Operations Management Forecasting MAD - Essay Example

Seasonal forecast takes into account the seasonal variations that occur during a specific time period. Seasonal variations are the regular ups and downs in a time series that relate to repetitive events such as holidays and weathers. Seasonal forecasting is very useful as it can be applied to hourly, daily, weekly, quarterly and yearly events. Seasonal forecasting can also be useful for the management of the organizations in order to plan the capacity so that the organization can handle peak loads.

Analyzing the data in the form of quarterly or monthly values makes it easy to detect seasonal patterns by the organizations’ management. By using this forecasting technique, the Great Northwest Outdoor Company can easily estimate the demand for its orders in the all the seasons as demand for its item is seasonal. Exponential smoothing is defined as a weighted moving-average forecasting method in which data points are weighted by an exponential function. The concept of exponential smoothing is not a complex.

In mathematical terms, exponential smoothing can be defined as the latest estimate of demand is equal to our old estimate adjusted by a fraction of the difference between the last period’s actual demand and the old estimate. Seasonal exponential smoothing takes into account the demand across four seasons in a year for this company. As this technique is easy to use and does not require a lot of past data, it is used in almost every type of business. This type of forecasting cannot take into account the seasonal variations that occur in each year for the demand of the company’s items.

If the company uses Seasonal Forecasting method, it can take into account the seasonal variations and produce a more accurate forecast for the four seasons of 2005. Following is the forecast for year 2005. If the MAD (Mean Absolute Deviation) is considered to recommend the most accurate forecasting method then it would be the Seasonal Forecasting. The MAD