Simulation
- Simulations Overview: Simulations run multiple trials to analyze how a system behaves under various scenarios and to compare different strategies.
- Random Sampling: Utilizes random sampling to deal with uncertain inputs, which is essential in real-world scenarios where parameters are not precisely known.
- Monte Carlo Simulation: A crucial tool in operations management, used when making decisions under uncertainty (e.g., market demands).
- Helps explore uncertainties by conducting simulations, providing insights into risks and possible outcomes.
- Generating Random Numbers: Monte Carlo simulations generate random numbers through computer algorithms, often using Excel functions.
- Demand Example: Utilizes historical data (e.g., weekly candy sales) to estimate supply quantities needed, demonstrating cumulative demand probabilities and how they relate to random number assignments.
- Average Revenue Calculation: Demand values correspond to set random numbers, and revenues are calculated accordingly.
- Expected Value Method: Established comparison of simulation results with traditional expected value calculations to assess typical demand, emphasizing the importance of extensive simulations for better accuracy.
- Excel Random Function: Functions like
RAND()andRANDBETWEEN()are essential for generating random numbers within a specified range and can be utilized for various models. - Cumulative Probabilities: Critical in determining ranges of random numbers assigned to given probabilities, directly impacting decision-making.
- Practical Application of Simulation: Use of tables to map random numbers to different sales categories and calculate corresponding profits based on demand and selling prices.
- Case Analysis: Discusses the importance of case analysis and helps in understanding forecasting and supply chain demand as part of operational strategies.