Quantitative Forecasts
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Simple Moving Average Calculation
The simple moving average is established through calculating the average annual sales over a specific number of years or period. Usually the majority of the moving averages are founded on closing sales. For instance, a 5 year simple moving average, in this case, will be five years sales sum of closing sales with the sum divided by five. As this methods name suggest a moving average is basically an average that moves. This is because as indicated in the calculation in the excel spreadsheet, old data is constantly left or dropped as new data becomes available. Thus, this results to the moving of averages along the same time scale.
For instance, based on the excel calculations, the first year of the moving average sales just covers the last five years of the Macy’s Inc.’s sales (Macy’s Inc. 2017). On the other hand, the second year of the moving average results in the dropping of the first Macy’s sales data $25,778 and proceeds to add new sales. In addition, the third year of the moving average proceeds through dropping the second sales data as well as adding the new sales. As it can be noted for the excel calculations the sales values keeps gradually increasing over years.
As compared to the exponential moving average method, the simple moving average is much simple and straight forward. However, the moving average value is slightly below the last sales value which causes the moving average to slow or lag.
Exponential Moving Average Calculation
On the other hand, the exponential moving averages as shown in the excel computation is known of minimizing the lag through applying much weight to the recent sales value. In addition, the weighting used in the computation used on the most recent price relies on the number of years in the moving average. As indicated in the excel calculation, there are three stages applied in calculating an exponential moving average. The initial stage or step involves calculating a simple moving average. This is because an exponential moving average has to begin from somewhere so as the simple moving average is applied to the previous years in the initial calculation. The second steps involve calculating the weighting multiplier while the third step constitutes of computing the exponential moving average.
For instance, as in the excel calculations, a 5 year period exponential moving average used a 33.33% weighting to the most recent sales value. The 5 year period exponential moving average can also be termed as a 33.33% exponential moving average. However, it is good to understand that the weighting for the shorter term period is considered more than the weighting for the longer time duration thus the Macy’s 5 year period is relevant.
Naive method
The naïve forecasting method is only essential or appropriate for a time series data. This is because all forecasts are just set to be the value indicated by the last observation. This can mean that the latest data presented by the operation dataset is considered the forecasted value for the following period. Therefore, in the case of the last period Macy’s sales operations dataset is the sales forecast for the period or the year 2017.
From the above three quantitative methods of the forecast, I would prefer the use of exponential moving average forecast method. This is because, despite the method being complex as compare to both the simple moving average and the naïve method, the method presents well-calculated forecast value that put into consideration a number of factors thus producing a forecast that is most accurate and feasible. In addition, the exponential moving average method minimizes the lag present in the simple moving average and advocate the use of shorter predictive durations boosting the level of accuracy.
A sales forecast for the Macy or any other firm can result in a mixture of the result, thus can either result to positive or negative financial metrics. On the positive side, sales forecast would result in enabling the firm to cater for all customer demand and at the right time since the company had anticipated for the customer demand and prepared for it. However, if the sales forecast is not accurate, it would not result in preparing for the right demand thus causing customer dissatisfaction. This would be harmful to the company’s financial metric and maximum sales would not be achieved.
References
Macy’s Inc. (2017). Sales Data. Retrieved from: http://investors.macysinc.com/phoenix.zhtml?c=84477&p=irol-sales
Michelle_Michy
businessoperationsdataset.docx
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