Forecasting is the art and science of predicting future events.

Slides:

Advertisements

Similar presentations

Agenda of Week V. Forecasting

Advertisements

Spreadsheet Modeling & Decision Analysis

Forecasting OPS 370.

Operations Management Forecasting Chapter 4

Analyzing and Forecasting Time Series Data

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.

1 Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Management Science, 3e by Cliff Ragsdale.

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

CHAPTER 3 Forecasting.

Chapter 3 Forecasting McGraw-Hill/Irwin

Chapter 13 Forecasting.

Roberta Russell & Bernard W. Taylor, III

Forecasting & Time Series Minggu 6. Learning Objectives Understand the three categories of forecasting techniques available. Become aware of the four.

Operations Management Forecasting Chapter 4

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J Operations Management Forecasting Chapter 4.

4 Forecasting PowerPoint presentation to accompany Heizer and Render

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.

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

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

Demand Forecasts The three principles of all forecasting techniques: –Forecasting is always wrong –Every forecast should include an estimate of error –The.

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

Time Series Analysis for e-business. “Forecasting is very dangerous, especially about the future.” --- Samuel Goldwyn.

Time Series “The Art of Forecasting”. What Is Forecasting? Process of predicting a future event Underlying basis of all business decisions –Production.

1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.

Production Planning and Control. 1. Naive approach 2. Moving averages 3. Exponential smoothing 4. Trend projection 5. Linear regression Time-Series Models.

CHAPTER 3 FORECASTING.

Demand Management and Forecasting

Chapter 3 Forecasting.

Business Forecasting Used to try to predict the future Uses two main methods: Qualitative – seeking opinions on which to base decision making – Consumer.

1 Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Management Science, 3e by Cliff Ragsdale.

Forecasting supply chain requirements

3-1Forecasting. 3-2Forecasting FORECAST:  A statement about the future value of a variable of interest such as demand.  Forecasts affect decisions and.

Forecasting Professor Ahmadi.

1 DSCI 3023 Forecasting Plays an important role in many industries –marketing –financial planning –production control Forecasts are not to be thought of.

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

Time Series Analysis and Forecasting

Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.

Lesson 4 -Part A Forecasting Quantitative Approaches to Forecasting Components of a Time Series Measures of Forecast Accuracy Smoothing Methods Trend Projection.

1 1 Slide Forecasting Professor Ahmadi. 2 2 Slide Learning Objectives n Understand when to use various types of forecasting models and the time horizon.

© 1999 Prentice-Hall, Inc. Chap Chapter Topics Component Factors of the Time-Series Model Smoothing of Data Series  Moving Averages  Exponential.

Business Processes Sales Order Management Aggregate Planning Master Scheduling Production Activity Control Quality Control Distribution Mngt. © 2001 Victor.

Welcome to MM305 Unit 5 Seminar Prof Greg Forecasting.

Time Series Analysis and Forecasting. Introduction to Time Series Analysis A time-series is a set of observations on a quantitative variable collected.

15-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Forecasting Chapter 15.

Learning Objectives Describe what forecasting is Explain time series & its components Smooth a data series –Moving average –Exponential smoothing Forecast.

PRODUCTION & OPERATIONS MANAGEMENT Module II Forecasting for operations Prof. A.Das, MIMTS.

4 - 1 Course Title: Production and Operations Management Course Code: MGT 362 Course Book: Operations Management 10 th Edition. By Jay Heizer & Barry Render.

1 1 Chapter 6 Forecasting n Quantitative Approaches to Forecasting n The Components of a Time Series n Measures of Forecast Accuracy n Using Smoothing.

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

Chapter 12 Forecasting. Lecture Outline Strategic Role of Forecasting in SCM Components of Forecasting Demand Time Series Methods Forecast Accuracy Regression.

3-1Forecasting CHAPTER 3 Forecasting McGraw-Hill/Irwin Operations Management, Eighth Edition, by William J. Stevenson Copyright © 2005 by The McGraw-Hill.

To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. Chapter 8 Forecasting To Accompany.

To accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Fourth Edition  1996 Addison-Wesley Publishing Company, Inc. All rights.

Times Series Forecasting and Index Numbers Chapter 16 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.

Managerial Decision Modeling 6 th edition Cliff T. Ragsdale.

McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 3 Forecasting.

Forecast 2 Linear trend Forecast error Seasonal demand.

Chapter 15 Forecasting. Forecasting Methods n Forecasting methods can be classified as qualitative or quantitative. n Such methods are appropriate when.

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.

TIME SERIES MODELS. Definitions Forecast is a prediction of future events used for planning process. Time Series is the repeated observations of demand.

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.

Forecasting Chapter 9.

Forecasting Methods Dr. T. T. Kachwala.

“The Art of Forecasting”

FORCASTING AND DEMAND PLANNING

Forecasting Elements of good forecast Accurate Timely Reliable

Chapter 8 Supplement Forecasting.

Presentation transcript:

Forecasting is the art and science of predicting future events

Forecasting Process 6. Check forecast accuracy with one or more measures 1. Identify the purpose of forecast 2. Collect historical data 3. Plot data and identify patterns 4. Select a forecast model that seems appropriate for data 5. Develop / compute forecast for period of historical data 8b. Select new forecast model or adjust parameters of existing model 8a. Forecast over planning horizon 9. Adjust forecast based on additional qualitative information and insight 10. Monitor results and measure forecast accuracy 7. Is accuracy of forecast acceptable?

Forecasting Methods Time Series Models (data changes with time) Causal Models (data is dependent on some other data variable) Qualitative Analysis (no relevant data is available)

Causal Forecasts Assumption: One or more variables can be identified which has a relationship with demand Approaches: Simple Linear Regression Multiple Linear Regression

“Time Series” Defn: A time-ordered sequence of observations that have been taken at regular intervals. Examples: past monthly demands, past annual demands. Assumption: Future values can be estimated from past values of the series.

Forecasting Process 6. Check forecast accuracy with one or more measures 1. Identify the purpose of forecast 2. Collect historical data 3. Plot data and identify patterns 4. Select a forecast model that seems appropriate for data 5. Develop / compute forecast for period of historical data 8b. Select new forecast model or adjust parameters of existing model 8a. Forecast over planning horizon 9. Adjust forecast based on additional qualitative information and insight 10. Monitor results and measure forecast accuracy 7. Is accuracy of forecast acceptable?

Demand Behavior Trend gradual, long-term up or down movement Cycle up & down movement repeating over long time frame Seasonal pattern periodic oscillation in demand which repeats based on calendar schedule Random movements (follow no pattern) Practice decomposing with TimeSeriesData.xls

Some Time Series Terms Stationary Component - a time series variable exhibiting no significant upward or downward trend over time. Nonstationary Component - a time series variable exhibiting a significant upward or downward trend over time. Seasonal Component - a time series variable exhibiting a repeating pattern at regular intervals over time. Irregular Component- something that is random and thus cannot be predicted.

Time Series Approaches S Moving Averages S Exponential Smoothing S Seasonal Adjustments S Linear Trend Lines

Moving Averages  No general method exists for determining k.  We must try out several k values to see what works best.

Forecast Error

Measuring Accuracy Magnitude Four common techniques are the: mean absolute deviation, mean absolute percent error, the mean square error, root mean square error. We will focus on MAD and MAPE.

A Comment on Comparing MAD and MAPE Values Care should be taken when comparing MAD and/or MAPE values of two different forecasting techniques. The lowest number may result from a technique that fits older values very well but fits recent values poorly. Plotting historical forecasts on the graph will help you identify this case. It is sometimes wise to compute the measures using only the most recent values.

Exponential Smoothing New forecast= Last Period Forecast + Correction for Error made Last Period = Last Period Forecast + α ( Last Period Demand – Last Period Forecast)

Examples of Two Exponential Smoothing Functions

Stationary Seasonal Effects

Stationary Data and Seasonal Effects Additive Effect: Multiplicative Effect: E t is the expected level at time period t. S t is the seasonal factor for time period t. p represents the number of seasonal periods