Unit –II / Operations Management / Forecasting Example

(08/02/2016)

Calculate MAD, MSE, MAPE and TS for the Data given in the table below

Period 1 2 3 4 5 6 7 8

Demand 217 213 216 210 213 219 216 212

Forecast 215 216 215 214 211 214 217 216

MAD Mean Absolute Deviation (MAD)

n

ForecastActualMAD

22 / 8 = 2.75

MSE Mean Squared Error

(MAPE)

1

)( 2

n

ForecastActualMSE

76/7 = 10.86

MAPE Mean Squared

Percentage Error (MAPE)

n

XActual

ForecastActual

MAPE

100

10.26/8 =1.28

TS

Tracking Signal

MAD

ForecastActual

)(Signal Tracking

-2/2.75 = -0.73

Period Actual (Demand) (A)

Forecast (F)

(Actual-Forecast)

i.e (A-F)

Error

I Actual – Forecast I

I Error I

I Error I ----------- x 100 Actual I Error I

I Error I 2

1 217 215 2 2 4 0.92

2 213 216 -3 3 9 1.41

3 216 215 1 1 1 0.46

4 210 214 -4 4 16 1.90

5 213 211 2 2 4 0.94

6 219 214 5 5 25 2.28

7 216 217 -1 1 1 0.46

8 212 216 -4 4 16 1.89

-2 22 76 10.26

2 MAPE = ------ x100 =0.92

217

Features Common to all Forecasts

Conditions in the past will continue in the future

Rarely perfect

Forecasts for groups tend to be more accurate than forecasts for individuals

Forecast accuracy declines as time horizon increases

Elements of a Good Forecast

Timely

Accurate

Reliable (should work consistently)

Forecast expressed in meaningful units

Communicated in writing

Simple to understand and use

Steps in Forecasting Process

Determine purpose of the forecast

Establish a time horizon

Select forecasting technique

Gather and analyze the appropriate data

Prepare the forecast

Monitor the forecast

Types of Forecasts

Qualitative (Subjective Inputs , Soft in formation i.e Human Factors and Personal

Opinions) 1. Judgment and opinion 2. Sales force 3. Consumer surveys 4. Delphi technique 5. Expert Opinion

Quantitative (Historical Inputs , Analyzing Objective or Hard Data , Avoiding Personal

Biases) 1. Regression and Correlation (associative) 2. Moving Average 3. Weighted Moving Average 4. Exponential Smoothing 5. Trend Analysis 6. Time Series

Forecasts Based on Time Series Data

Components (behavior) of Time Series data o Trend

o Cycle o Seasonal o Irregular o Random variations

Naïve Forecast Method:- A simple and widely approach to forecasting in the naïve approach . It is a forecast for any period that equals the previous periods actual value . Example :If the demand for a product last week was 20 Units , the forecast for this week is presumed 20 units. ( This method is for stable series only)