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Intermittent demand

Intermittent demand. Forecasting & Inventory control Ruud Teunter. Typical intermittent demand pattern. RAF data-set (5,000 items). What forecasting method to use?. Zero forecast Simple moving average Exponential smoothing Croston’s method

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Intermittent demand

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  1. Intermittent demand Forecasting & Inventory control Ruud Teunter

  2. Typical intermittent demand pattern

  3. RAF data-set (5,000 items)

  4. What forecasting method to use? • Zero forecast • Simple moving average • Exponential smoothing • Croston’s method • Syntetos-Boylan variation of Croston’s method • Levén-Segerstedt variation of Croston’s method • Bootstrapping

  5. Croston’s Method (1972) • If a demand occurs 1.Re-estimate mean demand size (S) using Exponential Smoothing: SSES 2.Re-Estimate mean demand interval (I1) using Exponential Smoothing: ISES 3. Re-estimate mean demand : D = SSES / ISES • Else if demand does not occur Do not re-estimate ^ ^ ^ ^ ^

  6. Bias in Croston’s method • E[D] = E[S/I] > E[S] / E[I] • Bias increases with variation of I • Bias increases with smoothing constant α • Syntetos-Boylan variation corrects for bias

  7. Syntetos-Boylan Approximation ^ Forecast of Mean Demand = (1 - /2) S SES ISES ^  = smoothing constant for I Comments • As easy to apply as Croston’s Method • Slight bias remains (downwards)

  8. Traditional performance measuresAccuracy (MAD, MSE) of next period forecast

  9. Results Traditional performance measures

  10. Zero forecast – Example

  11. Forecaster

  12. And stock controller

  13. Inventory performance measureService Level Accuracy

  14. Inventory policy parameters • RAF uses the order-up-to S policy • Assume normal lead time demand • L: Lead time • k: safety factor for achieving target stock-out service level (Normally distributed lead time demand) Forecast 1.25 ∙ MAD or √(MSE)

  15. Service Level Accuracy

  16. Initial Demand Size! Inventory S Initial Demand Size (Additional) Lead Time Demand Time Lead Time

  17. Include Initial Demand Size Forecasted number of periods between demands

  18. Conclusions • Dangerous to focus on standard accuracy measures (MAD, MSE) only • Although bias relates measures (Mean Error - ME) would have helped • Standard forecasting methods (MA, SES) do not provide enough information for intermittent demand • Original Croston, SB Croston variant, and Bootstrapping have similar performance for RAF sata set

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