Update src/streamlit_app.py

src/streamlit_app.py

@@ -8,7 +8,7 @@ import random
 
 # Page configuration
 st.set_page_config(
-    page_title="Rane Group - Ecosystem Supply Chain Hub",
+    page_title="Rane Group - Complete Supply Chain Hub",
     page_icon="🌐",
     layout="wide",
     initial_sidebar_state="expanded"
@@ -25,6 +25,20 @@ st.markdown("""
         text-align: center;
         margin-bottom: 2rem;
     }
+    .tab-header {
+        background: linear-gradient(90deg, #059669, #10b981);
+        padding: 0.8rem;
+        border-radius: 8px;
+        color: white;
+        margin-bottom: 1rem;
+    }
+    .alert-card {
+        background: #fff5f5;
+        padding: 1rem;
+        border-radius: 8px;
+        border-left: 6px solid #e53e3e;
+        margin: 0.5rem 0;
+    }
     .ecosystem-alert {
         background: #fef2f2;
         padding: 1rem;
@@ -32,6 +46,28 @@ st.markdown("""
         border-left: 6px solid #dc2626;
         margin: 0.5rem 0;
     }
+    .root-cause {
+        background: #fef7e7;
+        padding: 0.8rem;
+        border-radius: 6px;
+        margin: 0.3rem 0;
+        border-left: 3px solid #f6ad55;
+    }
+    .mitigation {
+        background: #e6fffa;
+        padding: 0.8rem;
+        border-radius: 6px;
+        margin: 0.3rem 0;
+        border-left: 3px solid #4fd1c7;
+    }
+    .best-option {
+        background: #f0fff4;
+        padding: 0.8rem;
+        border-radius: 6px;
+        margin: 0.3rem 0;
+        border-left: 4px solid #48bb78;
+        border: 2px solid #48bb78;
+    }
     .tier-impact {
         background: #fff7ed;
         padding: 0.8rem;
@@ -54,19 +90,19 @@ st.markdown("""
         margin: 0.3rem 0;
         border-left: 4px solid #3b82f6;
     }
-    .supplier-card {
-        background: #f8fafc;
-        padding: 1rem;
-        border-radius: 8px;
-        border: 1px solid #e2e8f0;
-        margin: 0.5rem 0;
+    .normal-status {
+        background: #f0fff4;
+        padding: 0.6rem;
+        border-radius: 6px;
+        border-left: 4px solid #48bb78;
+        margin: 0.2rem 0;
     }
-    .impact-timeline {
-        background: #fefce8;
+    .external-signal {
+        background: #f3e5f5;
         padding: 0.6rem;
         border-radius: 6px;
+        border-left: 4px solid #9c27b0;
         margin: 0.2rem 0;
-        border-left: 3px solid #eab308;
     }
 </style>
 """, unsafe_allow_html=True)
@@ -74,8 +110,78 @@ st.markdown("""
 # Initialize session state
 if 'executed_mitigations' not in st.session_state:
     st.session_state.executed_mitigations = []
-if 'supplier_disruptions' not in st.session_state:
-    st.session_state.supplier_disruptions = {}
+if 'external_signals' not in st.session_state:
+    st.session_state.external_signals = []
+
+# Generate forward-looking demand data
+@st.cache_data
+def generate_forward_demand_data():
+    today = datetime(2025, 8, 4)
+    dates = [today + timedelta(days=x) for x in range(14)]
+    
+    materials = [
+        'STG001-Steering Gear', 
+        'STG002-Steering Column', 
+        'STG003-Power Steering', 
+        'BRK001-Brake Pads', 
+        'SUS001-Shock Absorber'
+    ]
+    
+    all_data = []
+    
+    for material in materials:
+        np.random.seed(hash(material) % 1000)
+        
+        # Generate base demand
+        base_demand = np.random.normal(150, 20, 14)
+        demand_forecast = np.clip(base_demand, 80, 250).astype(int)
+        
+        # Generate supply capacity
+        supply_capacity = np.random.normal(160, 15, 14)
+        supply_plan = np.clip(supply_capacity, 100, 280).astype(int)
+        
+        # External impacts (as integers)
+        weather_impact = np.zeros(14, dtype=int)
+        policy_impact = np.zeros(14, dtype=int)
+        market_impact = np.zeros(14, dtype=int)
+        
+        # Weather impact (Chennai rains days 1-3)
+        weather_impact[1:4] = -25
+        
+        # EV policy impact (from day 7)
+        if 'STG' in material:
+            policy_impact[7:] = 15
+        
+        # Market sentiment (festive season days 5-9)
+        market_impact[5:9] = [8, 10, 12, 6]
+        
+        # Calculate adjusted demand and supply
+        adjusted_demand = demand_forecast + weather_impact + policy_impact + market_impact
+        adjusted_demand = np.clip(adjusted_demand, 50, 300).astype(int)
+        
+        # Supply with weather constraints
+        supply_actual = supply_plan.copy()
+        for i in range(1, 4):  # Weather impact on supply
+            supply_actual[i] = int(supply_actual[i] * 0.7)
+        
+        # Calculate gaps
+        supply_gap = supply_actual - adjusted_demand
+        
+        for i, date in enumerate(dates):
+            all_data.append({
+                'Date': date,
+                'Material': material,
+                'Demand_Forecast': int(demand_forecast[i]),
+                'Demand_Adjusted': int(adjusted_demand[i]),
+                'Supply_Plan': int(supply_plan[i]),
+                'Supply_Projected': int(supply_actual[i]),
+                'Gap': int(supply_gap[i]),
+                'Weather_Impact': int(weather_impact[i]),
+                'Policy_Impact': int(policy_impact[i]),
+                'Market_Impact': int(market_impact[i])
+            })
+    
+    return pd.DataFrame(all_data)
 
 # Tier 2 Supplier Data
 @st.cache_data
@@ -184,7 +290,68 @@ def generate_ecosystem_data():
     
     return pd.DataFrame(all_data)
 
-# Generate mitigation strategies
+# External signals data
+@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', 'Impact': 'Demand Increase', 'Confidence': 88},
+        {'Source': 'News Analytics', 'Signal': 'Upcoming festive season - historically 15% demand spike', 'Impact': 'Demand Surge', 'Confidence': 92},
+        {'Source': 'Supplier Network', 'Signal': 'Tier-2 supplier capacity increased by 20%', 'Impact': 'Supply Boost', 'Confidence': 98},
+        {'Source': 'Social Media', 'Signal': 'Positive sentiment around new Maruti EV model', 'Impact': 'Demand Growth', 'Confidence': 75},
+        {'Source': 'Government Portal', 'Signal': 'New EV subsidy policy effective next week', 'Impact': 'Market Expansion', 'Confidence': 100}
+    ]
+
+# Generate detailed alerts with root cause analysis
+def generate_detailed_alerts(df):
+    alerts = []
+    
+    for material in df['Material'].unique():
+        material_data = df[df['Material'] == material]
+        shortage_days = material_data[material_data['Gap'] < -5]
+        
+        if not shortage_days.empty:
+            for _, row in shortage_days.iterrows():
+                # Root cause analysis
+                root_causes = []
+                if row['Weather_Impact'] < -10:
+                    root_causes.append("Chennai plant disruption due to heavy rains (-25 units)")
+                if row['Policy_Impact'] > 10:
+                    root_causes.append(f"EV policy driving increased demand (+{row['Policy_Impact']} units)")
+                if row['Market_Impact'] > 5:
+                    root_causes.append(f"Festive season demand surge (+{row['Market_Impact']} units)")
+                if not root_causes:
+                    root_causes.append("Base demand exceeding current supply capacity")
+                
+                # Mitigation options
+                mitigation_options = [
+                    {"option": "Activate Pune 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 backup suppliers", "impact": "+40 units/day", "cost": "Very High", "timeline": "6 hours"},
+                    {"option": "Reallocate inventory from other plants", "impact": "+20 units/day", "cost": "Low", "timeline": "18 hours"}
+                ]
+                
+                # Determine best option based on severity
+                if row['Gap'] < -20:  # Critical shortage
+                    best_option = mitigation_options[2]  # Air freight
+                elif row['Gap'] < -10:  # High shortage
+                    best_option = mitigation_options[0]  # Pune backup
+                else:  # Medium shortage
+                    best_option = mitigation_options[1]  # Expedite suppliers
+                
+                alerts.append({
+                    'material': material,
+                    'date': row['Date'].strftime('%Y-%m-%d'),
+                    'shortage': abs(row['Gap']),
+                    'severity': 'Critical' if row['Gap'] < -20 else 'High' if row['Gap'] < -10 else 'Medium',
+                    'root_causes': root_causes,
+                    'mitigation_options': mitigation_options,
+                    'best_option': best_option
+                })
+    
+    return alerts
+
+# Generate mitigation strategies for ecosystem
 def generate_mitigation_strategies(supplier, material, impact_amount, impact_days):
     base_strategies = [
         {
@@ -194,7 +361,6 @@ def generate_mitigation_strategies(supplier, material, impact_amount, impact_day
             'cost': 'High (+15% unit cost)',
             'effectiveness': '90%',
             'capacity': f'+{impact_amount * 0.9:.0f} units/day',
-            'implementation_time': 2
         },
         {
             'strategy': 'Emergency Air Freight',
@@ -203,7 +369,6 @@ def generate_mitigation_strategies(supplier, material, impact_amount, impact_day
             'cost': 'Very High (+40% logistics cost)',
             'effectiveness': '75%',
             'capacity': f'+{impact_amount * 0.75:.0f} units/day',
-            'implementation_time': 0.5
         },
         {
             'strategy': 'Inventory Reallocation',
@@ -212,301 +377,438 @@ def generate_mitigation_strategies(supplier, material, impact_amount, impact_day
             'cost': 'Medium (+5% handling cost)',
             'effectiveness': '60%',
             'capacity': f'+{impact_amount * 0.6:.0f} units/day',
-            'implementation_time': 1
-        },
-        {
-            'strategy': 'Production Rescheduling',
-            'description': f'Reschedule production to prioritize {material}',
-            'timeline': '4-8 hours',
-            'cost': 'Low (internal reorg)',
-            'effectiveness': '45%',
-            'capacity': f'+{impact_amount * 0.45:.0f} units/day',
-            'implementation_time': 0.3
-        },
-        {
-            'strategy': 'Customer Communication',
-            'description': 'Proactive communication with OEM customers',
-            'timeline': '1-2 hours',
-            'cost': 'Very Low',
-            'effectiveness': '30% (delay mitigation)',
-            'capacity': 'Manages expectations',
-            'implementation_time': 0.1
         }
     ]
     
     # AI recommendation logic
     if impact_amount > 100:  # Critical impact
-        recommended = [0, 1, 4]  # Alternate supplier + Air freight + Communication
+        recommended = [0, 1]  # Alternate supplier + Air freight
     elif impact_amount > 50:  # High impact
-        recommended = [0, 2, 4]  # Alternate supplier + Reallocation + Communication
+        recommended = [0, 2]  # Alternate supplier + Reallocation
     else:  # Medium impact
-        recommended = [2, 3, 4]  # Reallocation + Rescheduling + Communication
+        recommended = [2]  # Reallocation
     
     return base_strategies, recommended
 
 # Load data
-df = generate_ecosystem_data()
+df_demand = generate_forward_demand_data()
+df_ecosystem = generate_ecosystem_data()
+external_signals = get_external_signals()
 suppliers = get_tier2_suppliers()
 
 # Header
 st.markdown("""
 <div class="main-header">
-    <h1>🌐 Rane Group - Ecosystem Supply Chain Command Center</h1>
-    <h3>Tier 2 Supplier Impact Analysis & Agentic AI Mitigation</h3>
-    <p>Real-time Ecosystem Monitoring | Supply Chain Resilience | Automated Response</p>
+    <h1>🌐 Rane Group - Complete Supply Chain Command Center</h1>
+    <h3>Forward-Looking Demand Sensing | Ecosystem Impact Analysis | Agentic AI Optimization</h3>
+    <p>Comprehensive Supply Chain Intelligence | Real-time Decision Support</p>
 </div>
 """, unsafe_allow_html=True)
 
-# Sidebar controls
-st.sidebar.title("🎯 Ecosystem Controls")
-
-# Supplier filter
-selected_suppliers = st.sidebar.multiselect(
-    "Monitor Suppliers:",
-    list(suppliers.keys()),
-    default=list(suppliers.keys())
+# Tab Navigation
+st.sidebar.title("🎯 Dashboard Navigation")
+dashboard_tab = st.sidebar.radio(
+    "Select Dashboard:",
+    ["📊 Demand & Supply Forecast", "🌐 Ecosystem Supplier Impact"],
+    index=0
 )
 
-# Disruption simulation
-st.sidebar.subheader("🎮 Disruption Simulation")
-disruption_scenario = st.sidebar.selectbox(
-    "Inject Additional Disruption:",
-    ["None", "Multi-supplier Strike", "Regional Power Outage", "Transportation Blockade", "Raw Material Shortage"]
-)
-
-if st.sidebar.button("🚨 Activate Disruption"):
-    st.sidebar.error(f"Simulating: {disruption_scenario}")
-
-# Main Dashboard - Ecosystem Alerts
-st.subheader("🚨 Live Ecosystem Supply Chain Alerts")
-
-# Generate real-time alerts
-ecosystem_alerts = []
-for supplier in selected_suppliers:
-    supplier_data = df[df['Supplier'] == supplier]
-    disrupted_data = supplier_data[supplier_data['Is_Disrupted'] == True]
-    
-    if not disrupted_data.empty:
-        for material in disrupted_data['Material'].unique():
-            material_disruptions = disrupted_data[disrupted_data['Material'] == material]
-            
-            total_impact = material_disruptions['Tier2_Impact'].sum()
-            impact_days = len(material_disruptions)
-            first_impact_date = material_disruptions['Date'].min()
+# TAB 1: DEMAND & SUPPLY FORECAST
+if dashboard_tab == "📊 Demand & Supply Forecast":
+    st.markdown("""
+    <div class="tab-header">
+        <h2>📊 Demand & Supply Forecast Dashboard</h2>
+        <p>Forward-Looking 14-Day Demand Sensing | External Signal Integration | AI-Driven Optimization</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Material selection
+    selected_materials_demand = st.sidebar.multiselect(
+        "Focus Materials:",
+        df_demand['Material'].unique(),
+        default=df_demand['Material'].unique()[:3]
+    )
+    
+    # Filter data
+    filtered_df_demand = df_demand[df_demand['Material'].isin(selected_materials_demand)]
+    
+    # Generate and display alerts
+    st.subheader("🚨 Detailed Supply Chain Alerts")
+    
+    alerts = generate_detailed_alerts(filtered_df_demand)
+    
+    if alerts:
+        for i, alert in enumerate(alerts[:3]):  # Show top 3 alerts
+            st.markdown(f"""
+            <div class="alert-card">
+                <h4>⚠️ {alert['material']} - {alert['severity']} Shortage Alert</h4>
+                <p><b>Date:</b> {alert['date']} | <b>Shortage:</b> {alert['shortage']} units</p>
+            </div>
+            """, unsafe_allow_html=True)
             
-            # Calculate Rane impact with lead time
-            rane_impacted = supplier_data[
-                (supplier_data['Material'] == material) & 
-                (supplier_data['Is_Rane_Impacted'] == True)
-            ]
+            # Root cause analysis
+            st.markdown("**🔍 Root Cause Analysis:**")
+            for cause in alert['root_causes']:
+                st.markdown(f"""
+                <div class="root-cause">
+                    🎯 {cause}
+                </div>
+                """, unsafe_allow_html=True)
             
-            if not rane_impacted.empty:
-                rane_impact_start = rane_impacted['Date'].min()
-                rane_impact_days = len(rane_impacted)
-                rane_total_impact = rane_impacted['Rane_Impact'].sum()
+            # Mitigation options
+            st.markdown("**⚡ Mitigation Options:**")
+            for option in alert['mitigation_options']:
+                is_best = option == alert['best_option']
+                option_class = "best-option" if is_best else "mitigation"
+                best_indicator = "🏆 **RECOMMENDED** " if is_best else ""
                 
-                ecosystem_alerts.append({
-                    'supplier': supplier,
-                    'material': material,
-                    'disruption_cause': material_disruptions.iloc[0]['Disruption_Cause'],
-                    'tier2_impact_start': first_impact_date,
-                    'tier2_impact_days': impact_days,
-                    'tier2_total_impact': total_impact,
-                    'rane_impact_start': rane_impact_start,
-                    'rane_impact_days': rane_impact_days,
-                    'rane_total_impact': rane_total_impact,
-                    'lead_time': material_disruptions.iloc[0]['Lead_Time_Days']
-                })
-
-# Display ecosystem alerts
-if ecosystem_alerts:
-    for alert in ecosystem_alerts:
-        st.markdown(f"""
-        <div class="ecosystem-alert">
-            <h4>⚠️ Tier 2 Supplier Disruption Alert</h4>
-            <p><b>Supplier:</b> {alert['supplier']} | <b>Material:</b> {alert['material']}</p>
-            <p><b>Root Cause:</b> {alert['disruption_cause']}</p>
+                st.markdown(f"""
+                <div class="{option_class}">
+                    {best_indicator}<b>{option['option']}</b><br>
+                    📈 Impact: {option['impact']} | 💰 Cost: {option['cost']} | ⏱️ Timeline: {option['timeline']}
+                </div>
+                """, unsafe_allow_html=True)
+            
+            # Action buttons
+            col1, col2, col3 = st.columns([2, 1, 1])
+            with col1:
+                if st.button(f"✅ Implement Solution", key=f"demand_implement_{i}"):
+                    st.success(f"Implementing: {alert['best_option']['option']}")
+            
+            st.markdown("---")
+    else:
+        st.markdown("""
+        <div class="normal-status">
+            ✅ <b>All Good!</b> No critical supply shortages detected in the next 14 days.
         </div>
         """, unsafe_allow_html=True)
+    
+    # Enhanced visualization
+    st.subheader("📊 14-Day Supply vs Demand Outlook")
+    
+    for material in selected_materials_demand:
+        material_data = filtered_df_demand[filtered_df_demand['Material'] == material]
         
-        # Detailed impact analysis
-        col1, col2 = st.columns(2)
+        st.markdown(f"**{material}**")
         
-        with col1:
-            st.markdown("**🏭 Tier 2 Supplier Impact:**")
-            st.markdown(f"""
-            <div class="tier-impact">
-                📅 <b>Impact Period:</b> {alert['tier2_impact_start'].strftime('%Y-%m-%d')} ({alert['tier2_impact_days']} days)<br>
-                📉 <b>Total Supply Lost:</b> {alert['tier2_total_impact']} units<br>
-                🎯 <b>Daily Impact:</b> {alert['tier2_total_impact'] // alert['tier2_impact_days']} units/day
-            </div>
-            """, unsafe_allow_html=True)
+        # Create clear chart
+        fig = go.Figure()
+        
+        # Add demand line
+        fig.add_trace(go.Scatter(
+            x=material_data['Date'],
+            y=material_data['Demand_Adjusted'],
+            mode='lines+markers',
+            name='Adjusted Demand',
+            line=dict(color='red', width=3),
+            marker=dict(size=8)
+        ))
+        
+        # Add supply line
+        fig.add_trace(go.Scatter(
+            x=material_data['Date'],
+            y=material_data['Supply_Projected'],
+            mode='lines+markers',
+            name='Projected Supply',
+            line=dict(color='green', width=3),
+            marker=dict(size=8)
+        ))
+        
+        # Highlight shortage areas
+        shortage_data = material_data[material_data['Gap'] < 0]
+        if not shortage_data.empty:
+            fig.add_trace(go.Scatter(
+                x=shortage_data['Date'],
+                y=shortage_data['Supply_Projected'],
+                mode='markers',
+                name='Shortage Days',
+                marker=dict(color='orange', size=12, symbol='x'),
+            ))
         
-        with col2:
-            st.markdown("**⚙️ Rane Group Impact (with Lead Time):**")
+        fig.update_layout(
+            title=f'{material} - Supply vs Demand Forecast',
+            xaxis_title='Date',
+            yaxis_title='Units',
+            height=350,
+            showlegend=True,
+            hovermode='x unified'
+        )
+        
+        st.plotly_chart(fig, use_container_width=True)
+    
+    # External demand sensing
+    st.subheader("📡 Real-time External Demand Sensing")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.write("**Active External Signals:**")
+        for signal in external_signals:
+            confidence_color = "🟢" if signal['Confidence'] > 90 else "🟡" if signal['Confidence'] > 80 else "🟠"
             st.markdown(f"""
-            <div class="tier-impact">
-                📅 <b>Impact Period:</b> {alert['rane_impact_start'].strftime('%Y-%m-%d')} ({alert['rane_impact_days']} days)<br>
-                📉 <b>Total Supply Lost:</b> {alert['rane_total_impact']} units<br>
-                ⏱️ <b>Lead Time Delay:</b> {alert['lead_time']} days
+            <div class="external-signal">
+                <b>{confidence_color} {signal['Source']}</b><br>
+                {signal['Signal']}<br>
+                <small>Impact: {signal['Impact']} | Confidence: {signal['Confidence']}%</small>
             </div>
             """, unsafe_allow_html=True)
+    
+    with col2:
+        st.write("**Scenario Planning:**")
         
-        # Generate and display mitigation strategies
-        strategies, recommended_indices = generate_mitigation_strategies(
-            alert['supplier'], 
-            alert['material'], 
-            alert['rane_total_impact'] // alert['rane_impact_days'],
-            alert['rane_impact_days']
-        )
+        scenario = st.selectbox("Select Scenario to Test:", 
+                               ["Base Case", "Heavy Monsoon", "Festive Surge", "EV Policy Boost"])
         
-        st.markdown("**🤖 Agentic AI Mitigation Strategies:**")
+        if st.button("🎮 Run Scenario", key="demand_scenario"):
+            if scenario == "Heavy Monsoon":
+                st.error("Scenario: 40% supply reduction for 5 days. Activating contingency plans...")
+            elif scenario == "Festive Surge":
+                st.warning("Scenario: 35% demand increase for 7 days. Scaling production...")
+            elif scenario == "EV Policy Boost":
+                st.info("Scenario: 25% demand boost for EV components. Optimizing product mix...")
+
+# TAB 2: ECOSYSTEM SUPPLIER IMPACT
+elif dashboard_tab == "🌐 Ecosystem Supplier Impact":
+    st.markdown("""
+    <div class="tab-header">
+        <h2>🌐 Ecosystem Supplier Impact Dashboard</h2>
+        <p>Tier 2 Supplier Disruption Analysis | Cascading Impact Modeling | Automated Mitigation Response</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Supplier selection
+    selected_suppliers = st.sidebar.multiselect(
+        "Monitor Suppliers:",
+        list(suppliers.keys()),
+        default=list(suppliers.keys())
+    )
+    
+    # Main Dashboard - Ecosystem Alerts
+    st.subheader("🚨 Live Ecosystem Supply Chain Alerts")
+    
+    # Generate real-time alerts
+    ecosystem_alerts = []
+    for supplier in selected_suppliers:
+        supplier_data = df_ecosystem[df_ecosystem['Supplier'] == supplier]
+        disrupted_data = supplier_data[supplier_data['Is_Disrupted'] == True]
         
-        for i, strategy in enumerate(strategies):
-            is_recommended = i in recommended_indices
-            is_executed = f"{alert['supplier']}_{alert['material']}_{i}" in st.session_state.executed_mitigations
-            
-            if is_executed:
-                card_class = "mitigation-executed"
-                status_prefix = "✅ **EXECUTED** "
-            elif is_recommended:
-                card_class = "mitigation-recommended"
-                status_prefix = "🏆 **AI RECOMMENDED** "
-            else:
-                card_class = "mitigation-recommended"
-                status_prefix = ""
-            
+        if not disrupted_data.empty:
+            for material in disrupted_data['Material'].unique():
+                material_disruptions = disrupted_data[disrupted_data['Material'] == material]
+                
+                total_impact = material_disruptions['Tier2_Impact'].sum()
+                impact_days = len(material_disruptions)
+                first_impact_date = material_disruptions['Date'].min()
+                
+                # Calculate Rane impact with lead time
+                rane_impacted = supplier_data[
+                    (supplier_data['Material'] == material) & 
+                    (supplier_data['Is_Rane_Impacted'] == True)
+                ]
+                
+                if not rane_impacted.empty:
+                    rane_impact_start = rane_impacted['Date'].min()
+                    rane_impact_days = len(rane_impacted)
+                    rane_total_impact = rane_impacted['Rane_Impact'].sum()
+                    
+                    ecosystem_alerts.append({
+                        'supplier': supplier,
+                        'material': material,
+                        'disruption_cause': material_disruptions.iloc[0]['Disruption_Cause'],
+                        'tier2_impact_start': first_impact_date,
+                        'tier2_impact_days': impact_days,
+                        'tier2_total_impact': total_impact,
+                        'rane_impact_start': rane_impact_start,
+                        'rane_impact_days': rane_impact_days,
+                        'rane_total_impact': rane_total_impact,
+                        'lead_time': material_disruptions.iloc[0]['Lead_Time_Days']
+                    })
+    
+    # Display ecosystem alerts
+    if ecosystem_alerts:
+        for alert in ecosystem_alerts:
             st.markdown(f"""
-            <div class="{card_class}">
-                {status_prefix}<b>{strategy['strategy']}</b><br>
-                📋 {strategy['description']}<br>
-                ⏱️ <b>Timeline:</b> {strategy['timeline']} | 💰 <b>Cost:</b> {strategy['cost']}<br>
-                📈 <b>Effectiveness:</b> {strategy['effectiveness']} | 🚀 <b>Capacity:</b> {strategy['capacity']}
+            <div class="ecosystem-alert">
+                <h4>⚠️ Tier 2 Supplier Disruption Alert</h4>
+                <p><b>Supplier:</b> {alert['supplier']} | <b>Material:</b> {alert['material']}</p>
+                <p><b>Root Cause:</b> {alert['disruption_cause']}</p>
             </div>
             """, unsafe_allow_html=True)
             
-            # Action buttons
-            col1, col2, col3 = st.columns([2, 1, 1])
-            
-            strategy_key = f"{alert['supplier']}_{alert['material']}_{i}"
+            # Detailed impact analysis
+            col1, col2 = st.columns(2)
             
             with col1:
-                if not is_executed:
-                    if st.button(f"🚀 Execute Strategy", key=f"execute_{strategy_key}"):
-                        st.session_state.executed_mitigations.append(strategy_key)
-                        st.success(f"Executing: {strategy['strategy']}")
-                        st.rerun()
-                else:
-                    st.success("Strategy Active")
+                st.markdown("**🏭 Tier 2 Supplier Impact:**")
+                st.markdown(f"""
+                <div class="tier-impact">
+                    📅 <b>Impact Period:</b> {alert['tier2_impact_start'].strftime('%Y-%m-%d')} ({alert['tier2_impact_days']} days)<br>
+                    📉 <b>Total Supply Lost:</b> {alert['tier2_total_impact']} units<br>
+                    🎯 <b>Daily Impact:</b> {alert['tier2_total_impact'] // alert['tier2_impact_days']} units/day
+                </div>
+                """, unsafe_allow_html=True)
             
             with col2:
-                if st.button(f"📊 Impact Analysis", key=f"analysis_{strategy_key}"):
-                    st.info(f"Detailed analysis for {strategy['strategy']}")
+                st.markdown("**⚙️ Rane Group Impact (with Lead Time):**")
+                st.markdown(f"""
+                <div class="tier-impact">
+                    📅 <b>Impact Period:</b> {alert['rane_impact_start'].strftime('%Y-%m-%d')} ({alert['rane_impact_days']} days)<br>
+                    📉 <b>Total Supply Lost:</b> {alert['rane_total_impact']} units<br>
+                    ⏱️ <b>Lead Time Delay:</b> {alert['lead_time']} days
+                </div>
+                """, unsafe_allow_html=True)
+            
+            # Generate and display mitigation strategies
+            strategies, recommended_indices = generate_mitigation_strategies(
+                alert['supplier'], 
+                alert['material'], 
+                alert['rane_total_impact'] // alert['rane_impact_days'],
+                alert['rane_impact_days']
+            )
+            
+            st.markdown("**🤖 Agentic AI Mitigation Strategies:**")
             
-            with col3:
-                if is_recommended:
-                    st.button("🏆 Recommended", key=f"rec_{strategy_key}", disabled=True)
+            for i, strategy in enumerate(strategies):
+                is_recommended = i in recommended_indices
+                is_executed = f"eco_{alert['supplier']}_{alert['material']}_{i}" in st.session_state.executed_mitigations
+                
+                if is_executed:
+                    card_class = "mitigation-executed"
+                    status_prefix = "✅ **EXECUTED** "
+                elif is_recommended:
+                    card_class = "mitigation-recommended"
+                    status_prefix = "🏆 **AI RECOMMENDED** "
+                else:
+                    card_class = "mitigation-recommended"
+                    status_prefix = ""
+                
+                st.markdown(f"""
+                <div class="{card_class}">
+                    {status_prefix}<b>{strategy['strategy']}</b><br>
+                    📋 {strategy['description']}<br>
+                    ⏱️ <b>Timeline:</b> {strategy['timeline']} | 💰 <b>Cost:</b> {strategy['cost']}<br>
+                    📈 <b>Effectiveness:</b> {strategy['effectiveness']} | 🚀 <b>Capacity:</b> {strategy['capacity']}
+                </div>
+                """, unsafe_allow_html=True)
+                
+                # Action buttons
+                strategy_key = f"eco_{alert['supplier']}_{alert['material']}_{i}"
+                
+                col1, col2 = st.columns([2, 1])
+                
+                with col1:
+                    if not is_executed:
+                        if st.button(f"🚀 Execute Strategy", key=f"execute_{strategy_key}"):
+                            st.session_state.executed_mitigations.append(strategy_key)
+                            st.success(f"Executing: {strategy['strategy']}")
+                            st.rerun()
+                    else:
+                        st.success("Strategy Active")
+                
+                with col2:
+                    if is_recommended:
+                        st.button("🏆 Recommended", key=f"rec_{strategy_key}", disabled=True)
+            
+            st.markdown("---")
+    else:
+        st.markdown("""
+        <div class="normal-status">
+            ✅ <b>Ecosystem Healthy!</b> No supplier disruptions detected in the current timeframe.
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Ecosystem visualization
+    st.subheader("📊 Ecosystem Supply Chain Flow Visualization")
+    
+    # Create ecosystem impact chart
+    fig = go.Figure()
+    
+    for supplier in selected_suppliers:
+        supplier_data = df_ecosystem[df_ecosystem['Supplier'] == supplier]
         
-        st.markdown("---")
-
-# Ecosystem visualization
-st.subheader("📊 Ecosystem Supply Chain Flow Visualization")
-
-# Create ecosystem impact chart
-fig = go.Figure()
-
-for supplier in selected_suppliers:
-    supplier_data = df[df['Supplier'] == supplier]
-    
-    # Sample one material per supplier for clarity
-    sample_material = supplier_data['Material'].iloc[0]
-    material_data = supplier_data[supplier_data['Material'] == sample_material]
-    
-    # Tier 2 supply line
-    fig.add_trace(go.Scatter(
-        x=material_data['Date'],
-        y=material_data['Tier2_Disrupted_Supply'],
-        mode='lines+markers',
-        name=f'{supplier} (Tier 2)',
-        line=dict(width=2, dash='dash'),
-        marker=dict(size=6)
-    ))
-    
-    # Rane impact line
-    fig.add_trace(go.Scatter(
-        x=material_data['Date'],
-        y=material_data['Rane_Impacted_Supply'],
-        mode='lines+markers',
-        name=f'Rane Impact from {supplier}',
-        line=dict(width=3),
-        marker=dict(size=8)
-    ))
-
-fig.update_layout(
-    title='Tier 2 Supplier Disruptions → Rane Group Supply Impact',
-    xaxis_title='Date',
-    yaxis_title='Supply Units',
-    height=500,
-    showlegend=True,
-    hovermode='x unified'
-)
-
-st.plotly_chart(fig, use_container_width=True)
-
-# Detailed ecosystem table
-st.subheader("📋 Detailed Ecosystem Impact Timeline")
-
-# Create comprehensive timeline table
-timeline_data = []
-for supplier in selected_suppliers:
-    supplier_data = df[df['Supplier'] == supplier]
-    
-    for _, row in supplier_data.iterrows():
-        if row['Is_Disrupted'] or row['Is_Rane_Impacted']:
-            timeline_data.append({
-                'Date': row['Date'].strftime('%Y-%m-%d'),
-                'Supplier': row['Supplier'],
-                'Material': row['Material'].split('-')[1],
-                'Tier2_Impact': f"-{row['Tier2_Impact']}" if row['Tier2_Impact'] > 0 else "Normal",
-                'Rane_Impact': f"-{row['Rane_Impact']}" if row['Rane_Impact'] > 0 else "Normal",
-                'Lead_Time': f"{row['Lead_Time_Days']} days",
-                'Disruption': row['Disruption_Cause'],
-                'Status': "🔴 Active" if row['Is_Disrupted'] else "🟡 Downstream Impact"
-            })
-
-if timeline_data:
-    timeline_df = pd.DataFrame(timeline_data)
-    st.dataframe(timeline_df, use_container_width=True, height=400)
-else:
-    st.info("🟢 No ecosystem disruptions detected in the current timeframe")
+        # Sample one material per supplier for clarity
+        sample_material = supplier_data['Material'].iloc[0]
+        material_data = supplier_data[supplier_data['Material'] == sample_material]
+        
+        # Tier 2 supply line
+        fig.add_trace(go.Scatter(
+            x=material_data['Date'],
+            y=material_data['Tier2_Disrupted_Supply'],
+            mode='lines+markers',
+            name=f'{supplier} (Tier 2)',
+            line=dict(width=2, dash='dash'),
+            marker=dict(size=6)
+        ))
+        
+        # Rane impact line
+        fig.add_trace(go.Scatter(
+            x=material_data['Date'],
+            y=material_data['Rane_Impacted_Supply'],
+            mode='lines+markers',
+            name=f'Rane Impact from {supplier}',
+            line=dict(width=3),
+            marker=dict(size=8)
+        ))
+    
+    fig.update_layout(
+        title='Tier 2 Supplier Disruptions → Rane Group Supply Impact',
+        xaxis_title='Date',
+        yaxis_title='Supply Units',
+        height=500,
+        showlegend=True,
+        hovermode='x unified'
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)
 
-# Summary metrics
-st.subheader("📊 Ecosystem Resilience Dashboard")
+# Performance summary (common for both tabs)
+st.subheader("📊 Performance Summary")
 
 col1, col2, col3, col4 = st.columns(4)
 
-total_suppliers_disrupted = len(df[df['Is_Disrupted'] == True]['Supplier'].unique())
-total_rane_impact_days = len(df[df['Is_Rane_Impacted'] == True])
-total_mitigation_strategies = len([s for s in st.session_state.executed_mitigations])
-avg_lead_time = df['Lead_Time_Days'].mean()
-
-with col1:
-    st.metric("Suppliers Disrupted", f"{total_suppliers_disrupted}")
-
-with col2:
-    st.metric("Rane Impact Days", f"{total_rane_impact_days}")
-
-with col3:
-    st.metric("Active Mitigations", f"{total_mitigation_strategies}")
+if dashboard_tab == "📊 Demand & Supply Forecast":
+    filtered_df = filtered_df_demand if 'filtered_df_demand' in locals() else df_demand
+    
+    total_shortage_days = len(filtered_df[filtered_df['Gap'] < 0])
+    critical_shortage_days = len(filtered_df[filtered_df['Gap'] < -20])
+    materials_at_risk = len(filtered_df[filtered_df['Gap'] < -5]['Material'].unique())
+    avg_gap = filtered_df['Gap'].mean()
+    
+    with col1:
+        st.metric("Days with Shortages", f"{total_shortage_days}")
+        
+    with col2:
+        st.metric("Critical Days", f"{critical_shortage_days}")
+        
+    with col3:
+        st.metric("Materials at Risk", f"{materials_at_risk}")
+        
+    with col4:
+        st.metric("Avg Supply Gap", f"{avg_gap:.0f} units")
 
-with col4:
-    st.metric("Avg Lead Time", f"{avg_lead_time:.1f} days")
+else:  # Ecosystem tab
+    total_suppliers_disrupted = len(df_ecosystem[df_ecosystem['Is_Disrupted'] == True]['Supplier'].unique())
+    total_rane_impact_days = len(df_ecosystem[df_ecosystem['Is_Rane_Impacted'] == True])
+    total_mitigation_strategies = len([s for s in st.session_state.executed_mitigations if 'eco_' in s])
+    avg_lead_time = df_ecosystem['Lead_Time_Days'].mean()
+    
+    with col1:
+        st.metric("Suppliers Disrupted", f"{total_suppliers_disrupted}")
+    
+    with col2:
+        st.metric("Rane Impact Days", f"{total_rane_impact_days}")
+    
+    with col3:
+        st.metric("Active Mitigations", f"{total_mitigation_strategies}")
+    
+    with col4:
+        st.metric("Avg Lead Time", f"{avg_lead_time:.1f} days")
 
 # Footer
 st.markdown("---")
 st.markdown("""
 <div style='text-align: center; color: #666;'>
-    <p>🌐 <b>Rane Group Ecosystem Command Center</b> | Tier 2 Impact Analysis | Agentic AI Response<br>
-    Supply Chain Resilience | Automated Mitigation | Real-time Ecosystem Monitoring</p>
+    <p>🌐 <b>Rane Group Complete Supply Chain Command Center</b> | Demand Sensing + Ecosystem Intelligence<br>
+    Powered by Agentic AI | Real-time Decision Support | Comprehensive Supply Chain Resilience</p>
 </div>
 """, unsafe_allow_html=True)