The best forecasting techniques that should be used in the future for Manychip’s strategy are the mix of Causal and Time series forecasting method. Since the Causal method is used whenever there is direct tie between demand and an environmental factor, it can be used to understand the connection between the cause and the external factors.
The time series forecasting method can also be used in order to have a strong background of what has happened in the past. It can be used in order to have a brief sketch of similar instances that can be of big help in determining the next action that must be taken by the company. In the manufacturing industry, the causal and time series techniques can be used in order to, first, determine the historical background of the existing demands in the market. Next, it can be used in order to predict the future needs in the industry by employing the causal strategy in discovering the link between the current environmental factor and the demand rate. In the manufacturing of clothes, the weather must be greatly considered in order to know what type of clothing must be manufactured in a larger scale in connection with the existing demand for that type of clothing. By using the historical data for previous years of the same weather condition, the company can be able to predict future needs for the type of clothing and be ahead of the industry. In the retail industry, the technique can be used in order to understand the existing environmental conditions, such as the stormy or rainy season, and its effect on the distribution of the retailed goods. Since retailing requires distribution of the goods, a good understanding of the existing demand of, say jerseys, in a certain area such as Toronto or Miami can help in determining the amount that is to be retailed.
Further, previous data can also help in identifying any existing trend or retailing pattern with regards to the retailing business of jerseys in Miami. In the health care industry, the causal forecasting method can help identify the usual diseases and number of expected patients through an understanding of the season, such as rainy or cold season and summer time. The knowledge on the increasing or decreasing number of patients during those seasons can help forecast the possible number of patients in the future, backed by the records of the hospital in the previous years of every season. Although the simulation forecasting technique can help, it should be used in decreasing frequencies. The reason behind this is because human behavior is not the same as computer simulated behavior and that human behavior can relatively change in the shortest span of time. More importantly, customer behavior can easily shift depending on many factors.
By limiting the understanding of customer behavior on mere computer simulation, the dangers can be devastating to the company. This is because if the company totally depends on simulation techniques, the company can greatly be damaged if the simulation fails to meet the real instances in the real world. The significance of forecasting error on the causal and time series forecasting technique rests on the fact that the environment as well as previous or historical data can lead to errors as well. With an understanding of forecasting error, these two techniques can be further refined. By taking into consideration the errors that may occur, little by little the causal and time series techniques can be reanalyzed or refined. The impact of error on the techniques can either be equally devastating or constructing, depending on the firm that will handle the error.
If, for instance, the company refuses to realign the techniques according to the errors that have occurred, then the results can devastate further the performance of the company. This is because the same error may happen again. On the other hand, if the company has been able to adjust themselves after the error has occurred, then there is little chance that they will greatly be affected again by the same error.
This is because the company has already been able to adjust the causal and time series techniques according to the error that has occurred.ReferencePeterson, R. (1969). A Note on the Determination of Optimal Forecasting Strategy. Management Science.
16 (4), B165-B169.