Assembly', 'CON001-Wire Connectors'],
-            'capacity': 170,
-            'reliability': 90,
-            'lead_time': 8,
-            'risk_factors': ['Monsoon disruptions', 'Regional political instability', 'Supply chain bottlenecks']
-        }
+        '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
     }
 
-# Generate ecosystem data with import focus
-@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()
-            
-            # Different disruption patterns for international suppliers
-            if 'Japan' in supplier_name:
-                disrupted_supply[5:9] = (disrupted_supply[5:9] * 0.4).astype(int)
-                disruption_cause = "JNPT port congestion - shipment delays"
-                disruption_days = list(range(5, 9))
-            elif 'Germany' in supplier_name:
-                disrupted_supply[7:11] = (disrupted_supply[7:11] * 0.3).astype(int)
-                disruption_cause = "European port strikes affecting shipments"
-                disruption_days = list(range(7, 11))
-            elif 'Thailand' in supplier_name:
-                disrupted_supply[3:6] = (disrupted_supply[3:6] * 0.5).astype(int)
-                disruption_cause = "Monsoon affecting Bangkok operations"
-                disruption_days = list(range(3, 6))
-            else:
-                disruption_cause = "No disruption"
-                disruption_days = []
-            
-            # Import lead time impact
-            lead_time = supplier_info['lead_time']
-            rane_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]
-                    rane_supply[arrival_day] = max(rane_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]),
-                    'Rane_Normal_Supply': int(normal_supply[i]),
-                    'Rane_Impacted_Supply': int(rane_supply[i]),
-                    'Rane_Impact': int(normal_supply[i] - rane_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_Rane_Impacted': rane_supply[i] < normal_supply[i]
-                })
-    
-    return pd.DataFrame(all_data)
-
-# Yazaki-specific external signals
-@st.cache_data
-def get_external_signals():
-    return [
-        {'Source': 'JNPT Port Authority', 'Signal': 'Port congestion expected for next 5 days', 'Impact': 'Import Delays', 'Confidence': 94},
-        {'Source': 'EV Market Intelligence', 'Signal': 'Electric vehicle wiring demand up 35% this quarter', 'Impact': 'Demand Surge', 'Confidence': 91},
-        {'Source': 'Currency Markets', 'Signal': 'INR weakening against JPY - 3% in last week', 'Impact': 'Cost Increase', 'Confidence': 98},
-        {'Source': 'Maruti Suzuki', 'Signal': 'New EV model launch - wiring harness orders expected +40%', 'Impact': 'Major Demand Increase', 'Confidence': 96},
-        {'Source': 'Weather Services', 'Signal': 'Heavy monsoon forecasted in Thailand next week', 'Impact': 'Supply Disruption', 'Confidence': 87},
-        {'Source': 'Government Policy', 'Signal': 'New import duty on auto components - 5% increase', 'Impact': 'Cost Impact', 'Confidence': 100}
-    ]
-
-# Generate alerts for Yazaki materials
-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'] > 3]  # Lower threshold due to import constraints
-        
-        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 > 8:
-                            root_causes.append(f"AI detected {diff} units additional demand from EV transition")
-                            
-                if row['Day'] >= 15 and row['Day'] <= 20:
-                    root_causes.append("Chennai port customs delays affecting imports")
-                elif row['Day'] >= 25 and row['Day'] <= 30:
-                    root_causes.append("INR currency volatility impacting import costs")
-                else:
-                    root_causes.append("OEM firm demand exceeding import supply capacity")
-                
-                if not root_causes:
-                    root_causes.append("Base demand exceeding current import supply capacity")
-                
-                # Yazaki-specific mitigation options
-                mitigation_options = [
-                    {"option": "Expedite air freight from Japan/Germany", "impact": "+25 units/day", "cost": "Very High", "timeline": "48 hours"},
-                    {"option": "Activate Thailand backup supplier", "impact": "+20 units/day", "cost": "High", "timeline": "72 hours"},
-                    {"option": "Reallocate inventory from other Yazaki plants", "impact": "+15 units/day", "cost": "Medium", "timeline": "24 hours"},
-                    {"option": "Request OEM demand deferral", "impact": "+30 units buffer", "cost": "Low", "timeline": "12 hours"}
-                ]
-                
-                if row['Shortfall'] > 25:
-                    best_option = mitigation_options[0]  # Air freight
-                elif row['Shortfall'] > 12:
-                    best_option = mitigation_options[1]  # Thailand backup
-                else:
-                    best_option = mitigation_options[2]  # Inventory reallocation
-                
-                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'] > 25 else 'High' if row['Shortfall'] > 12 else 'Medium',
-                    'root_causes': root_causes,
-                    'mitigation_options': mitigation_options,
-                    'best_option': best_option
-                })
-    
-    return alerts
+# 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 mitigation strategies similar but Yazaki-focused
-def generate_mitigation_strategies(supplier, material, impact_amount, impact_days):
-    base_strategies = [
-        {
-            'strategy': 'Activate Alternate Import Source',
-            'description': f'Engage backup international supplier for {material}',
-            'timeline': '48-72 hours',
-            'cost': 'Very High (+25% unit cost)',
-            'effectiveness': '85%',
-            'capacity': f'+{impact_amount * 0.85:.0f} units/day',
-        },
-        {
-            'strategy': 'Emergency Air Freight',
-            'description': f'Air freight {material} from Japan/Germany',
-            'timeline': '24-48 hours',
-            'cost': 'Extremely High (+60% logistics cost)',
-            'effectiveness': '90%',
-            'capacity': f'+{impact_amount * 0.9:.0f} units/day',
-        },
-        {
-            'strategy': 'Inventory Reallocation',
-            'description': f'Reallocate {material} from other Yazaki facilities',
-            'timeline': '12-36 hours',
-            'cost': 'Medium (+8% handling cost)',
-            'effectiveness': '70%',
-            'capacity': f'+{impact_amount * 0.7:.0f} units/day',
-        }
+# 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"
     ]
     
-    if impact_amount > 80:
-        recommended = [0, 1]
-    elif impact_amount > 40:
-        recommended = [0, 2]
-    else:
-        recommended = [2]
-        
-    return base_strategies, recommended
-
-# Load data
-df_demand = generate_8week_demand_data()
-df_ecosystem = generate_ecosystem_data()
-external_signals = get_external_signals()
-suppliers = get_tier2_suppliers()
+    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")
 
-# Yazaki India title
+# Clean filters section - ONLY changed plant names and some filters
 st.markdown("""
-<div class="modern-header">
-    <div class="header-title">🔌 Yazaki India Supply Chain Command Center</div>
-    <div class="header-subtitle">Import-Focused Supply Chain Intelligence | Automotive Wiring Systems | Global Supply Network</div>
+<div class="filter-container">
+    <div class="section-header">Import & Supply Chain Filters</div>
 </div>
 """, unsafe_allow_html=True)
 
-# Tab Navigation
-st.sidebar.title("🎯 Yazaki Dashboard Navigation")
-dashboard_tab = st.sidebar.radio(
-    "Select Dashboard:",
-    ["📊 Import Demand Forecast", "🌐 Global Supplier Impact", "🛡️ Import Buffer Optimizer"],
-    index=0
-)
+col1, col2, col3, col4 = st.columns(4)
+col5, col6, col7, col8 = st.columns(4)
 
-# TAB 1: Import Demand & Supply Forecast
-if dashboard_tab == "📊 Import Demand Forecast":
-    st.markdown("""
-    ### 📈 8-Week Import Planning Horizon | OEM Confirmed Orders (Days 1-14) | AI-Adjusted Import Forecasts (Days 15-56)
-    """)
-    
-    # Key metrics for Yazaki
-    col1, col2, col3, col4 = st.columns(4)
-    
-    total_shortage = df_demand['Shortfall'].sum()
-    avg_import_lead_time = 12  # Average from suppliers
-    import_dependency = 89  # High dependency on imports
-    active_suppliers = len(suppliers)
-    
-    with col1:
-        st.markdown(f"""
-        <div class="metric-card">
-            <div class="metric-number">{total_shortage}</div>
-            <div class="metric-label">Total Import Shortage (8 weeks)</div>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col2:
-        st.markdown(f"""
-        <div class="metric-card">
-            <div class="metric-number">{avg_import_lead_time}</div>
-            <div class="metric-label">Avg Import Lead Time (Days)</div>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col3:
-        st.markdown(f"""
-        <div class="metric-card">
-            <div class="metric-number">{import_dependency}%</div>
-            <div class="metric-label">Import Dependency</div>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col4:
-        st.markdown(f"""
-        <div class="metric-card">
-            <div class="metric-number">{active_suppliers}</div>
-            <div class="metric-label">Global Suppliers</div>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    # Material selection
-    selected_materials = st.multiselect(
-        "Select Yazaki Materials:",
-        df_demand['Material'].unique(),
-        default=list(df_demand['Material'].unique()[:2])
-    )
-    
-    if selected_materials:
-        filtered_df = df_demand[df_demand['Material'].isin(selected_materials)]
-        
-        # Create visualization
-        fig = px.line(filtered_df, x='Date', y=['Demand_Used', 'Supply_Projected'], 
-                     color='Material', title="Import Demand vs Supply Projection (8 weeks)",
-                     line_dash_map={'Demand_Used': 'solid', 'Supply_Projected': 'dash'})
-        
-        fig.update_layout(height=500, showlegend=True)
-        st.plotly_chart(fig, use_container_width=True)
-        
-        # Show detailed data
-        st.markdown("### 📊 Import Demand & Supply Detail")
-        display_columns = ['Date', 'Material', 'Week', 'Demand_Used', 'Supply_Projected', 'Shortfall', 'Demand_Type']
-        st.dataframe(filtered_df[display_columns], use_container_width=True)
-        
-        # Generate and display alerts
-        alerts = generate_detailed_alerts(filtered_df)
-        
-        if alerts:
-            st.markdown("### 🚨 Critical Import Alerts")
-            for alert in alerts[:5]:  # Show top 5 alerts
-                severity_class = f"{alert['severity'].lower()}-alert"
-                
-                st.markdown(f"""
-                <div class="alert-container {severity_class}">
-                    <h4>⚠️ {alert['severity']} Alert: {alert['material']}</h4>
-                    <p><strong>Date:</strong> {alert['date']} ({alert['week']}) | 
-                    <strong>Shortage:</strong> {alert['shortage']} units | 
-                    <strong>Type:</strong> {alert['demand_type']}</p>
-                    
-                    <h5>🔍 Root Causes:</h5>
-                    <ul>{''.join([f"<li>{cause}</li>" for cause in alert['root_causes']])}</ul>
-                    
-                    <h5>💡 Recommended Mitigation:</h5>
-                    <div class="mitigation-option">
-                        <strong>{alert['best_option']['option']}</strong><br>
-                        Impact: {alert['best_option']['impact']} | 
-                        Cost: {alert['best_option']['cost']} | 
-                        Timeline: {alert['best_option']['timeline']}
-                    </div>
-                    
-                    {'<button style="background: #dc2626; color: white; padding: 8px 16px; border: none; border-radius: 4px; margin-top: 10px;">🚀 Execute Mitigation</button>' if alert['severity'] == 'Critical' else ''}
-                </div>
-                """, unsafe_allow_html=True)
+with col1:
+    plant_location = st.selectbox("Yazaki Plant", ["Chennai (Main)", "Bawal (Haryana)", "Kanchipuram", "All Plants"], index=0)
 
-# TAB 2: Global Supplier Impact (same structure but Yazaki-focused)
-elif dashboard_tab == "🌐 Global Supplier Impact":
-    st.markdown("""
-    ### 🌏 Global Supplier Disruption Analysis | Import Cascade Modeling | International Supply Risk
-    """)
+with col2:
+    material_group = st.selectbox("Component Category", ["All Components", "Wire Harnesses", "Connectors", "Electronics"], index=0)
+
+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)
+
+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)
+
+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)
+
+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)
+
+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>
+    </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']
+    )
     
-    # Similar structure but with import focus...
-    # (Implementation similar to original but with Yazaki suppliers and import-specific risks)
+    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,
+        showlegend=True,
+        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
+    )
     
-    # Show ecosystem data
-    st.dataframe(df_ecosystem.head(20), use_container_width=True)
+    st.plotly_chart(fig1, use_container_width=True)
 
-# TAB 3: Import Buffer Optimizer
-elif dashboard_tab == "🛡️ Import Buffer Optimizer":
-    st.markdown("""
-    ### 🛡️ AI-Optimized Import Safety Stock | Currency Risk Buffer | Port Delay Contingency
-    """)
+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']
+    )
+    
+    fig2.update_layout(
+        plot_bgcolor='white',
+        paper_bgcolor='white',
+        font_family="Inter",
+        title_font_size=14,
+        title_font_color="#1e293b",
+        height=400
+    )
     
-    # Buffer optimization specific to import constraints
-    st.info("AI-driven safety-stock recommendations considering import lead times, currency volatility, and port disruption risks")
+    st.plotly_chart(fig2, use_container_width=True)
 
-# Footer
+# Clean footer - ONLY changed company name
 st.markdown("""
----
-**🔌 Yazaki India Global Supply Chain Command Center** | 
-Import-Focused Planning | OEM Integration + AI-Enhanced Forecasting | International Supplier Network Intelligence
-
-*Powered by Agentic AI | 8-Week Import Horizon | Comprehensive Global Supply Chain Resilience*
-""")
+<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>
+""".format(timestamp=datetime.now().strftime("%B %d, %Y at %I:%M %p")), unsafe_allow_html=True)