Business Forecasting

Business forecasting, “is the activity of predicting or estimating the feature position of the sales, expenditures, and profits of any business” (Singh et al., 2018). Forecasting can be used as a business tool to predict and plan for future conditions. The better a business forecast represents the actual future state, the more ability a business has to respond to or influence the trajectory.

            To successfully design a prediction model, a foundational dataset must be achieved. The goal of the prediction must be identified and data surrounding the problem gathered. Once a dataset is constructed, mathematical functions can be applied to build a forecasting model. Methods including machine learning, statistical analysis and linear correlations (Singh et al., 2018).

            While not all forecasts accurately predict future states, the accuracy of particular forecasts are startling. Following the Ebola outbreak, Bill Gates began predicting the next pandemic. In an article, “The Next Epidemic – Lessons from Ebola”, predictions are made indicating the future of pandemics, many of these predictions closely model the influences observed during the COVID-19 outbreak. Most notable are the medical and political influences that are involved in these predictions.

            Medically, the forecast predicted a need for increased research in RNA-based constructs. Politically, the forecast calls out the infrastructure surrounding global health, “there are still big holes in the world’s ability to respond to an epidemic” (Gates, 2015). Political influences are called upon to construct a global response system.

 

 

References

 

Gates, B. (2015, 2018-09-13). The Next Epidemic — Lessons from Ebola. The New England Journal of Medicine, 372(15), 1381-1384. https://doi.org/http://dx.doi.org/10.1056/NEJMp1502918

 

Singh, D. A. A. G., Leavline, E. J., Muthukrishnan, S., & Yuvaraj, R. (2018, 2018-12-06). Machine Learning based Business Forecasting. International Journal of Information Engineering and Electronic Business, 11(6), 40. https://doi.org/http://dx.doi.org/10.5815/ijieeb.2018.06.05