Restructure tabs: Strategy, Design, Logistics, Sourcing, Market

- Fix Fleet Scale Economics chart (show Profit Advantage instead of comparing similar-sized profits)
- Rename Operations → Logistics
- Move BOM/materials from Design → new Sourcing tab
- Design tab now focuses on circularity impact (grades, material composition)
- Expand Market tab with competitive positioning tables
- Update sidebar controls per tab

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

app.py

@@ -82,7 +82,7 @@ st.sidebar.markdown("---")
 st.sidebar.header("View")
 selected_view = st.sidebar.radio(
     "Select stakeholder view",
-    options=["Strategy", "Design", "Operations", "Product", "Growth"],
+    options=["Strategy", "Design", "Logistics", "Sourcing", "Market"],
     index=0,
     help="Each view shows relevant controls and insights"
 )
@@ -138,23 +138,25 @@ if selected_view == "Strategy":
 
 elif selected_view == "Design":
     st.sidebar.header("🎨 Design Controls")
-    st.session_state.mfg_cost = st.sidebar.slider(
-        "Manufacturing Cost", 50, 250, st.session_state.mfg_cost, step=10, format="€%d"
+    refurb_pct = st.sidebar.slider(
+        "Products Needing Repair", 5, 50, int(st.session_state.refurb_needed_pct * 100), format="%d%%"
     )
-    # Recycled content sliders will be in main content area
-
-elif selected_view == "Operations":
-    st.sidebar.header("🔧 Operations Controls")
+    st.session_state.refurb_needed_pct = refurb_pct / 100
     return_rate_pct = st.sidebar.slider(
         "Return Rate", 50, 100, int(st.session_state.return_rate * 100), format="%d%%"
     )
     st.session_state.return_rate = return_rate_pct / 100
 
+elif selected_view == "Logistics":
+    st.sidebar.header("🚚 Logistics Controls")
+    return_rate_pct = st.sidebar.slider(
+        "Return Rate", 50, 100, int(st.session_state.return_rate * 100), format="%d%%"
+    )
+    st.session_state.return_rate = return_rate_pct / 100
     refurb_pct = st.sidebar.slider(
         "Products Needing Repair", 5, 50, int(st.session_state.refurb_needed_pct * 100), format="%d%%"
     )
     st.session_state.refurb_needed_pct = refurb_pct / 100
-
     st.session_state.logistics = st.sidebar.slider(
         "Logistics (per cycle)", 5, 40, st.session_state.logistics, format="€%d"
     )
@@ -162,23 +164,14 @@ elif selected_view == "Operations":
         "Avg. Refurbishment Cost", 10, 80, st.session_state.avg_refurb_cost, format="€%d"
     )
 
-elif selected_view == "Product":
-    st.sidebar.header("📊 Product Controls")
-    st.session_state.lease_price = st.sidebar.slider(
-        "Monthly Lease Price", 10, 60, st.session_state.lease_price, format="€%d"
-    )
+elif selected_view == "Sourcing":
+    st.sidebar.header("📦 Sourcing Controls")
     st.session_state.mfg_cost = st.sidebar.slider(
         "Manufacturing Cost", 50, 250, st.session_state.mfg_cost, step=10, format="€%d"
     )
-    st.session_state.avg_refurb_cost = st.sidebar.slider(
-        "Avg. Refurbishment Cost", 10, 80, st.session_state.avg_refurb_cost, format="€%d"
-    )
-    st.session_state.logistics = st.sidebar.slider(
-        "Logistics (per cycle)", 5, 40, st.session_state.logistics, format="€%d"
-    )
 
-elif selected_view == "Growth":
-    st.sidebar.header("📈 Growth Controls")
+elif selected_view == "Market":
+    st.sidebar.header("📈 Market Controls")
     st.session_state.lease_price = st.sidebar.slider(
         "Monthly Lease Price", 10, 60, st.session_state.lease_price, format="€%d"
     )
@@ -236,13 +229,14 @@ if selected_view == "Strategy":
     circ_profit_5k = customers_5k * revenue_per_customer - circ_cost_5k
     lin_cost_5k = customers_5k * new_unit_cost
     lin_profit_5k = customers_5k * revenue_per_customer - lin_cost_5k
+    profit_advantage_5k = circ_profit_5k - lin_profit_5k
     products_saved_5k = customers_5k - fleet_5k
     co2_per_unit = config["environmental"]["co2_manufacturing_kg"]
     co2_saved_5k = products_saved_5k * co2_per_unit
 
     hero_cols = st.columns(4)
     with hero_cols[0]:
-        st.metric("Profit Advantage", f"€{circ_profit_5k - lin_profit_5k:,.0f}", delta="vs linear model")
+        st.metric("Profit Advantage", f"€{profit_advantage_5k:,.0f}", delta="vs linear model")
     with hero_cols[1]:
         st.metric("Products Saved", f"{products_saved_5k:,}", delta=f"at {fleet_5k:,} fleet")
     with hero_cols[2]:
@@ -267,6 +261,7 @@ if selected_view == "Strategy":
         linear_profit = linear_revenue - linear_cost
         products_saved = customers_served - fleet
         co2_saved = products_saved * co2_per_unit
+        profit_advantage = circular_profit - linear_profit
 
         scale_data.append({
             "Fleet Size": fleet,
@@ -274,7 +269,7 @@ if selected_view == "Strategy":
             "Customers Served": customers_served,
             "Circular Profit": circular_profit,
             "Linear Profit": linear_profit,
-            "Profit Advantage": circular_profit - linear_profit,
+            "Profit Advantage": profit_advantage,
             "Products Saved": products_saved,
             "CO2 Saved (kg)": co2_saved,
             "Linear Products Needed": customers_served,
@@ -285,23 +280,23 @@ if selected_view == "Strategy":
     scale_col1, scale_col2 = st.columns(2)
 
     with scale_col1:
-        fig_scale = go.Figure()
-        fig_scale.add_trace(go.Bar(
-            x=scale_df["Fleet Label"], y=scale_df["Circular Profit"],
-            name=f"Circular ({max_cycles} cycles)", marker_color="#2ecc71",
-            text=[f"€{v:,.0f}" for v in scale_df["Circular Profit"]], textposition="outside",
+        # Fixed chart: Show Profit Advantage as the primary metric
+        fig_advantage = go.Figure()
+        fig_advantage.add_trace(go.Bar(
+            x=scale_df["Fleet Label"],
+            y=scale_df["Profit Advantage"],
+            name="Circular Profit Advantage",
+            marker_color="#2ecc71",
+            text=[f"€{v:,.0f}" for v in scale_df["Profit Advantage"]],
+            textposition="outside",
         ))
-        fig_scale.add_trace(go.Bar(
-            x=scale_df["Fleet Label"], y=scale_df["Linear Profit"],
-            name="Linear (sell once)", marker_color="#e74c3c",
-            text=[f"€{v:,.0f}" for v in scale_df["Linear Profit"]], textposition="outside",
-        ))
-        fig_scale.update_layout(
-            title="Profit at Scale: Circular vs Linear",
-            xaxis_title="Fleet Size", yaxis_title="Total Profit (EUR)",
-            barmode="group", legend=dict(orientation="h", yanchor="bottom", y=1.02),
+        fig_advantage.update_layout(
+            title="Profit Advantage: Circular vs Linear",
+            xaxis_title="Fleet Size",
+            yaxis_title="Extra Profit from Circular Model (EUR)",
+            showlegend=False,
         )
-        st.plotly_chart(fig_scale, use_container_width=True)
+        st.plotly_chart(fig_advantage, use_container_width=True)
 
     with scale_col2:
         fig_products = go.Figure()
@@ -326,10 +321,9 @@ if selected_view == "Strategy":
     key_data = scale_df[scale_df["Fleet Size"] == 5000].iloc[0]
     st.success(f"""
     **The Business Case:** With **{5000:,} products** in our fleet, we serve **{key_data['Customers Served']:,} families**
-    and generate **€{key_data['Circular Profit']:,.0f}** in profit.
+    and generate **€{key_data['Profit Advantage']:,.0f} more profit** than a linear model.
 
-    A linear model would need **{key_data['Linear Products Needed']:,} products** to serve the same customers,
-    generating **€{key_data['Profit Advantage']:,.0f} less profit**.
+    A linear model would need **{key_data['Linear Products Needed']:,} products** to serve the same customers.
 
     Environmental impact: **{key_data['CO2 Saved (kg)']:,.0f} kg CO2 saved**.
     """)
@@ -400,178 +394,105 @@ if selected_view == "Strategy":
 # VIEW: DESIGN
 # ============================================================
 elif selected_view == "Design":
-    st.header("Design: Material & Manufacturing Impact")
+    st.header("Design: Product Design for Circularity")
     st.markdown("*For product designers and sustainability officers*")
 
-    has_materials = "materials" in config and "bom" in config.get("materials", {})
-    has_labor = "labor" in config
-    has_overhead = "overhead" in config
-
-    if has_materials and has_labor and has_overhead:
-        bom = config["materials"]["bom"]
-
-        # Recycled content sliders
-        with st.expander("Adjust Recycled Content", expanded=True):
-            st.markdown("**Adjust recycled material percentages to see cost impact:**")
-            recycled_sliders = {}
-            slider_cols = st.columns(3)
-            col_idx = 0
-
-            for component_key, component in bom.items():
-                if "virgin_cost_per_kg" in component:
-                    default_pct = component.get("recycled_content_pct", 0)
-                    with slider_cols[col_idx % 3]:
-                        recycled_sliders[component_key] = st.slider(
-                            f"{component.get('name', component_key)}",
-                            0, 100, int(default_pct * 100), step=5,
-                            key=f"recycled_{selected_product}_{component_key}",
-                            help=f"Virgin: €{component['virgin_cost_per_kg']:.2f}/kg, Recycled: €{component.get('recycled_cost_per_kg', component['virgin_cost_per_kg']):.2f}/kg"
-                        )
-                    col_idx += 1
-
-        # Calculate material costs
-        material_data = []
-        total_material_cost = 0
-        total_material_cost_virgin = 0
-        total_weight = 0
-
-        for component_key, component in bom.items():
-            weight = component.get("weight_kg", 0)
-            total_weight += weight
-
-            if "virgin_cost_per_kg" in component:
-                recycled_pct = recycled_sliders.get(component_key, component.get("recycled_content_pct", 0) * 100) / 100
-                virgin_cost = component["virgin_cost_per_kg"]
-                recycled_cost = component.get("recycled_cost_per_kg", virgin_cost)
-                cost_per_kg = (virgin_cost * (1 - recycled_pct)) + (recycled_cost * recycled_pct)
-                cost = weight * cost_per_kg
-                cost_virgin = weight * virgin_cost
-                recyclable = component.get("recyclable", False)
-            else:
-                cost_per_kg = component.get("cost_per_kg", 0)
-                cost = weight * cost_per_kg
-                cost_virgin = cost
-                recycled_pct = 0
-                recyclable = component.get("recyclable", False)
-
-            total_material_cost += cost
-            total_material_cost_virgin += cost_virgin
+    # Design for Circularity metrics
+    st.subheader("Design Impact on Circular Economics")
 
-            material_data.append({
-                "Component": component.get("name", component_key),
-                "Weight (kg)": weight,
-                "Cost/kg": cost_per_kg,
-                "Total Cost": cost,
-                "Recycled %": recycled_pct * 100,
-                "Recyclable": "Yes" if recyclable else "No",
-            })
+    design_cols = st.columns(4)
+    with design_cols[0]:
+        st.metric("EoLRR", f"{eolrr:.1%}", help="End-of-Life Recovery Rate")
+    with design_cols[1]:
+        st.metric("Grade A Rate", f"{grade_a_pct:.0%}", help="Products needing only cleaning")
+    with design_cols[2]:
+        st.metric("Repair Rate", f"{grade_b_pct:.0%}", help="Products needing parts replacement")
+    with design_cols[3]:
+        st.metric("Scrap Rate", f"{grade_c_pct:.0%}", help="Products that can't be reused")
 
-        labor_cost = config["labor"]["manufacturing"].get("total",
-                        config["labor"]["manufacturing"]["hours_per_unit"] *
-                        config["labor"]["manufacturing"]["hourly_rate"])
-        overhead_cost = config["overhead"]["manufacturing_per_unit"]
+    st.markdown("---")
 
-        cost_col1, cost_col2 = st.columns(2)
+    # Grade Distribution - Design Impact framing
+    st.subheader("Design Choices → Return Grades")
+    st.markdown("Better design for durability and repairability reduces the repair rate and increases Grade A returns.")
 
-        with cost_col1:
-            st.subheader("Cost Waterfall")
+    grades_df = pd.DataFrame({
+        "Grade": ["A (Clean only)", "B (Repair needed)", "C (Scrap)"],
+        "Percentage": [grade_a_pct * 100, grade_b_pct * 100, grade_c_pct * 100],
+        "Refurb Cost": [refurb_a, refurb_b, config["costs"]["refurbishment"]["grade_c"]],
+    })
 
-            fig_waterfall = go.Figure(go.Waterfall(
-                orientation="v",
-                measure=["relative", "relative", "relative", "total"],
-                x=["Materials", "Labor", "Overhead", "Total"],
-                y=[total_material_cost, labor_cost, overhead_cost, 0],
-                text=[f"€{total_material_cost:.0f}", f"€{labor_cost:.0f}",
-                      f"€{overhead_cost:.0f}", f"€{total_material_cost + labor_cost + overhead_cost:.0f}"],
-                textposition="outside",
-                connector={"line": {"color": "rgb(63, 63, 63)"}},
-                increasing={"marker": {"color": "#3498db"}},
-                totals={"marker": {"color": "#2ecc71"}},
-            ))
-            fig_waterfall.update_layout(title=f"Manufacturing Cost: €{mfg_cost:.0f}", yaxis_title="EUR", showlegend=False)
-            st.plotly_chart(fig_waterfall, use_container_width=True)
+    fig_grades = px.bar(
+        grades_df, x="Grade", y="Percentage", color="Grade",
+        color_discrete_map={"A (Clean only)": "#2ecc71", "B (Repair needed)": "#f1c40f", "C (Scrap)": "#e74c3c"},
+        title="Return Grade Distribution", text="Percentage",
+    )
+    fig_grades.update_traces(texttemplate='%{text:.0f}%', textposition='outside')
+    fig_grades.update_layout(showlegend=False, yaxis_title="% of Returns")
+    st.plotly_chart(fig_grades, use_container_width=True)
 
-        with cost_col2:
-            st.subheader("Bill of Materials")
-            mat_df = pd.DataFrame(material_data)
-            st.dataframe(mat_df.style.format({
-                "Weight (kg)": "{:.1f}", "Cost/kg": "€{:.2f}",
-                "Total Cost": "€{:.2f}", "Recycled %": "{:.0f}%",
-            }), use_container_width=True, hide_index=True)
+    st.info(f"**Design insight:** Reducing repair rate from {grade_b_pct:.0%} to {max(5, grade_b_pct*100-10):.0f}% would save €{(grade_b_pct - max(0.05, grade_b_pct-0.10)) * refurb_b:.0f} per returned product.")
 
-            recyclable_weight = sum(m["Weight (kg)"] for m in material_data if m["Recyclable"] == "Yes")
-            recyclable_pct = recyclable_weight / total_weight * 100 if total_weight > 0 else 0
-            cost_savings = total_material_cost_virgin - total_material_cost
+    st.markdown("---")
 
-            st.markdown(f"""
-            **Summary:** Total Weight: **{total_weight:.1f} kg** | Material Cost: **€{total_material_cost:.2f}** | Recyclable: **{recyclable_pct:.0f}%**
-            """)
+    # Material Composition Over Time
+    st.subheader("Material Composition Over Time")
+    st.markdown("How the product's material composition changes through repair cycles.")
 
-            if cost_savings > 0:
-                st.success(f"Recycled content saves **€{cost_savings:.2f}** vs 100% virgin ({cost_savings/total_material_cost_virgin*100:.1f}% reduction)")
+    lifecycle_data = []
+    original_material_pct = 100
+    new_parts_added_pct = 0
+    parts_replaced_grade_a = 0.05
+    parts_replaced_grade_b = 0.20
 
-        st.markdown("---")
+    for cycle in range(0, max_cycles + 1):
+        if cycle == 0:
+            lifecycle_data.append({
+                "Cycle": "New",
+                "Original Material %": original_material_pct,
+                "New Parts Added %": new_parts_added_pct,
+            })
+        else:
+            parts_from_a = grade_a_pct * parts_replaced_grade_a * 100
+            parts_from_b = grade_b_pct * parts_replaced_grade_b * 100
+            new_parts_added_pct += parts_from_a + parts_from_b
+            original_material_pct = max(0, 100 - new_parts_added_pct)
+
+            lifecycle_data.append({
+                "Cycle": f"Cycle {cycle}",
+                "Original Material %": original_material_pct,
+                "New Parts Added %": new_parts_added_pct,
+            })
 
-        # Material Composition Over Time
-        st.subheader("Material Composition Over Time")
-
-        lifecycle_data = []
-        original_material_pct = 100
-        new_parts_added_pct = 0
-        parts_replaced_grade_a = 0.05
-        parts_replaced_grade_b = 0.20
-
-        for cycle in range(0, max_cycles + 1):
-            if cycle == 0:
-                lifecycle_data.append({
-                    "Cycle": "New",
-                    "Original Material %": original_material_pct,
-                    "New Parts Added %": new_parts_added_pct,
-                })
-            else:
-                parts_from_a = grade_a_pct * parts_replaced_grade_a * 100
-                parts_from_b = grade_b_pct * parts_replaced_grade_b * 100
-                new_parts_added_pct += parts_from_a + parts_from_b
-                original_material_pct = max(0, 100 - new_parts_added_pct)
-
-                lifecycle_data.append({
-                    "Cycle": f"Cycle {cycle}",
-                    "Original Material %": original_material_pct,
-                    "New Parts Added %": new_parts_added_pct,
-                })
-
-        lifecycle_df = pd.DataFrame(lifecycle_data)
-
-        fig_materials = go.Figure()
-        fig_materials.add_trace(go.Scatter(
-            x=lifecycle_df["Cycle"], y=lifecycle_df["Original Material %"],
-            name="Original Parts", mode="lines", fill="tozeroy",
-            line=dict(color="#3498db", width=0), fillcolor="rgba(52, 152, 219, 0.7)",
-        ))
-        fig_materials.add_trace(go.Scatter(
-            x=lifecycle_df["Cycle"],
-            y=lifecycle_df["Original Material %"] + lifecycle_df["New Parts Added %"],
-            name="Replacement Parts", mode="lines", fill="tonexty",
-            line=dict(color="#2ecc71", width=0), fillcolor="rgba(46, 204, 113, 0.7)",
-        ))
-        fig_materials.update_layout(
-            yaxis_title="Material Composition (%)", yaxis_range=[0, 120],
-            legend=dict(orientation="h", yanchor="bottom", y=1.02),
-        )
-        st.plotly_chart(fig_materials, use_container_width=True)
+    lifecycle_df = pd.DataFrame(lifecycle_data)
 
-        final_original = lifecycle_df.iloc[-1]["Original Material %"]
-        final_new = lifecycle_df.iloc[-1]["New Parts Added %"]
-        st.info(f"After {max_cycles} cycles: **{final_original:.0f}%** original parts, **{final_new:.0f}%** replacement parts")
+    fig_materials = go.Figure()
+    fig_materials.add_trace(go.Scatter(
+        x=lifecycle_df["Cycle"], y=lifecycle_df["Original Material %"],
+        name="Original Parts", mode="lines", fill="tozeroy",
+        line=dict(color="#3498db", width=0), fillcolor="rgba(52, 152, 219, 0.7)",
+    ))
+    fig_materials.add_trace(go.Scatter(
+        x=lifecycle_df["Cycle"],
+        y=lifecycle_df["Original Material %"] + lifecycle_df["New Parts Added %"],
+        name="Replacement Parts", mode="lines", fill="tonexty",
+        line=dict(color="#2ecc71", width=0), fillcolor="rgba(46, 204, 113, 0.7)",
+    ))
+    fig_materials.update_layout(
+        yaxis_title="Material Composition (%)", yaxis_range=[0, 120],
+        legend=dict(orientation="h", yanchor="bottom", y=1.02),
+    )
+    st.plotly_chart(fig_materials, use_container_width=True)
 
-    else:
-        st.info("Detailed cost breakdown not available. Add `materials`, `labor`, and `overhead` sections to config.")
+    final_original = lifecycle_df.iloc[-1]["Original Material %"]
+    final_new = lifecycle_df.iloc[-1]["New Parts Added %"]
+    st.info(f"After {max_cycles} cycles: **{final_original:.0f}%** original parts remain, **{final_new:.0f}%** replaced through repairs.")
 
 # ============================================================
-# VIEW: OPERATIONS
+# VIEW: LOGISTICS
 # ============================================================
-elif selected_view == "Operations":
-    st.header("Operations: Logistics & Refurbishment Planning")
+elif selected_view == "Logistics":
+    st.header("Logistics: Operations & Warehouse Planning")
     st.markdown("*For logistics managers and warehouse operations*")
 
     # Return assumptions summary
@@ -679,152 +600,141 @@ elif selected_view == "Operations":
     st.info(f"Per 100 products: After {max_cycles} cycles, **{final_in_service:.0f}** still serviceable, **{final_lost:.0f}** lost")
 
 # ============================================================
-# VIEW: PRODUCT
+# VIEW: SOURCING
 # ============================================================
-elif selected_view == "Product":
-    st.header("Product: Lifecycle Economics & Pricing")
-    st.markdown("*For product managers and pricing analysts*")
-
-    # Key Metrics
-    st.subheader("Key Metrics")
-
-    col1, col2, col3, col4 = st.columns(4)
-    with col1:
-        st.metric("EoLRR", f"{eolrr:.1%}", delta=f"{(eolrr - config['cti_parameters']['target_eolrr']):.1%}",
-                  help="End-of-Life Recovery Rate = Return Rate × (Grade A + B)")
-    with col2:
-        st.metric("New Unit Cost", f"€{new_unit_cost:.0f}", help="Manufacturing + Logistics")
-    with col3:
-        st.metric("Refurb Unit Cost", f"€{refurb_unit_cost:.0f}", delta=f"-€{new_unit_cost - refurb_unit_cost:.0f} vs new")
-    with col4:
-        st.metric("Revenue per Customer", f"€{revenue_per_customer:.0f}", help=f"€{lease_price}/month × {lease_term} months")
-
-    st.markdown("---")
+elif selected_view == "Sourcing":
+    st.header("Sourcing: Material Costs & Supply")
+    st.markdown("*For procurement, supply chain, and finance*")
 
-    # Per-Product Economics
-    st.subheader("Per-Product Economics: Single Unit Over Multiple Cycles")
+    has_materials = "materials" in config and "bom" in config.get("materials", {})
+    has_labor = "labor" in config
+    has_overhead = "overhead" in config
 
-    product_data = []
-    circular_cum_cost = 0
-    circular_cum_revenue = 0
-    linear_cum_cost = 0
-    linear_cum_revenue = 0
+    if has_materials and has_labor and has_overhead:
+        bom = config["materials"]["bom"]
 
-    for cycle in range(1, max_cycles + 1):
-        if cycle == 1:
-            cycle_cost = new_unit_cost
-        else:
-            cycle_cost = refurb_unit_cost
-
-        circular_cum_cost += cycle_cost
-        circular_cum_revenue += revenue_per_customer
-        circular_cum_profit = circular_cum_revenue - circular_cum_cost
-
-        linear_cum_cost += new_unit_cost
-        linear_cum_revenue += revenue_per_customer
-        linear_cum_profit = linear_cum_revenue - linear_cum_cost
-
-        product_data.append({
-            "Cycle": cycle,
-            "Cycle Label": f"Cycle {cycle}",
-            "Circular: Cumulative Profit": circular_cum_profit,
-            "Circular: Cost per Use": circular_cum_cost / cycle,
-            "Linear: Cumulative Profit": linear_cum_profit,
-            "Linear: Cost per Use": new_unit_cost,
-        })
+        # Recycled content sliders
+        with st.expander("Adjust Recycled Content", expanded=True):
+            st.markdown("**Adjust recycled material percentages to see cost impact:**")
+            recycled_sliders = {}
+            slider_cols = st.columns(3)
+            col_idx = 0
 
-    df_product = pd.DataFrame(product_data)
+            for component_key, component in bom.items():
+                if "virgin_cost_per_kg" in component:
+                    default_pct = component.get("recycled_content_pct", 0)
+                    with slider_cols[col_idx % 3]:
+                        recycled_sliders[component_key] = st.slider(
+                            f"{component.get('name', component_key)}",
+                            0, 100, int(default_pct * 100), step=5,
+                            key=f"recycled_{selected_product}_{component_key}",
+                            help=f"Virgin: €{component['virgin_cost_per_kg']:.2f}/kg, Recycled: €{component.get('recycled_cost_per_kg', component['virgin_cost_per_kg']):.2f}/kg"
+                        )
+                    col_idx += 1
 
-    chart_col1, chart_col2 = st.columns(2)
+        # Calculate material costs
+        material_data = []
+        total_material_cost = 0
+        total_material_cost_virgin = 0
+        total_weight = 0
 
-    with chart_col1:
-        fig_profit = go.Figure()
-        fig_profit.add_trace(go.Scatter(
-            x=df_product["Cycle"], y=df_product["Circular: Cumulative Profit"],
-            name="Circular (1 product reused)", mode="lines+markers",
-            line=dict(color="#2ecc71", width=3), marker=dict(size=12),
-        ))
-        fig_profit.add_trace(go.Scatter(
-            x=df_product["Cycle"], y=df_product["Linear: Cumulative Profit"],
-            name="Linear (N new products)", mode="lines+markers",
-            line=dict(color="#e74c3c", width=3), marker=dict(size=12),
-        ))
-        fig_profit.update_layout(title="Cumulative Profit per Product", xaxis_title="Use Cycle", yaxis_title="Cumulative Profit (EUR)",
-                                 legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01))
-        st.plotly_chart(fig_profit, use_container_width=True)
+        for component_key, component in bom.items():
+            weight = component.get("weight_kg", 0)
+            total_weight += weight
 
-    with chart_col2:
-        fig_cost = go.Figure()
-        fig_cost.add_trace(go.Bar(
-            x=df_product["Cycle Label"], y=df_product["Circular: Cost per Use"],
-            name="Circular (amortized)", marker_color="#2ecc71",
-            text=[f"€{c:.0f}" for c in df_product["Circular: Cost per Use"]], textposition="outside",
-        ))
-        fig_cost.add_trace(go.Bar(
-            x=df_product["Cycle Label"], y=df_product["Linear: Cost per Use"],
-            name="Linear (always new)", marker_color="#e74c3c",
-            text=[f"€{c:.0f}" for c in df_product["Linear: Cost per Use"]], textposition="outside",
-        ))
-        fig_cost.update_layout(title="Cost per Use Cycle (Amortized)", xaxis_title="Use Cycle", yaxis_title="Cost per Use (EUR)",
-                               barmode="group", legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01))
-        st.plotly_chart(fig_cost, use_container_width=True)
+            if "virgin_cost_per_kg" in component:
+                recycled_pct = recycled_sliders.get(component_key, component.get("recycled_content_pct", 0) * 100) / 100
+                virgin_cost = component["virgin_cost_per_kg"]
+                recycled_cost = component.get("recycled_cost_per_kg", virgin_cost)
+                cost_per_kg = (virgin_cost * (1 - recycled_pct)) + (recycled_cost * recycled_pct)
+                cost = weight * cost_per_kg
+                cost_virgin = weight * virgin_cost
+                recyclable = component.get("recyclable", False)
+            else:
+                cost_per_kg = component.get("cost_per_kg", 0)
+                cost = weight * cost_per_kg
+                cost_virgin = cost
+                recycled_pct = 0
+                recyclable = component.get("recyclable", False)
 
-    # Profit summary
-    profit_advantage = df_product.iloc[-1]["Circular: Cumulative Profit"] - df_product.iloc[-1]["Linear: Cumulative Profit"]
-    cost_savings = (max_cycles * new_unit_cost) - circular_cum_cost
+            total_material_cost += cost
+            total_material_cost_virgin += cost_virgin
 
-    if profit_advantage > 0:
-        st.success(f"**Circular advantage:** Save **€{cost_savings:.0f}** in costs, earn **€{profit_advantage:.0f}** more profit per product lifecycle")
+            material_data.append({
+                "Component": component.get("name", component_key),
+                "Weight (kg)": weight,
+                "Cost/kg": cost_per_kg,
+                "Total Cost": cost,
+                "Recycled %": recycled_pct * 100,
+                "Recyclable": "Yes" if recyclable else "No",
+            })
 
-    st.markdown("---")
+        labor_cost = config["labor"]["manufacturing"].get("total",
+                        config["labor"]["manufacturing"]["hours_per_unit"] *
+                        config["labor"]["manufacturing"]["hourly_rate"])
+        overhead_cost = config["overhead"]["manufacturing_per_unit"]
 
-    # Pricing Analysis
-    st.subheader("Pricing Analysis: Finding the Sweet Spot")
+        cost_col1, cost_col2 = st.columns(2)
 
-    price_range = list(range(15, 51, 5))
-    pricing_data = []
+        with cost_col1:
+            st.subheader("Manufacturing Cost Waterfall")
 
-    for test_price in price_range:
-        test_revenue = test_price * lease_term
-        test_total_revenue = test_revenue * max_cycles
-        circ_cost = new_unit_cost + (max_cycles - 1) * refurb_unit_cost
-        circ_profit = test_total_revenue - circ_cost
+            fig_waterfall = go.Figure(go.Waterfall(
+                orientation="v",
+                measure=["relative", "relative", "relative", "total"],
+                x=["Materials", "Labor", "Overhead", "Total"],
+                y=[total_material_cost, labor_cost, overhead_cost, 0],
+                text=[f"€{total_material_cost:.0f}", f"€{labor_cost:.0f}",
+                      f"€{overhead_cost:.0f}", f"€{total_material_cost + labor_cost + overhead_cost:.0f}"],
+                textposition="outside",
+                connector={"line": {"color": "rgb(63, 63, 63)"}},
+                increasing={"marker": {"color": "#3498db"}},
+                totals={"marker": {"color": "#2ecc71"}},
+            ))
+            fig_waterfall.update_layout(title=f"Manufacturing Cost: €{mfg_cost:.0f}", yaxis_title="EUR", showlegend=False)
+            st.plotly_chart(fig_waterfall, use_container_width=True)
 
-        pricing_data.append({
-            "Monthly Price": test_price,
-            "Revenue per Cycle": test_revenue,
-            "Profit per Product": circ_profit,
-        })
+        with cost_col2:
+            st.subheader("Bill of Materials")
+            mat_df = pd.DataFrame(material_data)
+            st.dataframe(mat_df.style.format({
+                "Weight (kg)": "{:.1f}", "Cost/kg": "€{:.2f}",
+                "Total Cost": "€{:.2f}", "Recycled %": "{:.0f}%",
+            }), use_container_width=True, hide_index=True)
 
-    pricing_df = pd.DataFrame(pricing_data)
+        # Summary
+        recyclable_weight = sum(m["Weight (kg)"] for m in material_data if m["Recyclable"] == "Yes")
+        recyclable_pct = recyclable_weight / total_weight * 100 if total_weight > 0 else 0
+        cost_savings = total_material_cost_virgin - total_material_cost
 
-    fig_price_profit = go.Figure()
-    fig_price_profit.add_trace(go.Scatter(
-        x=pricing_df["Monthly Price"], y=pricing_df["Profit per Product"],
-        mode="lines+markers", name="Profit per Product",
-        line=dict(color="#2ecc71", width=3), marker=dict(size=10),
-    ))
+        st.markdown("---")
+        st.subheader("Sourcing Summary")
+
+        summary_cols = st.columns(4)
+        with summary_cols[0]:
+            st.metric("Total Weight", f"{total_weight:.1f} kg")
+        with summary_cols[1]:
+            st.metric("Material Cost", f"€{total_material_cost:.2f}")
+        with summary_cols[2]:
+            st.metric("Recyclable", f"{recyclable_pct:.0f}%", help="By weight")
+        with summary_cols[3]:
+            if cost_savings > 0:
+                st.metric("Recycled Savings", f"€{cost_savings:.2f}", delta=f"{cost_savings/total_material_cost_virgin*100:.1f}% vs virgin")
+            else:
+                st.metric("Recycled Savings", "€0", help="Adjust sliders above")
 
-    circ_cost_current = new_unit_cost + (max_cycles - 1) * refurb_unit_cost
-    current_profit = (lease_price * lease_term * max_cycles) - circ_cost_current
-    fig_price_profit.add_trace(go.Scatter(
-        x=[lease_price], y=[current_profit], mode="markers",
-        name=f"Current (€{lease_price}/mo)", marker=dict(size=16, color="#e74c3c", symbol="star"),
-    ))
-    fig_price_profit.add_hline(y=0, line_dash="dash", line_color="gray", annotation_text="Break-even")
-    fig_price_profit.update_layout(title="Profit vs Monthly Price", xaxis_title="Monthly Lease Price (EUR)",
-                                   yaxis_title="Profit per Product Lifecycle (EUR)")
-    st.plotly_chart(fig_price_profit, use_container_width=True)
+        if cost_savings > 0:
+            st.success(f"Using recycled content saves **€{cost_savings:.2f}** vs 100% virgin materials")
 
-    break_even_price = circ_cost_current / (lease_term * max_cycles)
-    st.info(f"**Break-even price:** €{break_even_price:.0f}/month (over {max_cycles} cycles)")
+    else:
+        st.info("Detailed cost breakdown not available. Add `materials`, `labor`, and `overhead` sections to config.")
 
 # ============================================================
-# VIEW: GROWTH
+# VIEW: MARKET
 # ============================================================
-elif selected_view == "Growth":
-    st.header("Growth: Customer Value & Marketing Story")
-    st.markdown("*For marketing, sales, and customer success*")
+elif selected_view == "Market":
+    st.header("Market: Customer Value & Positioning")
+    st.markdown("*For marketing, sales, and business development*")
 
     # Customer Value Proposition
     st.subheader("Customer Value Proposition")
@@ -847,7 +757,7 @@ elif selected_view == "Growth":
     st.subheader("Environmental Impact Story")
 
     co2_per_unit = config["environmental"]["co2_manufacturing_kg"]
-    co2_saved_per_customer = co2_per_unit * (1 - 1/max_cycles)  # Approximate savings per customer
+    co2_saved_per_customer = co2_per_unit * (1 - 1/max_cycles)
 
     env_cols = st.columns(3)
     with env_cols[0]:
@@ -866,7 +776,7 @@ elif selected_view == "Growth":
 
     st.markdown("---")
 
-    # Customer Value vs Retail
+    # Price vs Value Analysis
     st.subheader("Price vs Value Analysis")
 
     price_range = list(range(15, 51, 5))
@@ -905,9 +815,9 @@ elif selected_view == "Growth":
     if retail_price > 0 and total_lease_cost < retail_price:
         st.success(f"At €{lease_price}/mo, customers **save €{retail_price - total_lease_cost}** ({(retail_price - total_lease_cost)/retail_price*100:.0f}% less than buying)")
 
-    # Marketing messages
+    # Market Positioning
     st.markdown("---")
-    st.subheader("Key Marketing Messages")
+    st.subheader("Market Positioning")
 
     st.markdown(f"""
     | Message | Proof Point |
@@ -919,6 +829,18 @@ elif selected_view == "Growth":
     | **Circular economy** | One product serves {max_cycles} families |
     """)
 
+    st.markdown("---")
+    st.subheader("Competitive Advantages")
+
+    st.markdown("""
+    | vs Traditional Retail | vs Other Rental |
+    |----------------------|-----------------|
+    | Lower total cost | Professional refurbishment |
+    | No resale hassle | Quality guarantee |
+    | Sustainable choice | Circular model transparency |
+    | Flexibility to upgrade | CTI-certified process |
+    """)
+
 # ============================================================
 # FOOTER
 # ============================================================