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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
8-4Copyright 2011 John Wiley & Sons, Inc.
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.
8-5Copyright 2011 John Wiley & Sons, Inc.
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
8-6Copyright 2011 John Wiley & Sons, Inc.
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
8-7Copyright 2011 John Wiley & Sons, Inc.
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
8-8Copyright 2011 John Wiley & Sons, Inc.
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
8-9Copyright 2011 John Wiley & Sons, Inc.
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
8-10Copyright 2011 John Wiley & Sons, Inc.
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
8-11Copyright 2011 John Wiley & Sons, Inc.
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
8-12Copyright 2011 John Wiley & Sons, Inc.
8-13Copyright 2011 John Wiley & Sons, Inc.
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
8-15Copyright 2011 John Wiley & Sons, Inc.
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
8-17Copyright 2011 John Wiley & Sons, Inc.
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:
8-18Copyright 2011 John Wiley & Sons, Inc.
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 =
where: Ft+1 = forecast of demand for next period
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
8-19Copyright 2011 John Wiley & Sons, Inc.
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 =
8-20Copyright 2011 John Wiley & Sons, Inc.
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
8-21Copyright 2011 John Wiley & Sons, Inc.
Exponential Smoothing
A weighted average procedure is used to obtain a forecast:
Ft+1 =
where: Ft+1 = forecast of demand for next period
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
8-23Copyright 2011 John Wiley & Sons, Inc.
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
8-25Copyright 2011 John Wiley & Sons, Inc.
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
8-26Copyright 2011 John Wiley & Sons, Inc.
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
8-27Copyright 2011 John Wiley & Sons, Inc.
Seasonality Adjustment Example
Given the following table of customer traffic for an ice cream shop experiencing seasonal fluctuations.
8-28Copyright 2011 John Wiley & Sons, Inc.
# 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:
8-29Copyright 2011 John Wiley & Sons, Inc.
234
92
244
96
Seasonality Adjustment ExampleStep 2
– Compute a seasonal index for each season
8-30Copyright 2011 John Wiley & Sons, Inc.
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
8-31Copyright 2011 John Wiley & Sons, Inc.
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
8-32Copyright 2011 John Wiley & Sons, Inc.
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
8-33Copyright 2011 John Wiley & Sons, Inc.
Linear Regression Continued
8-34Copyright 2011 John Wiley & Sons, Inc.
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
8-35Copyright 2011 John Wiley & Sons, Inc.
[ 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
8-36Copyright 2011 John Wiley & Sons, Inc.
Linear Regression Example
Given the following four months of pizza sales and advertising dollars:
8-37Copyright 2011 John Wiley & Sons, Inc.
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
Linear Regression Example
Dependent Variable Y = Pizza Sales
Independent Variable X = Advertising $
8-38Copyright 2011 John Wiley & Sons, Inc.
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
Linear Regression Example
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
8-39Copyright 2011 John Wiley & Sons, Inc.
[ 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
8-40Copyright 2011 John Wiley & Sons, Inc.
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
8-41Copyright 2011 John Wiley & Sons, Inc.
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
8-42Copyright 2011 John Wiley & Sons, Inc.
n
ForecastActual
n
ForecastActual 2
Forecast Accuracy Example
Given the following two sets of forecasts:
Calculate the MAD and MSE for both methods
8-43Copyright 2011 John Wiley & Sons, Inc.
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 =
8-44Copyright 2011 John Wiley & Sons, Inc.
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)
8-45Copyright 2011 John Wiley & Sons, Inc.
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
8-46Copyright 2011 John Wiley & Sons, Inc.
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
8-47Copyright 2011 John Wiley & Sons, Inc.
S & OP Continued
8-48Copyright 2011 John Wiley & Sons, Inc.
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.
8-49Copyright 2011 John Wiley & Sons, Inc.
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.
8-50Copyright 2011 John Wiley & Sons, Inc.
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.
8-51Copyright 2011 John Wiley & Sons, Inc.
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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