Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,3 +1,4 @@
+#Stable version for Yazaki India Ltd
 import streamlit as st
 import pandas as pd
 import numpy as np
@@ -16,12 +17,13 @@ st.set_page_config(
 
 # Custom CSS (same as before)
 st.markdown("""
+<style>
+</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 = []
 
@@ -40,8 +42,11 @@ def generate_8week_demand_data():
     ]
     
     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
@@ -54,8 +59,9 @@ 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), considering Yazaki is in automotive electronics
-        external_factors[14:] += 10
+        # EV policy impact (weeks 5-8)
+        if 'YAZ' in material:
+            external_factors[14:] += 10
         # Festive season boost (weeks 6-7)
         external_factors[28:42] += 8
         
@@ -70,6 +76,7 @@ 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]
@@ -83,11 +90,12 @@ 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,
@@ -100,9 +108,10 @@ def generate_8week_demand_data():
                 'Demand_Type': demand_type,
                 'Gap': supply_actual[i] - demand_used
             })
+    
     return pd.DataFrame(all_data)
 
-# Updated ecosystem Tier-2 suppliers for Yazaki India Ltd
+# Updated Tier-2 suppliers for Yazaki India
 @st.cache_data
 def get_tier2_suppliers():
     return {
@@ -132,7 +141,7 @@ def get_tier2_suppliers():
         }
     }
 
-# Updated ecosystem data generation function for Yazaki
+# Updated ecosystem generation with Yazaki-specific data
 @st.cache_data
 def generate_ecosystem_data():
     today = datetime(2025, 8, 4)
@@ -140,20 +149,22 @@ def generate_ecosystem_data():
     suppliers = get_tier2_suppliers()
     
     all_data = []
+    
     for supplier_name, supplier_info in suppliers.items():
         for material in supplier_info['materials']:
             np.random.seed(hash(supplier_name + material) % 1000)
+            
             base_capacity = supplier_info['capacity']
             normal_supply = np.full(14, base_capacity, dtype=int)
             disrupted_supply = normal_supply.copy()
             
             if supplier_name == 'Electro Components Pvt Ltd':
                 disrupted_supply[3:7] = (disrupted_supply[3:7] * 0.3).astype(int)
-                disruption_cause = "Port delays in Chennai"
+                disruption_cause = "Port delays in Chennai affecting imports"
                 disruption_days = list(range(3, 7))
             elif supplier_name == 'Connectix Solutions':
                 disrupted_supply[5:8] = (disrupted_supply[5:8] * 0.5).astype(int)
-                disruption_cause = "Critical equipment failure"
+                disruption_cause = "Critical equipment failure at Ahmedabad facility"
                 disruption_days = list(range(5, 8))
             elif supplier_name == 'WireCraft Industries':
                 disrupted_supply[8:11] = (disrupted_supply[8:11] * 0.2).astype(int)
@@ -188,9 +199,10 @@ def generate_ecosystem_data():
                     'Is_Disrupted': i in disruption_days,
                     'Is_Yazaki_Impacted': yazaki_supply[i] < normal_supply[i]
                 })
+    
     return pd.DataFrame(all_data)
 
-# External signals unchanged
+# Keep external signals unchanged (these are general market signals)
 @st.cache_data
 def get_external_signals():
     return [
@@ -198,28 +210,33 @@ def get_external_signals():
         {'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 EV models', 'Impact': 'Demand Growth', 'Confidence': 75},
+        {'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 alerts for 8-week data — with Yazaki references and unchanged logic
+# UPDATED: Generate alerts for 8-week data
 def generate_detailed_alerts(df):
     alerts = []
+    
     for material in df['Material'].unique():
         material_data = df[df['Material'] == material]
         shortage_days = material_data[material_data['Shortfall'] > 5]
+        
         if not shortage_days.empty:
             for _, row in shortage_days.iterrows():
                 root_causes = []
+                
                 if row['Day'] > 14:
                     if row['Corrected_Demand'] and row['Customer_Demand']:
                         diff = row['Corrected_Demand'] - row['Customer_Demand']
                         if diff > 10:
                             root_causes.append(f"AI detected {diff} units additional demand from external signals")
-                    if 15 <= row['Day'] <= 18:
-                        root_causes.append("Chennai plant weather disruption reducing supply")
-                    else:
-                        root_causes.append("... Firm demand exceeding supply capacity")
+                    
+                    if row['Day'] >= 15 and row['Day'] <= 18:
+                        root_causes.append("Chennai supplier weather disruption reducing supply")
+                else:
+                    root_causes.append("Firm demand exceeding supply capacity")
+                
                 if not root_causes:
                     root_causes.append("Base demand exceeding current supply capacity")
                 
@@ -248,9 +265,10 @@ def generate_detailed_alerts(df):
                     'mitigation_options': mitigation_options,
                     'best_option': best_option
                 })
+    
     return alerts
 
-# Mitigation strategies unchanged but updated for Yazaki naming
+# Keep mitigation strategies unchanged
 def generate_mitigation_strategies(supplier, material, impact_amount, impact_days):
     base_strategies = [
         {
@@ -278,12 +296,14 @@ 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
@@ -292,10 +312,10 @@ df_ecosystem = generate_ecosystem_data()
 external_signals = get_external_signals()
 suppliers = get_tier2_suppliers()
 
-# Simple title
+# Simple title (header removed as requested)
 st.title("Supply Chain Command Center - Yazaki India Ltd")
 
-# Tab Navigation
+# Tab Navigation (same as before)
 st.sidebar.title("🎯 Dashboard Navigation")
 dashboard_tab = st.sidebar.radio(
     "Select Dashboard:",
@@ -303,132 +323,334 @@ dashboard_tab = st.sidebar.radio(
     index=0
 )
 
-# TAB 1: 8-WEEK DEMAND & SUPPLY FORECAST
+# UPDATED TAB 1: 8-WEEK DEMAND & SUPPLY FORECAST
 if dashboard_tab == "📊 Demand & Supply Forecast":
+    
     st.markdown("""
-    # 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.
-
-    """)
+    <div style='background: linear-gradient(90deg, #1e3c72 0%, #2a5298 100%); padding: 15px; border-radius: 10px; margin-bottom: 20px;'>
+        <h3 style='color: white; margin: 0; text-align: center;'>
+            📊 8-Week Planning Horizon | Firm Demand (Days 1-14) | AI-Corrected Demand (Days 15-56)
+        </h3>
+    </div>
+    """, unsafe_allow_html=True)
     
-    # Select material filter
-    material_selected = st.selectbox("Select Material", df_demand['Material'].unique())
-
-    demand_data = df_demand[df_demand['Material'] == material_selected]
-
+    # Material selector
+    materials = df_demand['Material'].unique()
+    selected_material = st.selectbox("🔍 Select Material for Analysis:", materials)
+    
+    # Filter data for selected material
+    material_data = df_demand[df_demand['Material'] == selected_material].copy()
+    
+    # Create forecast visualization
     fig = go.Figure()
+    
+    # Add firm demand (first 14 days)
+    firm_data = material_data[material_data['Day'] <= 14]
+    fig.add_trace(go.Scatter(
+        x=firm_data['Date'],
+        y=firm_data['Demand_Used'],
+        mode='lines+markers',
+        name='Firm Demand (Days 1-14)',
+        line=dict(color='#2E86AB', width=3),
+        marker=dict(size=8)
+    ))
+    
+    # Add customer shared demand (days 15-56)
+    future_data = material_data[material_data['Day'] > 14]
     fig.add_trace(go.Scatter(
-        x=demand_data['Date'], y=demand_data['Firm_Demand'], mode='lines+markers',
-        name='Firm Demand (Days 1-14)'
+        x=future_data['Date'],
+        y=future_data['Customer_Demand'],
+        mode='lines',
+        name='Customer Shared Demand',
+        line=dict(color='#F18F01', width=2, dash='dot'),
+        opacity=0.7
     ))
+    
+    # Add AI-corrected demand (days 15-56)
     fig.add_trace(go.Scatter(
-        x=demand_data['Date'], y=demand_data['Corrected_Demand'], mode='lines+markers',
-        name='AI-Corrected Demand (Days 15-56)'
+        x=future_data['Date'],
+        y=future_data['Corrected_Demand'],
+        mode='lines+markers',
+        name='AI-Corrected Demand',
+        line=dict(color='#C73E1D', width=3),
+        marker=dict(size=6)
     ))
+    
+    # Add supply projection
     fig.add_trace(go.Scatter(
-        x=demand_data['Date'], y=demand_data['Supply_Projected'], mode='lines+markers',
-        name='Projected Supply'
+        x=material_data['Date'],
+        y=material_data['Supply_Projected'],
+        mode='lines',
+        name='Supply Projection',
+        line=dict(color='#4CAF50', width=2),
+        fill='tonexty',
+        fillcolor='rgba(76, 175, 80, 0.1)'
     ))
+    
     fig.update_layout(
-        yaxis_title='Units',
-        xaxis_title='Date',
-        legend_title='Legend',
-        height=400
+        title=f"📈 8-Week Demand vs Supply Forecast: {selected_material}",
+        xaxis_title="Date",
+        yaxis_title="Units",
+        height=500,
+        hovermode='x unified',
+        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
     )
+    
     st.plotly_chart(fig, use_container_width=True)
+    
+    # Weekly aggregation
+    weekly_summary = material_data.groupby('Week').agg({
+        'Demand_Used': 'sum',
+        'Supply_Projected': 'sum',
+        'Shortfall': 'sum'
+    }).reset_index()
+    
+    # Display weekly summary
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.subheader("📊 Weekly Summary")
+        for _, week_data in weekly_summary.iterrows():
+            gap = week_data['Supply_Projected'] - week_data['Demand_Used']
+            status = "✅ Surplus" if gap > 0 else "⚠️ Shortage" if gap < 0 else "⚖️ Balanced"
+            
+            st.markdown(f"""
+            **{week_data['Week']}**: {status}
+            - Demand: {week_data['Demand_Used']:,} units
+            - Supply: {week_data['Supply_Projected']:,} units
+            - Gap: {gap:+,} units
+            """)
+    
+    with col2:
+        st.subheader("🚨 Shortage Alerts")
+        alerts = generate_detailed_alerts(material_data)
+        
+        if alerts:
+            for alert in alerts[:3]:  # Show top 3 alerts
+                severity_color = {"Critical": "#FF4444", "High": "#FF8800", "Medium": "#FFBB00"}[alert['severity']]
+                
+                st.markdown(f"""
+                <div style='border-left: 4px solid {severity_color}; padding: 10px; margin: 10px 0; background: #f8f9fa;'>
+                    <strong>{alert['severity']} Alert</strong><br>
+                    <strong>Date:</strong> {alert['date']} ({alert['week']}) | <strong>Shortage:</strong> {alert['shortage']} units | <strong>Type:</strong> {alert['demand_type']}<br>
+                    <strong>Root Cause:</strong> {alert['root_causes'][0]}<br>
+                    <strong>Best Mitigation:</strong> {alert['best_option']['option']} ({alert['best_option']['timeline']})
+                </div>
+                """, unsafe_allow_html=True)
+        else:
+            st.success("✅ No shortages detected for this material!")
 
-    # Display alerts if any
-    alerts = generate_detailed_alerts(demand_data)
-    if alerts:
-        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"- {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.success("No significant shortages detected for selected material in the next 8 weeks.")
-
-# TAB 2: ECOSYSTEM SUPPLIER IMPACT
+# TAB 2: ECOSYSTEM SUPPLIER IMPACT (updated variable names)
 elif dashboard_tab == "🌐 Ecosystem Supplier Impact":
+    
     st.markdown("""
-    # Tier-2 Supplier Supply Impact and Risk Analysis for Yazaki India Ltd
+    <div style='background: linear-gradient(90deg, #1e3c72 0%, #2a5298 100%); padding: 15px; border-radius: 10px; margin-bottom: 20px;'>
+        <h3 style='color: white; margin: 0; text-align: center;'>
+            🌐 Tier 2 Supplier Disruption Analysis | Cascading Impact Modeling | Automated Mitigation Response
+        </h3>
+    </div>
+    """, unsafe_allow_html=True)
     
-    This dashboard visualizes the supply disruptions and cascading impacts within the Yazaki supply chain ecosystem.
-    """)
+    # Supplier overview
+    st.subheader("📈 Supplier Performance Overview")
     
-    df_tier2 = df_ecosystem.copy()
+    supplier_summary = df_ecosystem.groupby('Supplier').agg({
+        'Tier2_Impact': 'sum',
+        'Yazaki_Impact': 'sum',
+        'Is_Disrupted': 'sum'
+    }).reset_index()
     
-    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())
+    supplier_summary.columns = ['Supplier', 'Total_Tier2_Impact', 'Total_Yazaki_Impact', 'Disruption_Days']
     
-    filtered_data = df_tier2[
-        (df_tier2['Supplier'].isin(supplier_filter)) &
-        (df_tier2['Material'].isin(material_filter))
-    ]
+    # Display supplier cards
+    cols = st.columns(len(supplier_summary))
+    for i, (_, supplier) in enumerate(supplier_summary.iterrows()):
+        with cols[i]:
+            supplier_info = suppliers[supplier['Supplier']]
+            disruption_status = "🔴 Disrupted" if supplier['Disruption_Days'] > 0 else "🟢 Normal"
+            
+            st.markdown(f"""
+            <div style='border: 2px solid #ddd; padding: 15px; border-radius: 10px; text-align: center;'>
+                <h4>{supplier['Supplier']}</h4>
+                <p><strong>Location:</strong> {supplier_info['location']}</p>
+                <p><strong>Status:</strong> {disruption_status}</p>
+                <p><strong>Yazaki Impact:</strong> {supplier['Total_Yazaki_Impact']} units</p>
+                <p><strong>Reliability:</strong> {supplier_info['reliability']}%</p>
+            </div>
+            """, unsafe_allow_html=True)
     
-    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'
+    # Detailed supplier analysis
+    st.subheader("🔍 Detailed Supplier Analysis")
+    
+    selected_supplier = st.selectbox("Select Supplier for Detailed Analysis:", df_ecosystem['Supplier'].unique())
+    supplier_data = df_ecosystem[df_ecosystem['Supplier'] == selected_supplier]
+    
+    # Create supplier timeline
+    fig_supplier = go.Figure()
+    
+    for material in supplier_data['Material'].unique():
+        material_data = supplier_data[supplier_data['Material'] == material]
+        
+        fig_supplier.add_trace(go.Scatter(
+            x=material_data['Date'],
+            y=material_data['Tier2_Normal_Supply'],
+            mode='lines',
+            name=f'{material} - Normal Supply',
+            line=dict(dash='dot'),
+            opacity=0.6
+        ))
+        
+        fig_supplier.add_trace(go.Scatter(
+            x=material_data['Date'],
+            y=material_data['Tier2_Disrupted_Supply'],
+            mode='lines+markers',
+            name=f'{material} - Actual Supply',
+            marker=dict(size=6)
+        ))
+    
+    fig_supplier.update_layout(
+        title=f"📊 Supply Timeline: {selected_supplier}",
+        xaxis_title="Date",
+        yaxis_title="Supply Units",
+        height=400
     )
-    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("---")
+    
+    st.plotly_chart(fig_supplier, use_container_width=True)
+    
+    # Disruption details
+    disrupted_data = supplier_data[supplier_data['Is_Disrupted'] == True]
+    if not disrupted_data.empty:
+        st.subheader("⚠️ Disruption Details")
+        
+        for material in disrupted_data['Material'].unique():
+            material_disruptions = disrupted_data[disrupted_data['Material'] == material]
+            if not material_disruptions.empty:
+                disruption_info = material_disruptions.iloc[0]
+                
+                col1, col2 = st.columns(2)
+                with col1:
+                    st.markdown(f"""
+                    <div style='border-left: 4px solid #FF4444; padding: 15px; background: #f8f9fa; margin: 10px 0;'>
+                        <strong>Supplier:</strong> {disruption_info['Supplier']}<br>
+                        <strong>Material:</strong> {disruption_info['Material']}<br>
+                        <strong>Root Cause:</strong> {disruption_info['Disruption_Cause']}<br>
+                        <strong>Impact Duration:</strong> {len(material_disruptions)} days<br>
+                        <strong>Total Impact:</strong> {material_disruptions['Tier2_Impact'].sum()} units
+                    </div>
+                    """, unsafe_allow_html=True)
+                
+                with col2:
+                    # Generate mitigation strategies
+                    strategies, recommended = generate_mitigation_strategies(
+                        disruption_info['Supplier'],
+                        disruption_info['Material'],
+                        material_disruptions['Tier2_Impact'].sum(),
+                        len(material_disruptions)
+                    )
+                    
+                    st.markdown("**🛠️ Recommended Mitigation Strategies:**")
+                    for idx in recommended:
+                        strategy = strategies[idx]
+                        st.markdown(f"""
+                        **{strategy['strategy']}**
+                        - {strategy['description']}
+                        - Timeline: {strategy['timeline']}
+                        - Cost: {strategy['cost']}
+                        - Capacity: {strategy['capacity']}
+                        """)
 
-# TAB 3: BUFFER OPTIMIZER
+# TAB 3: BUFFER OPTIMIZER (unchanged)
 elif dashboard_tab == "🛡️ Buffer Optimizer":
-    st.markdown("""
-    # Dynamic Buffer Stock Optimization for Yazaki India Ltd
-
-    This dashboard provides suggested mitigation strategies to address supply risks and optimize inventory buffers.
-    """)
     
-    supplier_selected = st.selectbox("Select Tier-2 Supplier", options=suppliers.keys())
-    material_selected = st.selectbox("Select Material", options=suppliers[supplier_selected]['materials'])
+    st.markdown("""
+    <div style='background: linear-gradient(90deg, #1e3c72 0%, #2a5298 100%); padding: 15px; border-radius: 10px; margin-bottom: 20px;'>
+        <h3 style='color: white; margin: 0; text-align: center;'>
+            🛡️ AI-driven safety-stock recommendations across the full network
+        </h3>
+    </div>
+    """, unsafe_allow_html=True)
     
-    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)
+    # Buffer analysis
+    st.subheader("📊 Current Buffer Analysis")
     
-    if st.button("Generate Mitigation Strategies"):
-        strategies, recommended_idxs = generate_mitigation_strategies(supplier_selected, material_selected, impact_amount, impact_days)
+    # Calculate buffer recommendations
+    buffer_data = []
+    for material in df_demand['Material'].unique():
+        material_demand = df_demand[df_demand['Material'] == material]
+        
+        avg_demand = material_demand['Demand_Used'].mean()
+        max_demand = material_demand['Demand_Used'].max()
+        demand_volatility = material_demand['Demand_Used'].std()
+        
+        # Calculate recommended buffer
+        safety_factor = 1.5  # Can be adjusted based on service level requirements
+        recommended_buffer = int(demand_volatility * safety_factor)
         
-        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("---")
+        # Current buffer (assumed)
+        current_buffer = int(avg_demand * 0.1)  # Assuming 10% of average demand
+        
+        buffer_data.append({
+            'Material': material,
+            'Avg_Demand': int(avg_demand),
+            'Max_Demand': int(max_demand),
+            'Demand_Volatility': int(demand_volatility),
+            'Current_Buffer': current_buffer,
+            'Recommended_Buffer': recommended_buffer,
+            'Buffer_Gap': recommended_buffer - current_buffer
+        })
+    
+    buffer_df = pd.DataFrame(buffer_data)
+    
+    # Display buffer table
+    st.dataframe(buffer_df, use_container_width=True)
+    
+    # Buffer optimization visualization
+    fig_buffer = go.Figure()
+    
+    fig_buffer.add_trace(go.Bar(
+        name='Current Buffer',
+        x=buffer_df['Material'],
+        y=buffer_df['Current_Buffer'],
+        marker_color='lightblue'
+    ))
+    
+    fig_buffer.add_trace(go.Bar(
+        name='Recommended Buffer',
+        x=buffer_df['Material'],
+        y=buffer_df['Recommended_Buffer'],
+        marker_color='orange'
+    ))
+    
+    fig_buffer.update_layout(
+        title='📊 Buffer Stock Analysis: Current vs Recommended',
+        xaxis_title='Material',
+        yaxis_title='Buffer Units',
+        barmode='group',
+        height=400
+    )
+    
+    st.plotly_chart(fig_buffer, use_container_width=True)
+    
+    # Buffer recommendations
+    st.subheader("💡 Buffer Optimization Recommendations")
+    
+    for _, row in buffer_df.iterrows():
+        if row['Buffer_Gap'] > 0:
+            st.warning(f"**{row['Material']}**: Increase buffer by {row['Buffer_Gap']} units to handle demand volatility")
+        elif row['Buffer_Gap'] < 0:
+            st.info(f"**{row['Material']}**: Current buffer is {abs(row['Buffer_Gap'])} units above recommendation - consider optimization")
+        else:
+            st.success(f"**{row['Material']}**: Buffer levels are optimal")
 
-# Footer or Additional Info can be added here if needed
+# Footer
+st.markdown("""
+---
+<div style='text-align: center; padding: 20px; background: linear-gradient(90deg, #1e3c72 0%, #2a5298 100%); border-radius: 10px; margin-top: 30px;'>
+    <h3 style='color: white; margin: 0;'>
+        📊 <strong>Yazaki India Ltd 8-Week Supply Chain Command Center</strong> | Firm + AI-Corrected Demand | Ecosystem Intelligence + Buffer Optimization
+    </h3>
+    <p style='color: white; margin: 10px 0 0 0;'>
+        Powered by Agentic AI | 8-Week Planning Horizon | Comprehensive Supply Chain Resilience
+    </p>
+</div>
+""", unsafe_allow_html=True)