Write a minimum of 250 words for each of the discussion questions below:

1. Explain how simulation is used in the real world. Provide a specific example from your own line of work, or a line of work that you find particularly interesting.

2. Identify that parts or aspects of a simulation process that you have found to be particularly challenging. Describe why you believe that such aspects of simulation are challenging and provide remedies to simplify those aspects.

Reply stu a

Manufacturing sector is common to use simulation methods in order to successfully experiment, validate, design, and configure the product for the end user (Mourtzis et al., 2014). Creating the finished product comes from the information that is given and then and using the raw materials to create the finished goods. I have remembered in the past that at my previous job, simulation is implemented in order to make sure that the products are followed through the simple sequence in order to identify where there are opportunities to look at different scenarios on what will happen and when it will happen if one or more of the sequences are being stopped. The manufacturing plant consists of these following processes, which include receiving, production processing, packaging, and shipping (“Example Simulation Models”, n.d.). Also, the testing plant consists the processes of assembly, electrical testing, software loading, final testing, and packaging (“Example Simulation Models”, n.d.). The use of simulation can determine whether if one or more of the raw materials can be defective along the process from the testing plant. Simulation is a useful tool to use in manufacturing in order to identify how many products that will be made based on the consumer tastes/client needs in order to keep up with the demand by having random numbers generated depending on the product line(s). In addition, the needs to use that tool shows that it can identify what can the manufacturer do to improve the process quicker whether there will be a scenario on needing the latest technology in order to outpace the competition and to maximize its sales. I remembered that in the past, simulation is used to randomly determine if the product will take a longer or shorter time to finish, and to acquire the raw materials at a timely manner if needed.

Manufacturing presents a numerous amount of challenges in simulation that can range from keeping up with the demand to implementing the latest technology that will be used in order to create a more innovative product for the end users. One major challenge that is notable in manufacturing is keeping up the globalization trends that is going to fulfill the needs for the international clients/customers that are interested in their products. Another major challenge that is used in simulation is to implement new technological tools as there are always a need to be current with the global trends and to research new ideas on what will be needed to become a top leader in the industry. Customization is another component that is a growing challenge in manufacturing as there are always a different set of samples that are generated when there are many combinations. By having a large amount of combinations in custom made goods, there will never be an end to simulation as there are always a new line of product that will be introduced, which it will become another set of simulations that will needed to be generated. There are many characteristics that go along with the customization such as color, shape, size, part, among others. Even though, technology makes the information becoming easier to access for data, the challenges that impacts it consists of machine learning, artificial intelligence, visualization, and big data (McGinnis & Rose, 2017). From those advancements, the technology makes the modeling much more complex as there are many changes where information can be gathered in real-time updates. Challenges also comes with rewards when the end goal is to constantly make improvements in order to get results quicker or improve the visualizations.

Reference:

“Example Simulation Models. (n.d.).” University of Houston. Retrieved from https://uh.edu/~lcr3600/simulation/models.html

McGinnis, L.F. & Rose, O. (2017). “History and Perspective of Simulation in Manufacturing.” INFORMS. Retrieved from https://www.informs-sim.org/wsc17papers/includes/files/027.pdf

Mourtzis, D., Doukas, M. & Bernidaki, D. (2014). “Simulation in Manufacturing: Review and Challenges.” ScienceDirect. Retrieved from https://core.ac.uk/download/pdf/82026304.pdf

Stu B:

I work in the banking industry, and a key part of our risk management process is to produce stress tests using different economic scenarios. The reason for doing this is to get an idea of how our portfolio’s probability of default (PD) may change given predicted changes in the economic environment. For example, let’s say we have determined that our portfolio’s PDs are linearly correlated to the unemployment rate using regression analysis. However, according to forecasts, the unemployment rate is expected to increase to 7%. In this case, we would want to use our regression model to determine how our PDs are expected to change given this forecasted spike in the unemployment rate. If the forecast period predicts a 2% increase in our PD rate, we would take this into consideration and allocate funds accordingly.

A common means of getting these forecasted variables is to use the economic scenarios provided by Moody’s. Moody’s provides forecasts for scenarios such as “Stagflation” and “Low Oil Price”. There are even scenarios related to topical issues such as the coronavirus. Moody’s is able to generate forecast values using simulation analysis: “We…develop the basic outlines of our alternative scenarios by running multiple simulations to develop a probability distribution of economic outcomes. From this, we produce fully-fledged economic scenarios” (Moody’s Analytics, 2020). Prior to running the simulations, Moody’s addresses two sources of uncertainty: “(a) shocks to original random variables in the model (e.g., policy surprises, productivity gains/losses, shocks to consumer preferences, etc.) and (b) the fact that estimated parameters are random variables” (Licari, 2015). However, the distributions derived from the simulated values allows Moody’s to estimate the values for shocks and parameters to generate meaningful forecasts.

Although I am not doing the simulations myself, I have noticed from using these forecasts that the forecasted values are constantly changing. This poses a challenge in banking because if we make decisions about our portfolios based on a forecast, we make the assumption that the forecast will not drastically deviate from the actual outcome. However, Moody’s has to constantly change the forecasted values for the baseline and adverse scenarios to account for new global and economic challenges that may arise. Economies can be particularly volatile because unprecedented issues that were impossible to predict can affect the economy greatly. For example, the coronavirus is something that nobody predicted, yet it may be the biggest threat to the global economy that we have seen in years. From a risk management perspective, the best way to prepare for events like this is to use a “worst case” scenario with extreme values to ensure that the bank is prepared to take a hit if the time comes.

References:

Moody’s Analytics. (2020). Economic Scenarios. Retrieved February 25, 2020 from https://www.economy.com/products/alternative-scenarios/standard-scenarios

Licari, J. (2015). Multi-Period Stochastic Scenario Generation. Retrieved February 25, 2020 from https://www.moodysanalytics.com/risk-perspectives-magazine/risk-data-management/principles-and-practices/multi-period-scenario-simulations