PeriodDemandseasonal factor Mar-1831,5500.84 Apr-1832,1250.86 May-1835,7000.95 Jun-1840,6201.08 Jul-1842,8101.14 Aug-1844,2301.18 Sep-1841,4821.11 Oct-1838,3481.02 Nov-1834,7220.93 Dec-1836,1900.97 Jan-1938,4501.03 Feb-1933,3300.89 total 449,557 Average 37,463 What is the forecast demand for march 2019 using simple exponential smoothing with an alpha = 0.3 ?

Name: PeriodDemandseasonal factor Mar-1831,5500.84 Apr-1832,1250.86 May-1835
Uploaded: 2024-03-20T07:06:41.000Z
Channel: Bartleby
Description: PeriodDemandseasonal factor Mar-1831,5500.84 Apr-1832,1250.86 May-1835,7000.95 Jun-1840,6201.08 Jul-1842,8101.14 Aug-1844,2301.18 Sep-1841,4821.11 Oct-1838,3481.02 Nov-1834,7220.93 Dec-1836,1900.97 Jan-1938,4501.03 Feb-1933,3300.89 total 449,557

Answered: Period Demand seasonal factor Mar-18…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Period	Demand	seasonal factor
Mar-18	31,550	0.84
Apr-18	32,125	0.86
May-18	35,700	0.95
Jun-18	40,620	1.08
Jul-18	42,810	1.14
Aug-18	44,230	1.18
Sep-18	41,482	1.11
Oct-18	38,348	1.02
Nov-18	34,722	0.93
Dec-18	36,190	0.97
Jan-19	38,450	1.03
Feb-19	33,330	0.89

total	449,557
Average	37,463

What is the forecast demand for march 2019 using simple exponential smoothing with an alpha = 0.3 ?

Expert Solution

Step 1

The idea behind Exponential Smoothing for making forecasts consists of estimating the data value of certain period based on the previous data value as well as the previous forecast so that to attempt to correct for the deviation between the previous actual value and the prediction. The following formula is used to estimate the data value during the period $n$

$\text{Forecast during period n} = F_n = F_{n-1} + \alpha (A_{n-1} - F_{n-1})$

The Exponential Smoothing method of forecasting is a commonly used method to make forecasts based on a times series data set. Other common methods are the naive forecast method, the weighted moving averages, the moving averages forecast method and the linear trend forecasting method, just to mention a few.

Here we assume no trend component. Computing exponential smoothening with trend component provides for a more accurate prediction.

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