1 Intermittent demand Forecasting & Inventory control Ruud Teunter.

Slides:

Advertisements

Similar presentations

Part II – TIME SERIES ANALYSIS C3 Exponential Smoothing Methods © Angel A. Juan & Carles Serrat - UPC 2007/2008.

Advertisements

Forecasting OPS 370.

4/16/ Ardavan Asef-Vaziri Variable of interest Time Series Analysis.

1 Forecasting for Operations Everette S. Gardner, Jr.

T T18-03 Exponential Smoothing Forecast Purpose Allows the analyst to create and analyze the "Exponential Smoothing Average" forecast. The MAD.

Forecasting 5 June Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.

Forecasting using simple models

Measures of Effectiveness 1 Ardavan Asef-Vaziri 6/4/2009 Forecasting - 4 Chapter 7 Demand Forecasting in a Supply Chain Forecasting - 3 Demand Pooling.

Forecasting Ross L. Fink.

Data Sources The most sophisticated forecasting model will fail if it is applied to unreliable data Data should be reliable and accurate Data should be.

OPIM 310 –Lecture # 1.2 Instructor: Jose M. Cruz

Chapter 3 Forecasting McGraw-Hill/Irwin

Chapter 13 Forecasting.

T T18-05 Trend Adjusted Exponential Smoothing Forecast Purpose Allows the analyst to create and analyze the "Trend Adjusted Exponential Smoothing"

Roberta Russell & Bernard W. Taylor, III

Demand Forecasting: Time Series Models Professor Stephen R. Lawrence College of Business and Administration University of Colorado Boulder, CO

Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.

Copyright 2013 John Wiley & Sons, Inc. Chapter 8 Supplement Forecasting.

(a) : If average demand per day is 20 units and lead time is 10 days. Assuming zero safety stock. ss=0, compute ROP? (b): If average demand per day is.

T T18-06 Seasonal Relatives Purpose Allows the analyst to create and analyze the "Seasonal Relatives" for a time series. A graphical display of.

Customer Demand Customer demand varies in both timing and quantity:

Slides 13b: Time-Series Models; Measuring Forecast Error

Fall, 2012 EMBA 512 Demand Forecasting Boise State University 1 Demand Forecasting.

A) If average demand per day is 20 units and lead time is 10 days. Assuming zero safety stock. ss=0, compute ROP. b) If average demand per day is 20 units.

Forecasting – stock control interactions: a simulation intensive investigation Aris A. Syntetos and Zied M. Babai CORAS - University of Salford Centre.

13 – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Forecasting 13.

The Importance of Forecasting in POM

TOPICS TO BE DISCUSSED 1. Introduction

Forecasting for Operations

SLR w SI = Simple Linear Regression with Seasonality Indices

1 FORECASTING Regression Analysis Aslı Sencer Graduate Program in Business Information Systems.

Forecasting OPS 370.

MBA.782.ForecastingCAJ Demand Management Qualitative Methods of Forecasting Quantitative Methods of Forecasting Causal Relationship Forecasting Focus.

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Chapter 9 Demand Forecasting.

Forecasting. 預測 (Forecasting) A Basis of Forecasting In business, forecasts are the basis for budgeting and planning for capacity, sales, production and.

4-1 Operations Management Forecasting Chapter 4 - Part 2.

Welcome to MM305 Unit 5 Seminar Prof Greg Forecasting.

IE5403 Facilities Design and Planning Instructor: Assistant Prof. Dr. Rıfat Gürcan Özdemir

Demand Forecasting: Time Series Models Professor Stephen R. Lawrence College of Business and Administration University of Colorado Boulder, CO

Time-Series Forecast Models  A time series is a sequence of evenly time-spaced data points, such as daily shipments, weekly sales, or quarterly earnings.

Quantitative Forecasting Methods (Non-Naive)

Forecasting is the art and science of predicting future events.

Chapter 7 Demand Forecasting in a Supply Chain

CHAPTER 12 FORECASTING. THE CONCEPTS A prediction of future events used for planning purpose Supply chain success, resources planning, scheduling, capacity.

Forecasting Chapter 5 OPS 370

ISEN 315 Spring 2011 Dr. Gary Gaukler. Forecasting for Stationary Series A stationary time series has the form: D t =  +  t where  is a constant.

Demand Management and Forecasting Chapter 11 Portions Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.

Demand Forecasting Production and Operations Management Judit Uzonyi-Kecskés Ph.D. Student Department of Management and Corporate Economics Budapest University.

Chapter 11 – With Woodruff Modications Demand Management and Forecasting Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.

3-1Forecasting Weighted Moving Average Formula w t = weight given to time period “t” occurrence (weights must add to one) The formula for the moving average.

Chapter 3 Lecture 4 Forecasting. Time Series is a sequence of measurements over time, usually obtained at equally spaced intervals – Daily – Monthly –

T T18-02 Weighted Moving Average Forecast Purpose Allows the analyst to create and analyze the "Weighted Moving Average" forecast for up to 5.

Welcome to MM305 Unit 5 Seminar Dr. Bob Forecasting.

Welcome to MM305 Unit 5 Seminar Forecasting. What is forecasting? An attempt to predict the future using data. Generally an 8-step process 1.Why are you.

Dr. Kostas Nikolopoulos

1 By: Prof. Y. Peter Chiu 9 / 1 / / 1 / 2012 Chapter 2 -A Forecasting.

Demand Forecasting: Time Series Models Professor Stephen R

Demand Forecasting Fall, 2016 EMBA 512 Demand Forecasting

The Handbook of Logistics and Distribution Management

American Lighting Products

Operations Management Contemporary Concepts and Cases

Forecasting Methods Dr. T. T. Kachwala.

Spare Parts inventory Management system

NATCOR Stochastic Modelling Course Inventory Control – Part 1

Traditional smoothing approaches versus the Croston method

Principles of Supply Chain Management: A Balanced Approach

Forecasting Elements of good forecast Accurate Timely Reliable

Setting the right stock levels

Chapter 8 Supplement Forecasting.

Chapter 12 Historical forecasting techniques

Presentation transcript:

1 Intermittent demand Forecasting & Inventory control Ruud Teunter

2 Typical intermittent demand pattern

3 RAF data-set (5,000 items) MinimumAverageMaximum Demand size Demands per year0.513 Lead time1 months9 months24 months Price0.3p£108£4962

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: S SES 2.Re-Estimate mean demand interval (I1) using Exponential Smoothing: I SES 3. Re-estimate mean demand : D = S SES / I SES 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 I SES  = smoothing constant for I Comments As easy to apply as Croston’s Method Slight bias remains (downwards) ^ ^

8 Traditional performance measures Accuracy (MAD, MSE) of next period forecast

9 Results Traditional performance measures

10 Zero forecast – Example

11 Forecaster

12 And stock controller

13 Inventory performance measure Service 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) Forecast1.25 ∙ MAD or √(MSE)

15 Service Level Accuracy

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

17 Forecasted number of periods between demands Include Initial Demand Size

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