copyright 2011 john wiley & sons, inc. chapter 8 forecasting & demand planning 8-1

Copyright 2011 John Wiley & Sons, Inc.

Chapter 8

Forecasting & Demand Planning

8-1

Lecture Outline

8-2

• What is Forecasting?

• The Forecasting Process

• Types of Forecasting Methods

• Time Series Forecasting Models

• Causal Models

• Measuring Forecast Accuracy

• Collaborative Forecasting and Demand Planning

Copyright 2011 John Wiley & Sons, Inc.

Forecasting vs. Planning

8-3Copyright 2011 John Wiley & Sons, Inc.

• Forecasting drives all other business decisions

• Planning requires organizing resources in anticipation of the forecast

Forecasting vs. Planning Continued


Planning involves the following decisions:

1. Scheduling existing resource

2. Determining future resource needs

3. Acquiring new resources

Demand Management

Demand management is the process of influencing demand

– promotional campaigns, advertisements, etc.


Impact on the Organization

Every organizational function relies on forecasting for numerous things

• Marketing– estimates of demand, future trends

• Finance– set budgets, predict stock prices

• Operations– capacity planning, scheduling, inventory levels

• Sourcing– make purchasing decisions, select suppliers


Impact on SCM

Demand forecast affects the plans made by each member of the supply chain

• Independent forecasting among supply chain members

– causes a mismatch between supply and demand

– gives rise to the bullwhip effect


Principles of Forecasting

1. Forecasts are rarely perfect

2. Forecasts are more accurate for groups than for individual items

3. Forecasts are more accurate for shorter than longer time horizons


Steps in the Forecasting Process

1. Decide what to forecast

2. Analyze appropriate data

• common patterns include:

– Level or horizontal– Trend– Seasonality– Cycles

• in addition to patterns, data contain random variation


Steps in the Forecasting Process Continued

3. Select the forecasting model– select the model best suited for the

identified data pattern

4. Generate the forecast

5. Monitor forecast accuracy– measure forecast error– use to improve the forecast process


Factors in Method Selection

The following factors should be considered when selecting a forecasting method:

• Amount and type of available data

• Degree of accuracy required

• Length of forecast horizon

• Patterns in the data


Types of Forecasting Methods

There are two groups of forecasting methods:

• Qualitative – based on subjective opinions– often called judgmental methods

• Quantitative– based on mathematical modeling– objective and consistent– can handle large amounts of data and

uncover complex relationships


Qualitative Forecasting Methods

Qualitative methods are useful when identifying customer buying patterns, expectations, and estimating sales of new products

• Executive Opinion– a group decision-making process, subject to bias

• Market Research– surveys and interviews used to collect preferences

• The Delphi Method– a consensus is developed from anonymously

contributed expert information8-14Copyright 2011 John Wiley & Sons, Inc.

Quantitative Forecasting Methods

Quantitative methods are based on mathematical concepts

Two categories:

• Time Series Models– generate the forecast from an analysis of a

“time series” of the data

• Causal Models– assume that the variable being forecast is

related to other variables in the environment


Time Series Models

A time series is a listing of data points of the variable being forecast over time

Models include:

• Mean

• Moving Averages

• Exponential Smoothing

• Trend Adjusted Exponential Smoothing

A Seasonality Adjustment can also be applied8-16Copyright 2011 John Wiley & Sons, Inc.

Mean

Forecast is made by taking an average:

Ft+1 =

where: Ft+1 = forecast of demand for next period

Dt = demand for current period

n = # of data points

– appropriate for a level data pattern– forecasts become more stable over time


n

Dt

What is the forecast for week 6?

Ft+1 =

Mean Example

Given the following sales for a drill over the past 5 weeks:


n

Dt

F6 = [ 8+10+9+12+10 ] /5 = 9.8 ≈ 10

Week Sales

1 8

2 10

3 9

4 12

5 10

6

Moving Averages

Forecast is made by averaging a specified number, n, of the most recent data:

Ft+1 =


Dt = demand for current period

n = # of data points in the moving average

– appropriate for a level data pattern– forecast becomes more responsive as n decreases


n

Dt

Moving Averages Example

Given the following sales for over 4 months:

What is the forecast for May using a three-period moving average?

Ft+1 =


n

Dt

FMay = [ 27+42+42 ] /3 = 37

Month Sales

Jan 38

Feb 27

March 42

April 42

May

Weighted Moving Averages

All data are weighted equally with a simple moving average (weight = 1/n)

• Weighted Moving Average– computation is the same as a simple

moving average except that managers have the option of specifying the weights assigned to data points


Exponential Smoothing

A weighted average procedure is used to obtain a forecast:

Ft+1 =


Dt = actual value for current period

Ft = forecast for current period

= smoothing coefficient (between 0 and 1)

– higher values of are more responsive to latest demand changes

– must set forecast for initial period8-22Copyright 2011 John Wiley & Sons, Inc.

tt F)1(D

Exponential Smoothing Example

Café Nervosa forecast a monthly usage of cream to be 24 gallons in May. The actual usage in May was 28 gallons. What is the forecast for June given = 0.7 ?

Ft+1 =

FJune = (0.70)(28) + (0.30)(24)

= 26.8 gallons


tt F)1(D

Trend Adjusted Exponential Smoothing

Forecast is modified to account for a trend in the data

FITt+1 = Ft+1 + Tt+1

Tt+1 =

where: FITt+1 = forecast including trend for next period

Ft+1 = unadjusted forecast for next period

Tt+1 = trend factor for next period

Tt = trend factor for current period

Ft = forecast for current period

= smoothing coefficient (between 0 and 1)8-24Copyright 2011 John Wiley & Sons, Inc.

tt1t T)1()FF(

Trend Adjusted Exponential Smoothing Example

Given a demand for December of 18 and a demand for January of 20, what is the trend adjusted forecast for February ( = 0.3, = 0.4)?

Unadjusted: Ft+1 = = 0.3(20) + (1 – 0.3)(18) = 18.6

Trend: Tt+1 = = 0.4(18.6 – 18) + (1 – 0.4)(0) = 0.24

Adjusted: FITt+1= Ft+1 + Tt+1 = 18.6 + 0.24 = 18.84


tt1t T)1()FF(

tt F)1(D

Seasonality Adjustment

The forecast can be adjusted to reflect the amount by which a season is above or below average

Steps:

1. Compute average demand for each season

– total annual demand divided by the # of seasons


Seasonality Adjustment Continued

2. Compute a seasonal index for each season

– divide the demand for each season by the average demand for each year

– average across years available

3. Adjust the average forecast for next year by the seasonal index


Seasonality Adjustment Example

Given the following table of customer traffic for an ice cream shop experiencing seasonal fluctuations.


# Customers (thousands)

Quarter Year 1 Year 2

Fall 14 15

Winter 25 26

Spring 20 20

Summer 33 35

Total 92 96

A forecast of 98,000 customers has been generated for next year

What is the seasonally adjusted forecast per quarter?

Seasonality Adjustment Example

Step 1– Compute the average demand for each season

Year 1:

Year 2:


234

92

244

96

Seasonality Adjustment ExampleStep 2

– Compute a seasonal index for each season


Seasonal Indexes Average

Quarter Year 1 Year 2 Index

Fall 0.620

Winter 1.085

Spring 0.850

Summer 1.425

63.024

15

08.124

26

83.024

20

42.124

35

61.023

14

09.123

25

87.023

20

43.123

33

Seasonality Adjustment ExampleStep 3

– Seasonally adjust the average forecast for next year

Next year forecast = 98,000 Average = 24,500


Number of Customers

Quarter Seasonally Adjusted Forecast

Fall 24,500 (0.620) = 15,190

Winter 24,500 (1.085) = 26, 583

Spring 24,500 (0.850) = 20,825

Summer 24,500 (1.425) = 34,913

Causal Models

Assume that the variable being forecast is related to other variables in the environment

• Linear Regression– a forecasting model that assumes a straight

line relationship between an independent variable and a single dependent variable

• Multiple Regression– extends linear regression by looking at a

relationship between an independent variable and multiple dependent variables


Linear Regression

The straight line equation for the model is:

Y = a + b X

where: Y = dependent variable

X = independent variable

a = Y intercept of the straight line

b = slope of the straight line


Linear Regression Continued


Linear Regression Steps

1. Compute parameter b:

b =

where Y = average of the Y valuesX = average of the X valuesn = # of data points

2. Compute parameter a:

a = Y – b X


[ XY - nXY ]

[∑X2 – nX2]

Linear Regression Steps Continued

3. Substitute values for a and b in the equation:

Y = a + b X

4. Generate a forecast for the dependent variable (Y)

– substitute the appropriate value for X


Linear Regression Example

Given the following four months of pizza sales and advertising dollars:


Pizza Sales Advertising $ 58 135

43 90

62 145

68 145

Use linear regression to estimate pizza sales if $150 is spent on advertising next month


Dependent Variable Y = Pizza Sales

Independent Variable X = Advertising $


compute X = 515/4 = 128.75 and Y = 231/4 = 57.75

Y X XY X2 Y2

58 135 7,830 18,225 3,364

43 90 3,870 8,100 1,849

62 145 8,990 21,025 3,844

68 145 9,860 21,025 4,624

Total 231 515 30,550 68,375 13,681


1. Compute parameter b:

b = = = 0.391

2. Compute parameter a:

a = Y – b X = 57.75 – (0.391)(128.75) = 7.48

3. Substitute a and b: Y = 7.48 + 0.391X

4. Forecast: Y = 7.48 +0.391(150) = 66.13 pizzas


[ XY – nXY ]

[∑X2 – nX2]

[ 30,550 – 4(128.75)(57.75)]

[68,375 – 4(128.75)2]

Multiple Regression

Multiple regression looks at the relationship between the independent variable and multiple dependent variables:

Y = β0 + β1X1 + β2X2 +…+ βkXk

where: Y = dependent variable

X1…Xk = independent variables

β0 = Y intercept

β1… βk = coefficients that represent theinfluence of the independent variables on the dependent variable


Measuring Forecast Accuracy

Two measures to help determine how our forecasting methods are performing:

• Mean Absolute Deviation (MAD)

• Mean Square Error (MSE)

First measure forecast error:

et = Dt – Ft

where: et = forecast error for period t Dt = actual demand for period t Ft = forecast for period t


Error Measures

• MAD is the average of the sum of the absolute errors:

MAD =

• MSE is the average of the squared errors:

MSE =

– for both measures, select the forecasting method that provides the lowest value


n

ForecastActual

n

ForecastActual 2

Forecast Accuracy Example

Given the following two sets of forecasts:

Calculate the MAD and MSE for both methods


Method A Method B

Month Sales Forecast e |e| e2 Forecast e |e| e2

Jan 40 42 -2 2 4 44 -4 4 16

Feb 28 29 1 1 1 31 -3 3 9

Mar 41 39 2 2 4 38 3 3 9

Apr 41 38 3 3 9 42 -1 1 1

May 39 41 -2 2 4 40 -1 1 1

Total 2 10 22 -6 12 36

Forecast Accuracy Example

• MAD =

MADA = MADB =

• MSE =

MSEA =MSEB =


n

ForecastActual

5.24

10 3

4

12

5.54

22 9

4

36

n

ForecastActual 2

Collaborative Forecasting & Demand Planning

Two common processes:

• Collaborative Planning, Forecasting and Replenishment (CPFR)

• Sales and Operations Planning (S&OP)


CPFR

CPFR is a collaborative process of developing joint forecasts and plans with supply chain partners

Five-Step Process:

1. Create joint objectives

2. Develop a business plan

3. Create a joint forecast

4. Agree on replenishment strategies

5. Agree on a technology partner to bring CPFR to fruition


S & OP

S&OP is a collaborative process for generating forecasts that all functional areas agree upon

Five-Step Process:

1. Generate quantitative sales forecast

2. Marketing adjusts the forecast

3. Operations checks forecast against existing capability

4. Marketing, operations, and finance jointly review forecast and resource issues

5. Executives finalize forecast and capacity decisions


S & OP Continued


Review

1. Forecasting is the process of attempting to predict future events. Planning is the process of selecting actions in anticipation of the forecast.

2. There are three principles of forecasting: (a) forecasts are rarely perfect; (b) forecasts are more accurate for aggregated items than for individual items; and (c) forecasts are more accurate for shorter than longer time horizons.


Review Continued

3. Data are composed of patterns and randomness. Four of the most common patterns are level, trend, seasonality, and cycle.

4. Forecasting methods can be divided into qualitative and quantitative. Qualitative methods are subjective and based on objectives. Quantitative methods are mathematically based, are objective and consistent.

5. Quantitative forecasting methods can be time series models and causal models.

6. A. Time series models generate the forecast by identifying and analyzing patterns in a “time series” of the data.


Review Continued

6. b. Causal models assume that the variable being forecast is related to other variables.

7. CPFR is a collaborative process of developing joint forecasts and plans with supply chain partners, rather than doing them independently.

8. Sales and Operations Planning (S&OP) is intended to match supply and demand through financial collaboration between marketing, operations, and finance, in order to ensure that supply can meet demand requirements.


Copyright 2011 John Wiley & Sons, Inc.All rights reserved. Reproduction or translation of this work beyond that permitted in section 117 of the 1976 United States Copyright Act without express permission of the copyright owner is unlawful. Request for further information should be addressed to the Permission Department, John Wiley & Sons, Inc. The purchaser may make back-up copies for his/her own use only and not for distribution or resale. The Publisher assumes no responsibility for errors, omissions, or damages caused by the use of these programs or from the use of the information herein.

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copyright 2011 john wiley & sons, inc. chapter 8 forecasting & demand planning 8-1

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