In the field of operations management, simulation is the second most popular technique after modeling. Health and healthcare operations employ computer simulation methods in various activities. The economic evaluation of health technologies and health products forms the primary applications of simulation technology in the healthcare sector. Some of the simulation modeling techniques that can be employed in the healthcare sector include the Monte Carlo Simulation, Discrete-Event Simulation, System Dynamics, and Agent-Based Simulation. Other simulation techniques include intelligent simulation, traffic simulation, distributed simulation, simulation gaming, virtual simulation, and Petri-Nets.
Simulations are also evident in other sectors. Depending on the sector and the uncertainty being addressed, different simulation techniques and models can be employed. The operation of an organization, whether manufacturing or a service company, may be faced with uncertainty that might require modeling. Operations models are widely employed in addressing such problems. Some of the applications of operations models, including bidding for contracts, estimation of warranty costs, and drug production with uncertainty yield ( Albright et al., 2017 ). Organizations can also develop financial simulation models to analyze uncertainty variables, such as future cash flows, future stock prices, and future interest rates, among others. Organizations develop advanced financial planning models to help in capital budgeting and financial planning processes.
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Elsewhere, organizations may also employ a cash balance model. Such a model is used to track company cash balance over time. Such models are used in crucial decision-making processes, particularly when an organization wishes to take short-term loans to maintain the minimum cash balance ( Albright et al., 2017 ). Financial models, particularly the investment models, can also be developed by organizations to help them in selecting specific investments that will facilitate the achievement of the set goals.
Marketing models are also quite common. Marketing departments may employ simulation models in various applications. These departments are often faced with uncertainties, especially when analyzing the brand-switching behavior of customers, the entry of a new brand into the market, customer preference for different attributes of a given product, and the impact of advertisement of sales ( Albright et al., 2017 ). Therefore, it is common to see models of customer loyalty and marketing and sales models. Irrespective of the sector, Monte Carlo simulation, along with discrete-event simulation, is the widely used simulation techniques.
Problem 45
In part A, a simulation was created to examine the cost at different levels of production along with the revenue linked to the random demand. Upon running seven simulations with 1000 iterations, the total profit was maximized when the capacity was 50,00. The mean profit when the capacity was 50,000 was $ 582,469.64.
| Name | Capacity | Min | Mean | Max | 5% | 95% |
| Total Profit | 30,000 | $337,088.30 | $441,694.17 | $450,000.00 | $406,842.38 | $450,000.00 |
| Total Profit | 35,000 | $358,070.41 | $503,777.51 | $525,000.00 | $449,488.34 | $525,000.00 |
| Total Profit | 40,000 | $363,070.41 | $552,437.47 | $600,000.00 | $471,921.36 | $600,000.00 |
| Total Profit | 45,000 | $363,314.30 | $580,630.89 | $675,000.00 | $470,949.88 | $660,317.97 |
| Total Profit | 50,000 | $323,782.01 | $582,469.64 | $750,000 | $447,824.43 | $691,613.27 |
| Total Profit | 55,000 | $251,312.74 | $555,628.24 | $802,822.81 | $391,577.28 | $695,990.91 |
| Total Profit | 60,000 | $168,812.74 | $502,434.52 | $796,540.22 | $314,995.16 | $669,841.38 |
In part two, a 95% interval around the mean total was computed.
They can be 95% certain that the profit will lie between $ 417,167.25 and $ 722,719.02.
Reference
Albright, C., & Winston, W. (2017). Business Analytics: Data Analysis and Decision Making. 6 th Ed. Cengage Learning.
