Intermittent Demand Forecasting. John E. Boylan

Table 3.1 Order comprising five order lines.Table 3.2 Distribution of demand over one week.Table 3.3 Probability distribution of total demand over two weeks.Table 3.4 Cumulative distribution of total demand over two weeks.Table 3.5 Distribution of total demand over two weeks conditional on non‐zero...Table 3.6 Fill rates per time period.Table 3.7 Distribution of lumpy demand over one week.Table 3.8 Traditional fill rate calculation (

and

;

).Table 3.9 Sobel's fill rate calculation (

,

,

).

      2 Chapter 4Table 4.1 Triangular distribution example.Table 4.2 Poisson probabilities (

).Table 4.3 Calculation of chi‐square goodness of fit statistic.Table 4.4 Example of demand incidences.Table 4.5 Weekly demand data.Table 4.6 Sequence of demand occurrences (1) and non‐occurrences (0).Table 4.7 Observed and estimated order incidences over four weeks.Table 4.8 Critical values of the chi‐square distribution for degrees of freed...

      3 Chapter 5Table 5.1 Poisson (

) and stuttering Poisson (

and

probabilities (prob) an...Table 5.2 Calculation of negative binomial probabilities (

and

).Table 5.3 Percentages of SKUs with strong fit (demand per period).Table 5.4 Percentages of SKUs with strong fit (lead time demand).Table 5.5 Variables to be forecasted for four demand distributions.Table 5.6 ‘Stars and bars’ diagrams.

      4 Chapter 6Table 6.1 SES bias (issue points only,

) as a percentage of average demand.Table 6.2 Intermittent demand series (first eight periods).Table 6.3 Series of demand sizes and demand intervals.Table 6.4 Intermittent demand series (first 10 periods).Table 6.5 Intermittent series (after demand occurrence in period zero).Table 6.6 Croston's bias as a percentage of average demand.Table 6.7 Bias correction factors.Table 6.8 Bias of SES (

) as a percentage of average demand conditional on de...

      5 Chapter 7Table 7.1 Updating of mean and variance using SES.Table 7.2 Updating of variance over protection interval: scaled and direct.Table 7.3 Distributions of demand over gamma distributed lead times.

      6 Chapter 8Table 8.1 Safety factors for CSL targets, normal demand.Table 8.2 Safety factors for fill rate (FR) targets, normal demand.Table 8.3 Asymmetric effect of under‐ and over‐forecasting.Table 8.4 Adjusted safety factors for cycle service levels.Table 8.5 Cycle service level for Poisson demand ((R+L)

).Table 8.6 Fill rate for Poisson distributed demand.Table 8.7 Cycle service levels for stuttering Poisson distributed demand.Table 8.8 Weighted cumulative probabilities.Table 8.9 Adjusted safety factors for fill rates.Table 8.10

component calculations for Poisson distributed demand.Table 8.11

calculations for Poisson distributed demand.Table 8.12 Fill rate calculations for Poisson demand.Table 8.13

component calculations for stuttering Poisson demand.

      7 Chapter 9Table 9.1 Mean error, mean square error, mean absolute error, and mean absolu...Table 9.2 Forecast value added (FVA) example.Table 9.3 MAPEFF and sMAPE for intermittent demand.Table 9.4 MAE : Mean ratios for multiple series.Table 9.5 Mean absolute error for zero forecasts.Table 9.6 Mean error (ME) and mean absolute error (MAE).Table 9.7 Scaled mean error for multiple series.

      8 Chapter 10Table 10.1 Reported usage of forecast methods in practice.Table 10.2 Judgemental adjustments: effect on cycle service levels.Table 10.3 Cumulative forecast error (CFE).Table 10.4 Mean square error (frequent zeroes).

      9 Chapter 13Table 13.1 Cumulative frequency percentages.Table 13.2 Three‐month overlapping blocks (OB) and non‐overlapping blocks (NO...Table 13.3 Resampling from previous observations.Table 13.4 VZ resampling method (

).Table 13.5 Most recent 10 observations from Table 13.2.Table 13.6 Conditional probabilities of demand occurrence.Table 13.7 Simple bootstrapping with Markov chain extension..Table 13.8 Theta function calculation (

,

), overlap of one period.

      10 Chapter 14Table 14.1 INAR(1) process example.Table 14.2 INMA(1) process example.Table 14.3 Four simplest INARMA models.Table 14.4 Empirical evidence on model identification.Table 14.5 Conditional probabilities of demand at time

(

) given demand at t...Table 14.6 Cumulative conditional probabilities at time

(

) given demand at ...Table 14.7 Cumulative probabilities of demand over two periods (

), given dem...

      11 Chapter 15Table 15.1 Software implementation.

      List of Illustrations

      1 Chapter 1Figure 1.1 Intermittent and lumpy demand.

      2 Chapter 2Figure 2.1 Bill of materials (BoM) example.Figure 2.2 Periodic review and continuous review systems.Figure 2.3 Continuous review

and