# Resource Management in FlexSim Modelling: Addressing Drawbacks and Improving Accuracy
## 4 Conclusions
### Highlight the Simulation  
The simulation of the system starts by generating incoming customers with inter-arrival times that are exponentially distributed with a mean of 60 s. A customer entering the queue waits to be served by the service desk. The service time of the server is a log normal 2 distribution with location, scale, and shape values of 90, 3.1, and 0.5, respectively. There is a second possible destination for the customers in the queue: if they wait for more than 200 s, they will leave the system as a frustrated customer. There are some managerial issues here that need to be addressed. Firstly, the owner of the system needs to know the average waiting times for the two categories (happy and unhappy customers). Theoretically, the waiting time of an unhappy customer is always 200 s; for happy customers, this is always less than 200 s. However, this only holds true if a timer is used for each incoming customer to keep track of the waiting times. Timers take up space in the memory and slow down the running speed of the simulation model; hence, a hidden resource is identified to replace the timer, which is the event of a customer entering the queue. Since the simulation software used is FlexSim, a discrete event-based simulation, the use of a free resource in the form of an incoming customer to trigger updates to the waiting times is very clever, as it reduces the number of objects and simplifies the model. However, it is only effective in terms of function, and one needs to consider the ripple effect on the measurement of the waiting time in the queue. From a 12 h run of the model, two startling results were found. Firstly, rather than a fixed waiting time of 200 s, the average, standard deviation, and maximum waiting times for unhappy customers were 237.88, 35.40, and 398.83 s, respectively. Secondly, despite the waiting time for happy customers being limited to less than 200 s, there were 152 happy customers whose waiting time was more than 200 s, representing 152/455 = 33% of them. The average, standard deviation, and maximum waiting times for happy customers were 162.38, 70.88, and 354.84 s, respectively. When one used a process flow to replace the 3D model, the simulation results were similar to the expected results. There were 302 unhappy customers, each of which spent 200 s in the queue. Moreover, there were 453 happy customers, all with waiting times of less than 200 s. The average, standard deviation, and maximum waiting times for happy customers were 127.35, 53.59, and 199.99 s, respectively. From a comparison of the statistics for the 3D model and the process flow, one can easily see that the results were quite different for both happy and unhappy customers. For unhappy customers, the average waiting time in the 3D model was 237.88 s, whereas for the process flow, this was 200 s. For happy customers, the average waiting time in the 3D model was 162.38 s, whereas for the process flow, it was 127.36 s. The 3D model shows a significant increase (118.9%) in the average waiting time for unhappy customers and this is even larger (127.5%) for happy customers. Fortunately, these misleading waiting time figures can be alleviated by applying the technical contradiction model to tackle the measurement error problem of simulations in FlexSim. Three resource levels are discovered in the technical contradiction model. Improvements can be made in three consecutive stages. First, one more event can be added: outgoing customers from the waiting line trigger an update to the waiting time of customers in the waiting line (this is called 3D Model: Stage 1). Second, in addition to outgoing customers from the waiting line, two more events are added: incoming customers to the service desk and outgoing customers from the service desk (this is called a 3D Model: Stage 2). Third, besides the events in the second stage, one more event is added: when the waiting time is not updated for more than 30 s, the system will update it automatically (this is called 3D Model: Stage 3).