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**Module 03: Forecasting**

In this module, you will identify elements of demand forecasting, analyze steps in the forecasting process, and you will focus on the importance of forecasting as it relates to operations management. Demand forecasting may be completed in a simple manner reviewing historical trends or through elaborate systems. This will include the meaningful units in forecasting, how those meaningful units may be different in organizations providing services rather than making products, and how writing down the forecast allows multiple people in multiple roles in the organization to give input to the forecast. We will also look at the impact that a forecasting technique has on receiving the level of detail needed for a forecast. Each organization chooses its own method or methods based on the level of accuracy that may be required.

**Discussion Question**

**Question Requirements****:**

**Forecasting**

You are in charge of creating a forecast for an organization that manufactures laptops. The industry is very competitive and supplies have to be purchased far enough ahead of time so that there is no delay in the manufacturing process should the demand increase.

Which forecasting technique would you choose?

What are the steps in the forecasting process?

Why is it important to create a forecast for the correct period of time?

What happens when a forecast is created for 3 months when an organization needs an accurate forecast for 6 months?

**Directions:**

Discuss the concepts, principles, and theories from your textbook. Cite your textbooks and cite any other sources.

Write a discussion that includes an **introduction paragraph**, the** body**, and a **conclusion paragraph** to address the assignment’s guide questions.

In this module, you will identify elements of demand forecasting, analyze steps in the forecasting

process, and you will focus on the importance of forecasting as it relates to operations

management. Demand forecasting may be completed in a simple manner reviewing historical trends

or through elaborate systems. This will include the meaningful units in forecasting, how those

meaningful units may be different in organizations providing services rather than making products, and

how writing down the forecast allows multiple people in multiple roles in the organization to give input

to the forecast. We will also look at the impact that a forecasting technique has on receiving the level

of detail needed for a forecast. Each organization chooses its own method or methods based on the

level of accuracy that may be required.

Discussion Question

Question Requirements:

Forecasting

You are in charge of creating a forecast for an organization that manufactures laptops. The industry is

very competitive and supplies have to be purchased far enough ahead of time so that there is no

delay in the manufacturing process should the demand increase.

1.

2.

3.

4.

Which forecasting technique would you choose?

What are the steps in the forecasting process?

Why is it important to create a forecast for the correct period of time?

What happens when a forecast is created for 3 months when an organization needs an

accurate forecast for 6 months?

Directions:

• Discuss the concepts, principles, and theories from your textbook. Cite your textbooks and

cite any other sources.

• Write a discussion that includes an introduction paragraph, the body, and a conclusion

paragraph to address the assignment’s guide questions.

• Your initial post should address all components of the question with a 600-word limit.

Learning Outcomes

1. Analyze the importance of forecasting in terms of operations management.

2. Articulate the importance of developing and monitoring forecasts.

Readings

Required:

• Chapter 3 in Operations Management

• Chapter 3 PowerPoint Presentation

• Che-Jung CHANG, Guiping LI, Jianhong GUO, & Kun-Peng YU. (2020). DataDriven Forecasting Model for Small Data Sets. Economic Computation & Economic

Cybernetics Studies & Research, 54(4), 217–

229.

• Piotrowska-Woroniak, J., & Szul, T. (2022). Application of a Model Based on Rough

Set Theory (RST) to Estimate the Energy Efficiency of Public Buildings. Energies

(19961073), 15(23), 8793.

Recommended:

• Shang, Z., Li, M., Chen, Y., Li, C., Yang, Y., & Li, L. (2022). A novel model based

on multiple input factors and variance reciprocal: application on wind speed

forecasting. Soft Computing – A Fusion of Foundations, Methodologies &

Applications, 26(17), 8857–8877.

Forecasting

3-1

You should be able to:

LO 3.1

List features common to all forecasts

LO 3.2 Explain why forecasts are generally wrong

LO 3.3 List the elements of a good forecast

LO 3.4 Outline the steps in the forecasting process

LO 3.5 Summarize forecast errors and use summaries to make decisions

LO 3.6 Describe four qualitative forecasting techniques

LO 3.7 Use a naïve method to make a forecast

LO 3.8 Prepare a moving average forecast

LO 3.9 Prepare a weighted-average forecast

LO 3.10 Prepare an exponential smoothing forecast

LO 3.11 Prepare a linear trend forecast

LO 3.12 Prepare a trend-adjusted exponential smoothing forecast

LO 3.13 Compute and use seasonal relatives

LO 3.14 Compute and use regression and correlation coefficients

LO 3.15 Construct control charts and use them to monitor forecast errors

LO 3.16 Describe the key factors and trade-offs to consider when choosing a

forecasting technique

3-2

Forecast – a statement about the future value of a

variable of interest

We make forecasts about such things as weather,

demand, and resource availability

Forecasts are important to making informed decisions

LO 3.1

3-3

Expected level of demand

The level of demand may be a function of some

structural variation such as trend or seasonal variation

Accuracy

Related to the potential size of forecast error

LO 3.1

3-4

• Accounting. New product/process cost estimates, profit projections,

cash management.

• Finance. Equipment/equipment replacement needs,

timing and amount of funding/borrowing needs.

• Human resources. Hiring activities, including recruitment,

interviewing, and training; layoff planning, including

outplacement counseling.

• Marketing. Pricing and promotion, e-business strategies, global

competition strategies.

• MIS. New/revised information systems, internet services.

• Operations. Schedules, capacity planning, work assignments and

workloads, inventory planning, make-or-buy decisions, outsourcing,

project management.

• Product/service design. Revision of current features, design of new

products or services.

LO 3.1

3-5

Plan the system

Generally involves long-range plans related to:

Types of products and services to offer

Facility and equipment levels

Facility location

Plan the use of the system

Generally involves short- and medium-range plans related to:

Inventory management

Workforce levels

Purchasing

Production

Budgeting

Scheduling

LO 3.1

3-6

1.

2.

3.

4.

LO 3.1

Techniques assume some underlying causal system that

existed in the past will persist into the future

Forecasts are not perfect

Forecasts for groups of items are more accurate than

those for individual items

Forecast accuracy decreases as the forecasting horizon

increases

3-7

Forecasts are not perfect:

Because random variation is always present, there will

always be some residual error, even if all other factors

have been accounted for.

LO 3.2

3-8

The forecast

Should be timely

Should be accurate

Should be reliable

Should be expressed in meaningful units

Should be in writing

Technique should be simple to understand and use

Should be cost-effective

LO 3.3

3-9

1.

2.

3.

4.

5.

6.

LO 3.4

Determine the purpose of the forecast

Establish a time horizon

Obtain, clean, and analyze appropriate data

Select a forecasting technique

Make the forecast

Monitor the forecast errors

3-10

Qualitative forecasting

Qualitative techniques permit the inclusion of soft information

such as:

Human factors

Personal opinions

Hunches

These factors are difficult, or impossible, to quantify

Quantitative forecasting

These techniques rely on hard data

Quantitative techniques involve either the projection of historical

data or the development of associative methods that attempt to use

causal variables to make a forecast

LO 3.6

3-11

Forecasts that use subjective inputs such as opinions from consumer

surveys, sales staff, managers, executives, and experts

Executive opinions

A small group of upper-level managers may meet and collectively develop a

forecast

Salesforce opinions

Members of the sales or customer service staff can be good sources of

information due to their direct contact with customers and may be aware of

plans customers may be considering for the future

Consumer surveys

Since consumers ultimately determine demand, it makes sense to solicit input

from them

Consumer surveys typically represent a sample of consumer opinions

Other approaches

Managers may solicit 0pinions from other managers or staff people or outside

experts to help with developing a forecast.

The Delphi method is an iterative process intended to achieve a consensus

LO 3.6

3-12

Forecasts that project patterns identified in recent

time-series observations

Time-series – a time-ordered sequence of observations

taken at regular time intervals

Assume that future values of the time-series can be

estimated from past values of the time-series

LO 3.6

3-13

Trend

Seasonality

Cycles

Irregular variations

Random variation

LO 3.6

3-14

Trend

A long-term upward or downward movement in data

Population shifts

Changing income

Seasonality

Short-term, fairly regular variations related to the calendar or time

of day

Restaurants, service call centers, and theaters all experience

seasonal demand

LO 3.6

3-15

Cycle

Wavelike variations lasting more than one year

These are often related to a variety of economic, political, or even

agricultural conditions

Irregular variation

Due to unusual circumstances that do not reflect typical behavior

Labor strike

Weather event

Random Variation

Residual variation that remains after all other behaviors have been

accounted for

LO 3.6

3-16

Naïve forecast

Uses a single previous value of a time series as the basis

for a forecast

The forecast for a time period is equal to the previous

time period’s value

Can be used with

A stable time series

Seasonal variations

Trend

LO 3.7

3-17

These techniques work best when a series tends to vary

about an average

Averaging techniques smooth variations in the data

They can handle step changes or gradual changes in the

level of a series

Techniques

1.

2.

3.

LO 3.7

Moving average

Weighted moving average

Exponential smoothing

3-18

Technique that averages a number of the most recent

actual values in generating a forecast

n

Ft = MA n =

A

t −i

i =1

n

At − n + … + At − 2 + At −1

=

n

where

Ft = Forecast for time period t

MA n = n period moving average

At −i = Actual value in period t − i

n = Number of periods in the moving average

LO 3.8

3-19

As new data become available, the forecast is updated

by adding the newest value and dropping the oldest

and then re-computing the average

The number of data points included in the average

determines the model’s sensitivity

Fewer data points used—more responsive

More data points used—less responsive

LO 3.7

3-20

The most recent values in a time series are given more

weight in computing a forecast

The choice of weights, w, is somewhat arbitrary and

involves some trial and error

Ft = wt ( At ) + wt −1 ( At −1 ) + … + wt − n ( At − n )

where

wt = weight for period t , wt −1 = weight for period t − 1, etc.

At = the actual value for period t , At −1 = the actual value for period t − 1, etc.

LO 3.9

3-21

A weighted averaging method that is based on the

previous forecast plus a percentage of the forecast

error

Ft = Ft −1 + ( At −1 − Ft −1 )

where

Ft = Forecast for period t

Ft −1 = Forecast for the previous period

= Smoothing constant

At −1 = Actual demand or sales from the previous period

LO 3.10

3-22

A simple data plot can reveal the existence and nature

of a trend

Linear trend equation

Ft = a + bt

where

Ft = Forecast for period t

a = Value of Ft at t = 0

b = Slope of the line

t = Specified number of time periods from

LO 3.11

t =0

3-23

Slope and intercept can be estimated from historical

data

b=

n ty − t y

( )

n t − t

2

2

y − b t

a=

or y − bt

n

where

n = Number of periods

y = Value of the time series

LO

3.11

3-24

The trend adjusted forecast consists of two

components

Smoothed error

Trend factor

TAFt +1 = St + Tt

where

St = Previous forecast plus smoothed error

Tt = Current trend estimate

LO 3.12

3-25

Alpha and beta are smoothing constants

Trend-adjusted exponential smoothing has the ability

to respond to changes in trend

TAFt +1 = St + Tt

St = TAFt + (At − TAFt )

Tt = Tt−1 + (TAFt − TAFt−1 − Tt−1 )

LO 3.12

3-26

Seasonality – regularly repeating movements in

series values that can be tied to recurring events

Expressed in terms of the amount that actual values

deviate from the average value of a series

Models of seasonality

Additive

Seasonality is expressed as a quantity that gets added to or

subtracted from the time-series average in order to

incorporate seasonality

Multiplicative

Seasonality is expressed as a percentage of the average (or

trend) amount which is then used to multiply the value of a

series in order to incorporate seasonality

LO 3.12

3-27

Seasonal relatives

The seasonal percentage used in the multiplicative seasonally

adjusted forecasting model

Using seasonal relatives

To deseasonalize data

Done in order to get a clearer picture of the nonseasonal (e.g.,

trend) components of the data series

Divide each data point by its seasonal relative

To incorporate seasonality in a forecast

1.

2.

LO 3.13

Obtain trend estimates for desired periods using a trend

equation

Add seasonality by multiplying these trend estimates by the

corresponding seasonal relative

3-28

Associative techniques are based on the

development of an equation that summarizes the

effects of predictor variables

Predictor variables – variables that can be used to

predict values of the variable of interest

Home values may be related to such factors as home and

property size, location, number of bedrooms, and number of

bathrooms

LO 3.14

3-29

Regression – a technique for fitting a line to a set of

data points

Simple linear regression – the simplest form of

regression that involves a linear relationship between

two variables

The object of simple linear regression is to obtain an equation

of a straight line that minimizes the sum of squared vertical

deviations from the line (i.e., the least squares criterion)

LO 3.14

3-30

yc = a + bx

where

yc = Predicted (dependent ) variable

x = Predictor (independe nt) variable

b = Slope of the line

a = Value of yc when x = 0 (i.e., the height of the line at the y intercept)

and

b=

n( xy) − ( x )( y )

(

)

n x 2 − ( x )

2

y − b x

a=

or y − b x

n

where

n = Number of paired observatio ns

LO 3.14

3-31

Correlation, r

A measure of the strength and direction of relationship between

two variables

Ranges between -1.00 and +1.00

r=

(

n( xy) − ( x )( y )

)

n x 2 − ( x )

2

(

)

n y 2 − ( y )

2

r2, square of the correlation coefficient

A measure of the percentage of variability in the values of y that is

“explained” by the independent variable

Ranges between 0 and 1.00

LO 3.14

3-32

Variations around the line are random

2. Deviations around the average value (the line)

should be normally distributed

3. Predictions are made only within the range of

observed values

1.

LO 3.14

3-33

Always plot the line to verify that a linear relationship

is appropriate

The data may be time-dependent

If they are

use analysis of time series

use time as an independent variable in a multiple regression

analysis

A small correlation may indicate that other variables

are important

LO 3.14

3-34

Allowances should be made for forecast errors

It is important to provide an indication of the extent to

which the forecast might deviate from the value of the

variable that actually occurs

Forecast errors should be monitored

Error = Actual – Forecast

If errors fall beyond acceptable bounds, corrective

action may be necessary

LO 3.5

3-35

Actual − Forecast

MAD =

t

MAD weights all errors

evenly

t

n

(Actual − Forecast )

MSE =

t

2

t

n −1

Actual t − Forecast t

100

Actual t

MAPE =

n

LO 3.5

MSE weights errors according

to their squared values

MAPE weights errors

according to relative error

3-36

Period

Actual

(A)

Forecast

(F)

(A-F)

Error

|Error|

Error2

[|Error|/Actual]x100

1

107

110

-3

3

9

2.80%

2

125

121

4

4

16

3.20%

3

115

112

3

3

9

2.61%

4

118

120

-2

2

4

1.69%

5

108

109

1

1

1

0.93%

Sum

13

39

11.23%

n=5

n-1 = 4

n=5

MAD

MSE

MAPE

= 2.6

= 9.75

= 2.25%

LO 3.5

3-37

Tracking forecast errors and analyzing them can provide useful

insight into whether forecasts are performing satisfactorily

Sources of forecast errors:

The model may be inadequate due to

a.

b.

c.

omission of an important variable

a change or shift in the variable the model cannot handle

the appearance of a new variable

Irregular variations may have occurred

Random variation

Control charts are useful for identifying the presence of non-

random error in forecasts

Tracking signals can be used to detect forecast bias

LO 3.15

3-38

1. Compute the MSE.

2. Estimate of standard deviation of the distribution of errors

s = MSE

3. UCL: 0 + z MSE

4. LCL: 0 – z MSE

where z = Number of standard deviations from the mean

LO 3.15

3-39

Factors to consider

Cost

Accuracy

Availability of historical data

Availability of forecasting software

Time needed to gather and analyze data and prepare a

forecast

Forecast horizon

LO 3.16

3-40

The better forecasts are, the more able organizations will

be to take advantage of future opportunities and reduce

potential risks

A worthwhile strategy is to work to improve short-term forecasts

Accurate up-to-date information can have a significant effect on

forecast accuracy:

Prices

Demand

Other important variables

Reduce the time horizon forecasts have to cover

Sharing forecasts or demand data through the supply chain can

improve forecast quality

LO 3.16

3-41

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