Inventory: the Forecasting Demand for the Company Products - Essay Example

Inventory
Abstract
This work reviews the current forecasting strategy of the company as it prepares to meet the requirements of demand and capacity for its planned future. As it makes a review of the current forecasting strategy, it will seek to give a forward for the, however, ties directly to the overall strategic planning methodology already established by the company. From the history of the company, it has used the time series method in its forecasting processes since its inception. The forecasting methods that are to be considered in this paper are:
• Qualitative: it involves human judgment, and it receives application when little data is available
• Simulation: it involves using the judgment of a computer to imitate customer behavior
• Causal: used in case of a direct relationship in demand and an environmental factor, like cold weather
• Time-series: it involves the use of data in historical to foretell the future needs

Inventory
Besides the techniques already listed in the abstract, there are other forecasting techniques available and already in operation. They are each used in line with the future needs that a company requires. Therefore, before using any of these methods, consider the time of preparation, cost, and accuracy of the technique. These techniques make different assumptions in their operation (Makridakis, Wheelwright, & Hyndman, 1998). In addition to the already mentioned methods are Regression methods, which involve the extension of linear regression that is a variable, linearly relates to some other independent variables. The other method of forecasting used is the multiple equation method, which involves some variables that are dependent on interacting with each other in a number of equations. This method receives proper application in the economic models of forecasting.

Qualitative forecasting techniques
These techniques have their basis on human judgment in the forecasting of the planned future of a given company. Therefore, these methods are said to be subjective based on the judgments and opinions given by the experts and the consumers. These techniques apply to decisions that are intermediate and long-range. They can be used in the creation of forecasts that are short-term and can supplement projections, which have their basis on any qualitative methods (Makridakis, Wheelwright, & Hyndman, 1998). The examples of the qualitative forecasting methods include the ones mentioned below.

• Executive opinions, which involves the averaging of views of different experts in the areas to be forecasted
• Delphi method, which involves the questioning of a panel of experts each at a time
• Sales-force polling where the salespeople who are in constant contact with consumers obtain forecast information
• Consumer surveys which involve surveying the consumer purchases in the market

An example of qualitative forecasting technique in the manufacturing and retail industries is, doing a market survey of the manufactured goods amongst the different retailers that sell the goods directly to customers. They will each give different feedback that they usually get from the consumers once they have sold the products from the company. These will enable the company to tell exactly what may probably happen if a given trend of the company continues. In health care industries, one-on-one interaction with patients and doctors can be done in some hospitals. In specific, doctors should be talked to at length since they will be able to provide a medically inclined response advising on the health effects of the given health product produced.

Among the methods listed, the causal method of forecasting should not be used because it limits itself to environmental factors tied to the demand of a given product. This is not a very conclusive way of determining future demand since not all produced goods will always be affected by environmental factors like rain. In the qualitative method of forecasting, it is necessary to forecast error. This is because the error will be able to show the amount of deviation to be taken in the forecasting. The error should not be neglected when forecasting. It may affect the forecast, therefore, giving very wrong feedback.