Introduction
Multiple probability simulations, or Monte Carlo Simulations, are a type of mathematical modelling used to assess the range of events that could occur in the face of uncertainty. Since random variables introduce unpredictability into a process, a Monte Carlo simulation is used to model the probability of various outcomes (Kaya, Ozturk, & Sariguzel, 2021). It is a method for analyzing how potential outcomes change when risk and uncertainty are included. To simulate something is to represent it as though it were something else. According to Pokorádi (2022), if privacy laws allow, providers will be able to use Monte Carlo clinical data analysis to reduce waste and enhance patient outcomes through individualized therapy. Education, assessment, research, and health system integration play significant roles in patient safety, which is why healthcare simulations are so important.
Monte Carlo simulation will be used to figure out the optimal production strategy for the prescription medicine Prizdol for the NuFeel company. A look at the market’s supply and demand has been made based on the collected data. Annual Prizdol demand has a mean of 50,000 units, with a standard deviation of 12,000. Prizdol’s expected demand is depicted in the following figure.
Total/ Profit Demand
The estimated profit margin over the next decade is as follows: $0.04 per capacity unit per year in fixed costs; $0.02 per manufactured unit per year in variable costs; $3.70 per sold unit in revenue; and the cost of the facility is approximately $16 per capacity unit. The management of NuFeel has made it clear that they want the company to make only the products that customers want. Figure 1 depicts the results of many situations ranging in size from 30,000 to 60,000 units. The following equations were used: A is the available manufacturing capacity, and B is the annual demand for Prizdol.
The annual capacity numbers for a business at 30,000, 35,000, 40,000, 45,000, 50,000, 55,000, and 60,000 units. With the help of simulation, we can determine that the optimal capacity level is 50,000 in terms of expected profit. Figure 1 depicts a normal distribution with a mean of 50,000 units each year. Therefore, if you want to maximize profits from the Prizdol drug, 50,000 units is the optimal production level. Mean was 99%, minimum and maximum were both 1%. Figure 2 below summarizes the results from the overview of total profit achieved after the ten years in several scenarios.
Results of The Monte Carlo Simulation
As indicated, a production capacity of 50,000 units garners the maximum mean total profit. When looking at lower capacity levels, total profit is pressured by factory capacity, causing an increase in overall profit if there is an increase in capacity. On the other hand, when the capacity values are higher, the profit is no longer contained by the capacity of the factory (Shalyari e al., 2019). Therefore, one can know that the total profit depends on the demand for Prizdol. The results obtained illustrate the units of Prizdol sold equate to product demand resulting in the highest profit margins for the organization. It considers the mean expected total profit for a production capacity of $632,400.27 and a standard deviation of $81,825.50.
It has been established that a manufacturing capacity of 50,000 units yields the highest mean total profit. By comparing lower capacity levels, we see that an increase in industrial capacity leads to a rise in overall profit. In addition, the factory’s profit potential limits disappear at greater capacity values. Therefore, it is deduced that the overall profit is linked to Prizdol’s market popularity. The data showed that the company’s profit maximization occurred when the number of units of Prizdol sold was directly proportional to the number of units expected to be purchased. However, the company can be 95% confident that the range of total profit is $428,481 to $736,393 if the mean predicted total profit for a production capacity is $582,437, and the standard deviation is $78,548.
Conclusion
Simulations have several applications in healthcare, including training and study. The field of simulation has made significant strides in recent years. In this report, simulation provided a venue for sharing scholarly practice to promote the use of simulation in the context of health and social care. Many types of distributions can be used in simulations. In order to discover the optimal option, healthcare administrators can perform many simulations. Use of simulation models has the potential benefit of providing different outcomes and rapid response on the output variables. The Prizdol medication project showed how uncertainty can be resolved through the use of many simulations in a safe and effective manner.
References
Kaya, G. K., Ozturk, F., & Sariguzel, E. E. (2021). System-based risk analysis in a tram operating system: Integrating Monte Carlo simulation with the functional resonance analysis method. Reliability Engineering & System Safety, 215, 107835. Web.
Pokorádi, L. (2022). Monte-Carlo Simulation-based Analysis of Bridge Structure Systems’ Reliability. In IOP Conference Series: Materials Science and Engineering (Vol. 1237, No. 1, p. 012004). IOP Publishing. Web.
Shalyari, N., Alinejad, A., Hashemi, A. H. G., RadFard, M., & Dehghani, M. (2019). Health risk assessment of nitrate in groundwater resources of Iranshahr using Monte Carlo simulation and geographic information system (GIS). MethodsX, 6, 1812-1821. Web.