Chapter 5

Forecasting

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You should be able to:
LO 3.1 List features common to all forecasts
LO 3.2 Explain why forecasts are generally wrong
LO 3.3 List elements of a good forecast
LO 3.4 Outline the steps in the forecasting process
LO 3.5 Summarize forecast errors and use summaries to make decisions
LO 3.6 Describe four qualitative forecasting techniques
LO 3.7 Use a naïve method to make a forecast
LO 3.8 Prepare a moving average forecast
LO 3.9 Prepare a weighted-average forecast
LO 3.10 Prepare an exponential smoothing forecast
LO 3.11 Prepare a linear trend forecast
LO 3.12

3-2
Forecast
▪ OM is mostly proactive not reactive
▪ It involves structured planning activities
▪ Planning requires data pertaining to the feature
▪ Forecast: A statement about the future
◦Not necessarily numerical
◦Weather forecasts

3-3
 Uses of Forecasts
Accounting Cost/profit estimates
Finance Cash flow and funding
Human Resources Hiring/recruiting/training
Marketing Pricing, promotion, strategy
MIS IT/IS systems, services
Operations Schedules, MRP, workloads
Product/service design New products and services
3-4
 REMARKS
• Assume a causal system
– Future resembles the past
• Forecasts rarely perfect because of randomness
• Forecasts more accurate for groups vs. individuals.
– Forecasting errors among items in a group usually
have a canceling effect.
• Forecast accuracy decreases as time horizon for
forecasts increases
• Ex. I can forecast this year’s class average better
than next year’s class average
3-5
Elements of a Good Forecast
Timely

Reliable Accurate

Written

3-6
 Steps in the Forecasting Process

“The forecast”

Step 6 Monitor the forecast

Step 5 Prepare the forecast
Step 4 Gather and analyze data
Step 3 Select a forecasting technique
Step 2 Establish a time horizon
Step 1 Determine purpose of forecast
3-7
Types of Forecasts
1. Judgmental - uses subjective inputs
2. Time series - uses historical data assuming the future will
be like the past
3. Associative models - uses explanatory variables to
predict the future

3-8
▪ Executive opinions (long-range planning): There are factors
hard to quantify
▪ Sales force composite: Retailer forecasts for the manufacturer
▪ Consumer surveys: The customer at the mall who asks if you
like cherry flavor in your shampoo
▪ Outside opinion: Financial and consulting gurus and
companies
▪ Opinions of managers and staff: Delphi method: A series of
questionnaires developed sequentially
3-9
 2-Time Series Forecasts
❑Trend - long-term movement in data
❑ Seasonality - short-term regular variations in data
❑ Cycle – wavelike variations of more than one year’s duration
❑Irregular variations - caused by unusual circumstances
❑ Random variations - caused by chance

3-10
Irregular
variation

Trend

Cycles

90
89
88
Seasonal variations

3-11
 Types of data used to forecast
▪ Extrinsic data
Basing a forecast for a product on data about economic
indicators such as: population trends, or employment
statistics

▪ Intrinsic data
collected on the product itself.

▪ Historical analogy
forecasts for one product on data from a similar product.
3-12
➢ Forecasters usually start by generating a quantitative
forecast

❖perhaps combining intrinsic and extrinsic forecasting

techniques) and then allow for adjustments to the forecast
using qualitative methods. This process often reduces bias,

➢ overly optimistic forecast modifications from an effect of

groupthink could increase bias.

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Forecasting Models

❖Moving average
❖Weighted moving average
❖Exponential smoothing

3-14
 1- Moving Average Example

Ft = MAn= At-n + … At-2 + At-1

n

A number of recent actual values, updated as new values become available. 3-15
 2- Weighted Moving Average Example
Period Demand Weight
1 42
2 40 10%
3 43 20%
4 40 30%
5 41 40%

Weighted moving average – More recent values in a series are given more
weight in computing the forecast. 3-16
3- Exponential Smoothing

Ft = Ft-1 + (At-1 - Ft-1)

❑ Assume: the most recent observations might have the highest
predictive value.
❑Uses most recent period’s actual and forecast data.
❑ Weighted averaging method based on previous forecast plus
a percentage of the forecast error
❑A-F is the error term,
❑α is the % feedback
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3-18
Forecast Accuracy
Error is the difference between actual value and predicted value
Mean Absolute Deviation (MAD)
❑ Mean Squared Error (MSE)
Gives more weight to larger errors, which typically cause
more problems
❑ Mean Absolute Percent Error (MAPE)
MAPE should be used when there is a need to put errors in
perspective. Hence, to put large errors in perspective, MAPE
would be used.
3-19
Period Actual Forecast {A – F} |Error| Error² [|Error|÷ Actual] * 100
Error

1 217 215 2 2 4 0.92 %

2 213 216 -3 3 9 1.41
3 216 215 1 1 1 .46
4 210 214 -4 4 16 1.90
5 213 211 2 2 4 .91
6 219 214 5 5 25 2.28
7 216 217 -1 1 1 .46
8 212 216 -4 4 16 1.89
Ʃ = -2 22 76 10.26
3-20
3-21
 Linear Trend Equation

Ft = a + bt

Ft = Forecast for period t

t = Specified number of time periods
a = Value of Ft at t = 0
b = Slope of the line

3-22
Calculating : a and b

3-23
Linear Trend Equation Example
t y
Week t2 Sales ty
1 1 150 150
2 4 157 314
3 9 162 486
4 16 166 664
5 25 177 885

t = 15 t2 = 5 5 y = 812 ty = 2 4 9 9
2
( t) = 2 2 5

3-24
Solution: Linear Trend Calculation

b = 5 (2499) - 15(812) =
12495 -12180 = 6.3
5(55) - 225 275 - 225

a = 812 - 6.3(15) = 143.5

5

y = 143.5 + 6.3t
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