Chapter 4:
Demand Forecasting
Long Queue
Traffic Congestion
Stock Market Crash
Forecasting Time Horizons
Short Range
Medium Range
Long Range
Has a time span of 1 year but Generally spans from 3
is generally less than 3
months to 3 years.
months.
3 years or more in time span.
Used for planning purchasing,
job scheduling, workforce
levels, job assignment, and
production levels.
Used in planning for new
products, capital
expenditures, facility location
or expansion, and research
and development.
Useful in sales planning,
production planning and
budgeting, cash budgeting,
and analysis of various
operating plans.
Types of Forecast
Economic
Technological
Demand
Address the business cycle by
predicting inflation rates,
money supplies, housing
starts, and other planning
indicators.
Concerned with rates of
technological progress, which
can result in the birth of
exciting new products,
requiring new plants and
equipment.
Drive a company`s
production, capacity, and
scheduling systems and
serve as an inputs to
financial, marketing, and
personal planning.
Medium to Long Range
Forecast
Long Range Forecast
Short Range Forecast
Strategic Importance of Forecasting
A good forecasts are of critical importance in all aspects of a business.
Forecasts of demand therefore drive decisions in many areas that creates impact to
product demand forecast of three activities:
1. Supply chain management
2. Human resources
3. Capacity
Seven Steps in the Forecasting System
 Determine the use of the forecast
 Select the items to be forecasted
 Determine the time horizon of the forecast
 Select the forecasting model
 Gather the data needed to make the forecast
Make the forecast
Validate and implement the results
Forecast Approach
Executive
Opinion
Market
Survey
Qualitative
Sales Force
Composite
Delphi
Method
Forecast Approach
Naïve
Approach
Linear
Regression
Moving
Average
Quantitative
Trend
Projection
Exponential
Smoothing
Kinds of Quantitative Methods
Time Series Models
A forecasting technique that uses a series of past data points
to make a forecast. Such as naïve, moving average, and exponential
smoothing.
Associative Models
A forecasting technique based on the development of an
equation that summarizes the effects of predictor variables. Such
as trend projection and linear regression.
Decomposition of a Time Series
Trend
The gradual upward or downward movement of the data over
time.
Seasonality
A pattern that repeats itself after a period of days, weeks,
months, or quarters.
Cycles
Patterns in the data that occur every several years.
Random Variations
These are “blips” in the data caused by chance and unusual
situations.
Naive Approach
A forecasting technique which assume that demand in the
next period is equal to demand in the recent periods.
Formula:
Naïve = Demand in the recent periods
Moving Average Approach
A forecasting method that uses an average of the n most
recent periods of data to forecast the next period.
Formula:
Moving Average =
demand in previous 𝑛 periods
𝑛
Weighted Moving Average Approach
Formula:
( Weight for period n demand in 𝑛 𝑝𝑒𝑟𝑖𝑜𝑑 … )
Weighted Moving Average =
𝑛
Exponential Smoothing Approach
A weighted moving average forecasting technique in which
data points are weighted by an exponential function.
Formula:
𝐹𝑡 = 𝐹𝑡−1 +α(𝐴𝑡−1 −𝐹𝑡−1 )
Where:
𝐹𝑡 = New forecast
𝐹𝑡−1 = Previous periods forecast
α = Smoothing constant
𝐴𝑡−1 = Previous period`s actual demand
Measuring Forecast Error
Formula:
Forecast Error = 𝐴𝑡 −𝐹𝑡
Where:
𝐴𝑡 = Actual Demand
𝐹𝑡 = Forecast Value
Mean Absolute Deviation
A measure of the overall forecast error for a model.
Formula:
MAD
=
(Actual −Forecast)
𝑛
Mean Squared Error
The average of the differences between the forecasted and
observed values.
Formula:
MSE
=
(Forecast Error)
𝑛
2
Mean Absolute Percentage Error
The average of the absolute differences between the forecast
and actual values, expressed as a percent of actual values.
Formula:
MAPE
=
Actual −Forecast
∗100%
Actual
𝑛
Trend Projection Approach
A time series forecasting method that fits a trend line to a
series of historical data points and then projects the line into the
future for forecast.
Formula:
𝑦= a + bx
Where:
𝑦 = computed value of the variable to be predicted
𝑎= y-axis intercept
b = slope of the regression line
𝑥 = the independent variable
Trend Projection Approach
Formula:
b=
𝑥𝑦 −𝑛𝑥𝑦
𝑥 2 −𝑛𝑥 2
a = 𝑦 − 𝑏𝑥
Where:
b = Slope
a = Intercept
= Summation sign
x = known values of the independent variables
𝑦 = known values of the dependent variables
𝑥 = average of the x values
𝑦 = average of the y values
n = number of data points or observations
Exponential Smoothing with Trend Adjustment Approach
Formula:
𝐹𝑡 = α(At−1)+(1−α)(Ft−1 +Tt−1 )
𝑇𝑡 = ẞ(Ft −Ft−1 )+(1- ẞ) Tt−1
𝐹𝐼𝑇 = Ft + Tt
Where:
𝐹𝑡 = Exponentially smoothed forecast average of the data series in period t
𝑇𝑡 = Exponentially smoothed trend in period t
𝐴𝑡 = Actual demand in period t
α = Smoothing constant for the average (0 ≤ α ≤ 1)
ẞ = Smoothing constant for the trend (0 ≤ ẞ ≤ 1)
Seasonal Variations
A regular upward or downward movements in a time series
that tie to recurring events.
Formula:
Average Monthly Demand =
Seasonal Index =
𝑇𝑜𝑡𝑎𝑙 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐴𝑛𝑛𝑢𝑎𝑙 𝐷𝑒𝑚𝑎𝑛𝑑
12 𝑚𝑜𝑛𝑡ℎ𝑠
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝐷𝑒𝑚𝑎𝑛𝑑 𝑓𝑜𝑟 𝑝𝑎𝑠𝑡 3 𝑦𝑒𝑎𝑟𝑠
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝐷𝑒𝑚𝑎𝑛𝑑
Correlation Coefficient for Regression Lines
A measure of the strength of the relationship between two
variables.
Formula:
r=
𝒏 𝒙𝒚− 𝒙 𝒚
𝒏 𝒙𝟐 −( 𝒙)𝟐 𝒏 𝒚𝟐 −( 𝒚)𝟐