Forecasting

OPS 370

Forecasting - Chapter 4

Forecasting

2

Forecasting - Chapter 43

What to Forecast?

Short Term(0-3 Months)

Demand for IndividualProducts & Services

Medium Term(3 Months – 2 Years)

Demand for Product & Service Families

Long Term(>2 Years)

Total Sales, New Offerings

Forecasting - Chapter 44

How to Forecast?

• Qualitative Methods– Based On Educated Opinion & Judgment

(Subjective)– Particularly Useful When Lacking Numerical Data

(Example: Design and Introduction Phases of a Product’s Life Cycle)

• Quantitative Methods– Based On Data (Objective)

Forecasting - Chapter 45

Qualitative Methods

• Executive Judgment

• Sales Force Composite

• Market Research/Survey

• Delphi Method

Forecasting - Chapter 46

Quantitative Methods• Time Series & Regression• Time Series Popular Forecasting Approach in

Operations Management• Assumption:

– “Patterns” That Occurred in the Past Will Continue to Occur In the Future

• Patterns– Random Variation– Trend– Seasonality– Composite

Monthly Champagne Sales

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0 12 24 36 48 60 72 84

Time (t)

U.K. Airline Miles

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180001 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94

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U.K. Airline MilesUK Airline MilesTh

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iles

Forecasting - Chapter 49

Forecasting StepsData Collection

Data Analysis

Model Selection

Monitoring

Collect Relevant/Reliable Data

Be Aware of “Garbage-In, Garbage Out”

Forecasting - Chapter 410

Forecasting StepsData Collection

Data Analysis

Model Selection

Monitoring

Plot the Data

Identify Patterns

Forecasting - Chapter 411

Forecasting StepsData Collection

Data Analysis

Model Selection

Monitoring

Choose Model Appropriate for Data

Consider Complexity Trade-Offs

Perform Forecast(s)

Select Model Based on Performance Measure(s)

Forecasting - Chapter 412

Forecasting StepsData Collection

Data Analysis

Model Selection

Monitoring

Track Forecast Performance (Conditions May and Often Do

Change)

Forecasting - Chapter 413

Time Series Models

• Short Term– Naïve – Simple Moving Average– Weighted Moving Average– Exponential Smoothing

Forecasting - Chapter 414

Forecasting Example

• L&F Bakery has been forecasting by “gut feel.” They would like to use a formal (i.e., quantitative) forecasting technique.

Forecasting - Chapter 415

Forecasting Methods

• Naïve

Forecasting - Chapter 416

Forecasting Methods

• Naïve (Excel)

=C4

=C5

Forecasting Methods

• Moving Average

Forecasting Methods

30 Day Moving Average of AAPL Price

Forecasting Methods

• Moving Average (Excel)

=AVERAGE(C4:C6)

= AVERAGE(C5:C7)

Forecasting Methods• Moving Average Example• Assume n = 2

Week Demand1 1252 1753 1504 1505 160

Forecasting Methods

• Weighted Moving Average

Forecasting Methods

• Weighted Moving Average

=$G$6*C6+$G$5*C5+$G$4*C4=$G$6*C7+$G$5*C6+$G$4*C5

Forecasting Methods• Weighted Moving Average Example

• Assume n = 2, W1 = 0.7, W2 = 0.3Week Demand

1 1252 1753 1504 1505 160

Forecasting Methods

• Exponential Smoothing

Forecasting Methods

• Exponential Smoothing

Month Actual Forecast ErrorJan (1) 200 200 0Feb (2) 300 200 100Mar (3) 200 230 -30Apr (4) 400 221 179May (5) 500 275 225Jun (6) 600 343 257Jul (7) - - -

Forecasting Methods

• Exponential Smoothing (Excel)

Initial forecast

=D4+$G$4*(C4-D4)=D5+$G$4*(C5-D5)

Forecasting Methods• Exponential Smoothing Example• Assume a = 0.4

Week Demand1 1252 1753 1504 1505 160

Forecasting Methods

• How to Select Value of a?• Alpha determine importance of recent forecast

results in new forecasts

Determining Forecast Quality

• How Well Did a Forecast Perform?• Determine Forecast Error

Error = Actual Demand – Forecasted DemandMonth Actual Forecast Error

Jan 200 200 0Feb 300 200 100Mar 200 230 -30Apr 400 221 179May 500 275 225Jun 600 343 257

Average Error121.8

Determining Forecast Quality

• Why is Average Error a Deceiving Measure of Quality?

• Better Measures:

Mean Absolute Deviation

Mean Squared Error

Root Mean Squared Error

n

eMAD

n

t 1

n

eMSE

n

t 1

2

Determining Forecast Quality

MAD

MSE

Measure of Bias: Tracking Signal =

Sum of Errors/MAD=731/131.8 = 5.55

*OK if between -4 and +4

Determining Forecast Quality

Week Demand Forecast1 125 --2 175 --3 150 1504 150 162.55 160 150

155

For this MA(2) forecast. What is MAD, MSE, and TS?

Linear Regression

• <SKIP Section in Textbook on Exponential Smoothing with Linear Trend>

Linear Trend Line• Given Data

– Y = Values of Response Variable– X = Values of Independent Variable

• Parameters to estimate– a = Y-intercept– b = slope

• Use “least squares” regression equations to estimate a and b.– Or …

Y a bX

Excel for Linear Regression

Use SLOPE Function

Use INTERCEPT Function