# Class 11 - AGENDA HOMEWORK 4 ON T DRIVE DUE OCT 1 QUIZ 3 ON...

This preview shows pages 1–11. Sign up to view the full content.

AGENDA HOMEWORK 4 ON T: DRIVE, DUE OCT. 1 QUIZ 3 ON TUES. MORE FORECASTING

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
þ Form of weighted moving average þ Weights decline exponentially þ Most recent data weighted most þ Requires smoothing constant ( α ) þ Ranges from 0 to 1 þ Subjectively chosen þ Involves little record keeping of past data Exponential Smoothing
Exponential Smoothing Last period’s forecast + α (Last period’s actual demand – Last period’s forecast) Ft = Ft – 1 + α (At – 1 - Ft – 1) where Ft = new forecast Ft – 1 = previous forecast α = smoothing (or weighting) constant (0 ≤ α ≤ 1)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant α = .20
Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant α = .20 New forecast = 142 + .2(153 – 142)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant α = .20 New forecast = 142 + .2(153 – 142) = 142 + 2.2 = 144.2 ≈ 144 cars
Effect of Smoothing Constants Weight Assigned to Most 2nd Most 3rd Most 4th Most 5th Most Recent Recent Recent Recent Recent Smoothing Period Period Period Period Period Constant ( α 29 α(1 -α 29 α(1 -α 292 α(1 -α 293 α(1 -α 294 α = .1 .1 .09 .081 .073 .066 α = .5 .5 .25 .125 .063 .031

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Impact of Different  225 200 175 150 | | | | | | | | | 1 2 3 4 5 6 7 8 9 Quarter Demand α = .1 Actual demand α = .5
Impact of Different  225 200 175 150 | | | | | | | | | 1 2 3 4 5 6 7 8 9 Quarter Demand α = .1 Actual demand α = .5 þ Chose high values of  when underlying average is likely to change þ Choose low values of  when underlying average is stable

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Choosing  The objective is to obtain the most accurate forecast no matter the technique We generally do this by selecting the model that gives us the lowest forecast error Forecast error = Actual demand - Forecast value = At - Ft
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern