1. Forecasting & Demand Planning
Chapter 8
Forecasting & Demand Planning

2. Lecture Outline What is Forecasting? The Forecasting Process
Types of Forecasting Methods
Time Series Forecasting Models
Causal Models
Measuring Forecast Accuracy
Collaborative Forecasting and Demand Planning
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3. Forecasting vs. Planning
Forecasting drives all other business decisions
Planning requires organizing resources in anticipation of the forecast
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4. Forecasting vs. Planning Continued
Planning involves the following decisions:
Scheduling existing resource
Determining future resource needs
Acquiring new resources
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5. Demand Management
Demand management is the process of influencing demand
promotional campaigns, advertisements, etc.
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6. 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
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7. 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
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8. Principles of Forecasting
Forecasts are rarely perfect
Forecasts are more accurate for groups than for individual items
Forecasts are more accurate for shorter than longer time horizons
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9. Steps in the Forecasting Process
Decide what to forecast
Analyze appropriate data
common patterns include:
Level or horizontal
Trend
Seasonality
Cycles
in addition to patterns, data contain random variation
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10. Steps in the Forecasting Process Continued
Select the forecasting model
select the model best suited for the identified data pattern
Generate the forecast
Monitor forecast accuracy
measure forecast error
use to improve the forecast process
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11. 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
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12. 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
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14. 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 information
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15. 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
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16. 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 applied
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17. 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
Copyright 2011 John Wiley & Sons, Inc.

18. Mean Example
Given the following sales for a drill over the past 5 weeks:
Week
Sales 1 8 2 10 3 9 4 12 5 6
What is the forecast for week 6?
Ft+1 =
F6 = [ ] /5 = 9.8 ≈ 10
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19. 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
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20. Moving Averages Example
Given the following sales for over 4 months:
Month
Sales
Jan 38
Feb 27
March 42
April
May
What is the forecast for May using a three-period moving average?
Ft+1 =
FMay = [ ] /3 = 37
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21. 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
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22. 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 period
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23. 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
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24. 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)
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25. 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 = = 18.84
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26. Seasonality Adjustment
The forecast can be adjusted to reflect the amount by which a season is above or below average
Steps:
Compute average demand for each season
total annual demand divided by the # of seasons
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27. Seasonality Adjustment Continued
Compute a seasonal index for each season
divide the demand for each season by the average demand for each year
average across years available
Adjust the average forecast for next year by the seasonal index
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28. 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
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?
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29. Seasonality Adjustment Example
Step 1
Compute the average demand for each season
Year 1:
Year 2:
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30. Seasonality Adjustment Example
Step 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
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31. Seasonality Adjustment Example
Step 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
(1.085)
= 26, 583
Spring
(0.850)
= 20,825
Summer
(1.425)
= 34,913
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32. 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
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33. 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
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34. Linear Regression Continued
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35. Linear Regression Steps
Compute parameter b:
b =
where Y = average of the Y values
X = average of the X values
n = # of data points
Compute parameter a:
a = Y – b X
[ XY - nXY ]
[∑X2 – nX2]
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36. Linear Regression Steps Continued
Substitute values for a and b in the equation:
Y = a + b X
Generate a forecast for the dependent variable (Y)
substitute the appropriate value for X
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37. Linear Regression Example
Given the following four months of pizza sales and advertising dollars:
Use linear regression to estimate pizza sales if $150 is spent on advertising next month
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38. Linear Regression Example
Dependent Variable Y = Pizza Sales Independent Variable X = Advertising $ 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
9,860
4,624
Total
231
515
30,550
68,375
13,681
compute X = 515/4 = and Y = 231/4 = 57.75
Copyright 2011 John Wiley & Sons, Inc.

39. Linear Regression Example
Compute parameter b:
b = = = 0.391
Compute parameter a:
a = Y – b X = – (0.391)(128.75) = 7.48
Substitute a and b: Y = X
Forecast: Y = (150) = pizzas
[ 30,550 – 4(128.75)(57.75)]
[68,375 – 4(128.75)2]
[ XY – nXY ]
[∑X2 – nX2]
Copyright 2011 John Wiley & Sons, Inc.

40. 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 the
influence of the independent variables on the dependent variable
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41. 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
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42. Error Measures MAD is the average of the sum of the absolute errors:
MSE is the average of the squared errors:
MSE =
for both measures, select the forecasting method that provides the lowest value
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43. 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
Jan 40 42 -2 2 4 44 -4 16
Feb 28 29 1 31 -3 3 9
Mar 41 39 38
Apr -1
May
Total 10 22 -6 12 36
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44. Forecast Accuracy Example
MAD =
MADA = MADB =
MSE =
MSEA = MSEB =
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45. Collaborative Forecasting & Demand Planning
Two common processes:
Collaborative Planning, Forecasting and Replenishment (CPFR)
Sales and Operations Planning (S&OP)
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46. CPFR Five-Step Process:
CPFR is a collaborative process of developing joint forecasts and plans with supply chain partners
Five-Step Process:
Create joint objectives
Develop a business plan
Create a joint forecast
Agree on replenishment strategies
Agree on a technology partner to bring CPFR to fruition
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47. S & OP Five-Step Process:
S&OP is a collaborative process for generating forecasts that all functional areas agree upon
Five-Step Process:
Generate quantitative sales forecast
Marketing adjusts the forecast
Operations checks forecast against existing capability
Marketing, operations, and finance jointly review forecast and resource issues
Executives finalize forecast and capacity decisions
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48. S & OP Continued
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49. Review
Forecasting is the process of attempting to predict future events. Planning is the process of selecting actions in anticipation of the forecast.
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.
Copyright 2011 John Wiley & Sons, Inc.

50. Review Continued
Data are composed of patterns and randomness. Four of the most common patterns are level, trend, seasonality, and cycle.
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.
Quantitative forecasting methods can be time series models and causal models.
A. Time series models generate the forecast by identifying and analyzing patterns in a “time series” of the data.
Copyright 2011 John Wiley & Sons, Inc.

51. Review Continued
b. Causal models assume that the variable being forecast is related to other variables.
CPFR is a collaborative process of developing joint forecasts and plans with supply chain partners, rather than doing them independently.
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.
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