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How to Do Call Volume Forecasting for Service Desks
B Defining full-time staffing levels for a service desk is very difficult without a definitive way to predict the demand for service. Workload forecasting is the basis of any good staffing plan. While there are many forecasting techniques available, one that is simple, easy to implement and can be applied to any size service desk is the best place to start. The forecasting process is a combination of using judgment and application of mathematics. The mathematical process takes past history and uses it to predict future events. Both these components should be used in order to come out with an accurate forecast. A working knowledge of the statistical techniques discussed here will make the process understandable. Even for organizations which use time series analysis software, it is critical to understand these calculations as it helps in correctly interpreting results and verifying the accuracy of the results generated by the software. Introduction to the MethodologyThere are two main approaches to forecasting. One is the explanatory method which is based on an analysis of factors which are believed to influence the call volume; the other is the exploration method where the prediction is based on an inferred study of past general call volume behavior over time. Even for a modest degree of accuracy the former method is more difficult to implement and validate than the latter approach. For this reason, the focus here is on the exploration or time series approach to forecasting. This approach is scientifically valid yet easy to follow and implement. For an IT service desk whose primary purpose is to coordinate and resolve incidents as quickly as possible, an optimum level of staff numbers is required. A forecast of volume of calls helps the service desk in computing the optimum number of staff numbers. As a first step the forecast requires the past data of call volume. Table 1 gives the data for the years 2004, 2005 and 2006. In this case, it is believed that the recent three years reflect the current business situation and it is expected these patterns to continue into 2007. This data is adjusted for variation to eliminate certain spurious differences which are caused by peculiarities of the calendar. For example, the call volume for the month of February may be less not because of any real drop in activity but because of the fact that February has fewer days. The data is plotted in Figure 1. The red line represents the original call volume. Within each year a decline in call volume is observed in the beginning and an increase in the middle of the year and again a decline during the end of the year. Between the given years call volume seems to generally increase overall.
Decomposing the Call Volume DataGeneral analysis of the Figure 1 time series plot shows that a variety of things are likely influencing the call volume. It is important that these influences be decomposed out of the raw call volume data shown in Table 1. Generally there are four types of patterns, movements or components of time series. They are:
To be able to make a proper call volume forecast, one must know to what extent each of the above components is present in the data. To understand and measure these components, the forecast procedure involves initially removing the component effects from the original data. This is called decomposition. After the effects are measured, making a call volume forecast involves putting back the components on new call volume estimate. This is called as recomposition. Deseasonalizing the Call Volume DataThis step explains the removal of seasonal effects in the data. Without deseasonalizing the original call volume, one may incorrectly infer that the observed growth patterns will continue indefinitely when actually the increase is just because of the time of the year. To measure seasonal effects, a series of seasonal indexes should be calculated. A practical and widely used method to compute these indexes is the ratio to moving average approach. These indexes quantitatively measure how far above or below a given period stands in comparison to the expected call volume. Procedure for calculation of seasonal indexes is:
The computation is shown in Tables 2 and 3. The removal of seasonality from the original data is depicted in Figure 1 by the blue line. Note that the deseasonalized call volumes do not oscillate as widely as the original call levels. The remaining up and down movement must therefore be due to trend, cyclic and irregular effects.
Measuring the Call Volume TrendMeasurement of trend component is done by fitting a line to the data given in Table 1. This fitted line is calculated by the method of least squares which represents the overall linear growth over time. The trend line equation is: Y = A + BX Where Y = Predicted call volume occurring in the period X due to the trend effect The trend line parameters are calculated by use of mathematical formulas or Excel. The trend line equation for this case is found to be: Y = 77,516 + 946(X) To illustrate how the above equation is used, suppose the organization's interest is in the predicted call volume accorded by trend for January of 2006. This period corresponds in the equation to X = 6.5. Thus the predicted call volume for January 2006 is 83,665. The trend line is depicted in Figure 1 by the blue line. Measuring the Cyclic EffectsTo measure how the general business cycle affects call volume, a series of cyclic indexes are calculated. The deseasonalized data still contains trend, cyclic and irregular components. Also the predicted call volume using the trend equation do represent pure trend effects. Thus, it stands to reason that the ratio of the deseasonalized call volume and the call volume derived from the trend line equation should provide an index which reflects cyclic and irregular components only. The cyclic index calculations are shown in Table 4.
The business cycle is longer than the seasonal cycle and it should be understood that cyclic analysis is not as accurate as seasonal analysis due to complexity of general economic factors over long periods of time. Thus a general approximation of the cyclic factor is what is required to forecast the call volume. To study the general cyclic movement rather than precise cyclic changes, the cyclic plot must be smoothed out by replacing each index calculation with a centered three-period moving average. This is shown in Table 4. Both the cyclic index and the smoothed cyclic index are depicted in Figure 2.
In Figure 2, it can be noted that cyclic peaks occurring in Periods 11 and 27, and Periods 5 and 24 are approximately of the same magnitude and may thus be parts of different business cycles. From this, it can be infer that the cyclic length, i.e., the elapsed time before the cycle repeats is approximately 20 months. In order to make call volume forecasts, the approximate continuation of this cycle curve is projected into the next few months of 2007 in the figure. Making the Call Volume ForecastAt this point of time, the study of the past data to understand the different components of the time series analysis is completed. Now an attempt can be made to forecast volumes for the first two months of 2007. The procedure is:
The actual call volumes for January and February 2007 were 94,530 and 87,224 respectively. Summary: Math and Firsthand KnowledgeThis call volume forecasting procedure can be applied to any service desk which has data for the past few years. The advantage of the procedure is that it is simple to understand and implement and at the same time a fairly accurate. An effective combination of the mathematical calculations with management's firsthand knowledge of the situation is required to achieve accurate forecasts. There are other more complex forecasting techniques, but organizations should go through an evolutionary progression in adopting them. Start with a simple forecasting method, gain knowledge and move towards more sophisticated methods if necessary. About the Author: Krishna Murthy Dasari is a software quality professional with 12 years of experience in quality. He has worked in manufacturing and information technology industries. He has specialized experience in ISO 9000, CMMI, ITIL, Six Sigma and information security management. He is currently working with Satyam Computer Services Ltd. He can be reached at dvkm_dasari@yahoo.com. Reproduction Without Permission Is Strictly Prohibited – Copyright Requests Publish an Article: Do you have a Six Sigma tip, learning or case study? Share it with the largest community of Six Sigma professionals, and be recognized by your peers. It's a great way to promote your expertise and/or build your resume. Read more about submitting an article. "The Bottom Line" Links
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