Forecasting Demand for Services. Learning Objectives l Recommend the appropriate forecasting model for a given situation. l Conduct a Delphi forecasting.

Slides:

Advertisements

Similar presentations

Forecasting Introduction

Advertisements

Forecasting OPS 370.

ISM 270 Service Engineering and Management Lecture 6: Forecasting.

Chubaka Producciones Presenta :.

Forecasting Demand for Services

Forecasting Demand ISQA 511 Dr. Mellie Pullman.

ISM 270 Service Engineering and Management Lecture 5: Facility Location, Project Management.

2012 JANUARY Sun Mon Tue Wed Thu Fri Sat

1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.

CHAPTER 3 Forecasting.

Forecasting Demand Chapter 11. Forecasting Demand Subjective Models Delphi Method Cross-Impact Historical Analogy Causal Models Regression Models Econometric.

FORECASTING. Types of Forecasts Qualitative Time Series Causal Relationships Simulation.

Demand Management and Forecasting. Types of Forecasts Qualitative Time Series Causal Relationships Simulation.

Learning Objectives  Recommend the appropriate forecasting model for a given situation.  Conduct a Delphi forecasting exercise.  Describe.

P Pathophysiology Calendar. SundayMondayTuesdayWednesdayThursdayFridaySaturday January 2012.

ISM 270 Service Engineering and Management Lecture 7: Forecasting and Managing Service Capacity.

Mr. David P. Blain. C.Q.E. Management Department UNLV

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

Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall7-1 Chapter 7: Forecasting.

Sales Management Sales Forecasting Topic 13. Sales Forecasting What is it? Why do it? Qualitative vs Quantitative Goal = Accuracy Commonly Done by Marketing.

IES 371 Engineering Management Chapter 13: Forecasting

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

1 Given the following data, calculate forecasts for months April through June using a three- month moving average and an exponential smoothing forecast.

Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.

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.

Forecasting Demand for Services. 2 Learning Objectives Recommend the appropriate forecasting model for a given situation. Recommend the appropriate forecasting.

MNG221 - Management Science Forecasting. Lecture Outline Forecasting basics Moving average Exponential smoothing Linear trend line Forecast accuracy.

WORD JUMBLE. Months of the year Word in jumbled form e r r f b u y a Word in jumbled form e r r f b u y a february Click for the answer Next Question.

Copyright ©2016 Cengage Learning. All Rights Reserved

McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting.

DATE POWER 2 INCOME JANUARY 100member X 25.00P2, FEBRUARY 200member X 25.00P5, MARCH 400member X 25.00P10, APRIL 800member.

Chapter 4 Class 2.

Welcome to MM305 Unit 5 Seminar Prof Greg Forecasting.

McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting.

2011 Calendar Important Dates/Events/Homework. SunSatFriThursWedTuesMon January

1 1 Slide © 2000 South-Western College Publishing/ITP Slides Prepared by JOHN LOUCKS.

Forecasting Demand. Forecasting Methods Qualitative – Judgmental, Executive Opinion - Internal Opinions - Delphi Method - Surveys Quantitative - Causal,

Quantitative Forecasting Methods (Non-Naive)

Chapter 4 Forecasting. Ch. 4: What is covered? Moving AverageMoving Average Weighted Moving AverageWeighted Moving Average Exponential SmoothingExponential.

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

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.

FORECASTIN G Qualitative Analysis ~ Quantitative Analysis.

Forecasting Demand. Problems with Forecasts Forecasts are Usually Wrong. Every Forecast Should Include an Estimate of Error. Forecasts are More Accurate.

July 2007 SundayMondayTuesdayWednesdayThursdayFridaySaturday

Northcutt Bikes Case Answers

Chapter 20 Time Series Analysis and Forecasting. Introduction Any variable that is measured over time in sequential order is called a time series. We.

TIM 270 Service Engineering and Management Lecture 6: Forecasting.

Operations Management Demand Forecasting. Session Break Up Conceptual framework Software Demonstration Case Discussion.

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.

BUS 173: Lecture 10 Forecasting for Businesses. Outline  What is a forecast?  Why do we need forecasting?  What are the common tools of forecasting?

Forecasting Demand Chapter 11.

Forecasting Chapter 9.

Quantitative Analysis for Management

Forecasting techniques

4 Forecasting Demand PowerPoint presentation to accompany

Module 2: Demand Forecasting 2.

McDonald’s Kalender 2009.

McDonald’s Kalender 2009.

Forecasting Elements of good forecast Accurate Timely Reliable

McDonald’s Kalender 2009.

Forecasting Qualitative Analysis Quantitative Analysis.

McDonald’s calendar 2007.

Forecasting - Introduction

McDonald’s calendar 2007.

Habitat Changes and Fish Migration

2015 January February March April May June July August September

Habitat Changes and Fish Migration

Presentation transcript:

Forecasting Demand for Services

Learning Objectives l Recommend the appropriate forecasting model for a given situation. l Conduct a Delphi forecasting exercise. l Describe the features of exponential smoothing. l Conduct time series forecasting using exponential smoothing with trend and seasonal adjustments.

Forecasting Models l Subjective Models Delphi Methods l Causal Models Regression Models l Time Series Models Moving Averages Exponential Smoothing

Delphi Forecasting Question: In what future election will a woman become president of the united states?

N Period Moving Average Let : MA T = The N period moving average at the end of period T A T = Actual observation for period T Then: MA T = (A T + A T-1 + A T-2 + …..+ A T-N+1 )/N Characteristics: Need N observations to make a forecast Very inexpensive and easy to understand Gives equal weight to all observations Does not consider observations older than N periods

Moving Average Example Saturday Occupancy at a 100-room Hotel Three-period Saturday Period Occupancy Moving Average Forecast Aug Sept

Exponential Smoothing Let : S T = Smoothed value at end of period T A T = Actual observation for period T F T+1 = Forecast for period T+1 Feedback control nature of exponential smoothing New value (S T ) = Old value (S T-1 ) + [ observed error ] or :

Exponential Smoothing Hotel Example Saturday Hotel Occupancy ( =0.5) Actual Smoothed Forecast Period Occupancy Value Forecast error Saturday t A t S t F t |A t - F t | Aug Sept MAD = Mean Absolute Deviation (MAD)

Exponential Smoothing Implied Weights Given Past Demand Substitute for If continued:

Exponential Smoothing Weight Distribution Relationship Between and N (exponential smoothing constant) : N (periods in moving average) :

Saturday Hotel Occupancy Effect of Alpha ( =0.1 vs. =0.5) Actual Forecast

Exponential Smoothing With Trend Adjustment Commuter Airline Load Factor Week Actual load factor Smoothed value Smoothed trend Forecast Forecast error t A t S t T t F t | A t - F t | MAD 6.7

Exponential Smoothing with Seasonal Adjustment Ferry Passengers taken to a Resort Island Actual Smoothed Index Period t A t value S t I t Forecast F t | A t - F t| 1995 January … ….. February … ….. March … ….. April … ….. May … ….. June … ….. July … ….. August … ….. September … ….. October … ….. November … ….. December … January ….. February March April May

Topics for Discussion l What characteristics of service organizations make forecast accuracy important? l For each of the three forecasting methods, what are the developmental costs and associated cost of forecast error? l Suggest independent variables for a regression model to predict the sales volume for a proposed video rental store location. l Suggest how the Delphi method can be incorporated into a cross-impact analysis.