Transcript chapter-18

18–1
Chapter Eighteen
McGraw-Hill/Irwin
Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.
18–2
• LO18–2: Evaluate demand using quantitative
forecasting models
• LO18–3: Apply qualitative techniques to forecast
demand
Copyright © 2014 by McGraw Hill Education (India) Private Limited. All rights reserved.
• LO18–1: Understand how forecasting is essential to
supply chain planning
• LO18–4: Apply collaborative techniques to forecast
demand
18–3
– Finance and accounting use forecasts as the basis for
budgeting and cost control.
– Marketing relies on forecasts to make key decisions such as
new product planning and personnel compensation.
– Production uses forecasts to select suppliers; determine
capacity requirements; and drive decisions about purchasing,
staffing, and inventory.
Copyright © 2014 by McGraw Hill Education (India) Private Limited. All rights reserved.
• Forecasting is a vital function and affects every significant
management decision.
• Different roles require different forecasting approaches.
– Decisions about overall directions require strategic forecasts.
– Tactical forecasts are used to guide day-to-day decisions.
18–4
• The choice of the decoupling point in a SC is
strategic.
• Forecasting helps determine the level of inventory
needed at the decoupling points.
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• Decoupling point: Point at which inventory is
stored, which allows SC to operate independently.
• The decision will be affected by the error produced
in the forecast and the type of product (easily
inventoried or easily perishable).
18–5
– Qualitative
– Time series analysis (primary focus of this
chapter)
– Causal relationships
– Simulation
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• There are four basic types of forecasts.
• Time series analysis is based on the idea
that data relating to past demand can be
used to predict future demand.
18–6
Trend
Seasonal
element
Cyclical
elements
Random
variation
Autocorrelation
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Average
demand for a
period of time
Excel: Components
of Demand
18–7
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• Identification of trend lines is a common
starting point when developing a forecast.
• Common trend types include linear, S-curve,
asymptotic, and exponential.
18–8
Short term – forecasting less than 3 months
• Used mainly for tactical decisions
Medium term – forecasting 3 months to 2 years
• Used to develop a strategy that will be implemented over the
next 6 to 18 months (e.g., meeting demand)
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• Using the past to predict the future
Long term – forecasting greater than 2 years
• Useful for detecting general trends and identifying major
turning points
18–9
– Time horizon to be forecast
– Data availability
– Accuracy required
– Size of forecasting budget
– Availability of qualified personnel
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• Choosing an appropriate forecasting
model depends upon
18–10
Amount of Historical
Data
Data Pattern
Forecast
Horizon
Simple moving
average
6 to 12 months;
weekly data are often
used
Stationary (i.e.,
no trend or
seasonality)
Short
Weighted moving
average and simple
exponential
smoothing
5 to 10 observations
needed to start
Stationary
Short
Stationary and
trend
Short
Stationary,
trend, and
seasonality
Short to
medium
Exponential
smoothing with
trend
Linear regression
5 to 10 observations
needed to start
10 to 20 observations
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Forecasting Method
18–11
• Useful when demand is not growing or declining
rapidly and no seasonality is present.
• Removes some of the random fluctuation from
the data.
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• Forecast is the average of a fixed number of past
periods.
• Selecting the period length is important.
– Longer periods provide more smoothing.
– Shorter periods react to trends more quickly.
18–12
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•
18–13
18-14
18–14
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• A weighted moving average allows unequal
weighting of prior time periods.
– The sum of the weights must be equal to one.
– Often, more recent periods are given higher
weights than periods farther in the past.
𝐹𝑡 = 𝑤1𝐴𝑡 − 1 + 𝑤2𝐴𝑡 − 2 +
…+
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• The simple moving average formula implies
equal weighting for all periods.
𝑤𝑛𝐴𝑡 − 𝑛
18–15
• The recent past is often the best indicator
of the future, so weights are generally
higher for more recent data.
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• Experience and/or trial-and-error are the
simplest approaches.
• If the data are seasonal, weights should
reflect this appropriately.
18–16
• More recent results weighted more heavily
• The most used of all forecasting techniques
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• A weighted average method that includes all
past data in the forecasting calculation
• An integral part of computerized forecasting
18–17
– Exponential models are surprisingly accurate
– Formulating an exponential model is relatively
easy
– The user can understand how the model works
– Little computation is required to use the model
– Computer storage requirements are small
– Tests for accuracy are easy to compute
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• Well accepted for six reasons
18–18
18-19
18–19
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Demand
Forecast
1
820
820
2
775
820
3
680
811
4
655
785
5
750
759
6
802
757
7
798
766
8
689
772
9
775
756
10
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Week
760
18-20
18–20
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• The presence of a trend in the data causes the
exponential smoothing forecast to always lag behind
the actual data
• This can be corrected by adding a trend adjustment
– The trend smoothing constant is delta (δ)
18–21
– The previous forecast including trend (FITt-1) is 110
and the previous estimate of the trend (Tt-1) is 10
– α = 0.2 and δ = 0.3
– Actual demand for period t-1 is 115
Ft = Ft-1 + α(At-1 – FITt-1) = 110 + 0.2(115-110) = 111.0
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• Calculate the new forecast, assuming the
following:
Tt = Tt-1 + δ(Ft-1 – FITt-1) = 10 + 0.3(111-110) = 10.3
FITt = Ft + Tt = 111.0 + 10.3 = 121.3
18–22
– Usually in the range 0.1 to 0.3
• α depends upon how much random
variation is present
• δ depends upon how steady the trend is
• Measurement of forecast error can be used
to select values of α and δ to minimize
overall forecast error
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• Relatively small values for α and δ are
common
18–23
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• Regression is used to identify the functional
relationship between two or more correlated variables,
usually from observed data.
• One variable (the dependent variable) is predicted for
given values of the other variable (the independent
variable).
• Linear regression is a special case that assumes the
relationship between the variables can be explained
with a straight line.
Y = a + bt
18–24
Quarter
Sales
Quarter
Sales
1
600
7
2,600
2
1,550
8
2,900
3
1,500
9
3,800
4
1,500
10
4,500
5
2,400
11
4,000
6
3,100
12
4,900
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• The least squares method
determines the parameters a
and b such that the sum of the
squared errors is minimized –
“least squares”
18–25
600
600
1
360,000
801.3
2
1,550
3,100
4
2,402,500
1,160.9
3
1,500
4,500
9
2,250,000
1,520.5
4
1,500
6,000
16
2,250,000
1,880.1
5
2,400
12,000
25
5,760,000
2,239.7
6
3,100
18,600
36
9,610,000
2,599.4
7
2,600
18,200
49
6,760,000
2,959.0
8
2,900
23,200
64
8,410,000
3,318.6
9
3,800
34,200
81
14,440,000
3,678.2
10
4,500
45,000
100
20,250,000
4,037.8
11
4,000
44,000
121
16,000,000
4,397.4
12
4,900
58,800
144
24,010,000
4,757.1
78
33,350
268,200
650
112,502,500
The forecast is extended to periods 13-16
Sum
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1
18-26
18–26
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• Microsoft
Excel
includes data
analysis
tools, which
can perform
least squares
regression on
a data set.
18–27
– Trend, seasonal, cyclical, autocorrelation, and
random
• Identifying these elements and separating
the time series data into these components
is known as decomposition.
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• Chronologically ordered data are referred
to as a time series.
• A time series may contain one or many
elements.
18–28
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• Seasonal variation may be either
additive or multiplicative (shown here
with a changing trend).
18–29
Average Sales for
Seasonal Factor
Each Season
Season
Past Sales
Spring
200
1000
4
Summer
350
1000
=
4
Fall
300
Winter
150
Total
1000
200
=
250
0.8
250
350
=
250
1.4
1000
=
4
250
300
=
250
1.2
1000
=
4
250
150
=
250
0.6
= 250
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• The seasonal factor (or index) is the ratio of
the amount sold during each season divided by
the average for all seasons.
18–30
Average
Sales for
Each Season
(1,100y4)
Next
Year’s
Seasonal
Forecast
Seasonal
Factor
Spring
275
X
0.8
=
220
Summer
275
X
1.4
=
385
Fall
275
X
1.2
=
330
Winter
275
X
0.6
=
165
1100
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Expected
Demand
for
Next Year
18–31
– Find seasonal component
– Deseasonalize the demand
– Find trend component
• Forecast future values of each component
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• Decompose the time series into its
components
– Project trend component into the future
– Multiply trend component by seasonal
component
18–32
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• Using the data for periods 1-12, apply time series
analysis (decomposition, linear regression, trend
estimate & seasonal indices) to forecast for periods 13-16
18–33
Regression Results:
Y = 555.0 + 342.2t
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• Develop a least squares regression line for the
deseasonalized data.
• Project the regression line through the period of the
forecast.
Forecast for
periods 13-16
18–34
Period Quarter Y from Regression
Seasonal
Factor
Forecast (F x
Seasonal Factor
13
I
5,003.5
0.82
4,102.87
14
II
5,345.7
1.10
5,880.27
15
III
5,687.9
0.97
5,517.26
16
IV
6,030.1
1.12
6,753.71
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• Create the final forecast by adjusting
the regression line by the seasonal
factor.
18–35
– Bias – when a consistent mistake is made
– Random – errors that are not explained by the model
being used
• Measures of error
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• Forecast error is the difference between the forecast
value and what actually occurred.
• All forecasts contain some level of error.
• Sources of error
– Mean absolute deviation (MAD)
– Mean absolute percent error (MAPE)
– Tracking signal
18–36
•
Tracking signal indicates whether
forecast errors are accumulating
over time (either positive or
negative errors).
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• Ideally, MAD will be zero (no • MAPE scales the forecast error to
the magnitude of demand.
forecasting error).
• Larger values of MAD
indicate a less accurate
model.
18–37
18–38
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– This independent variable must be a leading
indicator.
• Many apparently causal relationships are
actually just correlated events – care must be
taken when selecting causal variables.
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• Causal relationship forecasting uses
independent variables other than time to
predict future demand.
18–39
• In this case, the forecast analyst may
utilize multiple regression.
– Analogous to linear regression analysis, but
with multiple independent variables.
– Multiple regression supported by statistical
software packages.
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• Often, more than one independent
variable may be a valid predictor of
future demand.
18–40
–
–
–
–
Market research
Panel consensus
Historical analogy
Delphi method
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• Generally used to take advantage of expert
knowledge.
• Useful when judgment is required, when
products are new, or if the firm has little
experience in a new market.
• Examples
18–41
– Demand forecasting
– Production and purchasing
– Inventory replenishment
• Integrates all members of a supply chain –
manufacturers, distributors, and retailers.
• Depends upon the exchange of internal
information to provide a more reliable view of
demand.
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• A web-based process used to coordinate the
efforts of a supply chain.
18–42
Joint business
planning
Development
of demand
forecasts
Sharing
forecasts
Inventory
replenishment
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Creation of a
front-end
partnership
agreement
18–43
• Forecast effort should be proportional to the
magnitude of decisions being made.
• Web-based systems (CPFR) are growing in importance
and effectiveness.
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• Forecasting is a fundamental step in any planning
process.
• All forecasts have errors – understanding and
minimizing this error is the key to effective
forecasting processes.
18–44