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Solution Manual For Forecasting and Predictive Analytics with Forecast X (TM) 7th Edition By Barry Keating, Holton Wilson, John Solutions (All Chapters, 100% Original Verified, A+ Grade) $25.49
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Solution Manual For Forecasting and Predictive Analytics with Forecast X (TM) 7th Edition By Barry Keating, Holton Wilson, John Solutions (All Chapters, 100% Original Verified, A+ Grade)

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Solution Manual For Forecasting and Predictive Analytics with Forecast X (TM) 7th Edition By Barry Keating, Holton Wilson, John Solutions (All Chapters, 100% Original Verified, A+ Grade) Solution Manual For Forecasting and Predictive Analytics with Forecast X (TM) 7th Edition By Barry Keating,...

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Forecasting and Predictive Analytics with Forecast X (TM), 7e By Barry Keating, Holton Wilson, John
Solutions

Solution Manual, For Complete File, Download link at the end of this File

Solutions to Chapter 1 Exercises
1. Describe the three phases of the evolution of forecasting/prediction.


The earliest phase in the evolution of forecasting was when all forecasts were purely judgmental.
Forecasts were based solely on the intuition of managers who had to predict how much would be
sold, or how much inventory to order, or how many people to hire (or all of these). Later
quantitative methods and forms of numerical analyses started to be used to make predictions.
Methods such as moving averages and simple exponential smoothing were followed by more
sophisticated time series methods such as Holt’s and Winters’ exponential smoothing. Linear
time trends came into use, at first by an analysts drawing freehand lines through the data that
they “believed” best represented the trend in the data. Later basic regression tools were used for
such simple time trends. As quantitative methods became more sophisticated, and as computer
power enabled their use, quantitative forecasting became dominant and has been shown to
outperform qualitative methods most of the time. We are now into the “big data” phase in which
we have not only the ability to use numeric data but have immense amounts of non-numeric data
that can contribute to the accuracy of predictions.

2. How does the organization of the material in this book relate to the stages of the
evolution of prediction?

The text is organized around three major themes: 1) Time Series Models; 2) Demand Planning
Models; and 3) Analytics. The text has an overview of some qualitative methods but the real
powerful forecasting methods begin in Chapters 3 and 4 that focus on various time series
methods. Chapter 5 delves heavily into causal models using multiple regression that can include
variables over which the organization has partial or total control. Chapters 6 and 7 continue the
demand planning section. Then in Chapter 8 predictive analytics take center stage. This is the
new frontier of prediction and forecasting.

3. Write a paragraph in which you compare what you think are the advantages and
disadvantages of subjective forecasting methods. How do you think the use of
quantitative methods relates to these advantages and disadvantages?

One advantage of subjective forecasting is that this class of methods does not require quantitative
skill on the part of the forecaster or the user. Historically, a related advantage was that
sophisticated computer software is not needed for subjective forecasting. In addition, the results
of subjective forecasts were widely accepted by management. This acceptance may have been
due to the ability of successful forecasters to sense changes based on a subjective understanding
of the business/economic environment. Unfortunately it takes years for a forecaster to learn to
translate intuition into a reliable forecast. The ability to make subjective forecasts is very
difficult to transfer to other individuals which means that when that forecaster retires or moves to
another position forecast accuracy typically suffers. Thus, over time consistency in forecast


Copyright © 2019 McGraw-Hill Education. All rights reserved. No reproduction or distribution
without the prior written consent of McGraw-Hill Education.

,accuracy is difficult to maintain. Perhaps the biggest disadvantage of subjective forecasts is that
they almost always are biased.

4. Explain how forecasting relates to having an efficient supply chain.

For a supply chain to function efficiently all the participants along the chain must have accurate
forecasts in order to plan production and distribution. Consider a firm that makes seats for a
major auto manufacturer. This firm needs a reliable forecast of the production of vehicles so that
they can plan their raw material and human resource requirements.

5. The process of forecasting new products is difficult. Why? How can new products be
forecast?

The biggest hurtle to overcome in new product forecasting is the lack of historical data. In the
most extreme case of a totally new product there is no historical data that can be used to help
develop a forecast. Often a new product is not completely new to the market. There may be
similar products on the market for which there is a body of data that might be useful. Consider
the newest iPhone. Apple has historical data on previous versions of the iPhone that can be a
guide to expected sales of the newest model. When a product is totally new a firm can rely on
marketing research in the form of test markets and product clinics to develop estimates of future
sales. Once the product is introduced and a few data points have been observed the Bass model
may be useful. Products typically follow a product life cycle which provides a framework for
developing sales estimates.

6. In this chapter, you saw an example of a naive forecast. Why do you think it is given that
name? Describe how the naive forecast is developed.

Naïve forecasts are given that name because they simply assume that the next period will be the
same as the current period. This is truly a naïve assumption. Most people use a naïve forecast in
their daily lives. In the absence of a weather forecast, it is not uncommon to think that the
weather tomorrow will be the same as it is today. Most of the time this works well enough in the
very short term but such a forecast becomes absurd in a longer term. It is likely that the weather
on January 15 will be similar to the weather January 14th. But extending that reasoning six
months ahead the June 15 would be unreasonable. In the simplest form a naïve forecast takes
today’s sales as the forecast for tomorrow’s sales. That is the forecast for time period t+1 is the
actual value at time period t.

7. In the chapter, you learned about many metrics that can be used to evaluate forecast
accuracy. The MAPE was one of those that may be the most common in use. Explain
what the MAPE tells a forecaster.

MAPE stands for “Mean Absolute Percentage Error.” The MAPE is one form of an average
error in a forecast over a specific period of time. Because the absolute value of the errors are
used in the calculation it is not possible for positive errors to offset negative errors. Suppose you
have a forecast for four quarters with errors of 100. 50, -225, and 175. Calculating a simple
average would tell you that on average there was zero error. In fact this is quite obviously not

Copyright © 2019 McGraw-Hill Education. All rights reserved. No reproduction or distribution
without the prior written consent of McGraw-Hill Education.

,true. For simplicity assume that actual sales for the four periods were as shown in the table
below. The MAPE would be calculated as shown below:

Absolute Absolute
Actual Forecast Error Error % Error
400 300 100 100 25.00
200 150 50 50 25.00
500 725 -225 225 45.00
400 225 175 175 43.75
MAPE = 34.69

The MAPE for the same four periods would be 34.69 which would be a better indication of the
true errors in the forecast and would not mislead a manager into thinking the forecasts had all
been perfect. MAPE is also unit free since it is calculated as a percent. Thus, the MAPE can be
compared across different forecast items.

8. Suppose that you work for a U.S. senator who is contemplating writing a bill that would
put a national sales tax in place. Because the tax would be levied on the sales revenue of
retail stores, the senator has asked you to prepare a forecast of retail store sales for year 8,
based on data from year 1 through year 7. The data are:


(c1p8) Year Retail Store Sales
1 $ 1,225
2 1,285
3 1,359
4 1,392
5 1,443
6 1,474
7 1,467

1. Use the naive forecasting model presented in this chapter to prepare a forecast of retail
store sales for each year from 2 through 8.

Retail Naïve
Year Store Sales Forecast
1 1,225
2 1,285 1,225
3 1,359 1,285
4 1,392 1,359
5 1,443 1,392

Copyright © 2019 McGraw-Hill Education. All rights reserved. No reproduction or distribution
without the prior written consent of McGraw-Hill Education.

, 6 1,474 1,443
7 1,467 1,474
8 1,467




2. Prepare a time-series graph of the actual and forecast values of retail store sales for the
entire period. (You will not have a forecast for year 1 or an actual value for year 8.)




3. Calculate the MAPE for your forecast series using the values for year 2 through year 7.

Retail Store Naïve Absolute Absolute
Year Sales Forecast Error Error % Error
1 1225
2 1285 1225 60 60 4.67
3 1359 1285 74 74 5.45
4 1392 1359 33 33 2.37
5 1443 1392 51 51 3.53
6 1474 1443 31 31 2.10
7 1467 1474 -7 7 0.48
8 1467

MAPE = 3.10




9. Suppose that you work for a major U.S. retail department store that has outlets
nationwide. The store offers credit to customers in various forms, including store credit
cards, and over the years has seen a substantial increase in credit purchases. The manager
Copyright © 2019 McGraw-Hill Education. All rights reserved. No reproduction or distribution
without the prior written consent of McGraw-Hill Education.

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