Case Study – Help Desk

The department consists of 20 employees broken down Into 14 full-time customer service representatives (Cars), 3 CARS support employees and 3 managers (Collier & Evans, 2013). The senior manager of the Help Desk, Dot GifFord, has established a team to address short-term forecasting. The Help Desk currently handles approximately 2000 calls a week from over 50 different internal processes and organizational units. This comprises approximately 98% of their daily call volume.

The remaining 2% of calls originate from designated external customers that are mostly comprised of large estate and trust administrators.

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The team has several additional items to discern in addition to the short-term forecasting of call volume and must be ready to review all items at an Informal presentation in 10 days time. Service Management Characteristics The Initial question that must be asked Is, what are the service management characteristics of the CARS Job? We can start by looking at the service-encounter design for the Help Desk (Collier &amp: Evans, 2013). 1. Customer Contact: The CARS position requires a physical interaction between the customer and CARS and would be characterized as a high contact system.

As a exult, the CARS must be able to handle customer concerns by making the customer feel as if they are their only priority. 2. CARS Selection, Training and Empowerment: Each CARS needs to be able to handle the customer’s needs quickly and efficiently. Because of this requirement, Cars will need to be highly independent and self motivated when it comes to addressing the customers needs. The ability of a potential CARS to handle multiple concerns at a time will need to be demonstrated prior to selection.

Additionally, once trained, the CARS will need to have the ability to make decisions on their level as much s possible to provide the customer a sense of confidence that they are speaking to the right person for their problem.

3 CARS Recognition: Keeping can c R engaged In ten dally routine wall De Key to having an effective Help Desk experience for the customer. Management will need to provide not only an effective compensation package to retain Cars, but will need to regularly recognize consistent service excellence and those occasions where a CARS provides an outstanding service experience. 4.

Service Recovery: Tied closely with CARS empowerment, each CARS must be able to make the appropriate service recovery when necessary. While some service recoveries may require a manager’s involvement, this should be limited, with the largest portion of control for service recovery lying in the hands of the CARS. As Ford and Bowen (2008) point out, the management team of the Help Desk need to ensure each CARS is producing customer solutions through memorable experiences.

It is simply no longer acceptable to Just fix a problem for a customer, but each CARS needs to provide an experience that exceeds the customer’s expectations.

Help Desk Mission Statement The next question for the team is to define the mission statement and create an verbal strategy for the Help Desk. The mission statement for the Help Desk should identify “what business we are in? ” and “what is our business for? ” (Adair, 2012). Based on this definition, the Help Desk could define their mission statement as: The Help Desk unit is responsible for creating an world class customer experience, while providing timely and accurate solutions to their customer’s immediate concerns and needs.

From this mission statement we can create our strategy, which is closely tied into the characteristics of the CARS Job itself.

Our strategy will be to hire and retain Cars focused on service excellence, who will quickly and competently address our customers’ need in a professional and detailed manner. Our customers are not simply the person who contacted us, but the banking customer they are representing who put their faith and trust in our institution. With these tools now available to the Help Desk team, when a customer calls with an error from an inaccurate stock price we are now able to handle this concern in a proactive approach.

By being proactive, the help desk is acting upon their mission statement and strategy as defined above. The CARS, once contacted now is in control of the experience and is able to set expectations for the customer. Through this approach, the Car’s ability to address a customer’s concern creates the opportunity to improve the customer’s perception of the Help Desk and overall confidence in the business unit.

Forecasting Short-term Demand To forecast the Help Desks call demand, the team will need to identify a few key items. The first is the planning horizon or the length of time on which the forecast is based (Colliers & Evans, 2013).

As identified by Dot Gifford, the team is looking o forecast the short-term demand for the call center. Because we are looking at data based on a time series of daily call volume, we will create a forecast of the call demand using a time bucket value equal to days. As we begin to create our forecast, we need to remember that any forecast is subject to errors.

In the forecasting techniques we are evaluating, mean square error (MSIE) is being used. Using this accuracy measurement, we will look for the technique that provides us the lowest MSIE Walt ten closes t Allocates Detente ten actual dally calls receiver forecasted amount.

The first technique evaluated is simple moving average (MA) that uses an average of the most recent “k” or number of observations in a time series (Collier & Evans, 2013). As shown in Exhibit 1, using a “k” value of 2 days provides us with a MSIE of 2590. 11 and shows the closest distance between the actual call volume and the estimated call volume for this technique.

The next forecasting technique evaluated is single exponential smoothing (SEES) which uses a weighted average of past time-series values to forecast the value of the time series in the next period Collier & Evans, 2013).

The advantage of this technique over MA is that past data is not forgotten, but Just weighted less in comparison to more recent data. Using a smoothing constant between O and 1 controls this effect. The closer you move towards 1, the more emphasis you place on recent data. Exhibit 2 shows that using a smoothing constant of 0.

9 produces an MSIE of 1852. 74 and the closest distance between the actual call volume and the estimated call volume. Based upon the results of the two forecasting techniques, I would recommend using SEES with a smoothing constant of 0. As the preferred method in the short term.

The reason that I suggest limited use of this particular model is due to the large smoothing constant.

Ideally, a smoothing constant of 0. 1 to 0. 5 is recommended so that we do not overreact and adjust the forecasted too quickly or too aggressively (Collier & Evans, 2013). Because cost is an additional constraint for the Help Desk, I would recommend evaluating the forecast and time bucket over the upcoming weeks. By adjusting the time bucket, the Help Desk should be able to improve the forecast accuracy and reduce the smoothing constant to a more acceptable level.