Cutting Edge Case Solution
This case discusses the head of HR department: Mark Lawrence’s approval of creating and maintaining decentralized administrative centers,totaling to 35, in order to achieve the efficiency in overall control system. The main objective was to calculate the total number of employees to be hired in these centers of the cutting edge company. To do so,the company was asked to do the forecasting of an expected number of calls which a center is expected to receive in the call center. The main issue which Mark was facing, was the forecasting of the calls, as Mark foretasted the number of calls through judgmental approach, and it was 8000 per month, but it was a wrong forecast because it was three times higher than the actual numbers.So, the case further discusses various forecasting methods to finalize one accurate forecasting system.
Mark’s initial forecasting method was not accurate, because he was using judgmental method and had used his intuitions and guesses to forecast the number of calls per month. On the other hand; Mark had a wide range of data available to forecast, but he did not use all the data that was available which also contribute to wrong forecast. Mark did not use the historical data and also used only two variables that also did not help in generating an accurate result. And these are the reasons that deviated the results.
As there are several methods of forecasting and Mark had an option to use other appealing methods to generate accurate results, i.e. regress for the number of calls and time series. To add the seasonality factor in analysis; Mark should have used the data from more than one center. Furthermore, the time series analysis method considered the seasonality effect within the data of all centers. Mark should not have limited his knowledge when forecasting and should have taken t large samples of different centers from different locations, to forecast the number of calls accurately. There are also option of getting other variables in the analysis.
As in this comparison; the last value method that was used by Harry, was the simplest method among all, which states that the forecast of the last point is equal to the next data point in time series. It was not an-accurate analysis because Mark had only used one sample size. There are other factors and conditions that affect the last value ion the data point, and he was using that for the next value data. As we can see that in the data file that the table shows the accuracy points of forecasting methods and it is on number two. Its mean absolute deviation is 169, which explains that on average; there is a difference of 169 between the actual and foretasted number of the calls.
This forecasting method is quite changed and opposite of the methods discussed in last question. As this method of forecasting uses all the data points and considers these point relevant and also uses them for calculating the next value. This forecasting method takes the data points from the time series and takes their average and this is suitable for the data that is present in the table. There was a variability in the call volume and this average forecasting method was scaled last,because MAD was highest in this method………………………….
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