Introduction to operations and supply chain management 3e bozarth chapter 09

including moving average, exponential smoothing, and linear regression models.. Last Period Model Last Period Model - The simplest time series model that uses demand for the current pe

Chapter 9

Chapter Objectives

Be able to:

most appropriate type of forecasting approach, given different forecasting situations

including moving average, exponential smoothing, and linear regression models

regression and multiple regression

interpret the results

 Forecast – An estimate of the future level of some variable.

 Why Forecast?

 Assess long-term capacity needs

 Develop budgets, hiring plans, etc.

 Plan production or order materials

 Number of current producers and suppliers

 Projected aggregate supply levels

 Technological and political trends

 Cost of supplies and services

 Market price for firm’s product or service

Laws of Forecasting

(but they are still useful).

Forecasting Methods

techniques based on intuition or informed opinion.

 Used when data are scarce, not available, or

irrelevant.

models that use measurable, historical data to

generate forecasts

 Time series and causal models

Selecting a Forecasting Method

Qualitative Forecasting Methods

 Market surveys

 Build-up forecasts

 Life-cycle analogy method

 Panel consensus forecasting

 Delphi method

Quantitative Forecasting Methods

 Time series forecasting models – Models that use a series of observations in chronological order to develop forecasts.

 Causal forecasting models – Models in which forecasts are modeled as a function of

something other than time.

Demand movement

time period to the next.

series.

in a time series associated with certain times of the year.

Time series with randomness

Figure 9.3

Time series with Trend and Seasonality

Figure 9.4

Last Period Model

 Last Period Model - The simplest time series model that uses demand for the current

period as a forecast for the next period.

F t+1 = D t

where Ft+1= forecast for the next period, t+1 and Dt = demand for the current period, t

Last Period Model

Moving Average Model

 Moving Average Model – A time series

forecasting model that derives a forecast by taking an average of recent demand value.

n

D F

n

i

i t t

Moving Average Model

3-period moving average forecast for Period 8:

Weighted Moving Average Model

 Weighted Moving Average Model – A form of the moving average model that allows the

actual weights applied to past observations

to differ.

Weighted Moving Average Model

3-period weighted moving average forecast for Period 8=

Exponential Smoothing Model

moving average model in which the forecast for the next period is calculated as the weighted average of the current period’s actual value and forecast.

Exponential Smoothing Model

Adjusted Exponential Smoothing

 Adjusted Exponential Smoothing Model – An expanded

version of the exponential smoothing model that includes a trend adjustment factor.

AF t+1 = F t+1 +T t+1

where AFt+1 = adjusted forecast for the next period

Ft+1 = unadjusted forecast for the next period = Dt + (1 – ) Ft

Tt+1 = trend factor for the next period = (Ft+1 – Ft) + (1 – )Tt

T = trend factor for the current period

Linear Regression

Linear Regression

 How to calculate the a and b

Linear Regression – Example 9.3

Linear Regression – Example 9.3

Linear Regression – Example 9.3

The graph shows an upward trend of 7.33 sales per month.

Figure 9.12

Seasonal Adjustments

 Seasonality – Repeated patterns or drops in a time series associated with certain times of the year.

Seasonal Adjustments

 Four-step procedure:

 For each of the demand values in the time series, calculate the

corresponding forecast using the unadjusted forecast model.

 For each demand value, calculate (Demand/Forecast) If the ratio is less than 1, then the forecast model overforecasted; if it is greater than 1, then the model underforecasted.

 If the time series covers multiple years, take the average

(Demand/Forecast) for corresponding months or quarters to derive the seasonal index Otherwise use (Demand/Forecast) calculated in Step 2 as the seasonal index.

 Multiply the unadjusted forecast by the seasonal index to get the seasonally adjusted forecast value.

Seasonality – Example 9.4

Note that the regression forecast does not reflect the seasonality.

Seasonality – Example 9.4

Seasonality – Example 9.4

Calculate the (Demand/Forecast) for each of the time periods:

January 2012: (Demand/Forecast) = 51/106.9 = 477 January 2013: (Demand/Forecast) = 112/205.6 = 545

Calculate the monthly seasonal indices:

Monthly seasonal index, January = (.477 + 545)/2 = 511

Calculate the seasonally adjusted forecasts

Seasonally adjusted forecast = unadjusted forecast x seasonal index

January 2012: 106.9 x 511 = 54.63

Seasonality – Example 9.4

Note that the regression forecast now does reflect the

seasonality.

Figure 9.16

Causal Forecasting Models

 Linear Regression

 Multiple Regression

 Examples:

Multiple Regression

 Multiple Regression – A generalized form of linear regression that allows for more than one independent variable.

Forecast Accuracy

How do we know:

is prone to make?

Need measures of forecast accuracy

Measures of Forecast Accuracy

Measures of Forecast Accuracy

Forecast Accuracy – Example 9.7

Table 9.11

Forecast Accuracy – Example 9.7

 Calculate the forecast error for each week, the absolute deviation of the forecast error, and absolute percent errors.

Forecast Accuracy – Example 9.7

Forecast Accuracy – Example 9.7

 Model 2 has the lowest MFE so it is the least biased.

 Model 2 also has the lowest MAD and MAPE values so it appears to be superior.

 Calculate the tracking signal for the first 10 weeks.

Forecast Accuracy – Example 9.7

Forecast Accuracy – Example 9.7

 The tracking signal for Model 2 gets very low

in week 5, however the model recovers.

 You need to continue to update the tracking signal in the future.

Collaborative Planning, Forecasting, and Replenishment (CPFR)

 CPFR – A set of business processes, backed

up by information technology, in which

members agree to mutual business

objectives and measures, develop joint sales and operational plans, and collaborate

electronically to generate and update sales forecasts and replenishment plans.

Forecasting Case Study

Top-Slice Drivers

All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or

otherwise, without the prior written permission of the publisher

Printed in the United States of America.