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Static and dynamic simulation models are the two types of simulation models. Static simulation models feature systems that do not alter or evolve over time, as well as independent trials in which the findings of one trial have no impact on what happens in subsequent trials (Anderson et al., 2016). Similarly, dynamic simulation models are simulation models that must account for how the system changes or evolves over time (Anderson et al., 2016). When creating a data model, it is critical to consider CPU utilization, data partitioning, record size, and other factors. When a model is built on a worst-case scenario, maximum values are used. For example, if a variable has a character limit of 100, the model believes that the variable always has 100 characters (Static and Dynamic Models, 2022). A model that is based on a best-case scenario assumes that no single input record is discarded anywhere in the data flow. However, the accuracy of the model depends on factors such as the schema definition which refers to the size of the record, input data and the parallel processing environment (Static and Dynamic Models, 2022).

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A current problem my company is facing is that there is more employees than work needed to be done. Right now we are in the slow season of my work. That mean less people using our services and a bit of over staff. There’s a bit of uncertainty and a situation model can be used to access and address the issue. Simulation models are used for uncertainty in decisions specifically in business. A few ways we can address it is one market and promote ourselves more maybe reach a different target demographic per say. This would build more work to be done and usher in possibly new clientele. Two we could address it internally and offer VTO ( volunteer time off ) to our employees. It would save the company money , prevent idle time and overall success. On the extreme end layoffs, but that wouldn’t be the case as once the slow season is over business picks up quie a bit and can be daunting if we don’t have the man power to run business. The more likely situation would be to offer VTO to workers as it would assist with the slow time and two it would keep employees happy.

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A simulation model can be used to better inventory management by simulating future needs from historical and current data. Anderson et al., (2016) mentions that simulation allows for the consideration of different operating policies and changes to model parameters and then observe the impact of the changes on output measures such as profit or service level. As opposed to other modeling techniques such as linear and nonlinear modeling, simulations provide more likely outcomes and the user can tailor the inputs to determine an array of outcomes that may be singular in the form of a range. Additionally, altering assumptions or operating policies in a simulation model and rerunning it can provide results to understand how such changes will affect the operation of the real system (Anderson et al., 2016).

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I understand that simulation models are best for unknown situations that businesses are trying to find a solution to. It typically uses guesstimates for the data since there is uncertainty behind it. Recruitment, for example, is always unknown. Recruiters can find 20 candidates to interview and only 5 make it through as employees. I love simulation models for this aspect because there are many things dealing with a business that comes with the unknown but a solution must be given to figure out the next steps to take on the particular project. Do you think simulations give enough accurate information for companies to confidently use? Explain.

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One model that I addressed previously was the utilization of the decision tree model, which is utilized as a tool for selecting the ideal option among the alternatives. A decision tree often begins by graphically representing the problem as the initial node or root of a diagram and the various solutions as its branches (Anderson et al., 2016). Comparing the decision tree model to the simulation model is neither intuitive nor practical since the simulation model involves analyzing the impact of uncertainty on decisions being made. For example, if I am making plans for the weekend I have various possibilities, like going to the beach, seeing family, or going to the movies. My choice is influenced by a number of things, including the weather and whether or not I will get paid this week. I will go to the beach if the weather permits, if the weather is poor, my next decision will be influenced by whether or not I get paid; if I do get paid, I will go to the movies; if not, then I will visit my family. Utilizing the decision tree would be more effective since the simulation model focuses on uncertainty and all decisions being made in the example are dependent on a variable.

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In topic four discussion question one, I wrote about the benefits of using a linear optimization model in order to enhance my organization’s logistical processes. As opposed to utilizing linear modeling, simulation allows for the study of the subject’s range of behavior and performance (nonlinear) versus looking at one likely outcome (linear). In my opinion, if one wants to refine techniques and/or conduct a what-if analysis, then a simulation would be the best option. It is more fluid and interactive than a chart because it allows the user to enter different inputs to see how they effect the result. If the user has fixed values that ultimately lead to one general outcome, then a linear model would be used. Depending on one’s knowledge of the problem and available routes of studying and modeling it, the appropriate model is likely to be used. In addition, personal preference may be a contributing factor in the selection as well. Overall, as modeling is mainly about trying to predict outcomes, there will always be uncertainty in doing so. Therefore, when making a decision in the presence of uncertainty, decision makers should not only be interested in the average or expected outcome, but they should also be interested in information regarding the range of possible outcomes (Anderson et al., 2016).

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Linear programming focuses on producing results for companies or businesses that are looking for the minimum or maximum for their problem. For example, a company wants to know which department requires more funding compared to others. The company is looking to spend the minimum amount of money but yield maximum results. With simulation models, they compare the impact on uncertain decisions being made such as investments, project management, and marketing. There are several different models that can be used to find the best, base, or worse scenario, and based on the company’s wants/needs they will choose accordingly and create a simulation within excel. The problem at hand determines which model should be used. If it involves data that is certain linear programming should be used, however, if the data is unknown and uncertain, a simulation model should be used.

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