Challenges Faced By the Help Desk and Strategies to Mitigate Them

From the case study, the Help Desk at Bank USA faces the challenges of understaffing, poor
customer service, and lack of morale. When the trust administrator called the Investment
Management Help Desk, they had to wait for long before their call was answered, an indication
of poor service and lack of morale from the staff. The work seems to be stressful to the
employees, especially when they must keep calls down to a specific time frame. However, it is
critical for the staff members to ensure that the customer’s problems are resolved promptly since it seems the helpdesk support does not have enough staff. Poor customer service is
also indicative in the error the company has made in making the customer’s statement $42,000
too low. Chris Miami also sounds tired at 7:42 a.m., which shows that he may be overworking.
Strategies to mitigate the challenges would include boosting the employees’ morale, hiring more
workers to minimize the shift hours one should work, and retraining staff to improve customer
service (Hussain et al., 2019).

Preferred Forecasting Model

Using data on call volume in the case, one would choose the statistical forecasting model
to forecast the short-term demand. Statistical methods could be categorized as time-series
methods, thereby extrapolating historical time-series data. The best statistical analysis would be
possible with the record of detail event history or call by call to help calculate the summary of
the duration of call before it is answered and how long it takes the employee to resolve the call.
Further, statistical forecast would be applied because it calculates the random fluctuations in a
time series. The call volume in the case is fluctuating each day. The statistical forecasting
method would be preferred over other models because it offers room for regression due to
changes in factors that might affect the international call rate, such as market conditions or

CASE STUDY 3
political changes (Petropoulos, F., Kourentzes, N., Nikolopoulos, K., & Siemsen, E. (2018). Past
data is also useful in predicting what might happen in future.

CASE STUDY 4

References

Hussain, S. D., Khaliq, A., Nisar, Q. A., Kamboh, A. Z., & Ali, S. (2019). The Impact of
Employees’ Recognition, Rewards and Job Stress on Job Performance. SEISENSE
Journal of Management, 2(2), 69-82.
Petropoulos, F., Kourentzes, N., Nikolopoulos, K., & Siemsen, E. (2018). Judgmental selection
of forecasting models. Journal of Operations Management, 60, 34-46.