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2 Forecasting logistics requirements
2.1 Introduction2.2 Qualitative methods2.3 Quantitative methods2.4 Data preprocessing2.5 Choice of the forecasting method2.6 Advanced forecasting method2.7 Accuracy measure and forecasting monitoring2.8 Interval forecasts2.9 Case study: Forecasting methods at Adriatica Accumulatori
2.10 Case study: Sales forecasting at Orlea2.11 Questions and problems
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 1 / 17
2 Forecasting logistics requirements Quantitative methods
Quantitative forecasting methods (1/2)
- Used every time there are enough data;- yt , t = 1, . . . ,T : sequence of the T past observations of the
variable to be forecasted, arranged according to the time oftheir outcome (time series or historical data);
- all the periods are equally spaced in time;- choice of most suitable technique depends on:
> nature of the variable to be predicted;> amount and quality of the available data.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 2 / 17
2 Forecasting logistics requirements Quantitative methods
Quantitative forecasting methods (1/2)
- Used every time there are enough data;- yt , t = 1, . . . ,T : sequence of the T past observations of the
variable to be forecasted, arranged according to the time oftheir outcome (time series or historical data);
- all the periods are equally spaced in time;- choice of most suitable technique depends on:
> nature of the variable to be predicted;> amount and quality of the available data.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 2 / 17
2 Forecasting logistics requirements Quantitative methods
Quantitative forecasting methods (1/2)
- Used every time there are enough data;- yt , t = 1, . . . ,T : sequence of the T past observations of the
variable to be forecasted, arranged according to the time oftheir outcome (time series or historical data);
- all the periods are equally spaced in time;- choice of most suitable technique depends on:
> nature of the variable to be predicted;> amount and quality of the available data.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 2 / 17
2 Forecasting logistics requirements Quantitative methods
Quantitative forecasting methods (1/2)
- Used every time there are enough data;- yt , t = 1, . . . ,T : sequence of the T past observations of the
variable to be forecasted, arranged according to the time oftheir outcome (time series or historical data);
- all the periods are equally spaced in time;- choice of most suitable technique depends on:
> nature of the variable to be predicted;> amount and quality of the available data.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 2 / 17
2 Forecasting logistics requirements Quantitative methods
Quantitative forecasting methods (1/2)
- Used every time there are enough data;- yt , t = 1, . . . ,T : sequence of the T past observations of the
variable to be forecasted, arranged according to the time oftheir outcome (time series or historical data);
- all the periods are equally spaced in time;- choice of most suitable technique depends on:
> nature of the variable to be predicted;> amount and quality of the available data.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 2 / 17
2 Forecasting logistics requirements Quantitative methods
Quantitative forecasting methods (1/2)
- Used every time there are enough data;- yt , t = 1, . . . ,T : sequence of the T past observations of the
variable to be forecasted, arranged according to the time oftheir outcome (time series or historical data);
- all the periods are equally spaced in time;- choice of most suitable technique depends on:
> nature of the variable to be predicted;> amount and quality of the available data.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 2 / 17
2 Forecasting logistics requirements Quantitative methods
Quantitative forecasting methods (1/2)
- Used every time there are enough data;- yt , t = 1, . . . ,T : sequence of the T past observations of the
variable to be forecasted, arranged according to the time oftheir outcome (time series or historical data);
- all the periods are equally spaced in time;- choice of most suitable technique depends on:
> nature of the variable to be predicted;> amount and quality of the available data.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 2 / 17
2 Forecasting logistics requirements Quantitative methods
Graphical representation of time series (1/3)
- Cartesian diagram (t ,yt));- allows one to interpret the data more easily than in a table;- visual analysis of the diagram used as a support to most
complex methods (e.g. to identify a linear trend in the dataor to detect possible outliers) or even to visually extrapolatethe data;
- the T dots corresponding to the past observationsconnected through a continuous line (to emphasize themain features of the time series which is discrete in nature)
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 3 / 17
2 Forecasting logistics requirements Quantitative methods
Graphical representation of time series (1/3)
- Cartesian diagram (t ,yt));- allows one to interpret the data more easily than in a table;- visual analysis of the diagram used as a support to most
complex methods (e.g. to identify a linear trend in the dataor to detect possible outliers) or even to visually extrapolatethe data;
- the T dots corresponding to the past observationsconnected through a continuous line (to emphasize themain features of the time series which is discrete in nature)
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 3 / 17
2 Forecasting logistics requirements Quantitative methods
Graphical representation of time series (1/3)
- Cartesian diagram (t ,yt));- allows one to interpret the data more easily than in a table;- visual analysis of the diagram used as a support to most
complex methods (e.g. to identify a linear trend in the dataor to detect possible outliers) or even to visually extrapolatethe data;
- the T dots corresponding to the past observationsconnected through a continuous line (to emphasize themain features of the time series which is discrete in nature)
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 3 / 17
2 Forecasting logistics requirements Quantitative methods
Graphical representation of time series (1/3)
- Cartesian diagram (t ,yt));- allows one to interpret the data more easily than in a table;- visual analysis of the diagram used as a support to most
complex methods (e.g. to identify a linear trend in the dataor to detect possible outliers) or even to visually extrapolatethe data;
- the T dots corresponding to the past observationsconnected through a continuous line (to emphasize themain features of the time series which is discrete in nature)
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 3 / 17
2 Forecasting logistics requirements Quantitative methods
Graphical representation of time series (1/3)
- Cartesian diagram (t ,yt));- allows one to interpret the data more easily than in a table;- visual analysis of the diagram used as a support to most
complex methods (e.g. to identify a linear trend in the dataor to detect possible outliers) or even to visually extrapolatethe data;
- the T dots corresponding to the past observationsconnected through a continuous line (to emphasize themain features of the time series which is discrete in nature)
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 3 / 17
2 Forecasting logistics requirements Quantitative methods
Graphical representation of time series (1/3)
- Cartesian diagram (t ,yt));- allows one to interpret the data more easily than in a table;- visual analysis of the diagram used as a support to most
complex methods (e.g. to identify a linear trend in the dataor to detect possible outliers) or even to visually extrapolatethe data;
- the T dots corresponding to the past observationsconnected through a continuous line (to emphasize themain features of the time series which is discrete in nature)
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 3 / 17
2 Forecasting logistics requirements Quantitative methods
Graphical representation of time series (1/3)
- Cartesian diagram (t ,yt));- allows one to interpret the data more easily than in a table;- visual analysis of the diagram used as a support to most
complex methods (e.g. to identify a linear trend in the dataor to detect possible outliers) or even to visually extrapolatethe data;
- the T dots corresponding to the past observationsconnected through a continuous line (to emphasize themain features of the time series which is discrete in nature)
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 3 / 17
2 Forecasting logistics requirements Quantitative methods
Graphical representation of time series (2/3)
US GNPFigure 1 provides a graphical representation of the time seriesof the Gross National Product of the United States (in billions ofdollars) from 1889 to 1900.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 4 / 17
2 Forecasting logistics requirements Quantitative methods
Graphical representation of time series (3/3)
US GNP
0
10
20
30
40
50
60
70
80
90
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900
t
yt
Figure 1: Annual trend (t = 1, . . . ,12) of the Gross National Product ofthe United States (in billions of dollars) from 1889 to 1900.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 5 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (1/x)
- According to the density index: fraction of past observationswhich are zero;
- continuous time series: low density index (usually < 30%),see Figure 2;
- sporadic time series: significant proportion (usually morethan 30%) of zero values (see Figure 3);
> Typical sporadic time series: those of products whosesales volumes are low;
> periodic sporadic time series: if zero values alternateregularly with strictly positive observations;
> random sporadic time series: otherwise.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 6 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (1/x)
- According to the density index: fraction of past observationswhich are zero;
- continuous time series: low density index (usually < 30%),see Figure 2;
- sporadic time series: significant proportion (usually morethan 30%) of zero values (see Figure 3);
> Typical sporadic time series: those of products whosesales volumes are low;
> periodic sporadic time series: if zero values alternateregularly with strictly positive observations;
> random sporadic time series: otherwise.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 6 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (1/x)
- According to the density index: fraction of past observationswhich are zero;
- continuous time series: low density index (usually < 30%),see Figure 2;
- sporadic time series: significant proportion (usually morethan 30%) of zero values (see Figure 3);
> Typical sporadic time series: those of products whosesales volumes are low;
> periodic sporadic time series: if zero values alternateregularly with strictly positive observations;
> random sporadic time series: otherwise.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 6 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (1/x)
- According to the density index: fraction of past observationswhich are zero;
- continuous time series: low density index (usually < 30%),see Figure 2;
- sporadic time series: significant proportion (usually morethan 30%) of zero values (see Figure 3);
> Typical sporadic time series: those of products whosesales volumes are low;
> periodic sporadic time series: if zero values alternateregularly with strictly positive observations;
> random sporadic time series: otherwise.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 6 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (1/x)
- According to the density index: fraction of past observationswhich are zero;
- continuous time series: low density index (usually < 30%),see Figure 2;
- sporadic time series: significant proportion (usually morethan 30%) of zero values (see Figure 3);
> Typical sporadic time series: those of products whosesales volumes are low;
> periodic sporadic time series: if zero values alternateregularly with strictly positive observations;
> random sporadic time series: otherwise.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 6 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (1/x)
- According to the density index: fraction of past observationswhich are zero;
- continuous time series: low density index (usually < 30%),see Figure 2;
- sporadic time series: significant proportion (usually morethan 30%) of zero values (see Figure 3);
> Typical sporadic time series: those of products whosesales volumes are low;
> periodic sporadic time series: if zero values alternateregularly with strictly positive observations;
> random sporadic time series: otherwise.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 6 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (2/x)
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
t
yt
Figure 2: A continuous time series.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 7 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (3/x)
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
yt
t
Figure 3: A sporadic time series.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 8 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (4/x)
- regular time series: if it can be decomposed into four maincomponents, trend, cyclical variation, seasonal variationand residual variation (see Figure 4);
- irregular time series: otherwise (see Figure 5).
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 9 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (4/x)
- regular time series: if it can be decomposed into four maincomponents, trend, cyclical variation, seasonal variationand residual variation (see Figure 4);
- irregular time series: otherwise (see Figure 5).
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 9 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (4/x)
- regular time series: if it can be decomposed into four maincomponents, trend, cyclical variation, seasonal variationand residual variation (see Figure 4);
- irregular time series: otherwise (see Figure 5).
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 9 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (4/x)
- regular time series: if it can be decomposed into four maincomponents, trend, cyclical variation, seasonal variationand residual variation (see Figure 4);
- irregular time series: otherwise (see Figure 5).
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 9 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (4/x)
- regular time series: if it can be decomposed into four maincomponents, trend, cyclical variation, seasonal variationand residual variation (see Figure 4);
- irregular time series: otherwise (see Figure 5).
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 9 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (4/x)
- regular time series: if it can be decomposed into four maincomponents, trend, cyclical variation, seasonal variationand residual variation (see Figure 4);
- irregular time series: otherwise (see Figure 5).
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 9 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (4/x)
- regular time series: if it can be decomposed into four maincomponents, trend, cyclical variation, seasonal variationand residual variation (see Figure 4);
- irregular time series: otherwise (see Figure 5).
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 9 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (5/x)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
0
yt
t
Figure 4: A regular time series.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 10 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (6/x)
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
yt
t
Figure 5: An irregular time series.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 11 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (7/x)
- Trend> long-term modification of data patterns over time;> it may depend on changes in population and on the
product (or service) life cycle (see Figure 6).- Cyclical variation
> caused by the so-called business cycle, which dependson macro-economic issues;
> quite irregular, but its pattern is roughly periodic.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 12 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (7/x)
- Trend> long-term modification of data patterns over time;> it may depend on changes in population and on the
product (or service) life cycle (see Figure 6).- Cyclical variation
> caused by the so-called business cycle, which dependson macro-economic issues;
> quite irregular, but its pattern is roughly periodic.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 12 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (7/x)
- Trend> long-term modification of data patterns over time;> it may depend on changes in population and on the
product (or service) life cycle (see Figure 6).- Cyclical variation
> caused by the so-called business cycle, which dependson macro-economic issues;
> quite irregular, but its pattern is roughly periodic.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 12 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (7/x)
- Trend> long-term modification of data patterns over time;> it may depend on changes in population and on the
product (or service) life cycle (see Figure 6).- Cyclical variation
> caused by the so-called business cycle, which dependson macro-economic issues;
> quite irregular, but its pattern is roughly periodic.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 12 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (7/x)
- Trend> long-term modification of data patterns over time;> it may depend on changes in population and on the
product (or service) life cycle (see Figure 6).- Cyclical variation
> caused by the so-called business cycle, which dependson macro-economic issues;
> quite irregular, but its pattern is roughly periodic.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 12 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (7/x)
- Trend> long-term modification of data patterns over time;> it may depend on changes in population and on the
product (or service) life cycle (see Figure 6).- Cyclical variation
> caused by the so-called business cycle, which dependson macro-economic issues;
> quite irregular, but its pattern is roughly periodic.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 12 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (8/x)Demand
TimeGrowth Maturity Decline
Figure 6: Life cycle of a product or service.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 13 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (9/x)
- Seasonal variation> caused by the periodicity of several human activities.
Example. Ups and downs in the demand of someitems over the year;
> effect observed on a weekly horizon (e.g. some productsales are higher on weekends than on working days).
> Figure 4: length of seasonal cycle equal to six timeperiods.
- Residual variation> portion of the data pattern that cannot be interpreted as
trend, cyclical or seasonal variation;> result of numerous causes, each of which has a small
impact.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 14 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (9/x)
- Seasonal variation> caused by the periodicity of several human activities.
Example. Ups and downs in the demand of someitems over the year;
> effect observed on a weekly horizon (e.g. some productsales are higher on weekends than on working days).
> Figure 4: length of seasonal cycle equal to six timeperiods.
- Residual variation> portion of the data pattern that cannot be interpreted as
trend, cyclical or seasonal variation;> result of numerous causes, each of which has a small
impact.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 14 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (9/x)
- Seasonal variation> caused by the periodicity of several human activities.
Example. Ups and downs in the demand of someitems over the year;
> effect observed on a weekly horizon (e.g. some productsales are higher on weekends than on working days).
> Figure 4: length of seasonal cycle equal to six timeperiods.
- Residual variation> portion of the data pattern that cannot be interpreted as
trend, cyclical or seasonal variation;> result of numerous causes, each of which has a small
impact.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 14 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (9/x)
- Seasonal variation> caused by the periodicity of several human activities.
Example. Ups and downs in the demand of someitems over the year;
> effect observed on a weekly horizon (e.g. some productsales are higher on weekends than on working days).
> Figure 4: length of seasonal cycle equal to six timeperiods.
- Residual variation> portion of the data pattern that cannot be interpreted as
trend, cyclical or seasonal variation;> result of numerous causes, each of which has a small
impact.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 14 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (9/x)
- Seasonal variation> caused by the periodicity of several human activities.
Example. Ups and downs in the demand of someitems over the year;
> effect observed on a weekly horizon (e.g. some productsales are higher on weekends than on working days).
> Figure 4: length of seasonal cycle equal to six timeperiods.
- Residual variation> portion of the data pattern that cannot be interpreted as
trend, cyclical or seasonal variation;> result of numerous causes, each of which has a small
impact.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 14 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (9/x)
- Seasonal variation> caused by the periodicity of several human activities.
Example. Ups and downs in the demand of someitems over the year;
> effect observed on a weekly horizon (e.g. some productsales are higher on weekends than on working days).
> Figure 4: length of seasonal cycle equal to six timeperiods.
- Residual variation> portion of the data pattern that cannot be interpreted as
trend, cyclical or seasonal variation;> result of numerous causes, each of which has a small
impact.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 14 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (9/x)
- Seasonal variation> caused by the periodicity of several human activities.
Example. Ups and downs in the demand of someitems over the year;
> effect observed on a weekly horizon (e.g. some productsales are higher on weekends than on working days).
> Figure 4: length of seasonal cycle equal to six timeperiods.
- Residual variation> portion of the data pattern that cannot be interpreted as
trend, cyclical or seasonal variation;> result of numerous causes, each of which has a small
impact.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 14 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (9/x)
- Seasonal variation> caused by the periodicity of several human activities.
Example. Ups and downs in the demand of someitems over the year;
> effect observed on a weekly horizon (e.g. some productsales are higher on weekends than on working days).
> Figure 4: length of seasonal cycle equal to six timeperiods.
- Residual variation> portion of the data pattern that cannot be interpreted as
trend, cyclical or seasonal variation;> result of numerous causes, each of which has a small
impact.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 14 / 17
2 Forecasting logistics requirements Quantitative methods
Classification of time series (9/x)
- Seasonal variation> caused by the periodicity of several human activities.
Example. Ups and downs in the demand of someitems over the year;
> effect observed on a weekly horizon (e.g. some productsales are higher on weekends than on working days).
> Figure 4: length of seasonal cycle equal to six timeperiods.
- Residual variation> portion of the data pattern that cannot be interpreted as
trend, cyclical or seasonal variation;> result of numerous causes, each of which has a small
impact.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 14 / 17
2 Forecasting logistics requirements Quantitative methods
Forecasting process (1/3)
1. Data preprocessingData are seldom ready to be used to make a forecast.Outliers need to identified, and possibly removed in such away that they do not affect the predictions.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 15 / 17
2 Forecasting logistics requirements Quantitative methods
Forecasting process (1/3)
1. Data preprocessingData are seldom ready to be used to make a forecast.Outliers need to identified, and possibly removed in such away that they do not affect the predictions.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 15 / 17
2 Forecasting logistics requirements Quantitative methods
Forecasting process (1/3)
1. Data preprocessingData are seldom ready to be used to make a forecast.Outliers need to identified, and possibly removed in such away that they do not affect the predictions.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 15 / 17
2 Forecasting logistics requirements Quantitative methods
Forecasting process (2/3)
2. Choice of the forecasting methodThe most suitable forecasting method is selected among aset of alternative techniques based on the accuracy theywould have provided if used in the past.For parametrized methods, this step also includes thedetermination of the optimal value of each parameter.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 16 / 17
2 Forecasting logistics requirements Quantitative methods
Forecasting process (2/3)
2. Choice of the forecasting methodThe most suitable forecasting method is selected among aset of alternative techniques based on the accuracy theywould have provided if used in the past.For parametrized methods, this step also includes thedetermination of the optimal value of each parameter.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 16 / 17
2 Forecasting logistics requirements Quantitative methods
Forecasting process (2/3)
2. Choice of the forecasting methodThe most suitable forecasting method is selected among aset of alternative techniques based on the accuracy theywould have provided if used in the past.For parametrized methods, this step also includes thedetermination of the optimal value of each parameter.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 16 / 17
2 Forecasting logistics requirements Quantitative methods
Forecasting process (3/3)
3. Evaluation of the forecasting accuracyOnce the forecasted variable becomes known, an aposteriori error can be computed. Such errors are thencombined in order to assess the accuracy of the methodcurrently used.This measure can be used to finely tune up the parametersof the method or even to select an alternative technique.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 17 / 17
2 Forecasting logistics requirements Quantitative methods
Forecasting process (3/3)
3. Evaluation of the forecasting accuracyOnce the forecasted variable becomes known, an aposteriori error can be computed. Such errors are thencombined in order to assess the accuracy of the methodcurrently used.This measure can be used to finely tune up the parametersof the method or even to select an alternative technique.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 17 / 17
2 Forecasting logistics requirements Quantitative methods
Forecasting process (3/3)
3. Evaluation of the forecasting accuracyOnce the forecasted variable becomes known, an aposteriori error can be computed. Such errors are thencombined in order to assess the accuracy of the methodcurrently used.This measure can be used to finely tune up the parametersof the method or even to select an alternative technique.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 17 / 17
2 Forecasting logistics requirements Quantitative methods
Forecasting process (3/3)
3. Evaluation of the forecasting accuracyOnce the forecasted variable becomes known, an aposteriori error can be computed. Such errors are thencombined in order to assess the accuracy of the methodcurrently used.This measure can be used to finely tune up the parametersof the method or even to select an alternative technique.
G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 17 / 17
