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DSC Tech Library

Contact Center Equipment

telecommunications software solution This section of our technical library presents information and documentation relating to Call Center technology and Best Practices plus software and products. DSC is a leading provider of contact center technology and software solutions as well as predictive dialer phone systems for the modern call center. Customer contact center software includes CRM software and computer telephony integration solutions. These modern products help call center phone agents communicate effectively with your customers and prospects.

The following article presents product or service information relating to call centers and customer service help desks.

Call Center Simulation Modeling:
Methods, Challenges, And Opportunities

Page 7

By Vijay Mehrotra, Department of Decision Sciences
College of Business - San Francisco State University

Jason Fama, Engineering Group, Blue Pumpkin Software Inc.

5.2 Numerical Results

5.2.1 Determining the Number of Replications

For each of the individual scenarios that are discussed below, we ran multiple replications of the simulation model and computed estimates for performance measures based on the average of the run length.

For purposes of determining the number of runs for each scenario, we focused on average weekly Service Level for the Inbound queue as the statistic of interest. After each run, we would examine overall standard deviation of this statistic across all runs to date. We continued to run additional iterations until this overall standard deviation was under 2.5%, which we had set arbitrarily as our confidence threshold.

5.2.2 Base Case

Our baseline scenario is one with no Outbound Cross- Trained agents. This base case is listed as Scenario 1 in Table 1 below.

From this base case, it was clear that the Inbound Agent Group alone cannot deliver the desired Service Level (80% within 60 seconds), and that the Abandonment Rate is also much higher than desired.

5.2.3 Varying Cross-Training Levels

We then began to vary the number of Outbound-Skilled Agents who were included in the Cross-Trained Outbound group, assuming for these initial experiments that Inbound calls would immediately overflow to Cross-Trained Outbound agents whenever all Inbound Only agents were busy. The impact of this cross training on the population of Inbound callers is dramatic, as even limited cross training has a big impact on Service Levels and Abandonment Rates. In addition, there is an equally obvious negative impact of this cross training on the Outbound call statistics. These trade-offs are evident in Table 2 below. Based on these preliminary simulations, we chose to focus on cross-training a total of 30-40 Outbound agents. From here, we turned our attention to defining parameter for how long Inbound calls should wait before being made available to Cross-Trained Outbound agents.

5.2.4 Varying the Wait Time Parameter for Overflowing Inbound Calls

Results for different scenarios associated with 30 and 40 Cross-Trained Outbound agents are shown in Tables 2 and 3.

5.2.5 Summary

The different scenarios that we have simulated have enabled us to (a) hone in on the right levels of cross training to meet the Service Level goals with the current staffing levels and (b) examine trade-offs between different scenarios in terms of the key model outputs.

For example, consider Scenarios 3, 10, 14, and 15, all of which deliver SLs at or above the 80% target. The answer to which of these is the “best” choice will of course depend on the relative value of RPCs, Service Levels, and Abandoned customers. However, it is interesting to note that Scenario 3 produces essentially the same SL and RPC values as Scenarios 10 and 15 – but with a substantially higher abandonment rate. In turn, the tangible difference between Scenario 14 and 15 enables managers to explicitly quantify the level of increased Service Level and decreased abandonment rates against the decreased number of RPCs.

Finally, it is worth mentioning that while we have shown summary statistics for sixteen scenarios here, it is relatively easy for us to produce more detailed statistics and also to vary different parameters to examine any number of other cases. This flexibility, in turn, enables managers and analysts to develop a sense for system dynamics and also to proactively answer common senior management questions such as “what would a 10% increase in call volume next week do to us?” or “what is the value of adding an outsourcer to help us during our peak months?”

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