# ============================================ # ライブラリの読み込み # ============================================ import numpy as np # 数学計算用 import pandas as pd # データ処理用 import matplotlib.pyplot as plt # グラフ描画用 # 日本語フォント設定（グラフに日本語を表示するため） plt.rcParams[‘font.sans-serif’] = [‘Arial Unicode MS’, ‘Yu Gothic’, ‘Hiragino Sans’] plt.rcParams[‘axes.unicode_minus’] = False # ============================================ # 基準ケース（ベースライン）の設定 # ============================================ # 何と比較するかの「基準」を決める price_base = 1000 # 価格（円） cost_base = 600 # 原価（円） quantity_base = 10000 # 販売数量（個） fixed_cost_base = 2000000 # 固定費（円） # ============================================ # 利益を計算する関数 # ============================================ def calculate_profit(price, cost, quantity, fixed_cost): “”” 利益を計算する関数 利益 = 売上 – 変動費 – 固定費 = (価格 × 数量) – (原価 × 数量) – 固定費 = (価格 – 原価) × 数量 – 固定費 “”” revenue = price * quantity # 売上 variable_cost = cost * quantity # 変動費 profit = revenue – variable_cost – fixed_cost # 利益 return profit # 基準ケースの利益を計算 profit_base = calculate_profit(price_base, cost_base, quantity_base, fixed_cost_base) print(“【基準ケース（ベースライン）】”) print(f”価格: {price_base:,}円”) print(f”原価: {cost_base:,}円”) print(f”販売数量: {quantity_base:,}個”) print(f”固定費: {fixed_cost_base:,}円”) print() print(f”売上: {price_base * quantity_base:,}円”) print(f”変動費: {cost_base * quantity_base:,}円”) print(f”利益: {profit_base:,}円”)

# ============================================ # 価格の感度分析 # ============================================ # 価格を-30%〜+30%まで変化させて、利益がどう変わるか調べる print(“【価格の感度分析】”) print() # np.arange(-30, 35, 5): -30から30まで5刻みのリストを作成 # 結果: [-30, -25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25, 30] price_changes = np.arange(-30, 35, 5) # 結果を格納するリスト price_results = [] # 各変化率で利益を計算 for change in price_changes: # 新しい価格を計算 # 例: change=10なら、price = 1000 × (1 + 10/100) = 1100円 price = price_base * (1 + change/100) # 利益を計算（他の変数は固定） profit = calculate_profit(price, cost_base, quantity_base, fixed_cost_base) # 利益の変化率を計算 # 例: 利益が300万になったら、(300-200)/200 × 100 = 50%増 profit_change = (profit – profit_base) / profit_base * 100 # 結果を保存 price_results.append({ ‘価格変化率(%)’: change, ‘価格(円)’: price, ‘利益(円)’: profit, ‘利益変化率(%)’: profit_change }) # DataFrameに変換して表示 df_price = pd.DataFrame(price_results) print(df_price.to_string(index=False))

# ============================================ # 販売数量の感度分析 # ============================================ quantity_changes = np.arange(-30, 35, 5) quantity_results = [] for change in quantity_changes: quantity = quantity_base * (1 + change/100) profit = calculate_profit(price_base, cost_base, quantity, fixed_cost_base) profit_change = (profit – profit_base) / profit_base * 100 quantity_results.append({ ‘数量変化率(%)’: change, ‘利益変化率(%)’: profit_change }) df_quantity = pd.DataFrame(quantity_results) # ============================================ # 原価の感度分析 # ============================================ cost_changes = np.arange(-30, 35, 5) cost_results = [] for change in cost_changes: cost = cost_base * (1 + change/100) profit = calculate_profit(price_base, cost, quantity_base, fixed_cost_base) profit_change = (profit – profit_base) / profit_base * 100 cost_results.append({ ‘原価変化率(%)’: change, ‘利益変化率(%)’: profit_change }) df_cost = pd.DataFrame(cost_results) # ============================================ # 3つの変数を比較（10%変化時） # ============================================ print(“【感度の比較（各変数が10%変化した場合）】”) print() # 価格が10%上昇 price_10 = calculate_profit(price_base * 1.1, cost_base, quantity_base, fixed_cost_base) price_impact = (price_10 – profit_base) / profit_base * 100 # 販売数量が10%増加 quantity_10 = calculate_profit(price_base, cost_base, quantity_base * 1.1, fixed_cost_base) quantity_impact = (quantity_10 – profit_base) / profit_base * 100 # 原価が10%上昇（利益にはマイナスの影響） cost_10 = calculate_profit(price_base, cost_base * 1.1, quantity_base, fixed_cost_base) cost_impact = (cost_10 – profit_base) / profit_base * 100 print(f”価格が10%上昇 → 利益 {price_impact:+.1f}%”) print(f”販売数量が10%増加 → 利益 {quantity_impact:+.1f}%”) print(f”原価が10%上昇 → 利益 {cost_impact:+.1f}%”) print() # 影響度ランキング print(“【影響度ランキング】”) print(f”1位: 価格 → {abs(price_impact):.1f}%の影響”) print(f”2位: 原価 → {abs(cost_impact):.1f}%の影響”) print(f”3位: 販売数量 → {abs(quantity_impact):.1f}%の影響”)

# ============================================ # シナリオの定義 # ============================================ # 辞書形式で各シナリオの変化率を定義 scenarios = { ‘楽観シナリオ’: { ‘price_change’: 0.10, # 価格+10% ‘quantity_change’: 0.20, # 数量+20% ‘cost_change’: -0.10, # 原価-10% ‘fixed_cost_change’: -0.05 # 固定費-5% }, ‘標準シナリオ’: { ‘price_change’: 0.00, # 変化なし ‘quantity_change’: 0.00, ‘cost_change’: 0.00, ‘fixed_cost_change’: 0.00 }, ‘悲観シナリオ’: { ‘price_change’: -0.10, # 価格-10% ‘quantity_change’: -0.20, # 数量-20% ‘cost_change’: 0.10, # 原価+10% ‘fixed_cost_change’: 0.05 # 固定費+5% } } print(“【シナリオの設定】”) print() for name, changes in scenarios.items(): print(f”{name}:”) print(f” 価格: {changes[‘price_change’]:+.0%}”) print(f” 販売数量: {changes[‘quantity_change’]:+.0%}”) print(f” 原価: {changes[‘cost_change’]:+.0%}”) print(f” 固定費: {changes[‘fixed_cost_change’]:+.0%}”) print()

# ============================================ # 各シナリオの利益計算 # ============================================ scenario_results = [] for scenario_name, changes in scenarios.items(): # 変化後のパラメータを計算 # (1 + 変化率) を掛けて新しい値を求める price = price_base * (1 + changes[‘price_change’]) quantity = quantity_base * (1 + changes[‘quantity_change’]) cost = cost_base * (1 + changes[‘cost_change’]) fixed_cost = fixed_cost_base * (1 + changes[‘fixed_cost_change’]) # 各種指標を計算 revenue = price * quantity # 売上 variable_cost = cost * quantity # 変動費 contribution = revenue – variable_cost # 貢献利益 profit = contribution – fixed_cost # 利益 # 結果を保存 scenario_results.append({ ‘シナリオ’: scenario_name, ‘売上’: revenue, ‘変動費’: variable_cost, ‘貢献利益’: contribution, ‘固定費’: fixed_cost, ‘利益’: profit }) # 結果を表示 print(“【シナリオ分析結果】”) print(“=” * 60) for result in scenario_results: print() print(f”■ {result[‘シナリオ’]}”) print(“-” * 60) print(f”売上: {result[‘売上’]:>12,.0f}円”) print(f”変動費: {result[‘変動費’]:>12,.0f}円”) print(f”貢献利益: {result[‘貢献利益’]:>12,.0f}円”) print(f”固定費: {result[‘固定費’]:>12,.0f}円”) print(f”利益: {result[‘利益’]:>12,.0f}円”) print() print(“=” * 60)

# ============================================ # 結果の比較と評価 # ============================================ # 各シナリオの利益を取得 best_profit = scenario_results[0][‘利益’] # 楽観 base_profit = scenario_results[1][‘利益’] # 標準 worst_profit = scenario_results[2][‘利益’] # 悲観 print(“【利益の比較】”) print() print(f”楽観シナリオ: {best_profit:>12,.0f}円 ({(best_profit/base_profit-1)*100:+.1f}%)”) print(f”標準シナリオ: {base_profit:>12,.0f}円 (基準)”) print(f”悲観シナリオ: {worst_profit:>12,.0f}円 ({(worst_profit/base_profit-1)*100:+.1f}%)”) print() print(f”利益の振れ幅: {best_profit – worst_profit:,.0f}円”) print() # リスク評価 print(“【リスク評価】”) print() if worst_profit > 0: print(“✅ 悲観シナリオでも黒字”) print(“→ この計画はリスク許容範囲内”) else: print(“⚠️ 悲観シナリオでは赤字”) print(f” 赤字額: {abs(worst_profit):,.0f}円”) print(“→ リスク対策の検討が必要”)