MEASUREMENT OF ERROR IN FORECASTING

By-: Alok Kumar Yadav

MBA Ist Year

IILM Academy Of Higher Learning

Greater Noida

FORECASTING

Forecasting is a tool used for predicting

future demand based onpast demand information.

The biggest nightmare for any Demand Planner is forecasting inaccuracy. If the demand is underestimated, potential sales revenue will be lost and on the other hand if demand planner overestimates the demand, company will get stuck with non moving inventory. The term Forecast Error is used to measure the Forecast Accuracy. There are various methods to calculate Forecast Error. Each method has got its relevance under various circumstances. The below procedure explains them in details.

Basic Concept

Forecast Error :

Where :Ai – the actual value in time period

i

Fi – the forecast value in time period i

ii FAFE

Basic Concept

Mean Absolute Deviation (MAD)

First absolute deviation is calculated for each of the data point. Absolute Deviation is mod difference between forecast and actual value for the data point. It is averaged over selected time zone to get MAD. This means, there is no differentiation between positive and negative error. Also there is no reference to base on which the error is measured.

Mean Absolute Deviation

n

tt ForecastActualMAD

2

tt

1

ForecastActualMSE

n

MEAN SQUARE ERROR

 Mean Absolute Percentage Error (MAPE) –

First absolute percentage deviation is calculated by subtracting forecast from actual and then dividing it by actual value. The MAPE is expressed as average mod percentage value over selected time zone. MAPE does not differentiate between positive and negative error but it does have reference to the quantum of the value. MAPE is used where likelihood of positive and negative error is same and random

Mean Absolute Percentage Error

n

100

Actual

ForecastActual

MAPE t

tt

Forecast Error Calculation

PeriodActual

(A)Forecast

(F)(A-F) Error |Error| Error2 [|Error|/Actual]x100

1 107 110 -3 3 9 2.80%

2 125 121 4 4 16 3.20%

3 115 112 3 3 9 2.61%

4 118 120 -2 2 4 1.69%

5 108 109 1 1 1 0.93%

Sum 13 39 11.23%

n = 5 n-1 = 4 n = 5

MAD MSE MAPE

= 2.6 = 9.75 = 2.25%

Benefits

Improved Forecast Accuracy leading to better decision making

 Reduced cost because of reduction in inventory

 Higher sales revenue because of lesser stock outs. 

THANK YOU