Most of the ATMs have the problem of long queue of customers to undergo simple transaction at the peak hours and remain idle due to the lack of customer entry at the off peak hours.
Apart from ATM problem, simulation with queuing model had been used for various applications too:
According to Pieter Tjerk de Boer (1983), substantial focus has been dedicated to the estimation of overflow probabilities in queuing networks. A different adaptive method has applied to queuing problems than in the present work with few simple models been considered.The article of S.
S. Lavenberg(1989) has discussed that simulation is feasible for statistically studying a complex queuing model. Moderate simulation durations are found to be sufficient to obtain precise confidence interval estimates. As current configuration at each step of savings or insertion procedures is possibly infeasible, thus the alternative configuration is one that yields the largest savings in some criterion functions with these procedures can be found in Clarke and Wright (1964) or in Solomon (1987).
Christofides et al (1981) has discussed Lagrangean relaxation procedures for the queuing of customer in front of ATM.
Interactive optimization is incorporated into the problem-solving process with adaptations of this approach to queuing are presented by Krolak et al (1970). Brame and SimchiLevi (1995) has illustrated simulation model is worked by initially introducing the location based heuristic for general queuing problem as a location problem which is solved subsequently and the solution is transformed into solution to the queuing problem.
Simulation technique in queuing model is used for solving ATM waiting time problem since this problem cannot be solved with mathematical techniques and physical experimentation. Simulation technique helps identifying the pitfalls of existing 3 ATM services of 3 different banks at VIT (Vellore Institute of Technology). Initially, the simulation is being applied to see the rate of entry and exit, the waiting time of a customer with the ATM machine’s idle time after observation of the similar and continuous trend on weekdays and weekends separately. The next step i.e. utilize queuing model to examine the number of customers in the system to the customers wait before being served, thereby it proposes a new ATM service from any of these banks or other than the existing banks based upon the service required from the customers. A suitable simulation technique is also formulated to reduce idle time of servers and waiting time of customers for any bank having ATM facility.
The overall result shows the comparison between the three banks based on several characteristic, by applying mathematical formula, the simulation technique and queuing theory. From the simulation result, Indian Bank weekend has the lowest expected time customer spends in the system, 80; whereas Indian Bank weekdays free and Centurion bank have the highest in expected time customer spends in the system that is 120. Apart from that, the result also shows Indian Bank weekdays free and Centurion Bank has the 0 time for the customer expected to wait, whereas SBI weekend has the highest expected waiting time per customer, 12. From the queuing result, Centurion Bank has the lowest expected time customer spend in the system that is 189, whereas Indian Bank weekdays free, SBI weekdays free and SBI weekend has showed the highest, 250. For the expected waiting time per customer, Centurion Bank has showed the lowest 69, whereas SBI weekend with the highest result, 159.
By taking one day as a standard, a heavy crowd is found in prime hours during the weekdays in Indian Bank and SBI ATMs, the equipment ATM is 100% utilized by the customers. Utilization factor for Indian Bank and SBI in the non-busy hours is 50% and 55% respectively. In weekend period, the utilization factor for Indian Bank and SBI is 62% and 64% respectively. The reason shows that the SBI has obtained the highest utilization factor among the other 2 banks is because of the customers do not face the “Out of Service Problem” which is frequently occurred in Indian Bank and Centurion Bank, with an average of two times in a week. However, it takes more time to reload the currency in the ATM machine than Indian Bank in SBI. Few customers have the ATM transaction with Centurion Bank because of the dissatisfaction on its customer service and the minimum number of branches throughout the India. In addition, from the tabulate results, SBI ATM has obtained the minimum Ws and Wq than the other two banks which imply that it has attained the customer satisfaction on its services.
Every aspect that has an effect on making the waiting time longer in the ATM should be taken into the consideration. The aspects like the people that are not well versed with ATM, customers who have stand in the queue and leave, the time the workers take to feed the ATM with currency, out of stock situation and holidays which mostly after exams the utility of ATM should be taken into account of the waiting problem. Another recommendation is the sample size should be extended into larger sample size and more days of observation to obtain more accurate results. In addition, the consideration of waiting cost and service cost can help developing an efficient procedure for ATM queuing problem and to find out the best ATM facility.
Queuing Model and Simulation Model(SM) are used to define the queuing problems in terms of decision making to reduce the customer’s waiting time. After comparing the customers’ behaviour on different TM service at VIT, a new ATM machine (SBI) should be installed in men’s hostel to facilitate more customers towards the service by reducing the customers cost and service cost for the long run’s benefit.
In the last ten years, simulation software and methodology has been developed and used in the bank services. The previous sections have shown that using the simulation technique can increase sustainability of a bank with better customer service and enhanced customer satisfaction. However, this simulation technique is not limited only in modeling the customer in a bank, but the same concept can also be applied in defining and analyzing the model of a system or problem in the banking areas. Other areas can be modeled in banking areas such as customer flows to evaluate alternative layout within a branch bank, cash flows between branches and the bank’s central office. Once these models developed, it can easily be used to examine the effect of different parameters on the variables in the model.