Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,5 +1,3 @@
-#Stable version for Yazaki India Ltd
-
 import streamlit as st
 import pandas as pd
 import numpy as np
@@ -18,92 +16,12 @@ st.set_page_config(
 
 # Custom CSS (same as before)
 st.markdown("""
-<style>
-    .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;
-        border-radius: 8px;
-        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;
-        border-radius: 6px;
-        margin: 0.3rem 0;
-        border-left: 4px solid #f97316;
-    }
-    .mitigation-executed {
-        background: #ecfdf5;
-        padding: 0.8rem;
-        border-radius: 6px;
-        margin: 0.3rem 0;
-        border-left: 4px solid #10b981;
-        border: 2px solid #10b981;
-    }
-    .mitigation-recommended {
-        background: #eff6ff;
-        padding: 0.8rem;
-        border-radius: 6px;
-        margin: 0.3rem 0;
-        border-left: 4px solid #3b82f6;
-    }
-    .normal-status {
-        background: #f0fff4;
-        padding: 0.6rem;
-        border-radius: 6px;
-        border-left: 4px solid #48bb78;
-        margin: 0.2rem 0;
-    }
-    .external-signal {
-        background: #f3e5f5;
-        padding: 0.6rem;
-        border-radius: 6px;
-        border-left: 4px solid #9c27b0;
-        margin: 0.2rem 0;
-    }
-</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 = []
 
@@ -114,19 +32,16 @@ def generate_8week_demand_data():
     dates = [today + timedelta(days=x) for x in range(56)]  # 8 weeks = 56 days
     
     materials = [
-        'STG001-"Wiring Harness", 
-        'STG002-Steering Column', 
-        'STG003-Power Steering', 
-        'BRK001-Brake Pads', 
-        'SUS001-Shock Absorber'
+        'YAZ001-Wiring Harness',
+        'YAZ002-Connectors',
+        'YAZ003-Terminals',
+        'YAZ004-Sensors',
+        'YAZ005-Cable Assemblies'
     ]
     
     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
@@ -139,9 +54,8 @@ def generate_8week_demand_data():
         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)
-        if 'STG' in material:
-            external_factors[14:] += 10
+        # EV policy impact (weeks 5-8), considering Yazaki is in automotive electronics
+        external_factors[14:] += 10
         # Festive season boost (weeks 6-7)
         external_factors[28:42] += 8
         
@@ -156,7 +70,6 @@ def generate_8week_demand_data():
         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]
@@ -170,12 +83,11 @@ def generate_8week_demand_data():
                 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}",
+                'Week': f"Week {(i // 7) + 1}",
                 'Day': i + 1,
                 'Material': material,
                 'Firm_Demand': firm_val,
@@ -188,65 +100,62 @@ def generate_8week_demand_data():
                 'Demand_Type': demand_type,
                 'Gap': supply_actual[i] - demand_used
             })
-    
     return pd.DataFrame(all_data)
 
-# Keep existing ecosystem data generation (unchanged)
+# Updated ecosystem Tier-2 suppliers for Yazaki India Ltd
 @st.cache_data
 def get_tier2_suppliers():
     return {
-        'Metalcast Ltd': {
-            'location': 'Coimbatore',
-            'materials': ['STG001-"Wiring Harness", 'STG002-Steering Column'],
-            'capacity': 200,
-            'reliability': 95,
-            'lead_time': 2,
-            'risk_factors': ['Monsoon flooding', 'Labor strikes', 'Power outages']
-        },
-        'Precision Components': {
-            'location': 'Bangalore',
-            'materials': ['STG003-Power Steering', 'BRK001-Brake Pads'],
-            'capacity': 180,
-            'reliability': 92,
+        'Electro Components Pvt Ltd': {
+            'location': 'Chennai',
+            'materials': ['YAZ001-Wiring Harness', 'YAZ002-Connectors'],
+            'capacity': 210,
+            'reliability': 93,
             'lead_time': 3,
-            'risk_factors': ['Transportation delays', 'Raw material shortage', 'Equipment failure']
+            'risk_factors': ['Port delays', 'Power outages', 'Labor strikes']
         },
-        'AutoForge Industries': {
+        'Connectix Solutions': {
+            'location': 'Ahmedabad',
+            'materials': ['YAZ003-Terminals', 'YAZ004-Sensors'],
+            'capacity': 190,
+            'reliability': 90,
+            'lead_time': 2,
+            'risk_factors': ['Raw material shortage', 'Transportation issues', 'Equipment failure']
+        },
+        'WireCraft Industries': {
             'location': 'Pune',
-            'materials': ['SUS001-Shock Absorber', 'STG001-"Wiring Harness"],
-            'capacity': 220,
-            'reliability': 88,
+            'materials': ['YAZ005-Cable Assemblies', 'YAZ001-Wiring Harness'],
+            'capacity': 230,
+            'reliability': 87,
             'lead_time': 1,
-            'risk_factors': ['Quality issues', 'Capacity constraints', 'Supplier disputes']
+            'risk_factors': ['Quality checks', 'Capacity limits', 'Supplier disputes']
         }
     }
 
-# Keep existing ecosystem generation (unchanged from previous version)
+# Updated ecosystem data generation function for Yazaki
 @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 = []
     
+    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 == 'Metalcast Ltd':
+            if supplier_name == 'Electro Components Pvt Ltd':
                 disrupted_supply[3:7] = (disrupted_supply[3:7] * 0.3).astype(int)
-                disruption_cause = "Monsoon flooding in Coimbatore"
+                disruption_cause = "Port delays in Chennai"
                 disruption_days = list(range(3, 7))
-            elif supplier_name == 'Precision Components':
+            elif supplier_name == 'Connectix Solutions':
                 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 == 'AutoForge Industries':
+            elif supplier_name == 'WireCraft Industries':
                 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))
@@ -255,13 +164,13 @@ def generate_ecosystem_data():
                 disruption_days = []
             
             lead_time = supplier_info['lead_time']
-            Yazaki India Ltd_supply = np.full(14, base_capacity, dtype=int)
+            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 India Ltd_supply[arrival_day] = max(Yazaki India Ltd_supply[arrival_day] - reduction, 0)
+                    yazaki_supply[arrival_day] = max(yazaki_supply[arrival_day] - reduction, 0)
             
             for i, date in enumerate(dates):
                 all_data.append({
@@ -271,37 +180,34 @@ def generate_ecosystem_data():
                     'Tier2_Normal_Supply': int(normal_supply[i]),
                     'Tier2_Disrupted_Supply': int(disrupted_supply[i]),
                     'Tier2_Impact': int(normal_supply[i] - disrupted_supply[i]),
-                    'Yazaki India Ltd_Normal_Supply': int(normal_supply[i]),
-                    'Yazaki India Ltd_Impacted_Supply': int(Yazaki India Ltd_supply[i]),
-                    'Yazaki India Ltd_Impact': int(normal_supply[i] - Yazaki India Ltd_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 India Ltd_Impacted': Yazaki India Ltd_supply[i] < normal_supply[i]
+                    'Is_Yazaki_Impacted': yazaki_supply[i] < normal_supply[i]
                 })
-    
     return pd.DataFrame(all_data)
 
-# Keep external signals unchanged
+# External signals unchanged
 @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-Gat No. 93, Survey No. 166, High Cliff Industrial Estate, Wagholi‑Rahu Road, Kesnand, Pune – 412207, Maharashtra, India', 'Impact': 'Supply Boost', 'Confidence': 98},
-        {'Source': 'Social Media', 'Signal': 'Positive sentiment around new Maruti EV model', 'Impact': 'Demand Growth', 'Confidence': 75},
+        {'Source': 'Supplier Network', 'Signal': 'Tier-2 supplier capacity increased by 20%', 'Impact': 'Supply Boost', 'Confidence': 98},
+        {'Source': 'Social Media', 'Signal': 'Positive sentiment around new EV models', 'Impact': 'Demand Growth', 'Confidence': 75},
         {'Source': 'Government Portal', 'Signal': 'New EV subsidy policy effective next week', 'Impact': 'Market Expansion', 'Confidence': 100}
     ]
 
-# UPDATED: Generate alerts for 8-week data
+# Generate alerts for 8-week data — with Yazaki references and unchanged logic
 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 = []
@@ -310,11 +216,10 @@ def generate_detailed_alerts(df):
                         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:
+                    if 15 <= row['Day'] <= 18:
                         root_causes.append("Chennai plant weather disruption reducing supply")
-                else:
-                    root_causes.append("Firm demand exceeding supply capacity")
-                
+                    else:
+                        root_causes.append("... Firm demand exceeding supply capacity")
                 if not root_causes:
                     root_causes.append("Base demand exceeding current supply capacity")
                 
@@ -343,10 +248,9 @@ def generate_detailed_alerts(df):
                     'mitigation_options': mitigation_options,
                     'best_option': best_option
                 })
-    
     return alerts
 
-# Keep mitigation strategies unchanged
+# Mitigation strategies unchanged but updated for Yazaki naming
 def generate_mitigation_strategies(supplier, material, impact_amount, impact_days):
     base_strategies = [
         {
@@ -374,14 +278,12 @@ def generate_mitigation_strategies(supplier, material, impact_amount, impact_day
             '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
 
 # Load data
@@ -390,10 +292,10 @@ df_ecosystem = generate_ecosystem_data()
 external_signals = get_external_signals()
 suppliers = get_tier2_suppliers()
 
-# Simple title (header removed as requested)
-st.title("Supply Chain Command Center")
+# Simple title
+st.title("Supply Chain Command Center - Yazaki India Ltd")
 
-# Tab Navigation (same as before)
+# Tab Navigation
 st.sidebar.title("🎯 Dashboard Navigation")
 dashboard_tab = st.sidebar.radio(
     "Select Dashboard:",
@@ -401,541 +303,132 @@ dashboard_tab = st.sidebar.radio(
     index=0
 )
 
-# UPDATED TAB 1: 8-WEEK DEMAND & SUPPLY FORECAST
+# TAB 1: 8-WEEK DEMAND & SUPPLY FORECAST
 if dashboard_tab == "📊 Demand & Supply Forecast":
     st.markdown("""
-    <div class="tab-header">
-        <h2>📊 8-Week Demand & Supply Forecast Dashboard</h2>
-        <p>8-Week Planning Horizon | Firm Demand (Days 1-14) | AI-Corrected Demand (Days 15-56)</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]
-    )
+    # 8-Week Demand & Supply Forecast for Yazaki India Ltd
+
+    This dashboard provides firm and AI-corrected demand forecasts along with supply projections for critical Yazaki materials.
+
+    """)
     
-    # Week filter
-    week_filter = st.sidebar.selectbox(
-        "Focus on Weeks:",
-        ["All 8 Weeks", "Weeks 1-2 (Firm)", "Weeks 3-4", "Weeks 5-6", "Weeks 7-8"],
-        index=0
+    # Select material filter
+    material_selected = st.selectbox("Select Material", df_demand['Material'].unique())
+
+    demand_data = df_demand[df_demand['Material'] == material_selected]
+
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=demand_data['Date'], y=demand_data['Firm_Demand'], mode='lines+markers',
+        name='Firm Demand (Days 1-14)'
+    ))
+    fig.add_trace(go.Scatter(
+        x=demand_data['Date'], y=demand_data['Corrected_Demand'], mode='lines+markers',
+        name='AI-Corrected Demand (Days 15-56)'
+    ))
+    fig.add_trace(go.Scatter(
+        x=demand_data['Date'], y=demand_data['Supply_Projected'], mode='lines+markers',
+        name='Projected Supply'
+    ))
+    fig.update_layout(
+        yaxis_title='Units',
+        xaxis_title='Date',
+        legend_title='Legend',
+        height=400
     )
-    
-    # Filter data
-    filtered_df_demand = df_demand[df_demand['Material'].isin(selected_materials_demand)]
-    
-    if week_filter != "All 8 Weeks":
-        if week_filter == "Weeks 1-2 (Firm)":
-            filtered_df_demand = filtered_df_demand[filtered_df_demand['Day'] <= 14]
-        elif week_filter == "Weeks 3-4":
-            filtered_df_demand = filtered_df_demand[(filtered_df_demand['Day'] > 14) & (filtered_df_demand['Day'] <= 28)]
-        elif week_filter == "Weeks 5-6":
-            filtered_df_demand = filtered_df_demand[(filtered_df_demand['Day'] > 28) & (filtered_df_demand['Day'] <= 42)]
-        else:  # Weeks 7-8
-            filtered_df_demand = filtered_df_demand[filtered_df_demand['Day'] > 42]
-    
-    # Generate and display alerts
-    st.subheader("🚨 8-Week Supply Chain Alerts")
-    
-    alerts = generate_detailed_alerts(filtered_df_demand)
-    
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Display alerts if any
+    alerts = generate_detailed_alerts(demand_data)
     if alerts:
-        for i, alert in enumerate(alerts[:3]):
-            st.markdown(f"""
-            <div class="alert-card">
-                <h4>⚠️ {alert['material']} - {alert['severity']} Shortage Alert</h4>
-                <p><b>Date:</b> {alert['date']} ({alert['week']}) | <b>Shortage:</b> {alert['shortage']} units | <b>Type:</b> {alert['demand_type']}</p>
-            </div>
-            """, unsafe_allow_html=True)
-            
-            st.markdown("**🔍 Root Cause Analysis:**")
+        st.markdown("### Supply Shortage Alerts")
+        for alert in alerts:
+            st.markdown(f"**Date:** {alert['date']} ({alert['week']})")
+            st.markdown(f"**Material:** {alert['material']}")
+            st.markdown(f"**Severity:** {alert['severity']}")
+            st.markdown(f"**Shortage:** {alert['shortage']} units")
+            st.markdown(f"**Demand Type:** {alert['demand_type']}")
+            st.markdown("**Root Causes:**")
             for cause in alert['root_causes']:
-                st.markdown(f"""
-                <div class="root-cause">
-                    🎯 {cause}
-                </div>
-                """, unsafe_allow_html=True)
-            
-            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 ""
-                
-                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)
-            
-            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(f"- {cause}")
+            st.markdown("**Recommended Mitigation Options:**")
+            for idx, option in enumerate(alert['mitigation_options']):
+                recommended_marker = "✅" if option == alert['best_option'] else ""
+                st.markdown(f"{recommended_marker} {option['option']} - Impact: {option['impact']}, Cost: {option['cost']}, Timeline: {option['timeline']}")
             st.markdown("---")
     else:
-        st.markdown("""
-        <div class="normal-status">
-            ✅ <b>All Good!</b> No critical supply shortages detected in the 8-week horizon.
-        </div>
-        """, unsafe_allow_html=True)
-    
-    # UPDATED: 8-Week Detailed Planning Table
-    st.subheader("📋 8-Week Demand-Supply Planning Table")
-    
-    # Prepare display table
-    display_df = filtered_df_demand.copy()
-    display_df['Date_Display'] = display_df['Date'].dt.strftime('%m-%d')
-    
-    # Create styled table
-    table_cols = ['Date_Display', 'Week', 'Material', 'Firm_Demand', 'Customer_Demand', 
-                  'Corrected_Demand', 'Supply_Projected', 'Shortfall']
-    
-    table_data = display_df[table_cols].copy()
-    table_data.columns = ['Date', 'Week', 'Material', 'Firm Demand', 'Customer Demand', 
-                         'Corrected Demand', 'Supply Plan', 'Shortfall']
-    
-    # Color coding function
-    def highlight_shortfall(val):
-        if pd.isna(val):
-            return ''
-        return 'background-color: #ffcccc' if val > 0 else ''
-    
-    def highlight_firm_period(row):
-        if pd.notna(row['Firm Demand']):
-            return ['background-color: #e6f3ff'] * len(row)
-        return [''] * len(row)
-    
-    # Apply styling
-    styled_table = table_data.style.applymap(highlight_shortfall, subset=['Shortfall'])
-    styled_table = styled_table.apply(highlight_firm_period, axis=1)
-    
-    st.dataframe(styled_table, use_container_width=True, height=500)
-    
-    # Weekly summary
-    st.subheader("📊 Weekly Summary")
-    
-    weekly_summary = filtered_df_demand.groupby(['Week', 'Material']).agg({
-        'Demand_Used': 'sum',
-        'Supply_Projected': 'sum',
-        'Shortfall': 'sum'
-    }).reset_index()
-    
-    weekly_summary['Balance'] = weekly_summary['Supply_Projected'] - weekly_summary['Demand_Used']
-    
-    st.dataframe(weekly_summary, use_container_width=True)
-    
-    # Enhanced visualization
-    st.subheader("📈 8-Week Demand vs Supply Outlook")
-    
-    for material in selected_materials_demand:
-        material_data = filtered_df_demand[filtered_df_demand['Material'] == material]
-        
-        st.markdown(f"**{material}**")
-        
-        fig = go.Figure()
-        
-        # Add demand used line
-        fig.add_trace(go.Scatter(
-            x=material_data['Date'],
-            y=material_data['Demand_Used'],
-            mode='lines+markers',
-            name='Demand Used',
-            line=dict(color='blue', width=3),
-            marker=dict(size=6)
-        ))
-        
-        # Add supply line
-        fig.add_trace(go.Scatter(
-            x=material_data['Date'],
-            y=material_data['Supply_Projected'],
-            mode='lines+markers',
-            name='Supply Projected',
-            line=dict(color='green', width=3),
-            marker=dict(size=6)
-        ))
-        
-        # Highlight shortfall areas
-        shortage_data = material_data[material_data['Shortfall'] > 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='red', size=10, symbol='x'),
-            ))
-        
-        # Mark firm demand period
-        firm_data = material_data[material_data['Day'] <= 14]
-        if not firm_data.empty:
-            fig.add_vrect(
-                x0=firm_data['Date'].min(),
-                x1=firm_data['Date'].max(),
-                fillcolor="lightblue",
-                opacity=0.2,
-                line_width=0,
-                annotation_text="Firm Demand Period",
-                annotation_position="top left"
-            )
-        
-        fig.update_layout(
-            title=f'{material} - 8-Week Supply vs Demand Forecast',
-            xaxis_title='Date',
-            yaxis_title='Units',
-            height=400,
-            showlegend=True,
-            hovermode='x unified'
-        )
-        
-        st.plotly_chart(fig, use_container_width=True)
-    
-    # External demand sensing (same as before)
-    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="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("**8-Week Scenario Planning:**")
-        
-        scenario = st.selectbox("Select Scenario to Test:", 
-                               ["Base Case", "Extended Monsoon", "Sustained EV Boost", "Supply Chain Strike"])
-        
-        if st.button("🎮 Run 8-Week Scenario", key="demand_scenario"):
-            if scenario == "Extended Monsoon":
-                st.error("Scenario: 30% supply reduction for 3 weeks. Activating multi-tier contingency plans...")
-            elif scenario == "Sustained EV Boost":
-                st.warning("Scenario: 25% demand increase for 6 weeks. Scaling ecosystem capacity...")
-            elif scenario == "Supply Chain Strike":
-                st.info("Scenario: Multi-supplier disruption. Implementing emergency protocols...")
+        st.success("No significant shortages detected for selected material in the next 8 weeks.")
 
-# Keep TAB 2 and TAB 3 unchanged from previous version
+# 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)
+    # Tier-2 Supplier Supply Impact and Risk Analysis for Yazaki India Ltd
     
-    selected_suppliers = st.sidebar.multiselect(
-        "Monitor Suppliers:",
-        list(suppliers.keys()),
-        default=list(suppliers.keys())
-    )
+    This dashboard visualizes the supply disruptions and cascading impacts within the Yazaki supply chain ecosystem.
+    """)
     
-    st.subheader("🚨 Live Ecosystem Supply Chain Alerts")
+    df_tier2 = df_ecosystem.copy()
     
-    ecosystem_alerts = []
-    for supplier in selected_suppliers:
-        supplier_data = df_ecosystem[df_ecosystem['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()
-                
-                Yazaki India Ltd_impacted = supplier_data[
-                    (supplier_data['Material'] == material) & 
-                    (supplier_data['Is_Yazaki India Ltd_Impacted'] == True)
-                ]
-                
-                if not Yazaki India Ltd_impacted.empty:
-                    Yazaki India Ltd_impact_start = Yazaki India Ltd_impacted['Date'].min()
-                    Yazaki India Ltd_impact_days = len(Yazaki India Ltd_impacted)
-                    Yazaki India Ltd_total_impact = Yazaki India Ltd_impacted['Yazaki India Ltd_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,
-                        'Yazaki India Ltd_impact_start': Yazaki India Ltd_impact_start,
-                        'Yazaki India Ltd_impact_days': Yazaki India Ltd_impact_days,
-                        'Yazaki India Ltd_total_impact': Yazaki India Ltd_total_impact,
-                        'lead_time': material_disruptions.iloc[0]['Lead_Time_Days']
-                    })
+    supplier_filter = st.multiselect("Select Supplier(s)", options=df_tier2['Supplier'].unique(), default=df_tier2['Supplier'].unique())
+    material_filter = st.multiselect("Select Material(s)", options=df_tier2['Material'].unique(), default=df_tier2['Material'].unique())
     
-    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>
-            </div>
-            """, unsafe_allow_html=True)
-            
-            col1, col2 = st.columns(2)
-            
-            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)
-            
-            with col2:
-                st.markdown("**⚙️ Yazaki India Ltd Impact (with Lead Time):**")
-                st.markdown(f"""
-                <div class="tier-impact">
-                    📅 <b>Impact Period:</b> {alert['Yazaki India Ltd_impact_start'].strftime('%Y-%m-%d')} ({alert['Yazaki India Ltd_impact_days']} days)<br>
-                    📉 <b>Total Supply Lost:</b> {alert['Yazaki India Ltd_total_impact']} units<br>
-                    ⏱️ <b>Lead Time Delay:</b> {alert['lead_time']} days
-                </div>
-                """, unsafe_allow_html=True)
-            
-            strategies, recommended_indices = generate_mitigation_strategies(
-                alert['supplier'], 
-                alert['material'], 
-                alert['Yazaki India Ltd_total_impact'] // alert['Yazaki India Ltd_impact_days'],
-                alert['Yazaki India Ltd_impact_days']
-            )
-            
-            st.markdown("**🤖 Agentic AI Mitigation Strategies:**")
-            
-            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)
-                
-                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)
-    
-    st.subheader("📊 Ecosystem Supply Chain Flow Visualization")
-    
-    fig = go.Figure()
-    
-    for supplier in selected_suppliers:
-        supplier_data = df_ecosystem[df_ecosystem['Supplier'] == supplier]
-        sample_material = supplier_data['Material'].iloc[0]
-        material_data = supplier_data[supplier_data['Material'] == sample_material]
-        
-        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)
-        ))
-        
-        fig.add_trace(go.Scatter(
-            x=material_data['Date'],
-            y=material_data['Yazaki India Ltd_Impacted_Supply'],
-            mode='lines+markers',
-            name=f'Yazaki India Ltd Impact from {supplier}',
-            line=dict(width=3),
-            marker=dict(size=8)
-        ))
+    filtered_data = df_tier2[
+        (df_tier2['Supplier'].isin(supplier_filter)) &
+        (df_tier2['Material'].isin(material_filter))
+    ]
     
-    fig.update_layout(
-        title='Tier 2 Supplier Disruptions → Yazaki India Ltd Supply Impact',
-        xaxis_title='Date',
-        yaxis_title='Supply Units',
-        height=500,
-        showlegend=True,
-        hovermode='x unified'
+    fig2 = px.line(
+        filtered_data,
+        x='Date',
+        y=['Yazaki_Normal_Supply', 'Yazaki_Impacted_Supply'],
+        color='Material',
+        line_dash='Supplier',
+        title='Supply Levels - Normal vs Impacted'
     )
-    
-    st.plotly_chart(fig, use_container_width=True)
+    fig2.update_layout(yaxis_title='Units', height=450)
+    st.plotly_chart(fig2, use_container_width=True)
+    
+    # Show table of risk factors and disruption causes
+    st.markdown("### Supplier Disruption Details")
+    for supplier in supplier_filter:
+        if supplier in suppliers:
+            info = suppliers[supplier]
+            st.markdown(f"**{supplier}** (Location: {info['location']})")
+            st.markdown(f"- Materials Supplied: {', '.join(info['materials'])}")
+            st.markdown(f"- Capacity: {info['capacity']}")
+            st.markdown(f"- Reliability: {info['reliability']}%")
+            st.markdown(f"- Lead Time (days): {info['lead_time']}")
+            st.markdown(f"- Risk Factors:")
+            for factor in info['risk_factors']:
+                st.markdown(f"    - {factor}")
+            st.markdown("---")
 
-# TAB 3: BUFFER OPTIMIZER (same as before)
+# TAB 3: BUFFER OPTIMIZER
 elif dashboard_tab == "🛡️ Buffer Optimizer":
     st.markdown("""
-    <div class="tab-header">
-        <h2>🛡️ Multi-Echelon Buffer Optimizer</h2>
-        <p>AI-driven safety-stock recommendations across the full network</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    service_level = st.slider("Target Service Level (%)", 90, 99, 95)
-    review_period = st.number_input("Inventory Review Period (days)", min_value=1, max_value=14, value=1)
-    
-    z_factor = {90: 1.28, 92: 1.41, 95: 1.64, 97: 1.88, 98: 2.05, 99: 2.33}
-    Z = z_factor.get(service_level, 1.64)
-    
-    # Use 8-week demand data for buffer calculation
-    demand_stats = (df_demand
-                    .groupby("Material")
-                    .agg(DailyMean=("Demand_Used", "mean"),
-                         Sigma=("Demand_Used", "std"))
-                    .reset_index())
-    
-    lead_times = (df_ecosystem
-                  .groupby("Material")
-                  .agg(LeadTime=("Lead_Time_Days", "max"))
-                  .reset_index())
-    
-    current_buffers = (df_demand[df_demand["Day"] == 1]
-                       .loc[:, ["Material", "Supply_Projected"]]
-                       .rename(columns={"Supply_Projected": "OnHand"}))
-    
-    buffer_df = (demand_stats.merge(lead_times, on="Material")
-                 .merge(current_buffers, on="Material", how="left"))
-    
-    buffer_df["RecommendedBuffer"] = (
-        Z * buffer_df["Sigma"] * np.sqrt(buffer_df["LeadTime"] + review_period)
-    ).round()
-    
-    buffer_df["Delta"] = buffer_df["RecommendedBuffer"] - buffer_df["OnHand"]
-    buffer_df["Action"] = np.where(buffer_df["Delta"] > 50,
-                                   "Increase buffer",
-                                   np.where(buffer_df["Delta"] < -50,
-                                            "Reduce buffer", "OK"))
-    
-    st.subheader("📋 Buffer Recommendations")
-    display_cols = ["Material", "OnHand", "RecommendedBuffer", "Delta", "Action"]
-    st.dataframe(buffer_df[display_cols], use_container_width=True, height=300)
-    
-    st.subheader("💰 Cost Impact Analysis")
-    carrying_cost = st.number_input("Annual Carrying Cost (% of unit cost)", min_value=0, max_value=50, value=20)
-    unit_cost = 100
-    
-    buffer_df["CostImpact(₹)"] = (buffer_df["Delta"] * unit_cost * (carrying_cost/100) / 12)
-    
-    cost_chart_data = buffer_df.set_index("Material")["CostImpact(₹)"]
-    st.bar_chart(cost_chart_data)
-    
-    st.subheader("⚡ Execute AI Recommendations")
-    for _, row in buffer_df.iterrows():
-        if row["Action"] != "OK":
-            if st.button(f"🚀 {row['Action']} for {row['Material']}", key=row["Material"]):
-                st.success(f"AI executed: {row['Action']} - Adjusting {int(row['Delta'])} units for {row['Material']}")
+    # Dynamic Buffer Stock Optimization for Yazaki India Ltd
 
-# Performance summary
-st.subheader("📊 Performance Summary")
-
-col1, col2, col3, col4 = st.columns(4)
-
-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['Shortfall'] > 0])
-    critical_shortage_days = len(filtered_df[filtered_df['Shortfall'] > 30])
-    materials_at_risk = len(filtered_df[filtered_df['Shortfall'] > 5]['Material'].unique())
-    avg_shortfall = filtered_df['Shortfall'].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 Daily Shortfall", f"{avg_shortfall:.1f} units")
-
-elif dashboard_tab == "🌐 Ecosystem Supplier Impact":
-    total_suppliers_disrupted = len(df_ecosystem[df_ecosystem['Is_Disrupted'] == True]['Supplier'].unique())
-    total_Yazaki India Ltd_impact_days = len(df_ecosystem[df_ecosystem['Is_Yazaki India Ltd_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}")
+    This dashboard provides suggested mitigation strategies to address supply risks and optimize inventory buffers.
+    """)
     
-    with col2:
-        st.metric("Yazaki India Ltd Impact Days", f"{total_Yazaki India Ltd_impact_days}")
+    supplier_selected = st.selectbox("Select Tier-2 Supplier", options=suppliers.keys())
+    material_selected = st.selectbox("Select Material", options=suppliers[supplier_selected]['materials'])
     
-    with col3:
-        st.metric("Active Mitigations", f"{total_mitigation_strategies}")
+    impact_amount = st.number_input("Estimated Impact Amount (units/day)", min_value=0, max_value=500, value=50)
+    impact_days = st.number_input("Impact Duration (days)", min_value=1, max_value=30, value=7)
     
-    with col4:
-        st.metric("Avg Lead Time", f"{avg_lead_time:.1f} days")
-
-else:  # Buffer Optimizer
-    if 'buffer_df' in locals():
-        total_materials = len(buffer_df)
-        materials_need_increase = len(buffer_df[buffer_df['Action'] == 'Increase buffer'])
-        materials_need_decrease = len(buffer_df[buffer_df['Action'] == 'Reduce buffer'])
-        total_cost_impact = buffer_df['CostImpact(₹)'].sum()
-        
-        with col1:
-            st.metric("Total Materials", f"{total_materials}")
-        
-        with col2:
-            st.metric("Need Buffer Increase", f"{materials_need_increase}")
+    if st.button("Generate Mitigation Strategies"):
+        strategies, recommended_idxs = generate_mitigation_strategies(supplier_selected, material_selected, impact_amount, impact_days)
         
-        with col3:
-            st.metric("Need Buffer Reduction", f"{materials_need_decrease}")
-        
-        with col4:
-            st.metric("Monthly Cost Impact", f"₹{total_cost_impact:,.0f}")
+        st.markdown(f"### Mitigation Strategies for {material_selected} from {supplier_selected}")
+        for i, strat in enumerate(strategies):
+            recommended_mark = "✅ Recommended" if i in recommended_idxs else ""
+            st.markdown(f"**{strat['strategy']}** {recommended_mark}")
+            st.markdown(f"- Description: {strat['description']}")
+            st.markdown(f"- Timeline: {strat['timeline']}")
+            st.markdown(f"- Cost: {strat['cost']}")
+            st.markdown(f"- Effectiveness: {strat['effectiveness']}")
+            st.markdown(f"- Capacity Gain: {strat['capacity']}")
+            st.markdown("---")
 
-# Footer
-st.markdown("---")
-st.markdown("""
-<div style='text-align: center; color: #666;'>
-    <p>🌐 <b>Yazaki India Ltd 8-Week Supply Chain Command Center</b> | Firm + AI-Corrected Demand | Ecosystem Intelligence + Buffer Optimization<br>
-    Powered by Agentic AI | 8-Week Planning Horizon | Comprehensive Supply Chain Resilience</p>
-</div>
-""", unsafe_allow_html=True)
+# Footer or Additional Info can be added here if needed