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Introduction
The forecast process predicts the future outcome of events. The process is grounded on estimation. The forecast process includes the gathering of related data.
Thesis statement
The forecast enhances the operations head’s prerogatives.
Forecast
The Forecast Perspectives. There are several forecast process perspectives. The perspectives include the reasons for conducting the forecast, choice of forecast methods, and preferred forecast questions (Higgins, 2011). The other perspectives include salaries of workers, investment or budget amount, and raw materials used to generate the final product (Mahadevan, 2009).
Various methods of forecasting and applications. There are various forecasting methods and applications. The methods and applications are classified as either qualitative or quantitative. The qualitative method involves gathering the opinions of different individuals, experts, or groups. The research respondents can be experts on the research topic. The Gantt chart improves the forecasting process. The chart contains the time schedule of the project’s intertwined activities (Schwalbe, 2010). For example, the researcher asks the neighbor whether the Superman movie is worth viewing. The neighbor offers a personal opinion. Second, the researcher conducts market research. The researcher asks the McDonald’s customer how the food tastes. (Balakrishan, 2010).
The second group of forecasting methods is the quantitative group. Under this group, the last period method is based on the prior month’s actual output. If the current month’s revenue is $ 50,000, next month’s revenue forecast will remain at $50,000. If one Mcdonald’s store consistently generated a 10 percent increase in the prior month’s revenues, the researcher can predict next month’s profit will produce the same output (Balakrishan, 2010).
Time series forecasting has several approaches. The moving average approach creates an increase or decrease in the next month’s forecasting output. If the past several months’ revenues produced the same revenue, the next month’s forecast revenue is projected to be similar (Zhu, 2010). Further, The Delphi approach uses the computers’ speed in the prediction process. The Growth Curve approach focuses on the three product demand’s growth stages (Zhu, 2010). Extrapolation is another forecasting alternative. The alternative incorporates trending factors. If there are no external factors affecting the current production path, the future operating period’s outputs will continue on the same trend line path. If the graph’s trend line shows a 10 percent monthly production output increase, next month’s production output is projected to act in the same manner. Another name for the alternative is trend estimation (Zhu, 2010).
How to Evaluate the Forecast method’s Outputs/ Predictions. The evaluation of the predictive outcomes of the forecasting methods’ effectiveness can be done. Comparing the actual outcomes with the forecast figures will show the significant variances. To reduce the next period’s production output variance, the variances are incorporated into the next month’s production forecast. If there is a significant variance between one month’s projected 100,000 units and the 80,000 actual output, management must reduce the next forecast closer to the 80,000 actual production units (Zhu, 2010).
When analyzing the variance between the forecast amount and the actual amount, management must incorporate other external factors. For example, an unexpected hurricane destroyed one of the production facilities. Consequently, overall production performance drops. Further, new regulations or policies may prevent or reduce the production of banned products. Surely, incorporating material external factors into the forecasting process increases the effectiveness of the forecast results (Zhu, 2010).
By ensuring all relevant and valid data are retrieved, the effectiveness of the forecast outputs is increased. Further, a lack of relevant data may cast doubts on the accuracy of the forecast outputs. Likewise, using wrong forecast information produces wrong forecast amounts. Clearly, using both relevant and valid data ensures effective forecast outcomes (Shah, 2009).
Conclusion
The forecast perspectives help predict future outputs. There are several forecast methods and applications. By ensuring all relevant and valid data are used, the forecast outputs are more effective. Overwhelmingly, the forecast improves the effectiveness of the operations administrators’ prerogatives.
References
Balakrishan, N. (2010). Methods and Applications of Statistics. New York: J. Wiley & Sons.
Higgins, M. (2011). Advances in Forecasting. New York: W Upjohn Press.
Mahadevan, B. (2009). Operation Management. New York: Pearson Press.
Schwalbe, K. (2010). Information Technology Project Management. New York: Cengage Learning.
Shah, J. (2009). Supply Chain Management. New York: Pearson.
Zhu, J. (2010). Forecasting Models: Methods and Applications. New York: CreateSpace.
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