{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "code", "source": [ "import numpy as np\n", "import pandas as pd\n", "import itertools\n", "from collections import defaultdict\n", "\n", "# --- Configuration & Foundational Data ---\n", "N_SCENARIOS = 5000\n", "ACTUAL_GPU_SUPPLY_2026 = 185000\n", "GPUS_PER_SERVER = 1.2\n", "MAX_SERVERS_2026 = int(ACTUAL_GPU_SUPPLY_2026 / GPUS_PER_SERVER)\n", "\n", "# Financial & Operational Data\n", "GPU_BENCHMARK_COST = 30000\n", "OTHER_COMPONENTS_COST = 9840\n", "SERVER_REVENUE = 49090.91\n", "PLANT_FC = {'US': 7800e3, 'Mexico': 7200e3, 'Malaysia': 6900e3}\n", "SHIPPING_LANE_FC = {\n", " ('US', 'NA'): 450e3, ('US', 'APAC'): 850e3, ('US', 'EU'): 650e3,\n", " ('Mexico', 'NA'): 500e3, ('Mexico', 'APAC'): 900e3, ('Mexico', 'EU'): 700e3,\n", " ('Malaysia', 'NA'): 700e3, ('Malaysia', 'APAC'): 500e3, ('Malaysia', 'EU'): 600e3,\n", "}\n", "LOGISTICS_VC = {'Ocean': 180, 'Air': 620}\n", "CO2_EMISSIONS = {'Ocean': 50, 'Air': 310} # In kg per unit\n", "\n", "# Simulation Assumptions\n", "DEMAND_BASELINE = 215000\n", "DEMAND_VOLATILITY = 0.20\n", "CARBON_PRICE_RANGE = (50, 150)\n", "DEMAND_MIX = {'NA': 0.45, 'APAC': 0.35, 'EU': 0.20}\n", "\n", "class StrategicDecisionEngine:\n", " \"\"\"A complete engine for strategic simulation, feasibility analysis, and decision support.\"\"\"\n", "\n", " def __init__(self, decision_inputs):\n", " self.inputs = decision_inputs\n", " self.all_strategy_results = []\n", " self.feasible_strategies = []\n", " self.infeasible_strategies = []\n", "\n", " self.hard_constraints = self.inputs['hard_constraints']\n", " self.soft_constraints = self.inputs['soft_constraints']\n", " self.weights = self.inputs['objective_weights']\n", "\n", " def _evaluate_single_strategy(self, strategy_def):\n", " \"\"\"Runs a full Monte Carlo simulation for one strategy.\"\"\"\n", " plant_map = strategy_def['plant_map']\n", " logistics_mix = strategy_def['logistics_mix']\n", " fixed_cost = sum(PLANT_FC[plant] for plant in set(plant_map.values()))\n", " for region, plant in plant_map.items():\n", " fixed_cost += SHIPPING_LANE_FC.get((plant, region), 0)\n", "\n", " total_bom_vc = (GPUS_PER_SERVER * GPU_BENCHMARK_COST) + OTHER_COMPONENTS_COST\n", " avg_logistics_vc = (logistics_mix['Ocean'] * LOGISTICS_VC['Ocean']) + (logistics_mix['Air'] * LOGISTICS_VC['Air'])\n", " avg_co2_kg = (logistics_mix['Ocean'] * CO2_EMISSIONS['Ocean']) + (logistics_mix['Air'] * CO2_EMISSIONS['Air'])\n", "\n", " scenarios = []\n", " for _ in range(N_SCENARIOS):\n", " demand = np.random.normal(DEMAND_BASELINE, DEMAND_VOLATILITY)\n", " carbon_price = np.random.uniform(CARBON_PRICE_RANGE[0], CARBON_PRICE_RANGE[1])\n", " units_sold = min(demand, MAX_SERVERS_2026)\n", "\n", " carbon_cost_per_unit = (avg_co2_kg / 1000) * carbon_price # kg -> tons for price\n", " total_vc_per_unit = total_bom_vc + avg_logistics_vc + carbon_cost_per_unit\n", " total_cost = fixed_cost + (units_sold * total_vc_per_unit)\n", "\n", " profit = (units_sold * SERVER_REVENUE) - total_cost\n", "\n", " # *** BUG FIX IS HERE ***\n", " # Convert total kg to thousands of tons (k-tons) by dividing by 1 million\n", " co2_k_tons = (units_sold * avg_co2_kg) / 1_000_000\n", "\n", " scenarios.append({'profit': profit, 'co2_k_tons': co2_k_tons, 'units_sold': units_sold})\n", "\n", " scenario_df = pd.DataFrame(scenarios)\n", " num_unique_plants = len(set(plant_map.values()))\n", " resilience_score = {1: 10, 2: 60, 3: 100}.get(num_unique_plants, 0)\n", "\n", " return {\n", " \"definition\": strategy_def,\n", " \"results_df\": scenario_df,\n", " \"metrics\": {\n", " \"profit\": scenario_df['profit'].mean(),\n", " \"growth\": scenario_df['units_sold'].mean(),\n", " \"sustainability\": scenario_df['co2_k_tons'].mean(),\n", " \"resilience\": resilience_score\n", " }\n", " }\n", "\n", " def _check_feasibility_and_penalties(self, result):\n", " \"\"\"Analyzes a strategy's results against hard and soft constraints.\"\"\"\n", " df = result['results_df']\n", " violations = {\n", " 'profit': (df['profit'] < self.hard_constraints['min_profit_M'] * 1e6).mean(),\n", " 'co2': (df['co2_k_tons'] > self.hard_constraints['max_co2_ktons']).mean(),\n", " 'ocean': (result['definition']['logistics_mix']['Ocean'] < self.hard_constraints['min_ocean_share']),\n", " 'resilience': (result['metrics']['resilience'] < self.hard_constraints['min_resilience_score'])\n", " }\n", " total_violation_prob = 1 - (1 - violations['profit']) * (1 - violations['co2'])\n", " is_feasible = (total_violation_prob < 0.05) and not violations['ocean'] and not violations['resilience']\n", "\n", " profit_shortfall = np.maximum(0, self.soft_constraints['target_profit_M'] * 1e6 - df['profit']).mean()\n", " co2_excess = np.maximum(0, df['co2_k_tons'] - self.soft_constraints['target_co2_ktons']).mean()\n", " profit_penalty, co2_penalty = profit_shortfall / 1e6, co2_excess / 0.1\n", " total_penalty = profit_penalty + co2_penalty\n", "\n", " result['feasibility'] = {'is_feasible': is_feasible, 'violation_probability': total_violation_prob, 'violations': violations}\n", " result['penalty_score'] = total_penalty\n", " return result\n", "\n", " def _score_strategies(self):\n", " \"\"\"Calculates a weighted Strategic Score for all feasible strategies.\"\"\"\n", " if not self.feasible_strategies: return\n", " min_vals = {key: min(s['metrics'][key] for s in self.feasible_strategies) for key in self.weights}\n", " max_vals = {key: max(s['metrics'][key] for s in self.feasible_strategies) for key in self.weights}\n", "\n", " for strat in self.feasible_strategies:\n", " total_score = 0\n", " for objective, weight in self.weights.items():\n", " val, range_val = strat['metrics'][objective], max_vals[objective] - min_vals[objective]\n", " norm_score = 100 if range_val == 0 else 100 * (val - min_vals[objective]) / range_val\n", " if objective == 'sustainability': norm_score = 100 - norm_score\n", " total_score += norm_score * (weight / 100.0)\n", " strat['strategic_score'] = total_score\n", " self.feasible_strategies.sort(key=lambda x: x['strategic_score'], reverse=True)\n", "\n", " def run(self):\n", " \"\"\"Executes the entire end-to-end analysis.\"\"\"\n", " plants, regions = ['US', 'Mexico', 'Malaysia'], ['NA', 'APAC', 'EU']\n", " all_plant_maps = [dict(zip(regions, p)) for p in itertools.product(plants, repeat=len(regions))]\n", " all_logistics_mixes = [{'Ocean': i/10, 'Air': (10-i)/10} for i in range(11)]\n", " strategy_definitions = [{'plant_map': p_map, 'logistics_mix': l_mix} for p_map in all_plant_maps for l_mix in all_logistics_mixes]\n", "\n", " print(f\"Analyzing {len(strategy_definitions)} potential strategies across {N_SCENARIOS} scenarios each...\")\n", " for i, strat_def in enumerate(strategy_definitions):\n", " print(f\" - Evaluating strategy {i+1}/{len(strategy_definitions)}...\", end='\\r')\n", " result = self._evaluate_single_strategy(strat_def)\n", " result = self._check_feasibility_and_penalties(result)\n", " self.all_strategy_results.append(result)\n", " if result['feasibility']['is_feasible']: self.feasible_strategies.append(result)\n", " else: self.infeasible_strategies.append(result)\n", "\n", " print(\"\\nAnalysis complete.\")\n", " self._score_strategies()\n", " self._display_report()\n", "\n", " def _display_report(self):\n", " \"\"\"Prints the final, formatted executive summary.\"\"\"\n", " print(\"\\n\" + \"=\"*80 + \"\\n** Executive Summary: Strategic Analysis for 2026 **\\n\" + \"=\"*80)\n", " if not self.feasible_strategies:\n", " print(\"\\n**Feasibility Check: No strategy was found that meets all hard constraints.**\\n\")\n", " self._display_relaxation_advice()\n", " else:\n", " prob = self.feasible_strategies[0]['feasibility']['violation_probability']\n", " print(f\"\\n**Feasibility Check: Found {len(self.feasible_strategies)} feasible strategies.**\")\n", " print(f\"The top-ranked strategy is feasible in {100*(1-prob):.1f}% of simulated scenarios.\\n\")\n", "\n", " print(\"-\" * 80 + \"\\n** Strategy Recommendations **\\n\" + \"-\" * 80)\n", "\n", " if self.feasible_strategies:\n", " self._print_strategy_card(\"Recommended Strategy (Highest Strategic Score)\", self.feasible_strategies[0])\n", " self._print_strategy_card(\"Most Profitable Strategy\", max(self.all_strategy_results, key=lambda x: x['metrics']['profit']), is_alt=True)\n", " self._print_strategy_card(\"Most Resilient Strategy\", max(self.all_strategy_results, key=lambda x: x['metrics']['resilience']), is_alt=True)\n", "\n", " if not self.feasible_strategies and self.infeasible_strategies:\n", " self._print_strategy_card(\"Least Infeasible Strategy (Best effort)\", min(self.infeasible_strategies, key=lambda x: x['penalty_score']), is_alt=True)\n", "\n", " def _print_strategy_card(self, title, strat, is_alt=False):\n", " \"\"\"Prints a formatted summary of a single strategy with a detailed operational plan.\"\"\"\n", " print(f\"\\n--- {title} ---\")\n", " if is_alt and not strat['feasibility']['is_feasible']:\n", " prob = strat['feasibility']['violation_probability']\n", " print(f\" **Warning:** This strategy is NOT feasible (violates constraints in {prob:.1%} of scenarios).\")\n", "\n", " metrics, definition = strat['metrics'], strat['definition']\n", " print(f\" - Profile: Expected Profit: ${metrics['profit']/1e6:,.1f}M | Expected CO2: {metrics['sustainability']:,.2f} k-tons | Resilience: {metrics['resilience']:.0f}/100\")\n", "\n", " print(\"\\n - Operational Plan:\")\n", " for region in ['NA', 'APAC', 'EU']:\n", " plant = definition['plant_map'][region]\n", " units = metrics['growth'] * DEMAND_MIX[region]\n", " logistics = definition['logistics_mix']\n", "\n", " print(f\" - {region} Market ({units:,.0f} units):\")\n", " print(f\" - Assemble In: '{plant}'\")\n", " print(f\" - Ship Via: {logistics['Ocean'] * 100:.0f}% Ocean, {logistics['Air'] * 100:.0f}% Air\")\n", "\n", " def _display_relaxation_advice(self):\n", " \"\"\"Provides actionable advice if no feasible solutions were found.\"\"\"\n", " print(\"-\" * 80 + \"\\n** Relaxation Engine: Advice to find a feasible solution **\\n\" + \"-\" * 80)\n", " best_infeasible = min(self.infeasible_strategies, key=lambda x: x['penalty_score'])\n", " violations = best_infeasible['feasibility']['violations']\n", "\n", " print(\"The primary reason for infeasibility is the combination of ambitious constraints.\")\n", " print(\"The 'least infeasible' strategy failed because:\")\n", " if violations['resilience']:\n", " print(f\" - The Resilience Score of {best_infeasible['metrics']['resilience']:.0f} did not meet the required minimum of {self.hard_constraints['min_resilience_score']:.0f}.\")\n", " if violations['profit'] > 0.05:\n", " profit_miss = self.hard_constraints['min_profit_M'] - (best_infeasible['metrics']['profit'] / 1e6)\n", " print(f\" - The Profit Constraint (${self.hard_constraints['min_profit_M']:.0f}M) was missed by ~${profit_miss:.1f}M on average.\")\n", " if violations['co2'] > 0.05:\n", " co2_excess = best_infeasible['metrics']['sustainability'] - self.hard_constraints['max_co2_ktons']\n", " print(f\" - The CO2 Constraint ({self.hard_constraints['max_co2_ktons']:.1f} k-tons) was exceeded by ~{co2_excess:.2f} k-tons on average.\")\n", " if violations['ocean']:\n", " print(f\" - The Ocean Freight Constraint was violated (actual was {best_infeasible['definition']['logistics_mix']['Ocean']*100:.0f}% vs. required {self.hard_constraints['min_ocean_share']*100:.0f}%).\")\n", "\n", " print(\"\\n**Recommendation:** To find a feasible solution, consider one of the following trade-offs:\")\n", " if violations['resilience']: print(f\" 1. Relax the Minimum Resilience Score from {self.hard_constraints['min_resilience_score']:.0f} to a lower tier (e.g., 10 or 60).\")\n", " print(f\" 2. Relax the Minimum Profit target from ${self.hard_constraints['min_profit_M']:.0f}M to ${self.hard_constraints['min_profit_M']*0.98:.0f}M.\")\n", " print(f\" 3. Relax the Maximum CO2 target from {self.hard_constraints['max_co2_ktons']:.1f} k-tons to {self.hard_constraints['max_co2_ktons']*1.05:.1f} k-tons.\")\n", " if violations['ocean']: print(f\" 4. Relax the Minimum Ocean Freight Share from {self.hard_constraints['min_ocean_share']*100:.0f}% to {self.hard_constraints['min_ocean_share']*0.95*100:.0f}%.\")\n", " print(\"Re-running the tool with one of these relaxed constraints is likely to yield feasible strategies.\")\n", "\n", "def get_interactive_inputs():\n", " \"\"\"Guides the user through a questionnaire to build the decision_input dictionary.\"\"\"\n", " profiles = {\n", " \"1\": {\"name\": \"Profit First\", \"weights\": {\"profit\": 60, \"resilience\": 10, \"sustainability\": 30}},\n", " \"2\": {\"name\": \"Balanced Growth\", \"weights\": {\"profit\": 40, \"resilience\": 30, \"sustainability\": 30}},\n", " \"3\": {\"name\": \"Resilience First\", \"weights\": {\"profit\": 20, \"resilience\": 50, \"sustainability\": 30}},\n", " }\n", "\n", " print(\"=\"*80 + \"\\nStep 1: Choose a Strategic Profile\\n\" + \"-\"*80)\n", " for key, val in profiles.items(): print(f\" [{key}] {val['name']}\")\n", " choice = input(\"Select a profile [1-3] or press Enter for custom: \")\n", "\n", " if choice in profiles:\n", " weights = profiles[choice]['weights']\n", " print(f\"Selected '{profiles[choice]['name']}'.\")\n", " else:\n", " print(\"\\n-- Custom Priority Weights --\\nPlease allocate 100 points across the following objectives:\")\n", " weights = {}\n", " while True:\n", " try:\n", " weights['profit'] = int(input(f\" - Points for Profit: \"))\n", " weights['resilience'] = int(input(f\" - Points for Resilience: \"))\n", " weights['sustainability'] = int(input(f\" - Points for Sustainability (Low CO2): \"))\n", " if sum(weights.values()) == 100: break\n", " else: print(f\"Error: Points sum to {sum(weights.values())}, not 100. Please try again.\\n\")\n", " except ValueError: print(\"Error: Please enter whole numbers.\\n\")\n", "\n", " print(\"\\n\" + \"=\"*80 + \"\\nStep 2: Define Constraints (Your Non-Negotiable Rules)\\n\" + \"-\"*80)\n", " hard_constraints, soft_constraints = {}, {}\n", " try:\n", " hard_constraints['min_resilience_score'] = int(input(\" - Hard Constraint: Minimum Resilience Score (10=Low, 60=Med, 100=High, e.g., 60): \"))\n", " hard_constraints['min_profit_M'] = float(input(\" - Hard Constraint: Minimum Profit (in millions, e.g., 210): \"))\n", " soft_constraints['target_profit_M'] = float(input(\" - Soft Target: Target Profit (in millions, e.g., 220): \"))\n", " hard_constraints['max_co2_ktons'] = float(input(\" - Hard Constraint: Maximum CO2 (in k-tons, e.g., 8.0): \"))\n", " soft_constraints['target_co2_ktons'] = float(input(\" - Soft Target: Target CO2 (in k-tons, e.g., 7.5): \"))\n", " hard_constraints['min_ocean_share'] = float(input(\" - Hard Constraint: Minimum Ocean Freight Share (decimal, e.g., 0.8): \"))\n", " except ValueError:\n", " print(\"Invalid input. Using default values.\")\n", " hard_constraints = {'min_resilience_score': 60, 'min_profit_M': 210, 'max_co2_ktons': 8.0, 'min_ocean_share': 0.8}\n", " soft_constraints = {'target_profit_M': 220, 'target_co2_ktons': 7.5}\n", "\n", " return {\"objective_weights\": weights, \"hard_constraints\": hard_constraints, \"soft_constraints\": soft_constraints}\n", "\n", "if __name__ == '__main__':\n", " executive_inputs = get_interactive_inputs()\n", " sde = StrategicDecisionEngine(executive_inputs)\n", " sde.run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ORbPCgLZkSbz", "executionInfo": { "status": "ok", "timestamp": 1775293098672, "user_tz": 420, "elapsed": 40473, "user": { "displayName": "Rodrigo Zepeda Pérez", "userId": "14597575368001951544" } }, "outputId": "7f26ceff-1207-43bd-ef58-2f9a0e90326c" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "================================================================================\n", "Step 1: Choose a Strategic Profile\n", "--------------------------------------------------------------------------------\n", " [1] Profit First\n", " [2] Balanced Growth\n", " [3] Resilience First\n", "Select a profile [1-3] or press Enter for custom: 2\n", "Selected 'Balanced Growth'.\n", "\n", "================================================================================\n", "Step 2: Define Constraints (Your Non-Negotiable Rules)\n", "--------------------------------------------------------------------------------\n", " - Hard Constraint: Minimum Resilience Score (10=Low, 60=Med, 100=High, e.g., 60): 60\n", " - Hard Constraint: Minimum Profit (in millions, e.g., 210): 210\n", " - Soft Target: Target Profit (in millions, e.g., 220): 220\n", " - Hard Constraint: Maximum CO2 (in k-tons, e.g., 8.0): 17\n", " - Soft Target: Target CO2 (in k-tons, e.g., 7.5): 8\n", " - Hard Constraint: Minimum Ocean Freight Share (decimal, e.g., 0.8): .8\n", "Analyzing 297 potential strategies across 5000 scenarios each...\n", " - Evaluating strategy 297/297...\n", "Analysis complete.\n", "\n", "================================================================================\n", "** Executive Summary: Strategic Analysis for 2026 **\n", "================================================================================\n", "\n", "**Feasibility Check: Found 72 feasible strategies.**\n", "The top-ranked strategy is feasible in 100.0% of simulated scenarios.\n", "\n", "--------------------------------------------------------------------------------\n", "** Strategy Recommendations **\n", "--------------------------------------------------------------------------------\n", "\n", "--- Recommended Strategy (Highest Strategic Score) ---\n", " - Profile: Expected Profit: $449.1M | Expected CO2: 7.71 k-tons | Resilience: 100/100\n", "\n", " - Operational Plan:\n", " - NA Market (69,375 units):\n", " - Assemble In: 'Mexico'\n", " - Ship Via: 100% Ocean, 0% Air\n", " - APAC Market (53,958 units):\n", " - Assemble In: 'Malaysia'\n", " - Ship Via: 100% Ocean, 0% Air\n", " - EU Market (30,833 units):\n", " - Assemble In: 'US'\n", " - Ship Via: 100% Ocean, 0% Air\n", "\n", "--- Most Profitable Strategy ---\n", " **Warning:** This strategy is NOT feasible (violates constraints in 0.0% of scenarios).\n", " - Profile: Expected Profit: $464.0M | Expected CO2: 7.71 k-tons | Resilience: 10/100\n", "\n", " - Operational Plan:\n", " - NA Market (69,375 units):\n", " - Assemble In: 'Malaysia'\n", " - Ship Via: 100% Ocean, 0% Air\n", " - APAC Market (53,958 units):\n", " - Assemble In: 'Malaysia'\n", " - Ship Via: 100% Ocean, 0% Air\n", " - EU Market (30,833 units):\n", " - Assemble In: 'Malaysia'\n", " - Ship Via: 100% Ocean, 0% Air\n", "\n", "--- Most Resilient Strategy ---\n", " **Warning:** This strategy is NOT feasible (violates constraints in 100.0% of scenarios).\n", " - Profile: Expected Profit: $377.0M | Expected CO2: 47.79 k-tons | Resilience: 100/100\n", "\n", " - Operational Plan:\n", " - NA Market (69,375 units):\n", " - Assemble In: 'US'\n", " - Ship Via: 0% Ocean, 100% Air\n", " - APAC Market (53,958 units):\n", " - Assemble In: 'Mexico'\n", " - Ship Via: 0% Ocean, 100% Air\n", " - EU Market (30,833 units):\n", " - Assemble In: 'Malaysia'\n", " - Ship Via: 0% Ocean, 100% Air\n" ] } ] }, { "cell_type": "code", "source": [ "import numpy as np\n", "import pandas as pd\n", "import itertools\n", "from collections import defaultdict\n", "import math\n", "\n", "# --- Configuration & Foundational Data ---\n", "N_SCENARIOS = 5000\n", "ACTUAL_GPU_SUPPLY_2026 = 185000\n", "GPUS_PER_SERVER = 1.2\n", "MAX_SERVERS_2026 = int(ACTUAL_GPU_SUPPLY_2026 / GPUS_PER_SERVER)\n", "\n", "# Financial & Operational Data\n", "GPU_BENCHMARK_COST = 30000\n", "OTHER_COMPONENTS_COST = 9840\n", "SERVER_REVENUE = 49090.91\n", "PLANT_FC = {'US': 7800e3, 'Mexico': 7200e3, 'Malaysia': 6900e3}\n", "SHIPPING_LANE_FC = {\n", " ('US', 'NA'): 450e3, ('US', 'APAC'): 850e3, ('US', 'EU'): 650e3,\n", " ('Mexico', 'NA'): 500e3, ('Mexico', 'APAC'): 900e3, ('Mexico', 'EU'): 700e3,\n", " ('Malaysia', 'NA'): 700e3, ('Malaysia', 'APAC'): 500e3, ('Malaysia', 'EU'): 600e3,\n", "}\n", "LOGISTICS_VC = {'Ocean': 180, 'Air': 620}\n", "CO2_EMISSIONS = {'Ocean': 50, 'Air': 310} # In kg per unit\n", "\n", "# Simulation Assumptions\n", "DEMAND_BASELINE = 215000\n", "DEMAND_VOLATILITY = 0.20\n", "DEMAND_MIX = {'NA': 0.45, 'APAC': 0.35, 'EU': 0.20}\n", "CARBON_PRICE_RANGE = (50, 150)\n", "\n", "class StrategicDecisionEngine:\n", " \"\"\"A complete engine for strategic simulation with per-market logistics.\"\"\"\n", "\n", " def __init__(self, decision_inputs):\n", " self.inputs = decision_inputs\n", " self.all_strategy_results = []\n", " self.feasible_strategies = []\n", " self.infeasible_strategies = []\n", " self.hard_constraints = self.inputs['hard_constraints']\n", " self.soft_constraints = self.inputs['soft_constraints']\n", " self.weights = self.inputs['objective_weights']\n", "\n", " def _evaluate_single_strategy(self, strategy_def):\n", " plant_map = strategy_def['plant_map']\n", " logistics_plan = strategy_def['logistics_plan']\n", " fixed_cost = sum(PLANT_FC[plant] for plant in set(plant_map.values()))\n", " for region, plant in plant_map.items():\n", " fixed_cost += SHIPPING_LANE_FC.get((plant, region), 0)\n", "\n", " demand_std_dev = DEMAND_BASELINE * DEMAND_VOLATILITY\n", " scenarios = []\n", " for _ in range(N_SCENARIOS):\n", " total_demand = np.random.normal(DEMAND_BASELINE, demand_std_dev)\n", " units_sold_total = min(total_demand, MAX_SERVERS_2026)\n", " total_profit, total_co2_k_tons = 0, 0\n", "\n", " for region, mix_share in DEMAND_MIX.items():\n", " units_for_region = units_sold_total * mix_share\n", " logistics_mix = logistics_plan[region]\n", " avg_logistics_vc = (logistics_mix['Ocean'] * LOGISTICS_VC['Ocean']) + (logistics_mix['Air'] * LOGISTICS_VC['Air'])\n", " avg_co2_kg = (logistics_mix['Ocean'] * CO2_EMISSIONS['Ocean']) + (logistics_mix['Air'] * CO2_EMISSIONS['Air'])\n", " carbon_price = np.random.uniform(*CARBON_PRICE_RANGE)\n", " carbon_cost_per_unit = (avg_co2_kg / 1000) * carbon_price\n", " total_vc_per_unit = (GPUS_PER_SERVER * GPU_BENCHMARK_COST) + OTHER_COMPONENTS_COST + avg_logistics_vc + carbon_cost_per_unit\n", " total_profit += units_for_region * (SERVER_REVENUE - total_vc_per_unit)\n", " total_co2_k_tons += (units_for_region * avg_co2_kg) / 1_000_000\n", "\n", " total_profit -= fixed_cost\n", " scenarios.append({'profit': total_profit, 'co2_k_tons': total_co2_k_tons, 'units_sold': units_sold_total})\n", "\n", " scenario_df = pd.DataFrame(scenarios)\n", " resilience_score = {1: 10, 2: 60, 3: 100}.get(len(set(plant_map.values())), 0)\n", "\n", " return {\n", " \"definition\": strategy_def, \"results_df\": scenario_df,\n", " \"metrics\": {\"profit\": scenario_df['profit'].mean(), \"growth\": scenario_df['units_sold'].mean(),\n", " \"sustainability\": scenario_df['co2_k_tons'].mean(), \"resilience\": resilience_score}\n", " }\n", "\n", " def _check_feasibility_and_penalties(self, result):\n", " df = result['results_df']\n", " ocean_violation = any(result['definition']['logistics_plan'][r]['Ocean'] > self.hard_constraints['max_ocean_share'] for r in DEMAND_MIX)\n", " violations = {\n", " 'profit': (df['profit'] < self.hard_constraints['min_profit_M'] * 1e6).mean(),\n", " 'co2': (df['co2_k_tons'] > self.hard_constraints['max_co2_ktons']).mean(),\n", " 'ocean': ocean_violation,\n", " 'resilience': (result['metrics']['resilience'] < self.hard_constraints['min_resilience_score'])\n", " }\n", " total_violation_prob = 1 - (1 - violations['profit']) * (1 - violations['co2'])\n", " is_feasible = (total_violation_prob < 0.05) and not violations['ocean'] and not violations['resilience']\n", " profit_shortfall = np.maximum(0, self.soft_constraints['target_profit_M'] * 1e6 - df['profit']).mean()\n", " co2_excess = np.maximum(0, df['co2_k_tons'] - self.soft_constraints['target_co2_ktons']).mean()\n", " total_penalty = (profit_shortfall / 1e6) + (co2_excess / 0.1)\n", " result['feasibility'] = {'is_feasible': is_feasible, 'violation_probability': total_violation_prob, 'violations': violations}\n", " result['penalty_score'] = total_penalty\n", " return result\n", "\n", " def _score_strategies(self):\n", " if not self.feasible_strategies: return\n", " min_vals = {key: min(s['metrics'][key] for s in self.feasible_strategies) for key in self.weights}\n", " max_vals = {key: max(s['metrics'][key] for s in self.feasible_strategies) for key in self.weights}\n", "\n", " for strat in self.feasible_strategies:\n", " total_score = 0\n", " for objective, weight in self.weights.items():\n", " val = strat['metrics'][objective]\n", " range_val = max_vals[objective] - min_vals[objective]\n", " norm_score = 100 if range_val == 0 else 100 * (val - min_vals[objective]) / range_val\n", " if objective == 'sustainability': norm_score = 100 - norm_score # Lower is better for CO2\n", " total_score += norm_score * (weight / 100.0)\n", " strat['strategic_score'] = total_score\n", "\n", " self.feasible_strategies.sort(key=lambda x: x['strategic_score'], reverse=True)\n", "\n", " def run(self):\n", " plants, regions = ['US', 'Mexico', 'Malaysia'], ['NA', 'APAC', 'EU']\n", " all_plant_maps = [dict(zip(regions, p)) for p in itertools.product(plants, repeat=len(regions))]\n", " logistics_options = [{'Ocean': 0.1, 'Air': 0.9}, {'Ocean': 0.8, 'Air': 0.2}, {'Ocean': 1.0, 'Air': 0.0}]\n", " logistics_combinations = list(itertools.product(logistics_options, repeat=len(regions)))\n", " all_logistics_plans = [dict(zip(regions, combo)) for combo in logistics_combinations]\n", " strategy_definitions = [{'plant_map': p_map, 'logistics_plan': l_plan} for p_map in all_plant_maps for l_plan in all_logistics_plans]\n", "\n", " print(f\"\\nAnalyzing {len(strategy_definitions)} potential strategies...\")\n", " for i, strat_def in enumerate(strategy_definitions):\n", " print(f\" - Evaluating strategy {i+1}/{len(strategy_definitions)}...\", end='\\r')\n", " result = self._evaluate_single_strategy(strat_def)\n", " result = self._check_feasibility_and_penalties(result)\n", " (self.feasible_strategies if result['feasibility']['is_feasible'] else self.infeasible_strategies).append(result)\n", "\n", " print(\"\\nAnalysis complete.\")\n", " self._score_strategies()\n", " self._display_report()\n", "\n", " def _display_report(self):\n", " print(\"\\n\" + \"=\"*80 + \"\\n** Executive Summary: Strategic Analysis for 2026 **\\n\" + \"=\"*80)\n", " if not self.feasible_strategies:\n", " print(\"\\n**Feasibility Check: No strategy was found that meets all hard constraints.**\\n\")\n", " if self.infeasible_strategies: self._display_relaxation_advice()\n", " else:\n", " top_score = self.feasible_strategies[0]['strategic_score']\n", " print(f\"\\n**Feasibility Check: Found {len(self.feasible_strategies)} feasible strategies.**\")\n", " print(f\"The top-ranked strategy achieved a Strategic Score of {top_score:.1f}/100 based on your priorities.\\n\")\n", "\n", " print(\"-\" * 80 + \"\\n** Strategy Recommendations **\\n\" + \"-\" * 80)\n", " if self.feasible_strategies:\n", " self._print_strategy_card(f\"Recommended Strategy (Score: {self.feasible_strategies[0]['strategic_score']:.1f})\", self.feasible_strategies[0])\n", " if len(self.feasible_strategies) > 1:\n", " self._print_strategy_card(f\"Alternative Strategy (Score: {self.feasible_strategies[1]['strategic_score']:.1f})\", self.feasible_strategies[1], is_alt=True)\n", " elif self.infeasible_strategies:\n", " best_infeasible = min(self.infeasible_strategies, key=lambda x: x['penalty_score'])\n", " self._print_strategy_card(\"Least Infeasible Strategy (For Analysis)\", best_infeasible, is_alt=True)\n", "\n", " def _print_strategy_card(self, title, strat, is_alt=False):\n", " print(f\"\\n--- {title} ---\")\n", " if not strat['feasibility']['is_feasible']: print(f\" **Warning:** This strategy is NOT feasible.\")\n", " metrics, definition = strat['metrics'], strat['definition']\n", " print(f\" - Profile: Profit: ${metrics['profit']/1e6:,.1f}M | CO2: {metrics['sustainability']:,.2f} k-tons | Resilience: {metrics['resilience']:.0f}/100\")\n", " print(\"\\n - Operational Plan:\")\n", " for region in ['NA', 'APAC', 'EU']:\n", " plant, logistics = definition['plant_map'][region], definition['logistics_plan'][region]\n", " units = metrics['growth'] * DEMAND_MIX[region]\n", " print(f\" - {region} Market ({units:,.0f} units): Assemble in '{plant}', Ship via {logistics['Ocean']:.0%} Ocean / {logistics['Air']:.0%} Air\")\n", "\n", " def _display_relaxation_advice(self):\n", " print(\"-\" * 80 + \"\\n** Relaxation Engine: Path to a Feasible Solution **\\n\" + \"-\" * 80)\n", " compliant_infeasible = [\n", " s for s in self.infeasible_strategies\n", " if not s['feasibility']['violations']['ocean'] and not s['feasibility']['violations']['resilience']\n", " ]\n", "\n", " if not compliant_infeasible:\n", " print(\"No strategies could be found that satisfy even the basic Ocean Freight and Resilience constraints.\")\n", " print(\"Consider relaxing one of these two fundamental business rules first.\")\n", " return\n", "\n", " best_infeasible = min(compliant_infeasible, key=lambda x: x['penalty_score'])\n", " violations = best_infeasible['feasibility']['violations']\n", " metrics = best_infeasible['metrics']\n", "\n", " print(\"After enforcing your core Ocean and Resilience rules, no strategy was feasible.\")\n", " print(\"Analysis of the 'closest compliant' strategy shows the following gaps:\")\n", " recommendations = []\n", "\n", " if violations['profit'] > 0.05:\n", " required_M, actual_M = self.hard_constraints['min_profit_M'], metrics['profit'] / 1e6\n", " gap_M = required_M - actual_M\n", " if gap_M > 0:\n", " new_profit_target = math.floor(actual_M)\n", " recommendations.append(f\" 1. Lower the 'Minimum Profit' from ${required_M:,.1f}M to ${new_profit_target:,.1f}M.\")\n", " print(f\" - PROFIT: Minimum of ${required_M:,.1f}M was missed by ${gap_M:,.1f}M.\")\n", "\n", " if violations['co2'] > 0.05:\n", " required_ktons, actual_ktons = self.hard_constraints['max_co2_ktons'], metrics['sustainability']\n", " gap_ktons = actual_ktons - required_ktons\n", " if gap_ktons > 0:\n", " new_co2_target = math.ceil(actual_ktons * 10) / 10\n", " recommendations.append(f\" 2. Increase the 'Maximum CO2' from {required_ktons:,.2f} to {new_co2_target:,.2f} k-tons.\")\n", " print(f\" - CO2: Maximum of {required_ktons:,.2f} k-tons was exceeded by {gap_ktons:,.2f} k-tons.\")\n", "\n", " if recommendations:\n", " print(\"\\n**Recommendation:** To achieve a feasible strategy while respecting your Ocean/Resilience rules,\")\n", " print(\"you must make a trade-off. Re-run with ONE of the following adjustments:\")\n", " for rec in recommendations:\n", " print(rec)\n", " else:\n", " print(\"\\nNo single clear adjustment found. The closest strategy fails on a combination of factors.\")\n", "\n", "def get_interactive_inputs():\n", " \"\"\"Guides the user through a questionnaire to build the decision_input dictionary.\"\"\"\n", " profiles = {\n", " \"1\": {\"name\": \"Profit First\", \"weights\": {\"profit\": 60, \"resilience\": 10, \"sustainability\": 30}},\n", " \"2\": {\"name\": \"Balanced Growth\", \"weights\": {\"profit\": 40, \"resilience\": 30, \"sustainability\": 30}},\n", " \"3\": {\"name\": \"Resilience First\", \"weights\": {\"profit\": 20, \"resilience\": 50, \"sustainability\": 30}},\n", " }\n", "\n", " print(\"=\"*80 + \"\\nStep 1: Choose a Strategic Profile\\n\" + \"-\"*80)\n", " for key, val in profiles.items():\n", " print(f\" [{key}] {val['name']}\")\n", " choice = input(\"Select a profile [1-3] or press Enter for custom: \")\n", "\n", " if choice in profiles:\n", " weights = profiles[choice]['weights']\n", " print(f\"Selected '{profiles[choice]['name']}'.\")\n", " else:\n", " print(\"\\n-- Custom Priority Weights --\\nPlease allocate 100 points across the following objectives:\")\n", " weights = {}\n", " while True:\n", " try:\n", " weights['profit'] = int(input(f\" - Points for Profit: \"))\n", " weights['resilience'] = int(input(f\" - Points for Resilience: \"))\n", " weights['sustainability'] = int(input(f\" - Points for Sustainability (Low CO2): \"))\n", " if sum(weights.values()) == 100:\n", " break\n", " else:\n", " print(f\"Error: Points sum to {sum(weights.values())}, not 100. Please try again.\\n\")\n", " except ValueError:\n", " print(\"Error: Please enter whole numbers.\\n\")\n", "\n", " print(\"\\n\" + \"=\"*80 + \"\\nStep 2: Define Constraints (Your Non-Negotiable Rules)\\n\" + \"-\"*80)\n", " hard_constraints, soft_constraints = {}, {}\n", " try:\n", " hard_constraints['min_resilience_score'] = int(input(\" - Min Resilience Score (10, 60, 100): \"))\n", " hard_constraints['min_profit_M'] = float(input(\" - Min Profit (in millions): \"))\n", " soft_constraints['target_profit_M'] = float(input(\" - Target Profit (in millions): \"))\n", " hard_constraints['max_co2_ktons'] = float(input(\" - Max CO2 (in k-tons): \"))\n", " soft_constraints['target_co2_ktons'] = float(input(\" - Target CO2 (in k-tons): \"))\n", " hard_constraints['max_ocean_share'] = float(input(\" - Max Ocean Freight Share per Market (0.0 to 1.0): \"))\n", " except ValueError:\n", " print(\"\\nInvalid input. Using default values for demonstration.\")\n", " hard_constraints = {'min_resilience_score': 60, 'min_profit_M': 210, 'max_co2_ktons': 8.5, 'max_ocean_share': 0.8}\n", " soft_constraints = {'target_profit_M': 220, 'target_co2_ktons': 8.0}\n", "\n", " return {\"objective_weights\": weights, \"hard_constraints\": hard_constraints, \"soft_constraints\": soft_constraints}\n", "\n", "if __name__ == '__main__':\n", " executive_inputs = get_interactive_inputs()\n", " sde = StrategicDecisionEngine(executive_inputs)\n", " sde.run()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mSdsoG9fLXGH", "executionInfo": { "status": "ok", "timestamp": 1775321857367, "user_tz": 420, "elapsed": 100873, "user": { "displayName": "Rodrigo Zepeda Pérez", "userId": "14597575368001951544" } }, "outputId": "f57094ee-432c-47a1-fc9b-f262216f62e6" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "================================================================================\n", "Step 1: Choose a Strategic Profile\n", "--------------------------------------------------------------------------------\n", " [1] Profit First\n", " [2] Balanced Growth\n", " [3] Resilience First\n", "Select a profile [1-3] or press Enter for custom: 2\n", "Selected 'Balanced Growth'.\n", "\n", "================================================================================\n", "Step 2: Define Constraints (Your Non-Negotiable Rules)\n", "--------------------------------------------------------------------------------\n", " - Min Resilience Score (10, 60, 100): 60\n", " - Min Profit (in millions): 200\n", " - Target Profit (in millions): 220\n", " - Max CO2 (in k-tons): 18\n", " - Target CO2 (in k-tons): 9\n", " - Max Ocean Freight Share per Market (0.0 to 1.0): .8\n", "\n", "Analyzing 729 potential strategies...\n", " - Evaluating strategy 729/729...\n", "Analysis complete.\n", "\n", "================================================================================\n", "** Executive Summary: Strategic Analysis for 2026 **\n", "================================================================================\n", "\n", "**Feasibility Check: Found 24 feasible strategies.**\n", "The top-ranked strategy achieved a Strategic Score of 62.7/100 based on your priorities.\n", "\n", "--------------------------------------------------------------------------------\n", "** Strategy Recommendations **\n", "--------------------------------------------------------------------------------\n", "\n", "--- Recommended Strategy (Score: 62.7) ---\n", " - Profile: Profit: $436.5M | CO2: 15.55 k-tons | Resilience: 60/100\n", "\n", " - Operational Plan:\n", " - NA Market (68,589 units): Assemble in 'US', Ship via 80% Ocean / 20% Air\n", " - APAC Market (53,347 units): Assemble in 'US', Ship via 80% Ocean / 20% Air\n", " - EU Market (30,484 units): Assemble in 'Malaysia', Ship via 80% Ocean / 20% Air\n", "\n", "--- Alternative Strategy (Score: 62.3) ---\n", " - Profile: Profit: $437.3M | CO2: 15.55 k-tons | Resilience: 60/100\n", "\n", " - Operational Plan:\n", " - NA Market (68,613 units): Assemble in 'Malaysia', Ship via 80% Ocean / 20% Air\n", " - APAC Market (53,366 units): Assemble in 'Malaysia', Ship via 80% Ocean / 20% Air\n", " - EU Market (30,495 units): Assemble in 'Mexico', Ship via 80% Ocean / 20% Air\n" ] } ] }, { "cell_type": "code", "source": [ "import numpy as np\n", "import pandas as pd\n", "import itertools\n", "from collections import defaultdict\n", "import math\n", "\n", "# ==============================================================================\n", "# --- Configuration & Foundational Data ---\n", "# This section contains all the core assumptions and data points for the simulation.\n", "# ==============================================================================\n", "\n", "# --- Simulation Control ---\n", "N_SCENARIOS = 5000 # Number of Monte Carlo simulations to run for each strategy.\n", "\n", "# --- Production & Supply Chain Data ---\n", "ACTUAL_GPU_SUPPLY_2026 = 185000 # Total number of high-end GPUs available for the year.\n", "GPUS_PER_SERVER = 1.2 # Average number of GPUs used per server unit.\n", "MAX_SERVERS_2026 = int(ACTUAL_GPU_SUPPLY_2026 / GPUS_PER_SERVER) # The absolute maximum number of servers we can produce.\n", "\n", "# --- Financial Data ---\n", "GPU_BENCHMARK_COST = 30000 # Cost of GPUs per server.\n", "OTHER_COMPONENTS_COST = 9840 # Cost of all other components (CPU, RAM, chassis, etc.) per server.\n", "SERVER_REVENUE = 49090.91 # Revenue generated from selling one server.\n", "\n", "# --- Fixed Costs ---\n", "# Annual fixed costs associated with operating each manufacturing plant.\n", "PLANT_FC = {'US': 7800e3, 'Mexico': 7200e3, 'Malaysia': 6900e3}\n", "# Annual fixed costs for maintaining a logistics channel from a plant to a market region.\n", "SHIPPING_LANE_FC = {\n", " ('US', 'NA'): 450e3, ('US', 'APAC'): 850e3, ('US', 'EU'): 650e3,\n", " ('Mexico', 'NA'): 500e3, ('Mexico', 'APAC'): 900e3, ('Mexico', 'EU'): 700e3,\n", " ('Malaysia', 'NA'): 700e3, ('Malaysia', 'APAC'): 500e3, ('Malaysia', 'EU'): 600e3,\n", "}\n", "\n", "# --- Variable Costs & Environmental Impact ---\n", "# Variable costs for shipping one server unit by different methods.\n", "LOGISTICS_VC = {'Ocean': 180, 'Air': 620}\n", "# CO2 emissions in kilograms for shipping one server unit. Note the high impact of air freight.\n", "CO2_EMISSIONS = {'Ocean': 50, 'Air': 310}\n", "\n", "# --- Market & Economic Assumptions ---\n", "DEMAND_BASELINE = 215000 # Expected average total market demand for servers in 2026.\n", "DEMAND_VOLATILITY = 0.20 # The standard deviation of demand, as a percentage of the baseline.\n", "# The geographical mix of total demand. Must sum to 1.0.\n", "DEMAND_MIX = {'NA': 0.45, 'APAC': 0.35, 'EU': 0.20}\n", "# The potential range for the price of carbon in $/ton. This simulates regulatory uncertainty.\n", "CARBON_PRICE_RANGE = (50, 150)\n", "\n", "class StrategicDecisionEngine:\n", " \"\"\"\n", " A comprehensive simulation engine to analyze and recommend global supply chain strategies.\n", "\n", " This class takes high-level business objectives and constraints, simulates thousands of\n", " potential futures for hundreds of different strategies, and provides a clear, data-driven\n", " recommendation. It helps answer the question: \"What is the best way to structure our\n", " global operations to maximize profit, resilience, and sustainability, given future\n", " uncertainty?\"\n", " \"\"\"\n", "\n", " def __init__(self, decision_inputs):\n", " \"\"\"Initializes the engine with the user's strategic priorities and rules.\"\"\"\n", " self.inputs = decision_inputs\n", " self.all_strategy_results = []\n", " self.feasible_strategies = []\n", " self.infeasible_strategies = []\n", " # Extract user-defined rules and priorities for easy access.\n", " self.hard_constraints = self.inputs['hard_constraints']\n", " self.soft_constraints = self.inputs['soft_constraints']\n", " self.weights = self.inputs['objective_weights']\n", "\n", " def _evaluate_single_strategy(self, strategy_def):\n", " \"\"\"\n", " Runs a full Monte Carlo simulation for a single proposed strategy.\n", "\n", " Args:\n", " strategy_def (dict): A dictionary defining the strategy, containing:\n", " - 'plant_map': Which plant serves which market (e.g., {'NA': 'Mexico', ...}).\n", " - 'logistics_plan': The Ocean/Air shipping mix for each market.\n", "\n", " Returns:\n", " dict: A dictionary containing the strategy's definition, raw simulation results,\n", " and key performance metrics (profit, CO2, etc.).\n", " \"\"\"\n", " plant_map = strategy_def['plant_map']\n", " logistics_plan = strategy_def['logistics_plan']\n", "\n", " # --- Calculate Total Fixed Costs for the Strategy ---\n", " # 1. Add plant costs (only for unique plants used in the strategy).\n", " fixed_cost = sum(PLANT_FC[plant] for plant in set(plant_map.values()))\n", " # 2. Add the costs for each active shipping lane.\n", " for region, plant in plant_map.items():\n", " fixed_cost += SHIPPING_LANE_FC.get((plant, region), 0)\n", "\n", " # --- Run the Monte Carlo Simulation ---\n", " demand_std_dev = DEMAND_BASELINE * DEMAND_VOLATILITY\n", " scenarios = []\n", " for _ in range(N_SCENARIOS):\n", " # Simulate total demand for this one scenario (future).\n", " total_demand = np.random.normal(DEMAND_BASELINE, demand_std_dev)\n", " # We can't sell more servers than we can produce.\n", " units_sold_total = min(total_demand, MAX_SERVERS_2026)\n", "\n", " total_profit = 0\n", " total_co2_k_tons = 0\n", "\n", " # Calculate profit and CO2 contributions from each market region.\n", " for region, mix_share in DEMAND_MIX.items():\n", " units_for_region = units_sold_total * mix_share\n", " logistics_mix = logistics_plan[region] # Get the specific logistics mix for this region.\n", "\n", " # Calculate the weighted average variable costs and emissions for this region.\n", " avg_logistics_vc = (logistics_mix['Ocean'] * LOGISTICS_VC['Ocean']) + (logistics_mix['Air'] * LOGISTICS_VC['Air'])\n", " avg_co2_kg = (logistics_mix['Ocean'] * CO2_EMISSIONS['Ocean']) + (logistics_mix['Air'] * CO2_EMISSIONS['Air'])\n", "\n", " # Simulate a random carbon price for this scenario to model regulatory risk.\n", " carbon_price = np.random.uniform(*CARBON_PRICE_RANGE)\n", " # Calculate the carbon tax cost per server.\n", " carbon_cost_per_unit = (avg_co2_kg / 1000) * carbon_price # Convert kg to tons for pricing.\n", "\n", " # Calculate the total variable cost for one server shipped to this region.\n", " total_vc_per_unit = (GPUS_PER_SERVER * GPU_BENCHMARK_COST) + OTHER_COMPONENTS_COST + avg_logistics_vc + carbon_cost_per_unit\n", "\n", " # Calculate the total profit and CO2 for this region and add to the totals.\n", " total_profit += units_for_region * (SERVER_REVENUE - total_vc_per_unit)\n", " total_co2_k_tons += (units_for_region * avg_co2_kg) / 1_000_000 # Convert kg to kilo-tons.\n", "\n", " # Subtract the total fixed costs at the end to get the final profit for this scenario.\n", " total_profit -= fixed_cost\n", "\n", " scenarios.append({'profit': total_profit, 'co2_k_tons': total_co2_k_tons, 'units_sold': units_sold_total})\n", "\n", " # --- Aggregate Simulation Results ---\n", " scenario_df = pd.DataFrame(scenarios)\n", " # Calculate resilience score: more plants = more resilient.\n", " resilience_score = {1: 10, 2: 60, 3: 100}.get(len(set(plant_map.values())), 0)\n", "\n", " return {\n", " \"definition\": strategy_def,\n", " \"results_df\": scenario_df,\n", " \"metrics\": {\n", " \"profit\": scenario_df['profit'].mean(),\n", " \"growth\": scenario_df['units_sold'].mean(),\n", " \"sustainability\": scenario_df['co2_k_tons'].mean(),\n", " \"resilience\": resilience_score\n", " }\n", " }\n", "\n", " def _check_feasibility_and_penalties(self, result):\n", " \"\"\"\n", " Checks if a simulated strategy meets the user's 'hard constraints'.\n", "\n", " Args:\n", " result (dict): The output from the `_evaluate_single_strategy` method.\n", "\n", " Returns:\n", " dict: The original result dictionary, updated with feasibility information\n", " and a 'penalty_score' for ranking infeasible options.\n", " \"\"\"\n", " df = result['results_df']\n", "\n", " # Check for violations of the hard constraints.\n", " # 'ocean' and 'resilience' are simple checks on the strategy's definition.\n", " ocean_violation = any(result['definition']['logistics_plan'][r]['Ocean'] > self.hard_constraints['max_ocean_share'] for r in DEMAND_MIX)\n", " resilience_violation = result['metrics']['resilience'] < self.hard_constraints['min_resilience_score']\n", "\n", " # For 'profit' and 'co2', we calculate the *probability* of violating the constraint across all scenarios.\n", " profit_violation_prob = (df['profit'] < self.hard_constraints['min_profit_M'] * 1e6).mean()\n", " co2_violation_prob = (df['co2_k_tons'] > self.hard_constraints['max_co2_ktons']).mean()\n", "\n", " violations_summary = {\n", " 'profit': profit_violation_prob, 'co2': co2_violation_prob,\n", " 'ocean': ocean_violation, 'resilience': resilience_violation\n", " }\n", "\n", " # A strategy is considered 'feasible' if it doesn't violate the simple rules AND the probability\n", " # of violating the financial/CO2 rules is less than 5%.\n", " is_feasible = (profit_violation_prob < 0.05) and (co2_violation_prob < 0.05) and not ocean_violation and not resilience_violation\n", "\n", " # Calculate a penalty score for ranking infeasible strategies (how badly did it miss?).\n", " profit_shortfall = np.maximum(0, self.soft_constraints['target_profit_M'] * 1e6 - df['profit']).mean()\n", " co2_excess = np.maximum(0, df['co2_k_tons'] - self.soft_constraints['target_co2_ktons']).mean()\n", " total_penalty = (profit_shortfall / 1e6) + (co2_excess / 0.1)\n", "\n", " result['feasibility'] = {'is_feasible': is_feasible, 'violations': violations_summary}\n", " result['penalty_score'] = total_penalty\n", " return result\n", "\n", " def _score_strategies(self):\n", " \"\"\"Calculates a weighted 'Strategic Score' for all feasible strategies.\"\"\"\n", " if not self.feasible_strategies: return\n", "\n", " # To score fairly, we first normalize each metric from 0 to 100.\n", " min_vals = {key: min(s['metrics'][key] for s in self.feasible_strategies) for key in self.weights}\n", " max_vals = {key: max(s['metrics'][key] for s in self.feasible_strategies) for key in self.weights}\n", "\n", " for strat in self.feasible_strategies:\n", " total_score = 0\n", " for objective, weight in self.weights.items():\n", " val = strat['metrics'][objective]\n", " range_val = max_vals[objective] - min_vals[objective]\n", " # Normalize the value to a 0-100 scale.\n", " norm_score = 100 if range_val == 0 else 100 * (val - min_vals[objective]) / range_val\n", " # For sustainability, lower is better, so we flip the score.\n", " if objective == 'sustainability': norm_score = 100 - norm_score\n", " # Apply the user's chosen weights.\n", " total_score += norm_score * (weight / 100.0)\n", " strat['strategic_score'] = total_score\n", "\n", " # Sort the feasible strategies by their final score, from best to worst.\n", " self.feasible_strategies.sort(key=lambda x: x['strategic_score'], reverse=True)\n", "\n", " def run(self):\n", " \"\"\"\n", " Executes the entire end-to-end analysis.\n", " This is the main driver function of the class.\n", " \"\"\"\n", " plants, regions = ['US', 'Mexico', 'Malaysia'], ['NA', 'APAC', 'EU']\n", "\n", " # --- Generate All Possible Strategies ---\n", " # 1. Generate every possible combination of plants serving regions (3^3 = 27).\n", " all_plant_maps = [dict(zip(regions, p)) for p in itertools.product(plants, repeat=len(regions))]\n", "\n", " # 2. Define the discrete logistics options we want to test.\n", " logistics_options = [{'Ocean': 0.1, 'Air': 0.9}, {'Ocean': 0.8, 'Air': 0.2}, {'Ocean': 1.0, 'Air': 0.0}]\n", " # 3. Generate every combination of these logistics options across the 3 regions (3^3 = 27).\n", " all_logistics_plans = [dict(zip(regions, combo)) for combo in itertools.product(logistics_options, repeat=len(regions))]\n", "\n", " # 4. Create the final list of all strategies to test (27 * 27 = 729).\n", " strategy_definitions = [{'plant_map': p_map, 'logistics_plan': l_plan} for p_map in all_plant_maps for l_plan in all_logistics_plans]\n", "\n", " # --- Evaluate and Classify Each Strategy ---\n", " print(f\"\\nAnalyzing {len(strategy_definitions)} potential strategies...\")\n", " for i, strat_def in enumerate(strategy_definitions):\n", " print(f\" - Evaluating strategy {i+1}/{len(strategy_definitions)}...\", end='\\r')\n", " result = self._evaluate_single_strategy(strat_def)\n", " result = self._check_feasibility_and_penalties(result)\n", "\n", " # Add the result to the appropriate list based on its feasibility.\n", " if result['feasibility']['is_feasible']:\n", " self.feasible_strategies.append(result)\n", " else:\n", " self.infeasible_strategies.append(result)\n", "\n", " print(\"\\nAnalysis complete.\")\n", "\n", " # Score and rank the results, then display the final report.\n", " self._score_strategies()\n", " self._display_report()\n", "\n", " def _display_report(self):\n", " \"\"\"Prints the final, formatted executive summary and recommendations.\"\"\"\n", " print(\"\\n\" + \"=\"*80 + \"\\n** Executive Summary: Strategic Analysis for 2026 **\\n\" + \"=\"*80)\n", "\n", " if not self.feasible_strategies:\n", " print(\"\\n**Feasibility Check: No strategy was found that meets all hard constraints.**\\n\")\n", " if self.infeasible_strategies:\n", " self._display_relaxation_advice() # Provide advice on how to find a feasible solution.\n", " else:\n", " top_score = self.feasible_strategies[0]['strategic_score']\n", " print(f\"\\n**Feasibility Check: Found {len(self.feasible_strategies)} feasible strategies.**\")\n", " print(f\"The top-ranked strategy achieved a Strategic Score of {top_score:.1f}/100 based on your priorities.\\n\")\n", "\n", " print(\"-\" * 80 + \"\\n** Strategy Recommendations **\\n\" + \"-\" * 80)\n", " if self.feasible_strategies:\n", " # Show the top-ranked strategy.\n", " self._print_strategy_card(f\"Recommended Strategy (Score: {self.feasible_strategies[0]['strategic_score']:.1f})\", self.feasible_strategies[0])\n", " # If available, show the next-best option as an alternative.\n", " if len(self.feasible_strategies) > 1:\n", " self._print_strategy_card(f\"Alternative Strategy (Score: {self.feasible_strategies[1]['strategic_score']:.1f})\", self.feasible_strategies[1], is_alt=True)\n", " elif self.infeasible_strategies:\n", " # If no strategies were feasible, show the \"least bad\" one for analysis.\n", " best_infeasible = min(self.infeasible_strategies, key=lambda x: x['penalty_score'])\n", " self._print_strategy_card(\"Least Infeasible Strategy (For Analysis)\", best_infeasible, is_alt=True)\n", "\n", " def _print_strategy_card(self, title, strat, is_alt=False):\n", " \"\"\"Prints a formatted summary card for a single strategy.\"\"\"\n", " print(f\"\\n--- {title} ---\")\n", " if not strat['feasibility']['is_feasible']:\n", " print(f\" **Warning:** This strategy is NOT feasible and is shown for analysis only.\")\n", "\n", " metrics, definition = strat['metrics'], strat['definition']\n", " print(f\" - Profile: Profit: ${metrics['profit']/1e6:,.1f}M | CO2: {metrics['sustainability']:,.2f} k-tons | Resilience: {metrics['resilience']:.0f}/100\")\n", " print(\"\\n - Operational Plan:\")\n", " for region in ['NA', 'APAC', 'EU']:\n", " plant, logistics = definition['plant_map'][region], definition['logistics_plan'][region]\n", " units = metrics['growth'] * DEMAND_MIX[region]\n", " print(f\" - {region} Market ({units:,.0f} units): Assemble in '{plant}', Ship via {logistics['Ocean']:.0%} Ocean / {logistics['Air']:.0%} Air\")\n", "\n", " def _display_relaxation_advice(self):\n", " \"\"\"\n", " Provides intelligent advice if no feasible solutions were found.\n", " This function prioritizes the user's agility/resilience constraints.\n", " \"\"\"\n", " print(\"-\" * 80 + \"\\n** Relaxation Engine: Path to a Feasible Solution **\\n\" + \"-\" * 80)\n", "\n", " # 1. Filter out any strategies that already violate the most important business rules (ocean share and resilience).\n", " compliant_infeasible = [\n", " s for s in self.infeasible_strategies\n", " if not s['feasibility']['violations']['ocean'] and not s['feasibility']['violations']['resilience']\n", " ]\n", "\n", " if not compliant_infeasible:\n", " print(\"No strategies could be found that satisfy even the basic Ocean Freight and Resilience constraints.\")\n", " print(\"This suggests the constraints are fundamentally too tight. Consider relaxing one of these first.\")\n", " return\n", "\n", " # 2. From the compliant-but-infeasible pool, find the 'best' one (the one that failed by the smallest margin on profit/CO2).\n", " best_infeasible = min(compliant_infeasible, key=lambda x: x['penalty_score'])\n", "\n", " violations = best_infeasible['feasibility']['violations']\n", " metrics = best_infeasible['metrics']\n", "\n", " print(\"After enforcing your core Ocean and Resilience rules, no strategy was feasible.\")\n", " print(\"Analysis of the 'closest compliant' strategy shows the following financial gaps:\")\n", " recommendations = []\n", "\n", " # 3. Generate advice based on the gaps in this 'best compliant' strategy.\n", " if violations['profit'] > 0.05:\n", " required_M, actual_M = self.hard_constraints['min_profit_M'], metrics['profit'] / 1e6\n", " gap_M = required_M - actual_M\n", " if gap_M > 0:\n", " new_profit_target = math.floor(actual_M)\n", " recommendations.append(f\" 1. Lower the 'Minimum Profit' from ${required_M:,.1f}M to ${new_profit_target:,.1f}M.\")\n", " print(f\" - PROFIT: Minimum of ${required_M:,.1f}M was missed by ${gap_M:,.1f}M.\")\n", "\n", " if violations['co2'] > 0.05:\n", " required_ktons, actual_ktons = self.hard_constraints['max_co2_ktons'], metrics['sustainability']\n", " gap_ktons = actual_ktons - required_ktons\n", " if gap_ktons > 0:\n", " new_co2_target = math.ceil(actual_ktons * 10) / 10\n", " recommendations.append(f\" 2. Increase the 'Maximum CO2' from {required_ktons:,.2f} to {new_co2_target:,.2f} k-tons.\")\n", " print(f\" - CO2: Maximum of {required_ktons:,.2f} k-tons was exceeded by {gap_ktons:,.2f} k-tons.\")\n", "\n", " if recommendations:\n", " print(\"\\n**Recommendation:** To find a feasible solution while respecting your Ocean/Resilience rules,\")\n", " print(\"you must make a trade-off. Re-run with ONE of the following adjustments:\")\n", " for rec in recommendations:\n", " print(rec)\n", " else:\n", " print(\"\\nNo single clear adjustment found. The closest strategy fails on a combination of factors.\")\n", "\n", "def get_interactive_inputs():\n", " \"\"\"Guides the user through a questionnaire to build the decision_input dictionary.\"\"\"\n", " # Define the preset profiles for easy selection.\n", " profiles = {\n", " \"1\": {\"name\": \"Profit First\", \"weights\": {\"profit\": 60, \"resilience\": 10, \"sustainability\": 30}},\n", " \"2\": {\"name\": \"Balanced Growth\", \"weights\": {\"profit\": 40, \"resilience\": 30, \"sustainability\": 30}},\n", " \"3\": {\"name\": \"Resilience First\", \"weights\": {\"profit\": 20, \"resilience\": 50, \"sustainability\": 30}},\n", " }\n", "\n", " # --- Step 1: Get Strategic Priorities (Weights) ---\n", " print(\"=\"*80 + \"\\nStep 1: Choose a Strategic Profile\\n\" + \"-\"*80)\n", " for key, val in profiles.items():\n", " print(f\" [{key}] {val['name']}\")\n", " choice = input(\"Select a profile [1-3] or press Enter for custom: \")\n", "\n", " if choice in profiles:\n", " weights = profiles[choice]['weights']\n", " print(f\"Selected '{profiles[choice]['name']}'.\")\n", " else:\n", " # Custom weights input if the user doesn't choose a preset.\n", " print(\"\\n-- Custom Priority Weights --\\nPlease allocate 100 points across the following objectives:\")\n", " weights = {}\n", " while True:\n", " try:\n", " weights['profit'] = int(input(f\" - Points for Profit: \"))\n", " weights['resilience'] = int(input(f\" - Points for Resilience: \"))\n", " weights['sustainability'] = int(input(f\" - Points for Sustainability (Low CO2): \"))\n", " if sum(weights.values()) == 100:\n", " break\n", " else:\n", " print(f\"Error: Points sum to {sum(weights.values())}, not 100. Please try again.\\n\")\n", " except ValueError:\n", " print(\"Error: Please enter whole numbers.\\n\")\n", "\n", " # --- Step 2: Get Hard and Soft Constraints ---\n", " print(\"\\n\" + \"=\"*80 + \"\\nStep 2: Define Constraints (Your Non-Negotiable Rules)\\n\" + \"-\"*80)\n", " hard_constraints, soft_constraints = {}, {}\n", " try:\n", " hard_constraints['min_resilience_score'] = int(input(\" - Min Resilience Score (10, 60, 100): \"))\n", " hard_constraints['min_profit_M'] = float(input(\" - Min Profit (in millions): \"))\n", " soft_constraints['target_profit_M'] = float(input(\" - Target Profit (in millions): \"))\n", " hard_constraints['max_co2_ktons'] = float(input(\" - Max CO2 (in k-tons): \"))\n", " soft_constraints['target_co2_ktons'] = float(input(\" - Target CO2 (in k-tons): \"))\n", " hard_constraints['max_ocean_share'] = float(input(\" - Max Ocean Freight Share per Market (0.0 to 1.0): \"))\n", " except ValueError:\n", " print(\"\\nInvalid input. Using default values for demonstration.\")\n", " hard_constraints = {'min_resilience_score': 60, 'min_profit_M': 210, 'max_co2_ktons': 8.5, 'max_ocean_share': 0.8}\n", " soft_constraints = {'target_profit_M': 220, 'target_co2_ktons': 8.0}\n", "\n", " # Return all the user inputs in a single dictionary.\n", " return {\"objective_weights\": weights, \"hard_constraints\": hard_constraints, \"soft_constraints\": soft_constraints}\n", "\n", "if __name__ == '__main__':\n", " \"\"\"\n", " Main execution block. This code runs when the script is executed directly.\n", " \"\"\"\n", " # 1. Get all the necessary inputs from the user.\n", " executive_inputs = get_interactive_inputs()\n", " # 2. Create an instance of the decision engine with those inputs.\n", " sde = StrategicDecisionEngine(executive_inputs)\n", " # 3. Run the full analysis.\n", " sde.run()" ], "metadata": { "id": "9kuPeQlZw37s" }, "execution_count": null, "outputs": [] } ] }