Topics: Forecasting, Regression analysis, Exponential smoothing Pages: 33 (9606 words) Published: September 22, 2013



Have you ever gone to a restaurant and been told that they are sold out of their “special,” or gone to the university bookstore and found that the texts for your course are on backorder? Have you ever had a party at your home only to realize that you don’t have enough food for everyone invited? Just like getting caught unprepared in the rain, these situations show the consequences of poor forecasting. Planning for any event requires a forecast of the future. Whether in business or in our own lives, we make forecasts of future events. Based on those forecast, we make plans and take action. Forecasting is one of the most important business functions because all other business decision is based on a forecast of the future. Decisions such as which markets to pursue, which products to produce, how much inventory to carry, and how many people to hire all require a forecast. Poor forecasting results in incorrect business decisions and leaves the company unprepared to meet future demands. The consequences can be very costly in terms of lost sales and can even force a company out of business. Forecast are so important that companies are investing billions of dollars in technologies that can help them better plan for the future. For example, the ice –cream giant Ben & Jerry’s has invested in business intelligence software that tracks the life of each pint of ice cream, from ingredients to sale. Each pint is stamped with a tracking number that is stored in an Oracle database. Then the company uses the information to track trends, problems, and new business opportunities. They can track such things as seeing if the ice cream flavor. Chocolate Chip Cookie Dough is gaining on Cherry Garcia for the top sales spot, product sales by location, and rates of change. This information is then used to more accurately forecast products sales. Numerous other companies, such as Procter & Gamble, General Electric, Land’s End, Sears, and Red Robin Gourmet Burgers, are investing in the same type of software in order to improve forecast accuracy.

In this chapter you will learn about forecasting, the different types of forecasting methods available, and how to select and use the proper techniques. You will also learn about the latest available software that can help managers analyze and process data to generate forecasts.

There are many types of forecasting models. They differ in their degree of complexity, the amount of data they use, and the way they generate the forecast. However, some features are common to all forecasting models. They include the following: 1. Forecasts are rarely perfect. Forecasting the feature involves uncertainty. Therefore it is almost impossible to make a perfect prediction. Forecasters know that they have to live with a certain amount of error, which is the difference between what is forecast and what actually

happens. The goal of forecasting is to generate good forecasts on the average overtime and to keep forecast errors as low as possible.

2. Forecast is more accurate of families of items rather than for individual items. When items are grouped together, their individual high and low values can cancel each other out. The data for a group of items can be stable even when individual items in the group are very unstable. Consequently, one can obtain a higher degree of accuracy when forecasting for a group or items rather than for individual items. For example, you cannot expect the same degree of accuracy if you are forecasting sales of long sleeved hunter green polo shirts that you can expect when forecasting sales of all polo shirt.

3. Forecast are more accurate for shorter than longer time horizons. The shorter the time horizon of the forecast, the lower the degree of uncertainty. Data do not change very much in the short...
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