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MGMT Session 3

by: Emily McIlhattan

MGMT Session 3 MGMT 382

Emily McIlhattan
GPA 3.72
Management Information Systems
Dr. Dejoie

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About this Document

This Session introduces the idea of forecasting sales, and how to measure how accurate these forecasts are.
Management Information Systems
Dr. Dejoie
One Day of Notes
25 ?




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This 3 page One Day of Notes was uploaded by Emily McIlhattan on Wednesday January 28, 2015. The One Day of Notes belongs to MGMT 382 at Purdue University taught by Dr. Dejoie in Winter2015. Since its upload, it has received 108 views. For similar materials see Management Information Systems in Business, management at Purdue University.

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Popular in Business, management


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Date Created: 01/28/15
MGMT 3631 Lecture 3 Outline of Last Lecture Outline of Current Lecture RECAP Forecastingbackg round Forecasting Models Measuring how good a forecast is Forecasting Background i What needs to be forecasted EX Walt Disneyworld of visitors of showsrides to open of fooddrinks ii What decisions are made on the basis of these forecasts staffing parking inventory pricing scheduling b Poor forecasting cost money in your personal life think data plan on phone c Types i Qualitative gut feeling can not be taught based off experience education background unique ii Quantitative science can be learned easy to mimic Time Series Forecasts assume past history is best predictor of the future i Forecasting is uctuating around the mean for existing products most common ii Fluctuates and increases or decreases as a trend new iPhone new products do this b Basic Times Series uctuating around a constant mean i At actual demand you can observe ii Ft1 forecasted demand 1 Demand Mean Random Fluctuation 2 Forecast current estimate of the mean c Method 1 Na39I39ve Method simple especially when you do not have a lot of data i Equation Ft1At ii Saying that the forecasted demand in the next period is equal to the Actual demand in the period before d Method 2 The Simple Average Ft1 A1A2t how you would calculate any average e Method 3 The simple Moving Average the recent history is more relevant i Assign equal weight to each month ii You start using data once you hit the speci ed shifts based on n using most recent months 1 Eqn Ft1 Atn1At1Atn f Method 4 The Weighted Moving Average weights each of the n most recent demands differently i Like the weighted Attribute Model ii Biggest weight to most recent month g Method 5 Exponential Smoothing Forecast similar to Method 4 but tell you how to assign weight i Wt1 d1dquott ii Eqn Ft1 XAt 1XFt iii If you don t have forecasted number for period 1 1 Use Nai39ve Forecast for period 2 then go from there 2 Use a reasonable number to forecast period 1 iv We put emphasis on demand so the outcomes are similar Mean Demand Level Shifts a Longtermpermanent shift give the small n to moving average and larger d for exponential smoothing b Random event shortterm give large n to moving average and smaller 01 for exponential smoothing Measuring Forecast Errors helps to determine which method to use a EtAtFt i If MFE is zero it may not mean that you error is perfect its actually a poor prediction because there could be a lot of deviation 1 You nd the difference add up the error and then find to average to get the MFE a MAD Mean Absolute Difference nd the difference between demand and forecasted for each period to get error take the absolute value of that error then add up and average b MAPE Mean Absolute Percentage Error same as above only you nd the percentage or error within the actual demand and nd the absolute value of that percentage before nding the mean


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