Simulation of a Call Center
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[pic 1][pic 2][pic 3]Table of ContentsOBJECTIVE 3CURRENT SITUATION 3DATA COLLECTION 3FITTING DATA 4DATA ASSUMPTIONS 5MODEL BUILDING 6MODEL RESULTS and PRECISION 6ANALYSIS OF ALTERNATE MODELS 8CONCLUSION 10OBJECTIVEThe objective of the project is to understand the One Stop Information helpline (call center) structure and come up with an improvement in the current resource structure to try to improve the waiting time or total time spent in system for callers.I decided to try to simulate the call center because I had faced very high waiting times while trying to contact them. I decided to use Arena Simulation Software with the help of Arena Process Analyzer to improve the existing structure.CURRENT SITUATIONThe one stop information helpline is open on weekdays from 8AM – 5PM (9 hours). Students who have an issue with their Finances, Registration, Bursar or any other Issue can call the One Stop Information helpline to have their queries resolved. Upon calling the helpline, they are welcomed with a message and asked to enter their University ID. After this, the callers are asked to select the category of their query:FinancesRegistrationBillingOtherAfter selecting one of these options they proceed to join a queue, with an unusually long waiting time to speak to one of 4 agents. The time spent waiting for the agent and service times are very high.This project aims to analyze the benefits of adding additional trunk lines or an additional resource and to see the improvement in waiting times for callers.DATA COLLECTIONData collection for the project proved to be a challenge because the One Stop Office staff were not prepared to share their data, and were unavailable to answer my questions. I was left with the option of calling the One Stop Information helpline and tracking the waiting times for each trial and conducting surveys of multiple call center agents. I was able to collect data about delays in the welcome message and the Interactive Voice response system myself, but had to rely on the agents for data about number of people calling per day, and service times for each call type.
FITTING DATAThe Input Analyzer for Arena was used to fit data to distributions. The following Distributions were fitted to the respective call types. (All times in minutes)Triangular (4,5,7) –Service time for Registration QueriesTriangular (4,6,7) –Service time for Financial queries5+4*Beta (2,1.6) –Service time for Billing QueriesTriangular (7,8,9) –Service time for any Other queriesRegistration queries- Service time[pic 4]Financial Queries- Service time[pic 5]Billing queries- Service time [pic 6]Other queries- Service time[pic 7]Apart from this, the following distributions were also usedWelcome message – Uniform (45,50) SecondsEnter MID – Uniform (15,20) secondsCategory type message – Uniform (30,40) secondsDATA ASSUMPTIONSProportion of calls made and their category. This information was obtained through Call center agent survey25% of calls are made for Registration queries15% of Calls are made for Financial queries 10% of calls are made for Billing queries50%- All other queriesNo information about the number of Trunk lines available was available. It has been assumed in the model that there are 25 available trunk lines which are available for callers. In case a call arrives when all the 25 Trunk lines are seized, then that call will be dropped Inter-arrival time of Exponential (1.8) minutes. This was obtained from a survey question to the agents about the number of people that they talk to every day MODEL BUILDINGArrive Module: The call arrives with a first creation value of Expo (1.8) minutes and inter-arrival time of Expo(1.8) minutesRecord Module: For recording the total number of calls that were made in the system including dropped/ rejected callsDecide Module: A decide module has been used to ascertain the number of Trunk lines that have been seized at that moment. If the number of Trunk lines seized is less than 26 then the call proceeds to seize one of the available trunk lines. Else the call is rejected using a dispose module.Seize and Release Modules: The calls which pass the Decide module proceed to seize one of the available Trunk line resources. This trunk line is released just before the call exits the system after speaking to the agentDelay modules: 3 Delay modules have been used to replicate the following stages in the callSoothing music and welcome messageThe Interactive voice system asks the user to enter their University IDThe user is then instructed to choose the Query category from Registration, Finance, Billing and othersAssign Module: Assign module has been used with the “Disc” function to segment the calls into the 4 categories based on their probabilitiesProcess Module: The Process module has been used along with the ‘Expression’ feature to specify different service times for different categories of queryDispose Module: The caller exits the system after releasing the Trunk line that the caller had seizedFlow Chart Screenshot[pic 8]MODEL RESULTS AND PRECISIONThe model was initially run for 30 replications to determine the number of replications required for a particular precision value.