Forecasting in Communication
Forecasting plays an important role to improve communication in the organization by providing important information to employees, managers and others stakeholders. Foresting provides an idea about sales units that will be consumed by the customers that helps to communicate the production department about the number of units to be produced during a particular time period. Forecasting involves sales forces to forecast in the organization that make an easy communication about predicted results and also the efforts that would be required to achieve the objectives (Smaros, 2004). Forecasting is also made by taking the consideration of all the departments that is quite effective to facilitate an effective communication among different departments. Forecasting is also important to understand and determine the changes in customers’ needs and to generate relevant and rich information of the product that is important to improve effectiveness. Forecasting improves communication by reducing problems of every department that are working on the basis of its goals and forecasts by determining the efforts of other departments also (Smaros, 2004).
Reasons of Wrong Forecasting
There are several reasons in the organization that affect forecasted results from the actual results. Forecasting is an important and most difficult aspect that is based on uncertainty of future that create difficulty in making the forecast in a proper way. Forecasting is performed by using past data of the organization that may change due to change in the business or market environment. The lack of future aspects and changes in forecasting also make it wrong. In addition, several potential errors may create problems that affect a forecasting method’s accuracy. Similarly, forecaster cannot forecast in a right way if there is lack of time and resources to forecast as he/she may fail to judge all the areas that may affect the forecasting (Ahrens, 2007). Forecasting also goes wrong due to lack of proper communication among the employees, managers, and sales forces of the organizations. Selection of inappropriate forecasting method as per nature of forecasting is also a reason behind wrong forecast because an appropriate method helps in proper decision-making to a manager.
Ways to Make Effective Forecast
There are several principles through which a research could increase the chances that a forecast will be effective. Firstly, researcher can define cone of uncertainty of the world that outlines all possibilities related to particular event and movement of forecasting. Secondly, forecaster can use S-curve that starts with slowly at initial stage and then suddenly explode in growth phase and then it drop down in maturity and saturation stage that would be effective to enhance the significance of forecasting (Saffo, 2007). It is because it will help to cover all the aspects that will not fit with the forecasting. Similarly, the researcher should not over rely on one part of strong information, but he should apply strong opinions during the period by including the contingencies.
Models for Forecasting
In forecasting, more complicated models are better than less complicated methods because they can fit all data in forecasting, whereas less complicated models has less adjustable or uncertain parameters in the forecasting (Yuen, 2010). More complicated models can cover large output space in comparison to less complicated models that also provides more accurate results. Autoregressive Integrated Moving Average Models is an important more complicated method that fits with time series to understand data and to predict future points in series effectively. This model is used to predict time series of economics and industries that is helpful to remove persistence and to provide accurate estimation (Wang, 2010). More complicated models helps to understand about a certain problem that can be solved accurately by using potential process. At the same time, the more complicated methods include all the environmental aspects in forecasting that produces better results.
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Reference
Ahrens, C.D. (2007). Meteorology today: an introduction to weather, climate, and the environment (8th ed.). Canada: Cengage Learning.
Saffo, P. (2007). Six Rules for Effective Forecasting. Retrieved September 27, 2011, from http://www.usc.edu/schools/annenberg/asc/projects/wkc/pdf/200912digitalleadership_saffo.pdf.
Smaros, J. (2004). Using the assortment forecasting method to enable sales force involvement in forecasting: A case study. International Journal of Physical Distribution & Logistics Management, 34 (2), 140-157.
Wang, F.L. (2010). Artificial Intelligence and Computational Intelligence: International Conference, AICI 2010, Sanya, China, October 23-24, 2010, Proceedings. Germany: Springer.
Yuen, K.V. (2010). Bayesian Methods for Structural Dynamics and Civil Engineering. Singapore: John Wiley and Sons.