Category Archives: Forecasting

Problem solve Problem 2 A specific forecasting model was used to forecast demands for a product. The fore a t and the corresponding demands that subsequently occurred are shown below. Use the MAD and tracking signal technique to evaluate the forecasting model. Solution Evaluate the forecasting model using MAD, MAPE, and Jacking signal. Forecast model is well within the distribution. 1. Demand for stereo headph

Solved Problems Problem 1 Sunrise Baking Company markets doughnuts through a chain of food stores and has been experiencing over- and underproduction because of forecasting errors. The following data are their daily demands in dozens of doughnuts for the past four weeks. The bakery is closed Saturday so Friday’s production must satisfy demand for both Saturday and Sunday. Make a forecast for this week on

Internet Exercise The Wharton School at the University of Pennsylvania has a website devoted to forecasting at www.rnarketing.wharton.upenn.edu/forecastlwelcome.html. Visit this website and select a firm that provides business forecasting software. Visit that firm’s website and perform the following: Describe the company. Select one of its forecasting software products and describe it in detail including

Review and Discussion questions  1. Examine Exhibit 9.3 and suggest which forecasting technique you might use for (a) bathing suits. (b) demand for new house-. (e) electrical power usage. (d) new plant’ expansion plans. 2. In terms of the errors, why would the operations manager wish to use the least method when doing simple linear regression? 3. All forecasting methods using exponential smoothing. adaptive

Key Formulas Simple Moving Average Forecast Weighted moving Average Forecast and Exponential Smoothing Exponential Smoothing with Trend Effects Mean Absolute Deviation (MAD) Tracking Signal

The Application of Forecasting in Service Operations Service managers are recognizing the important distribution that forecasting can make in improving both the efficiency and the level or service in a service operation. Point-of-sale (POS) equipment can now provide! the service manager with historical sales data in time increments that are as small as 15 minutes. The av availability of these data permits accur

neural Networks neural networks represent a relatively new and growing area of forecasting. Unlike the more common statistical forecasting techniques such as time-series analysis and regression analysis neural networks simulate human learning. Thus. over time and with repeated use. neural networks can develop an understanding of the complex relationships that exist been input into a forecasting model and the out

Reliability of the Data With causal relationship forecasting we are concerned with how much of the changes in the dependent variable are being “explained” by changes in the independent variable. This is measured by the variance. The greater the proportion of the variance that can be explained by the independent variable. the stronger the relationship. The coefficient of determination (2) measures th

Causal Relationship Forecasting Any independent variable, to be of value from a forecasting perspective, must be a leading indicator. For example, if the weather service or the Farmer’s Almanac predicts that next winter is going to have an abnormally large number of snow terms, people would probably go out and buy snow shovels and snow blowers in the fall. Thus. the weather prediction or the Fanner’

Measurement of Error Several of the common terms used to describe the degree of error associated with forecasting are standard error, mean squared error (or variance), and mean absolute deviation. In addition. tracking signals may be used to indicate the existence of any positive or negative bias in the forecast. Standard error is discussed in the section on linear regression later in the chapter. Since the st