This repository showcases the creator's expertise in generating decision trees for operational risk management within supply chain contexts. It features models and analytical tools aimed at identifying, assessing, and mitigating potential risks, thus improving decision-making and operational efficiency in supply chain management.
1. Technology Selection for Enhancing Coal Mining Safety- Steve Hinton analyzes the costs and potential profits associated with using different communication technologies (microwave, cellular, infrared) to enhance safety in the coal mining industry. Steve Hinton's decision tree leads to selecting the infrared technology as the most cost-effective, with the best balance between equipment costs and R&D expenditure.
2. Insurance Purchase Timing for Hurricane Protection- A manufacturing company in Miami evaluates whether to purchase hurricane insurance six months before the season or at the beginning, considering the cost differences and probabilities of hurricane occurrences.The decision tree analysis recommends purchasing the insurance policy 6 months before hurricane season as the most financially advantageous strategy, saving on higher costs and potential losses.
3. Real Estate Investment Decision Making- An investor assesses the potential profits from different types of real estate (apartment, office, warehouse) under varying market conditions (strong vs. weak markets). The decision tree concludes that purchasing an apartment building offers the most stable profit across different market conditions, making it the best investment choice.
4. Bidding Strategy for Oil and Gas Leasing Rights- Southern Gas Company (SGC) decides on a bidding strategy (high, low, or not bidding) for oil and gas leasing rights in the Gulf of Mexico, considering competitors' actions and geological data. SGC's decision tree analysis reveals that placing a high bid of $16 million is the most profitable strategy, with an expected profit of $107 million considering all probabilities and outcomes.