Index

Absolute errors, 103

Absolute percentage errors (APEs), 108

Accuracy, 102–107

Actual inventory management systems, 18

Advertising data, 74–75

Anecdotal pareto principle, 81

ARIMA (Autoregressive Integrated Moving Average) models

    autocorrelation and partial autocorrelation, 66–68

    autoregression, 61–62

    integration, 62–64

    moving averages, 64–66

Audi A3, 40

Autocorrelation function (ACF), 66–68

Autoregression (AR), 61–62

    autocorrelation and partial autocorrelation functions, 67

 

The Bank of England (BoE), 14–15

Bias, 102–107

Biased forecast, 103

Big data, 74

“black box” argument, 92

Bottom-up forecasting, 137–139

Box–Jenkins models. See ARIMA models

“Brute force” technique, 65

Bureau of Labor Statistics, 42, 44

 

Causal modelling, 71–78

Chi-squared test, 115

Coefficient alpha (α), 52

Cognitive biases, 19

Combination method, 95–98

Continuous normal distribution, 79

Correction method, 95–98

Corresponding error, 102

Count data distributions, 79

Count data forecasts, quality measures for, 116–118

Critical fractile, 16

Cross-functional communication, 6

Croston’s method, 83–87

 

Dampening, 57

Data availability, for time series analysis, 33–35

Decision making, 28–29

Decomposition, 41

    methods, 42–44

Demand averaging, 5

Demand chasing, 90. See also Naïve forecasting

Demand forecasting, 27

    meaning of, 35

Dickey–Fuller test, 36

Domain-specific knowledge, 91–93

 

Economic order quantity model, 85

Economies of scale in forecasting, 38

Error definition, 102

Error measures, 101

Excel, 7, 43

Exponential smoothing model, 23, 34, 77

    change and noise, 51–54

    extensions, 55–58

    optimal smoothing parameters, 54–55

    weights for past demand under, 54

 

Fill rates. See Type II service levels

Financial forecasting, 33

First-order differencing, process of, 64

Forecast errors

    empirical distribution of, 25–28

    standard deviation of, 25–28

    symmetry of, 11–12

Forecast quality measures, 101–118

    assessing prediction intervals, 114–116

    bias and accuracy, 102–107

    for count data forecasts, 116–118

    percentage and scaled errors, 107–114

“Forecast Value Added”, 37

    analysis, 95

Forecastability, and scale, 37–40

Forecasting

    easy- and hard-to–forecast time series, 4–5

    decision making, 28–29

    preparation of, 3

    methods, 9–11

    point, 21–25, 27f

    prediction interval, 25–28

    from probabilistic perspective, 13

    reporting uncertainty, 11–15

    service levels, 15–18

    software, 6–7, 27

    time series, 22. See also Time series forecasting

    value of, 3–6

Forecasting competitions, 119–125

    additional aspects, 124–125

    data, 121–122

    description, 119

    planning, 119–121

    procedure, 122–124

Forecasting hierarchies, 135–145

    bottom-up forecasting, 137–139

    middle-out forecasting, 141–142

    multivariate time series, 135–137

    optimal reconciliation forecasting, 142–144

    other approaches, 144–145

    top-down forecasting, 139–141

Forecasting model

    domain-specific knowledge in, 91–93

     group decision making, 97

    judgemental

        combination method, 95–98

        correction method, 95–98

    and leading indicators, 71–72

    with multiple methods, 77

    political and incentive aspects, 93–95

    traditional, 82–83

Forecasting organization, 129–130

Free statistical software R, 7

 

GARCH (generalized autoregressive conditional heteroscedasticity), 69

GDP growth, 14–15

Goethe, 11

Google Correlate, 74

Google Trends, 74

Group decision making, 97

 

Hedging, 94

Hindsight bias, 90

Holt’s method, 55

Holt–Winters exponential smoothing, 57

Human judgment, in forecasting, 9

 

Illusionary trend perception, 21

In-stock probability. See Type I service level

Inherent forecastability, 4

Integration, 62–64

Interaction effect, 92

Intermittent demand series, 81

    Croston’s method, 83–87

Intuition vs. Cognition, 89–91

 

Judgmental forecasts, 95

 

Leading indicators, 71–74

    illustration, 72

    and time series, 74–78

    use of, 72–73

Level of time series, 41

    additive/multiplicative, 46

“Level only” model, 24

Lumpy demand, 81

 

M-competitions, 51

Make-to-order system, 3–4

Managerial thinking, common mistake in, 36–37

Mean absolute deviation (MAD), 103

Mean absolute error (MAE), 25, 76, 103

Mean absolute scaled error (MASE), 112

Mean error (ME), 103

Mean squared error (MSE), 25, 105

Microsoft Excel, 7

Middle-out forecasting, 141–142

Modern ERP software, 35

Moving averages (MA), 64–66

    autocorrelation and partial autocorrelation functions, 67

Multiplicative seasonality, 46

Multiplicative trend, 46

Multistep-ahead forecasts, 113

Multivariate time series, 135–137

 

Naïve forecasting, 5

News-vendor problem, 16

Normal distribution

    continuous, 79

    unbounded, 79

Normally distributed demands, 80

 

One-step-ahead forecast, 21, 113

Optimal reconciliation forecasting, 142–144

Organizational barriers, 130–133

 

Partial autocorrelation functions (PACF), 66–68

Pearson’s χ2 test, 115

Pegels’ classification, 58

Percentage errors, 107–114

Performance measurement, 101

Point forecast. See Forecast

Poisson-distributed demands, 80

Prediction interval, 25–28, 27f

    assessment of, 114–116

Professional forecasters, 9

 

Random noise, 41, 45

Regression equation, 75–76

Retail organizations, 10

Rolling origin forecast, 75

Rolling regression forecasts, 75–76, 77

Root mean squared error (RMSE), 106

 

Sales and operations planning (S&OP), 129–133

    forecasting organization, 129–130

    organizational barriers, 130–133

    steps in, 130

Sandbagging, 94

Scaled errors, 107–114

Seasonality, 34, 42

    additive/multiplicative, 46

    ARIMA model, 62

    methods to remove, 43

    stability of, 44–45

    variants of, 58

Seasonality parameters, 57

“Seasonally adjusted”, 42

Second guessing, 94

Service levels, 15–18

Shrinkage methods, 34

Single exponential smoothing model, 24

Smoothing parameter, 23

Software

    forecasting process, 6–7, 27

Spinning, 94

Spreadsheet model, 9

    software, 7

Squared errors, 106

Stationarity, 35–37

Statistical model, 9

Statistical software R, 58

Stock-keeping units (skus), 135

Syntetos-boylan approximation, 86

System neglect, 19

 

Theory-based formulas, 25–28

Time series forecasting, 72, 74–78

    additive and multiplicative components, 46

    cognitive biases in, 89

    combination with, 74–78

    components of, 41–42

    decomposition methods, 42–44

    data availability, 33–35

    forecastability and scale, 37–40

    on sales and advertising, 72

    stationarity, 35–37

    stability of components, 44–45

Top-down forecasting, 139–141

Traditional forecasting methods, 82–83

Trend

    additive/multiplicative, 46

    methods to remove, 43

    stability of, 44–45

    in time series, 41–42

Trends in time series

    ARIMA model, 62

    variants of, 58

Two-step-ahead forecast, 21

Type I service level, 16

Type II service level, 16

 

Unbiased forecast, 103

Unbounded normal distribution, 79

Uncertainty, reporting forecast, 11–15

 

Volkswagen Touran, 40

 

Weighted MAPE (wMAPE), 110

Wisdom of crowds literature, 96

Withholding, 94

 

X-13ARIMA-SEATS algorithm, 44

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