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In the decision-making process, ambiguity, variability, and uncertainty are common challenges. This is why Risk Analysis plays a crucial role in any decision-making process. Even with unprecedented access to information, accurately predicting the future remains a challenge. To address this, Monte Carlo simulation is utilized to provide a comprehensive view of all possible outcomes, assess the impacts of risks, and facilitate better decision-making in uncertain situations.
Monte Carlo Simulation:
Monte Carlo Simulation is a computerized mathematical technique that relies on a broad class of computer algorithms.
It enables individuals and organizations to calculate risks, leading to improved decision-making processes.
This technique can be described as a problem-solving approach that calculates the probability of outcomes by utilizing random variables and conducting multiple trials, known as simulations (Berg, 2004). Professionals across various fields such as engineering, medicine, physics, chemistry, project management, manufacturing, research and development, environmental studies, oil and gas, and various business functions utilize Monte Carlo Simulation. This reflective report focuses on the case study of the Fennel Design Project at Laura Watson's company to predict the demand for greeting cards.
The objective of this report is to analyze the Fennel Design project's situation and provide insights for companies facing similar circumstances.
This report utilizes discrete data within a continuous range.
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Discrete data involves scenarios with a limited number of possibilities, such as a coin flip resulting in either heads or tails. On the other hand, continuous data involves a range of values, like the fluctuating temperatures of a running engine. As a new company, Laura Watson's business may encounter uncertainties related to production timing, supply and demand analysis, and other influencing factors.
Monte Carlo Simulation is employed to forecast demand, conduct risk analysis, and offer valuable information for informed decision-making.
The report is structured into three main sections.
While Monte Carlo Simulation allows for numerous trials to achieve accurate results, the case study conducted by business managers Alex and Laura involved one thousand trials. Increasing the number of trials enhances the probability of profitability, provides closer results, and minimizes risks. Descriptive Analysis Function in Microsoft Excel was utilized to calculate the card prices, automatically generating statistical data like mean, median, mode, and standard deviation. In the second task, WHAT IF analysis was employed to explore different scenarios within the specified number of trials, including base case, best case, and worst-case scenarios.
Adjusting the values of these scenarios leads to automatic changes in outcomes, aiding managers in decision-making processes. The third task involved using risk simulation and random functions to strike a balance between mean and standard deviation concerning the projected demand. The Rand command was used to determine part costs, while random discreet methods and Variance Reduction were employed to minimize inaccuracies in profits. Each function was independently calculated in this report to enhance clarity and understanding. Through Monte Carlo Simulation, all necessary parameters in the case study were computed, and risks were predicted, enabling managers to make swift and informed decisions.
Bibliography
Berg, A. B. (2004). Markov Chain Monte Carlo Simulation and their Statistical Analysis. New Jersey: World Scientific.
Enhancing Decision-Making with Monte Carlo Simulation. (2017, Jan 04). Retrieved from https://studymoose.com/management-accounting-report-essay
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