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Forecasting Tools in the Sales Rates Prediction Case Study

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Updated: Apr 2nd, 2021


Identifying possible sales rates and making forecasts is a challenging task, as the subject matter depends on a variety of factors and, therefore, may be altered by the slightest changes in either the company’s operations or the target market. However, several tools for locating possible changes in the sales rates as well as estimating the latter rather accurately have been developed. Particularly, the phenomena known as MAPE and SES deserve to be mentioned.

While SES does provide grounds for making long-term forecasts, its accuracy is rather low, whereas MAPE (mean absolute percentage error) and its modifications (ME (mean error), MPE (mean percentage error), etc.) (Ord and Fildes 47). It helps identify the possible outcomes in a rather precise manner due to the approximation technique used. Therefore, MAPE must be viewed as the most reasonable choice for the firm under analysis.

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According to the results of the forecast, the company is likely to experience minor drops in sales in May, June, July, and November. However, the calculations show that the general prognosis for the sales rates is rather positive. Indeed, as the formula shows, the prognosis for the sales makes y = 0.1887x + 260.06, which is rather impressive (Lawrence and Geurts 88).

According to the sales forecast provided, the mean error of the outcomes calculated with the help of the tool in question makes approximately 35.5. Despite the fact that the specified data can be viewed as rather significant, one must admit that, compared to the average size of data sets provided, the specified mean error can hardly be deemed as significant. Indeed, the estimated mean error is unlikely to affect the sales forecasts considerably and, therefore, is unlikely to jeopardize the organization’s performance in the future.

The MAPE rates are very low as well, according to the estimations carried out. Landing at 0.2994, the specified percentage cannot be viewed as a serious impediment to developing a sales forecast. The specified tool, therefore, can be considered very efficient in terms of outlining the possible obstacles in the company’s way; particularly, certain financial inconsistencies regarding drops in sales can be predicted with rather high accuracy rates (Swanson and Tayman 385).

Compared to the method of SES, the tool in question is clearly more efficient, as linear regression and the MAPE analysis provide solid premises for making very accurate calculations. The company’s safety, which hinges on the choice of the forecast tool incorporated into the analysis, therefore, is facilitated by the linear regression approach and the adoption of the tools such as MAPE, ME, and MPE.

It is strongly recommended, therefore, that the linear regression analysis should be carried out (Dursun 102). The specified tool allows identifying the chances for evaluating every single opportunity available and at the same reduces the MAPE rates considerably. Indeed, the linear regression approach helps not only identify the opportunities that the organization is going to have when entering the market next year but also calculates the approximate amount of the company’s revenues based on the current ratio (Davis and Pecar 217).

The SES analysis, in its turn, though admittedly efficient, still lacks precision. Moreover, the SES tool cannot provide the organization with information regarding the short-term options (e.g., the opportunities for sales increase that may occur next month). Indeed, the MAPE approach has been deemed by a variety of experts as more valid and “superior to SES with regard to the MAPE for smoothing constants” (Lawrence and Geurts 129). In other words, the tool in question offers a perfect method for results approximation and, therefore, creates premises from making the outcomes of the evaluation as close to the future result as possible. Hence, the MAPE tool can be viewed as a much more reliable instrument than SES when it comes to forecasting sales.

The significance of the SES tool is not to be underrated, either, though, it would be wrong to claim that the SES analysis is entirely invalid when it comes to the assessment of the sales rates at a specific point in time. Quite on the contrary, the specified method turns out to be rather useful when applied to the cases with an updating trend (Chase 194).

Nevertheless, when it comes to carrying out the process of exponential smoothing and, therefore, more precise identification of the linear trend for a specific process, sales being the case in point, the MAPE tool turns out to be much more efficient. As it has been stressed above, MAPE creates opportunities for more accurate calculations and the exact location of the possible error in calculations.


It is, thus, strongly suggested that the MAPE tool should be used as the means for carrying out any further calculations of possible future sales rates. The above-mentioned approach should be credited for the opportunity to approximate the data, thus, allowing for a smoother analysis of the future sales and, therefore, a more precise forecast. Efficient and rather simple, the specified method deserves to be deemed as the foundation for the company’s further financial forecasting strategy.

Works Cited

Chase, Charles W. Demand-Driven Forecasting: A Structured Approach to Forecasting. Somerset, New Jersey: John Wiley & Sons, 2013. Print.

Davis, Glyn, and Branko Pecar. Quantitative Methods for Decision Making Using Excel. Oxford: OUP Oxford, 2012. Print.

Dursun, Omur. Early Estimation of Project Determinants: Predictions through Establishing the Basis of New Building Projects in Germany. Munchen: Walter de Gruyter, 2014. Print.

Lawrence, Kenneth D. and Michael D. Geurts. Advances in Business and Management Forecasting. Vol. 5. Bingley, Washington: Emerald Group Publishing, 2010. Print.

Ord, Keith and Robert Fildes. Principles of Business Forecasting. Boston, Massachusetts: Cengage Learning, 2012. Print.

Swanson, David A. and Jeff Tayman. Subnational Population Estimates. Berlin: Springer Science & Business Media, 2012. Print.

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