Operational Forecasting on the Workflow Process and Calculations

Published: 2021-09-10 07:05:08
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Category: Finances

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Operations Forecasting
Quantitative Forecast Methods
By using the 2013 – 2016 operational sales data for Macy’s Inc., it was possible to compare three different quantitative forecast methods. The three methods which are being compared are; simple moving average, exponential moving average and the naïve forecasting method. Each of these three methods have different pros and cons when conducting a sales forecast for an organization.
The simple moving average method is a technique which is used to calculate the average, or trend, in any given dataset for any period of time. The simple moving average, also referred to as SMA, is mostly used to calculate a long-term trend. In the dataset containing the financial data for Macy’s the SMA was used to calculate the average sales for every quarter starting in 2013 and ending in 2016. This was done on a 4 period trend. In addition to calculating the moving average, the error forecasting was also calculated. It can be determined that the Mean Squared Error, MSE, was 1695153.927. The MSE calculates the squared average of the forecasting errors. The Mean Absolute Deviation, MAD, was 1175. The Mean Absolute Deviation describes the variation in the dataset. The forecast errors simply indicate the difference between the forecasted values and the actual sales values. It can be noted that the difference between the forecasted sales and the actual sales is fairly different, this could occur for numerous different reasons; inventory management, seasonal sales and so on.The exponential moving average, also referred to as EMA, is similar to the simple moving average. The main difference is that there is more weight added to the EMA and the data reacts faster to sudden changes in the data. EMA is used for quick, short term trends. Many financial analysts will use the EMA method to make a quick business decision on whether to invest into the market. The EMA provides short and more accurate trend data. Using the Macy’s dataset and applying the formula for EMA it can be determined that over a 10-preiod trend there is a 18.18% weight applied to the latest sales data.
Naïve forecasting indicates that the last period’s actual sales data is used as the current periods forecasted sales data. Naïve forecasting is mainly used for data comparisons and does not actually offer solid forecasting data. One way to use naïve forecasting would be in seasonal trends. For example; last summer or the 3rd quarter the actual sales were $ 5,626. Naïve forecasting would indicate that the forecasted sales for 2017 quarter 3 would be approximately $ 5,626.
Suggested Forecasting Method
Based on the three quantitative forecasting methods the simple moving average, SMA, method would be best for performing a sales forecast for Macy’s. The reason being that the simple moving average forecasts a longer trend in the dataset. When forecasting sales for an entire year, 4 quarters, it is better to use a method that shows a longer trend. The exponential moving average is used for short-term trend analysis and the naïve method does not provide any concreate data. Therefore, to get an actual long-term trend the simple moving average would be the better option for forecasting.
Financial Impact
The financial impact that the simple moving average method will have on Macy’s would be beneficial for the organization. The reason being that the simple moving average will provide a long-term sales forecast for the investors, stakeholders and management team to evaluate. The management team would be able to prepare for the increase or decrease in sales based on the forecasted dataset. The company would also be able to use the forecasted data to determine staffing needs and ensuring that the right amount of inventory is on hand to meet the needs of the customers.
Conclusion
Using the three forecasting methods was certainly an interesting exercise and displayed the differences in forecasting results. The simple moving average was the most insightful for Macy’s as the data displayed a long-term trend of forecasted sales values. There are always multiple ways to calculate data and it is a good idea to try different methods to determine which one suits the company or industry best.

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