Add Fleet Scale Economics section for volume advantage pitch

Shows circular vs linear comparison at 500, 1000, 2000, 5000 products:
- Profit at scale comparison chart
- Products needed to serve same customers
- Business case summary for Jonas's pitch (5k products = 15k customers)
- CO2 savings from fewer manufactured products

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

app.py

@@ -1045,7 +1045,198 @@ st.markdown(f"""
 """)
 
 # ============================================================
-# SECTION 7: PRICING SENSITIVITY
+# SECTION 7: FLEET SCALE ECONOMICS
+# ============================================================
+st.markdown("---")
+st.header("Fleet Scale Economics: Volume Advantage")
+
+st.markdown("""
+**The service model advantage:** With fewer products in circulation, serve more customers over time.
+This is the key to proving the circular business case at scale.
+""")
+
+# Fleet sizes to analyze (Jonas's request: 500, 1000, 5000)
+fleet_sizes = [500, 1000, 2000, 5000]
+
+scale_data = []
+for fleet in fleet_sizes:
+    # Circular: fleet × cycles = total customers served
+    customers_served = fleet * max_cycles
+
+    # Circular costs: first cycle mfg, subsequent cycles refurb
+    circular_mfg_cost = fleet * new_unit_cost
+    circular_refurb_cost = fleet * (max_cycles - 1) * refurb_unit_cost
+    circular_total_cost = circular_mfg_cost + circular_refurb_cost
+    circular_revenue = customers_served * revenue_per_customer
+    circular_profit = circular_revenue - circular_total_cost
+
+    # Linear equivalent: to serve same customers, need to manufacture that many products
+    linear_products_needed = customers_served  # 1 product per customer
+    linear_cost = linear_products_needed * new_unit_cost
+    linear_revenue = customers_served * revenue_per_customer
+    linear_profit = linear_revenue - linear_cost
+
+    # Savings
+    products_saved = linear_products_needed - fleet
+    cost_saved = linear_cost - circular_total_cost
+    profit_advantage = circular_profit - linear_profit
+
+    # CO2 savings (fewer products manufactured)
+    co2_per_unit = config["environmental"]["co2_manufacturing_kg"]
+    co2_saved = products_saved * co2_per_unit
+
+    scale_data.append({
+        "Fleet Size": fleet,
+        "Fleet Label": f"{fleet:,}",
+        "Customers Served": customers_served,
+        "Circular Profit": circular_profit,
+        "Linear Profit": linear_profit,
+        "Profit Advantage": profit_advantage,
+        "Products Saved": products_saved,
+        "Cost Saved": cost_saved,
+        "CO2 Saved (kg)": co2_saved,
+        "Linear Products Needed": linear_products_needed,
+    })
+
+scale_df = pd.DataFrame(scale_data)
+
+scale_col1, scale_col2 = st.columns(2)
+
+with scale_col1:
+    st.subheader("Profit at Scale: Circular vs Linear")
+
+    fig_scale = go.Figure()
+
+    # Circular profit
+    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",
+    ))
+
+    # Linear profit (same customers)
+    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(
+        xaxis_title="Fleet Size (products in circulation)",
+        yaxis_title="Total Profit (EUR)",
+        barmode="group",
+        legend=dict(orientation="h", yanchor="bottom", y=1.02),
+    )
+
+    st.plotly_chart(fig_scale, width="stretch")
+
+with scale_col2:
+    st.subheader("Products Needed to Serve Same Customers")
+
+    fig_products = go.Figure()
+
+    # Circular: products needed
+    fig_products.add_trace(go.Bar(
+        x=scale_df["Fleet Label"],
+        y=scale_df["Fleet Size"],
+        name="Circular (reuse)",
+        marker_color="#2ecc71",
+        text=[f"{v:,}" for v in scale_df["Fleet Size"]],
+        textposition="outside",
+    ))
+
+    # Linear: products needed
+    fig_products.add_trace(go.Bar(
+        x=scale_df["Fleet Label"],
+        y=scale_df["Linear Products Needed"],
+        name="Linear (sell each)",
+        marker_color="#e74c3c",
+        text=[f"{v:,}" for v in scale_df["Linear Products Needed"]],
+        textposition="outside",
+    ))
+
+    fig_products.update_layout(
+        xaxis_title="Fleet Size Target",
+        yaxis_title="Products to Manufacture",
+        barmode="group",
+        legend=dict(orientation="h", yanchor="bottom", y=1.02),
+    )
+
+    st.plotly_chart(fig_products, width="stretch")
+
+# Key insight box for Jonas's pitch
+st.subheader("The Business Case")
+
+# Use 5000 as the key example
+key_fleet = 5000
+key_data = scale_df[scale_df["Fleet Size"] == key_fleet].iloc[0]
+
+metric_cols = st.columns(4)
+with metric_cols[0]:
+    st.metric(
+        "Fleet Size",
+        f"{key_fleet:,}",
+        help="Products in circulation"
+    )
+with metric_cols[1]:
+    st.metric(
+        "Customers Served",
+        f"{key_data['Customers Served']:,}",
+        delta=f"{max_cycles} cycles",
+        help="Total families served over product lifetime"
+    )
+with metric_cols[2]:
+    st.metric(
+        "Products Saved",
+        f"{key_data['Products Saved']:,}",
+        delta=f"vs linear",
+        help="Fewer products to manufacture"
+    )
+with metric_cols[3]:
+    st.metric(
+        "Extra Profit",
+        f"€{key_data['Profit Advantage']:,.0f}",
+        delta=f"vs selling {key_data['Linear Products Needed']:,}",
+        help="Circular profit advantage"
+    )
+
+st.success(f"""
+**Jonas's pitch:** With **{key_fleet:,} products** in our fleet, we serve **{key_data['Customers Served']:,} families**
+and generate **€{key_data['Circular Profit']:,.0f}** in profit.
+
+A linear model would need to **manufacture {key_data['Linear Products Needed']:,} products** to serve the same customers,
+costing **€{key_data['Cost Saved']:,.0f} more** and generating **€{key_data['Profit Advantage']:,.0f} less profit**.
+
+Environmental impact: **{key_data['CO2 Saved (kg)']:,.0f} kg CO2 saved** by manufacturing fewer products.
+""")
+
+# Summary table
+with st.expander("Full Scale Comparison Table"):
+    display_df = scale_df[["Fleet Label", "Customers Served", "Circular Profit", "Linear Profit",
+                           "Profit Advantage", "Products Saved", "CO2 Saved (kg)"]].copy()
+    display_df.columns = ["Fleet", "Customers", "Circular Profit", "Linear Profit",
+                          "Advantage", "Products Saved", "CO2 Saved"]
+    st.dataframe(
+        display_df.style.format({
+            "Customers": "{:,}",
+            "Circular Profit": "€{:,.0f}",
+            "Linear Profit": "€{:,.0f}",
+            "Advantage": "€{:,.0f}",
+            "Products Saved": "{:,}",
+            "CO2 Saved": "{:,.0f} kg",
+        }),
+        width="stretch",
+        hide_index=True,
+    )
+
+# ============================================================
+# SECTION 8: PRICING SENSITIVITY
 # ============================================================
 st.markdown("---")
 st.header("Pricing Analysis: Finding the Sweet Spot")