Chapter 6
Forecasting supply chain
requirements
Chapter 11: Strategic Leadership
Contents
• Introduction
• Forecasting in general
• Types of forecasting
• Long-term and short-term forecasting
• The forecasting process
• Appropriate forecasting models
• Validating forecasting models
• Stationary data
• Data with trend
• Forecasting seasonality
• Conclusion
Chapter 11: Strategic Leadership
Introduction
• Demand forecasting refers to the process of
determining the amount of product and related
information that consumers will require, either in
the short or long term.
• The responsibility for preparing demand
forecasting usually lies in the marketing and sales
departments.
Chapter 11: Strategic Leadership
Forecasting in general:
features of forecasting
• It is difficult to forecast accurately.
• Forecasts for groups of items are often more
accurate than forecasting demand for a single
item.
• Forecasts for a shorter time period are usually
more accurate than forecasting for a longer time
horizon.
• What happened in the past can be used as an
important guideline when making forecasts.
Chapter 11: Strategic Leadership
Types of forecasting
• A time series is a time-ordered sequence of
observations taken at regular intervals, e.g.
daily, weekly, monthly, quarterly or annually.
• Explanatory models, also called regression
models, rely on the identification of related
variables that can be used to predict values of
the variable of interest. A mathematical
relationship is developed between demand,
for example, and some other factors that
cause demand behaviour.
Chapter 11: Strategic Leadership
Types of forecasting:
qualitative forecasting
techniques
Qualitative forecasting techniques are appropriate
when little or no quantitativeinformation is
available, but sufficient qualitative knowledge
exists. It is usually a product of judgement and
accumulated knowledge.
Four techniques:
• The Delphi method
• Jury of executive option
• Sales force composite
• Consumer market survey
Chapter 11: Strategic Leadership
Long-term and short-term
forecasting
• Short-term forecasting is crucial for day-to-day
planning, such as determining safety stock levels
and production plans.
• Medium-term forecasts are usually needed for
budget purposes, including forecasts of sales,
prices and costs for the entire company and
different divisions.
• Long-term forecasting is primarily needed for
capital expansion plans, selecting research and
development projects, launching new projects,
formulating long-term goals and strategies and
adapting to environmental changes.
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The forecasting process
• Specify the objectives clearly.
• Determine what to forecast.
• Determine the time horizon of the forecast.
• Gather the data needed to make the forecast.
• Do the model selection.
• Validate the forecasting models.
• Make the forecasts.
• Implement the results.
• Track the results. Stopped for FT on 10/09/15
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Appropriate forecasting
models: types of data patterns
a. Level or horizontal pattern
b. Trend pattern
c. Seasonal pattern
d. Cycle
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Appropriate forecasting
models: selecting an
appropriate forecasting model
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Measures of accuracy
These measures evaluate how well each
forecasting method explains past behaviour of
the time series variable. Three of the most
common quantitative measures of accuracy are
as follows:
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Evaluate forecasting
technique
Step 1: Divide time series into an initialisation set
and a test set.
Step 2: Choose an appropriate forecasting
method.
Step 3: Use the initialisation set to estimate the
trend and seasonal components. Establish fit
accuracy.
Chapter 11: Strategic Leadership
Evaluate forecasting
technique (continued)
Step 4: Apply the method to the test set and
establish forecast accuracy.
Step 5: Repeat steps 2 to 4 for several forecasting
techniques, and select the forecasting method
which performs best with regard to forecast
accuracy and which is also appropriate depending
on the data pattern.
Step 6: Use the complete data set and apply the
selected technique to do the forecast.
Chapter 11: Strategic Leadership
Stationary data: naïve
forecasting
The naïve method assumes that the next period’s
forecast is equal to the current period’s actual
value.
The forecast is as follows:
Ft+1 = At, where
Ft+1 is the forecast for the next period t+1
A t is the actual value for the current period t and
T is the current time period.
Week number
t
1
2
3
4
5
6
7
8
Chapter 11: Strategic9Leadership
Actual sales
Naïve forecast
At
Ft
250
245
247
251
250
250
245
247
251
250
250
250
250
Note that F6 = A5
and F7 = F6, etc.
Stationary data: moving
averages
The simple moving average uses an average of n of the most
recent observations to create a forecast for the next period.
The following formula is used:
where
Ft+1 is the forecast for the next period t+1;
A t is the actual value for the current period t;
t is the current time period, and
n is the number of periods included in the moving average.
Chapter 11: Strategic Leadership
Stationary data: moving
averages (continued)
Week number
Actual sales
MA-3
t
At
Ft
1
250
2
245
3
247
4
251
247,33
5
250
247,67
6
245
249,33
7
248,67
8
247,89
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Stationary data: simple
exponential smoothing
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Data with trend: Holt’s
method
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Data with trend: linear
regression
Chapter 11: Strategic Leadership
Forecasting seasonality
Multiplicative seasonality: seasonality is expressed as a
percentage of the average.
Step 1: Calculate the forecast demand for the known
observations using linear regression or Holt’s method.
Step 2: Calculate the actual sales as a percentage of the
trend by dividing the actual sales by the forecast sales for
each time period.
Step 3: Calculate the average seasonal index for each
season by adding up the values calculated in Step 2 for that
season and dividing by the number of time periods.
Step 4: Multiply the forecast by the average seasonal index
for the corresponding season. This will produce a forecast
adjusted for seasons.
Chapter 11: Strategic Leadership
Forecasting seasonality:
example
Quarte
r
Time
period
Unit
sales
Linear
Actual
as %
Seasonal
X
Y
forecast
of
trend
forecast
1
1
240
242,37
0,99
239,63
2
3
4
2
3
4
245
247
249
243,33
244,28
245,24
1,01
1,01
1,02
1
5
243
246,19
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
245
248
251
245
252
253
255
251
254
257
259
257
256
259
263
247,15
248,11
249,06
250,02
250,97
251,93
252,88
253,84
254,79
255,75
256,71
257,66
258,62
259,57
260,53
261,48
262,44
263,40
4
24
Chapter 11: Strategic Leadership
264,35
Coefficien
ts
Interce
pt
241,4158
242,79
245,14
247,69
X
0,955639
0,99
243,41
Quarter
0,99
1,00
1,01
0,98
1,00
1,00
1,01
0,99
1,00
1,00
1,01
1,00
0,99
1,00
1,01
246,61
248,98
251,55
247,19
250,42
252,82
255,41
250,97
254,24
256,65
259,27
254,75
258,05
260,49
263,13
258,53
261,86
264,32
1
2
3
4
266,99
Seasonal
index
0,988685
0,997807
1,003527
1,009983
1. Forecast demand
using linear
regression.
2. Divide actual sales by
linear forecast to get
actual as % of trend.
3. Calculate average
seasonal index for
each quarter.
4. Calculate the seasonal
forecast by
multiplying the linear
forecast with the
appropriate seasonal
index.
Conclusion
• This chapter presented several methods for forecasting
future demand or sales.
• Different methods for stationary time series data and nonstationary time series data were presented.
• A practical method for addressing seasonality was also
suggested.
• In each case, the goal was to fit models to the past
behaviour of a time series, then to first test the models on
test data, and then to fit the appropriate model to project
future values.
• More advanced techniques were discussed in the appendix
of the chapter.
• This chapter is only an introduction to forecasting and the
techniques should be seen as such.
Chapter 11: Strategic Leadership