Overhaul Strategy page with new economic model

- Add 8 new sliders: linear price, lease price, mfg cost, fleet size,
utilization rate, logistics cost, repair cost/month, cycle length
- Add cumulative earnings over time chart (0-120 months)
- Add scenario comparison: save/clear scenarios feature
- Add breakeven analysis with marker on chart
- Add cycle transition markers (vertical dashed lines)
- Update hero metrics: breakeven month, monthly net revenue, 10yr profit
- Update Fleet Scale Economics to use new model
- Replace cycle sensitivity with cycle length analysis
- Switch Strategy view to NOK currency (other views remain EUR)
- Add model parameters summary expander

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

app.py

@@ -123,19 +123,73 @@ if "logistics" not in st.session_state:
 if "max_cycles_override" not in st.session_state:
     st.session_state.max_cycles_override = max_cycles
 
+# Strategy view session state (NOK currency - approximately EUR × 10)
+if "saved_scenarios" not in st.session_state:
+    st.session_state.saved_scenarios = []
+if "strategy_linear_price" not in st.session_state:
+    st.session_state.strategy_linear_price = int(retail_price * 10)  # EUR to NOK
+if "strategy_lease_price" not in st.session_state:
+    st.session_state.strategy_lease_price = int(default_lease_price * 10)  # EUR to NOK
+if "strategy_mfg_cost" not in st.session_state:
+    st.session_state.strategy_mfg_cost = int(default_mfg_cost * 10)  # EUR to NOK
+if "strategy_fleet_size" not in st.session_state:
+    st.session_state.strategy_fleet_size = 1000
+if "strategy_utilization" not in st.session_state:
+    st.session_state.strategy_utilization = 85
+if "strategy_logistics_cost" not in st.session_state:
+    st.session_state.strategy_logistics_cost = int(default_logistics * 10)  # EUR to NOK
+if "strategy_repair_cost" not in st.session_state:
+    st.session_state.strategy_repair_cost = 50
+if "strategy_cycle_length" not in st.session_state:
+    st.session_state.strategy_cycle_length = 24
+
 # Render sidebar based on selected view
 if selected_view == "Strategy":
-    st.sidebar.header("💼 Strategy Controls")
-    st.session_state.lease_price = st.sidebar.slider(
-        "Monthly Lease Price", 10, 60, st.session_state.lease_price, format="€%d"
+    st.sidebar.header("💼 Strategy Controls (NOK)")
+    st.sidebar.caption("Economic model for product lifespan profitability")
+
+    st.sidebar.subheader("Pricing")
+    st.session_state.strategy_linear_price = st.sidebar.slider(
+        "Linear Model Price", 500, 2000, st.session_state.strategy_linear_price, step=50, format="%d NOK",
+        help="What customers pay to buy outright"
     )
-    st.session_state.mfg_cost = st.sidebar.slider(
-        "Manufacturing Cost", 50, 250, st.session_state.mfg_cost, step=10, format="€%d"
+    st.session_state.strategy_lease_price = st.sidebar.slider(
+        "Monthly Lease Price", 100, 600, st.session_state.strategy_lease_price, step=10, format="%d NOK",
+        help="Monthly revenue per leased product"
+    )
+
+    st.sidebar.subheader("Costs")
+    st.session_state.strategy_mfg_cost = st.sidebar.slider(
+        "Manufacturing Cost", 500, 2500, st.session_state.strategy_mfg_cost, step=50, format="%d NOK",
+        help="Cost to produce one unit"
+    )
+    st.session_state.strategy_logistics_cost = st.sidebar.slider(
+        "Logistics Cost", 50, 400, st.session_state.strategy_logistics_cost, step=10, format="%d NOK",
+        help="Cost between cycles (pickup + delivery)"
     )
-    st.session_state.max_cycles_override = st.sidebar.slider(
-        "Max Reuse Cycles", 1, 5, st.session_state.max_cycles_override
+    st.session_state.strategy_repair_cost = st.sidebar.slider(
+        "Repair Cost/Month", 10, 100, st.session_state.strategy_repair_cost, step=5, format="%d NOK",
+        help="Monthly degradation cost (wear & tear)"
     )
 
+    st.sidebar.subheader("Fleet & Operations")
+    st.session_state.strategy_fleet_size = st.sidebar.slider(
+        "Fleet Size", 100, 10000, st.session_state.strategy_fleet_size, step=100,
+        help="Total products in fleet"
+    )
+    st.session_state.strategy_utilization = st.sidebar.slider(
+        "Utilization Rate", 50, 100, st.session_state.strategy_utilization, format="%d%%",
+        help="Percentage of fleet currently leased out"
+    )
+    st.session_state.strategy_cycle_length = st.sidebar.slider(
+        "Cycle Length", 12, 60, st.session_state.strategy_cycle_length, step=6, format="%d months",
+        help="Duration of one lease cycle"
+    )
+
+    # Keep EUR values in sync for other views (convert NOK back to EUR)
+    st.session_state.lease_price = st.session_state.strategy_lease_price // 10
+    st.session_state.mfg_cost = st.session_state.strategy_mfg_cost // 10
+
 elif selected_view == "Design":
     st.sidebar.header("🎨 Design Controls")
     refurb_pct = st.sidebar.slider(
@@ -216,6 +270,68 @@ elif operating_margin >= 0:
 else:
     model_status, status_color = "Loss", "#e74c3c"
 
+
+# ============================================================
+# STRATEGY: EARNINGS CALCULATION FUNCTION
+# ============================================================
+def calculate_cumulative_earnings(
+    fleet_size: int,
+    utilization_rate: float,
+    lease_price: float,
+    mfg_cost: float,
+    logistics_cost: float,
+    repair_cost_per_month: float,
+    cycle_length_months: int,
+    max_months: int = 120,
+) -> pd.DataFrame:
+    """
+    Calculate cumulative earnings over time for a circular lease model.
+
+    Returns DataFrame with columns: month, cumulative_profit, cycle_transitions
+    """
+    products_in_use = fleet_size * utilization_rate
+
+    # Initial investment (manufacturing cost for entire fleet)
+    initial_investment = fleet_size * mfg_cost
+
+    data = []
+    cumulative_profit = -initial_investment  # Start negative (investment)
+    cycle_transitions = 0
+
+    for month in range(max_months + 1):
+        if month == 0:
+            data.append({
+                "month": month,
+                "cumulative_profit": cumulative_profit,
+                "is_cycle_transition": False,
+            })
+            continue
+
+        # Monthly revenue from products in use
+        monthly_revenue = products_in_use * lease_price
+
+        # Monthly degradation/repair cost
+        monthly_degradation = products_in_use * repair_cost_per_month
+
+        # Check for cycle transition (every cycle_length_months)
+        is_transition = (month % cycle_length_months == 0) and (month > 0)
+        transition_cost = 0
+        if is_transition:
+            # Logistics cost for the entire fleet at cycle end
+            transition_cost = fleet_size * logistics_cost
+            cycle_transitions += 1
+
+        # Update cumulative profit
+        cumulative_profit += monthly_revenue - monthly_degradation - transition_cost
+
+        data.append({
+            "month": month,
+            "cumulative_profit": cumulative_profit,
+            "is_cycle_transition": is_transition,
+        })
+
+    return pd.DataFrame(data)
+
 # ============================================================
 # SHARED PRODUCT HEADER (compact)
 # ============================================================
@@ -275,56 +391,218 @@ st.markdown("---")
 # ============================================================
 if selected_view == "Strategy":
     st.header("Strategy: Business Model at Scale")
-    st.markdown("*For executives, investors, and board members*")
-
-    # Hero metrics
-    fleet_5k = 5000
-    customers_5k = fleet_5k * max_cycles
-    circ_cost_5k = fleet_5k * new_unit_cost + fleet_5k * (max_cycles - 1) * refurb_unit_cost
-    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
+    st.markdown("*For executives, investors, and board members — All values in NOK*")
+
+    # Get Strategy slider values
+    s_fleet_size = st.session_state.strategy_fleet_size
+    s_utilization = st.session_state.strategy_utilization / 100  # Convert to decimal
+    s_lease_price = st.session_state.strategy_lease_price
+    s_mfg_cost = st.session_state.strategy_mfg_cost
+    s_logistics = st.session_state.strategy_logistics_cost
+    s_repair_cost = st.session_state.strategy_repair_cost
+    s_cycle_length = st.session_state.strategy_cycle_length
+    s_linear_price = st.session_state.strategy_linear_price
+
+    # ============================================================
+    # EARNINGS OVER TIME CHART
+    # ============================================================
+    st.subheader("Cumulative Earnings Over Time")
+
+    # Calculate current scenario
+    max_display_months = 120
+    current_earnings = calculate_cumulative_earnings(
+        fleet_size=s_fleet_size,
+        utilization_rate=s_utilization,
+        lease_price=s_lease_price,
+        mfg_cost=s_mfg_cost,
+        logistics_cost=s_logistics,
+        repair_cost_per_month=s_repair_cost,
+        cycle_length_months=s_cycle_length,
+        max_months=max_display_months,
+    )
+
+    # Find breakeven month
+    breakeven_month = None
+    for idx, row in current_earnings.iterrows():
+        if row["cumulative_profit"] >= 0 and idx > 0:
+            breakeven_month = row["month"]
+            break
+
+    # Create the chart
+    fig_earnings = go.Figure()
+
+    # Add saved scenarios first (so current is on top)
+    scenario_colors = ["#9b59b6", "#e67e22", "#1abc9c", "#e74c3c", "#3498db"]
+    for i, scenario in enumerate(st.session_state.saved_scenarios):
+        color = scenario_colors[i % len(scenario_colors)]
+        fig_earnings.add_trace(go.Scatter(
+            x=scenario["data"]["month"],
+            y=scenario["data"]["cumulative_profit"],
+            mode="lines",
+            name=scenario["name"],
+            line=dict(color=color, width=2, dash="dot"),
+            opacity=0.7,
+        ))
+
+    # Add current scenario
+    fig_earnings.add_trace(go.Scatter(
+        x=current_earnings["month"],
+        y=current_earnings["cumulative_profit"],
+        mode="lines",
+        name="Current Scenario",
+        line=dict(color="#2ecc71", width=3),
+    ))
+
+    # Add breakeven marker
+    if breakeven_month:
+        breakeven_profit = current_earnings[current_earnings["month"] == breakeven_month]["cumulative_profit"].values[0]
+        fig_earnings.add_trace(go.Scatter(
+            x=[breakeven_month],
+            y=[breakeven_profit],
+            mode="markers+text",
+            name=f"Breakeven (Month {breakeven_month})",
+            marker=dict(size=12, color="#f1c40f", symbol="star"),
+            text=[f"Month {breakeven_month}"],
+            textposition="top center",
+        ))
+
+    # Add cycle transition markers (vertical dashed lines)
+    for month in range(s_cycle_length, max_display_months + 1, s_cycle_length):
+        fig_earnings.add_vline(
+            x=month, line_dash="dash", line_color="rgba(255,255,255,0.3)",
+            annotation_text=f"Cycle {month // s_cycle_length}" if month <= s_cycle_length * 3 else None,
+            annotation_position="top",
+        )
+
+    # Add zero line (breakeven threshold)
+    fig_earnings.add_hline(y=0, line_color="rgba(255,255,255,0.5)", line_width=1)
+
+    fig_earnings.update_layout(
+        xaxis_title="Months",
+        yaxis_title="Cumulative Profit (NOK)",
+        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="left", x=0),
+        hovermode="x unified",
+    )
+
+    st.plotly_chart(fig_earnings, use_container_width=True)
+
+    # Save/Clear scenario buttons
+    btn_col1, btn_col2, btn_col3 = st.columns([1, 1, 3])
+    with btn_col1:
+        if st.button("💾 Save Current Scenario", use_container_width=True):
+            scenario_num = len(st.session_state.saved_scenarios) + 1
+            st.session_state.saved_scenarios.append({
+                "name": f"Scenario {scenario_num}",
+                "params": {
+                    "fleet_size": s_fleet_size,
+                    "utilization": s_utilization,
+                    "lease_price": s_lease_price,
+                    "mfg_cost": s_mfg_cost,
+                    "logistics": s_logistics,
+                    "repair_cost": s_repair_cost,
+                    "cycle_length": s_cycle_length,
+                },
+                "data": current_earnings.to_dict(),
+            })
+            st.rerun()
+    with btn_col2:
+        if st.button("🗑️ Clear All Scenarios", use_container_width=True):
+            st.session_state.saved_scenarios = []
+            st.rerun()
+
+    st.markdown("---")
+
+    # ============================================================
+    # HERO METRICS (updated for new model)
+    # ============================================================
+    # Calculate key metrics
+    products_in_use = s_fleet_size * s_utilization
+    monthly_net_revenue = products_in_use * (s_lease_price - s_repair_cost)
+    annual_profit = monthly_net_revenue * 12 - (s_fleet_size * s_logistics)  # One cycle transition per year approx
+
+    # Linear model comparison: selling s_fleet_size products
+    linear_profit = s_fleet_size * (s_linear_price - s_mfg_cost)
+
+    # Minimum lifespan calculation (months to breakeven)
+    min_lifespan = breakeven_month if breakeven_month else "> 120"
 
     hero_cols = st.columns(4)
     with hero_cols[0]:
-        st.metric("Profit Advantage", f"€{profit_advantage_5k:,.0f}", delta="vs linear model")
+        st.metric(
+            "Breakeven",
+            f"Month {breakeven_month}" if breakeven_month else "Not reached",
+            delta="profitable after" if breakeven_month else "needs adjustment",
+            help="When cumulative profit crosses zero"
+        )
     with hero_cols[1]:
-        st.metric("Products Saved", f"{products_saved_5k:,}", delta=f"at {fleet_5k:,} fleet")
+        st.metric(
+            "Monthly Net Revenue",
+            f"{monthly_net_revenue:,.0f} NOK",
+            delta=f"{products_in_use:,.0f} products in use",
+            help="(Lease price - repair cost) × products in use"
+        )
     with hero_cols[2]:
-        st.metric("CO2 Avoided", f"{co2_saved_5k:,.0f} kg", delta="fewer manufactured")
+        profit_at_120 = current_earnings[current_earnings["month"] == 120]["cumulative_profit"].values[0]
+        st.metric(
+            "Profit at Year 10",
+            f"{profit_at_120:,.0f} NOK",
+            delta="positive" if profit_at_120 > 0 else "loss",
+            help="Cumulative profit after 120 months"
+        )
     with hero_cols[3]:
-        st.metric("Customers Served", f"{customers_5k:,}", delta=f"{max_cycles} cycles")
+        # Compare circular vs linear (10-year horizon)
+        circular_10yr = profit_at_120
+        linear_10yr = linear_profit  # One-time sale
+        advantage = circular_10yr - linear_10yr
+        st.metric(
+            "vs Linear (10yr)",
+            f"{advantage:+,.0f} NOK",
+            delta="circular wins" if advantage > 0 else "linear wins",
+            help="Circular 10yr profit minus one-time linear sale profit"
+        )
 
     st.markdown("---")
 
     # Fleet Scale Economics
     st.subheader("Fleet Scale Economics: Volume Advantage")
 
+    # Calculate 10-year profit for different fleet sizes using the new model
     fleet_sizes = [500, 1000, 2000, 5000]
     scale_data = []
+    co2_per_unit = config["environmental"]["co2_manufacturing_kg"]
+
     for fleet in fleet_sizes:
-        customers_served = fleet * max_cycles
-        circular_total_cost = fleet * new_unit_cost + fleet * (max_cycles - 1) * refurb_unit_cost
-        circular_revenue = customers_served * revenue_per_customer
-        circular_profit = circular_revenue - circular_total_cost
-        linear_cost = customers_served * new_unit_cost
-        linear_revenue = customers_served * revenue_per_customer
-        linear_profit = linear_revenue - linear_cost
+        # Calculate 10-year (120 month) earnings for this fleet size
+        earnings_df = calculate_cumulative_earnings(
+            fleet_size=fleet,
+            utilization_rate=s_utilization,
+            lease_price=s_lease_price,
+            mfg_cost=s_mfg_cost,
+            logistics_cost=s_logistics,
+            repair_cost_per_month=s_repair_cost,
+            cycle_length_months=s_cycle_length,
+            max_months=120,
+        )
+        circular_profit_10yr = earnings_df[earnings_df["month"] == 120]["cumulative_profit"].values[0]
+
+        # Linear model: one-time sale
+        linear_profit = fleet * (s_linear_price - s_mfg_cost)
+
+        # Number of cycles in 10 years
+        num_cycles = 120 // s_cycle_length
+        customers_served = fleet * num_cycles
+
+        # Products saved (circular reuses, linear needs one per customer)
         products_saved = customers_served - fleet
         co2_saved = products_saved * co2_per_unit
-        profit_advantage = circular_profit - linear_profit
 
         scale_data.append({
             "Fleet Size": fleet,
             "Fleet Label": f"{fleet:,}",
             "Customers Served": customers_served,
-            "Circular Profit": circular_profit,
+            "Circular Profit (10yr)": circular_profit_10yr,
             "Linear Profit": linear_profit,
-            "Profit Advantage": profit_advantage,
+            "Profit Advantage": circular_profit_10yr - linear_profit,
             "Products Saved": products_saved,
             "CO2 Saved (kg)": co2_saved,
             "Linear Products Needed": customers_served,
@@ -335,22 +613,20 @@ if selected_view == "Strategy":
     scale_col1, scale_col2 = st.columns(2)
 
     with scale_col1:
-        # 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"]],
+            marker_color=["#2ecc71" if v > 0 else "#e74c3c" for v in scale_df["Profit Advantage"]],
+            text=[f"{v:,.0f} NOK" for v in scale_df["Profit Advantage"]],
             textposition="outside",
         ))
         fig_advantage.update_layout(
-            title="Profit Advantage: Circular vs Linear",
+            title="10-Year Profit Advantage: Circular vs Linear",
             xaxis_title="Fleet Size",
-            yaxis_title="Extra Profit from Circular Model (EUR)",
+            yaxis_title="Extra Profit from Circular Model (NOK)",
             showlegend=False,
-            yaxis=dict(rangemode='tozero'),
         )
         st.plotly_chart(fig_advantage, use_container_width=True)
 
@@ -367,41 +643,86 @@ if selected_view == "Strategy":
             text=[f"{v:,}" for v in scale_df["Linear Products Needed"]], textposition="outside",
         ))
         fig_products.update_layout(
-            title="Products Needed to Serve Same Customers",
+            title="Products Needed to Serve Same Customers (10yr)",
             xaxis_title="Fleet Size Target", yaxis_title="Products to Manufacture",
             barmode="group", legend=dict(orientation="h", yanchor="bottom", y=1.02),
             yaxis=dict(rangemode='tozero'),
         )
         st.plotly_chart(fig_products, use_container_width=True)
 
-    # Business case summary
-    key_data = scale_df[scale_df["Fleet Size"] == 5000].iloc[0]
+    # Business case summary using current fleet size
+    current_fleet_data = None
+    for row in scale_data:
+        if row["Fleet Size"] == s_fleet_size or row == scale_data[-1]:
+            current_fleet_data = row
+            if row["Fleet Size"] == s_fleet_size:
+                break
+
+    # If current fleet size not in list, calculate it
+    if current_fleet_data["Fleet Size"] != s_fleet_size:
+        current_10yr = current_earnings[current_earnings["month"] == 120]["cumulative_profit"].values[0]
+        num_cycles = 120 // s_cycle_length
+        customers_served = s_fleet_size * num_cycles
+        products_saved = customers_served - s_fleet_size
+        current_fleet_data = {
+            "Fleet Size": s_fleet_size,
+            "Customers Served": customers_served,
+            "Circular Profit (10yr)": current_10yr,
+            "Linear Profit": s_fleet_size * (s_linear_price - s_mfg_cost),
+            "Profit Advantage": current_10yr - s_fleet_size * (s_linear_price - s_mfg_cost),
+            "Products Saved": products_saved,
+            "CO2 Saved (kg)": products_saved * co2_per_unit,
+        }
+
     st.success(f"""
-    **The Business Case:** With **{5000:,} products** in our fleet, we serve **{key_data['Customers Served']:,} families**
-    and generate **€{key_data['Profit Advantage']:,.0f} more profit** than a linear model.
+    **The Business Case:** With **{s_fleet_size:,} products** in our fleet at **{s_utilization*100:.0f}% utilization**,
+    we serve **{current_fleet_data['Customers Served']:,} families** over 10 years.
 
-    A linear model would need **{key_data['Linear Products Needed']:,} products** to serve the same customers.
+    Circular model generates **{current_fleet_data['Profit Advantage']:+,.0f} NOK** compared to linear model.
 
-    Environmental impact: **{key_data['CO2 Saved (kg)']:,.0f} kg CO2 saved**.
+    Environmental impact: **{current_fleet_data['CO2 Saved (kg)']:,.0f} kg CO2 saved** ({current_fleet_data['Products Saved']:,} fewer products manufactured).
     """)
 
     st.markdown("---")
 
     # Cycle Sensitivity
-    st.subheader("Sensitivity: Impact of Reuse Cycles")
+    st.subheader("Sensitivity: Impact of Cycle Length")
 
+    # Test different cycle lengths
+    cycle_lengths = [12, 18, 24, 36, 48, 60]
     sensitivity_data = []
-    for test_cycles in range(1, 6):
-        circ_cost = new_unit_cost + (test_cycles - 1) * refurb_unit_cost
-        circ_revenue = test_cycles * revenue_per_customer
-        circ_profit = circ_revenue - circ_cost
-        lin_cost = test_cycles * new_unit_cost
-        lin_profit = test_cycles * revenue_per_customer - lin_cost
+
+    for test_cycle in cycle_lengths:
+        # Calculate 10-year profit for this cycle length
+        earnings_df = calculate_cumulative_earnings(
+            fleet_size=s_fleet_size,
+            utilization_rate=s_utilization,
+            lease_price=s_lease_price,
+            mfg_cost=s_mfg_cost,
+            logistics_cost=s_logistics,
+            repair_cost_per_month=s_repair_cost,
+            cycle_length_months=test_cycle,
+            max_months=120,
+        )
+        profit_10yr = earnings_df[earnings_df["month"] == 120]["cumulative_profit"].values[0]
+
+        # Find breakeven
+        be_month = None
+        for idx, row in earnings_df.iterrows():
+            if row["cumulative_profit"] >= 0 and idx > 0:
+                be_month = row["month"]
+                break
+
+        num_cycles = 120 // test_cycle
+        num_transitions = num_cycles  # Logistics cost at each cycle end
 
         sensitivity_data.append({
-            "Cycles": test_cycles,
-            "Profit Advantage": circ_profit - lin_profit,
-            "Cost Saved": lin_cost - circ_cost,
+            "Cycle Length": f"{test_cycle}mo",
+            "Cycle Months": test_cycle,
+            "10yr Profit": profit_10yr,
+            "Breakeven Month": be_month if be_month else 120,
+            "Num Cycles": num_cycles,
+            "Total Logistics": num_transitions * s_fleet_size * s_logistics,
         })
 
     sens_df = pd.DataFrame(sensitivity_data)
@@ -411,44 +732,67 @@ if selected_view == "Strategy":
     with sens_col1:
         fig_sens = go.Figure()
         fig_sens.add_trace(go.Bar(
-            x=sens_df["Cycles"], y=sens_df["Profit Advantage"],
-            marker_color=["#e74c3c" if x <= 0 else "#2ecc71" for x in sens_df["Profit Advantage"]],
-            text=[f"€{v:,.0f}" for v in sens_df["Profit Advantage"]], textposition="outside",
+            x=sens_df["Cycle Length"], y=sens_df["10yr Profit"],
+            marker_color=["#2ecc71" if x > 0 else "#e74c3c" for x in sens_df["10yr Profit"]],
+            text=[f"{v:,.0f} NOK" for v in sens_df["10yr Profit"]], textposition="outside",
         ))
         fig_sens.update_layout(
-            title="Profit Advantage by # of Cycles",
-            xaxis_title="Number of Use Cycles", yaxis_title="Profit Advantage (EUR)",
-            yaxis=dict(rangemode='tozero'),
+            title="10-Year Profit by Cycle Length",
+            xaxis_title="Cycle Length", yaxis_title="Cumulative Profit (NOK)",
         )
-        fig_sens.add_vline(x=max_cycles, line_dash="dash", line_color="blue",
-                          annotation_text=f"Current: {max_cycles}")
+        # Mark current cycle length
+        current_cycle_label = f"{s_cycle_length}mo"
+        if current_cycle_label in sens_df["Cycle Length"].values:
+            fig_sens.add_vline(
+                x=sens_df[sens_df["Cycle Length"] == current_cycle_label].index[0],
+                line_dash="dash", line_color="#f1c40f",
+                annotation_text=f"Current: {s_cycle_length}mo"
+            )
         st.plotly_chart(fig_sens, use_container_width=True)
 
     with sens_col2:
-        fig_cost = go.Figure()
-        fig_cost.add_trace(go.Bar(
-            x=sens_df["Cycles"], y=sens_df["Cost Saved"],
+        fig_be = go.Figure()
+        fig_be.add_trace(go.Bar(
+            x=sens_df["Cycle Length"], y=sens_df["Breakeven Month"],
             marker_color="#3498db",
-            text=[f"€{v:,.0f}" for v in sens_df["Cost Saved"]], textposition="outside",
+            text=[f"{v:.0f}mo" for v in sens_df["Breakeven Month"]], textposition="outside",
         ))
-        fig_cost.update_layout(
-            title="Cost Savings from Reuse",
-            xaxis_title="Number of Use Cycles", yaxis_title="Cost Saved vs Linear (EUR)",
+        fig_be.update_layout(
+            title="Breakeven Month by Cycle Length",
+            xaxis_title="Cycle Length", yaxis_title="Months to Breakeven",
             yaxis=dict(rangemode='tozero'),
         )
-        st.plotly_chart(fig_cost, use_container_width=True)
+        st.plotly_chart(fig_be, use_container_width=True)
 
     # Summary table
     with st.expander("Full Scale Comparison Table"):
-        display_df = scale_df[["Fleet Label", "Customers Served", "Circular Profit", "Linear Profit",
+        display_df = scale_df[["Fleet Label", "Customers Served", "Circular Profit (10yr)", "Linear Profit",
                                "Profit Advantage", "Products Saved", "CO2 Saved (kg)"]].copy()
-        display_df.columns = ["Fleet", "Customers", "Circular Profit", "Linear Profit",
+        display_df.columns = ["Fleet", "Customers (10yr)", "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",
+            "Customers (10yr)": "{:,}", "Circular Profit": "{:,.0f} NOK", "Linear Profit": "{:,.0f} NOK",
+            "Advantage": "{:+,.0f} NOK", "Products Saved": "{:,}", "CO2 Saved": "{:,.0f} kg",
         }), use_container_width=True, hide_index=True)
 
+    # Model parameters summary
+    with st.expander("Current Model Parameters"):
+        params_col1, params_col2, params_col3 = st.columns(3)
+        with params_col1:
+            st.markdown("**Pricing**")
+            st.write(f"- Linear Price: {s_linear_price:,} NOK")
+            st.write(f"- Monthly Lease: {s_lease_price:,} NOK")
+        with params_col2:
+            st.markdown("**Costs**")
+            st.write(f"- Manufacturing: {s_mfg_cost:,} NOK")
+            st.write(f"- Logistics/cycle: {s_logistics:,} NOK")
+            st.write(f"- Repair/month: {s_repair_cost:,} NOK")
+        with params_col3:
+            st.markdown("**Operations**")
+            st.write(f"- Fleet Size: {s_fleet_size:,}")
+            st.write(f"- Utilization: {s_utilization*100:.0f}%")
+            st.write(f"- Cycle Length: {s_cycle_length} months")
+
 # ============================================================
 # VIEW: DESIGN
 # ============================================================