FORECASTING pankajchaudhary582@gmail.com

1. A
PRESENTATION
ON
“FORECASTING”
UNDER THE SUPERVISION OF: PRESENTED BY:
Dr. A.K BHARDWAJ RANJANA SINGH
PID NO. 14MTEEPS016
DEPARTMENT OF ELECTRICAL ENGINEERING
SAM HIGGINBOTTOM INSTITUTE OF AGRICULTURE, TECHNOLOGY & SCIENCES
ALLAHABAD
1

2. Forecasting
 Forecasting is the process of
making statements about
events whose actual outcomes
have not yet been observed.
 Educated Guessing
 Underlying basis of
all business decisions
 Production
 Inventory
 Personnel
 Facilities
??
2

3. Types of Forecasts
 Economic forecasts
 Address business cycle – inflation rate, money
supply, housing starts, etc.
 Technological forecasts
 Predict rate of technological progress
 Impacts development of new products
 Demand forecasts
 Predict sales of existing product
3

4. Forecasting Approaches
Qualitative Methods
 Used when situation is vague and little data
exist
 New products
 New technology
 Involves intuition, experience
 e.g., forecasting sales on Internet
4

5. Forecasting Approaches
Quantitative Methods
 Used when situation is ‘stable’ and historical
data exist
 Existing products
 Current technology
 Involves mathematical techniques
 e.g., forecasting sales of color televisions
5

6. Types of Forecasts by Time Horizon
Short-range forecast
Usually < 3 months
Job scheduling, worker assignments
Medium-range forecast
3 months to 2 years
Sales/production planning
Long-range forecast
> 2 years
New product planning
6

7. To Use a Forecasting Method
Collect historical data
Select a model
Selections should produce a good forecast
7

8. A Good Forecast
 Has a small error
 Forecast Error = Demand – Forecast
n

t=1
FE =
= Actual value at time t
t t A -F
= Forecast value at time t
= Number of periods to be averaged
t A
t F
n 8

9. Measures of Forecast Error
 A - F
t t
n
MAD =
n
t=1
 MAD = Mean Absolute Deviation
 MSE = Mean Squared Error  A - F

n
MSE =
n
t=1
2
 t t
 RMSE = Root Mean Squared Error
RMSE = MSE
 Ideal values =0 (i.e., no forecasting error)
9

10. FE/MAD Example
Month Sales (At) Forecast(Ft)
1 220 n/a
2 250 255
3 210 205
4 300 320
5 325 315
MAD =
A - F
t t
n
n

t=1
|At – Ft|
5
5
20
10
= 40 = 40
4
=10
FE=
n = 4
10

11. MSE/RMSE Example
Month Sales(At) Forecast(Ft)
1 220 n/a
2 250 255
3 210 205
4 300 320
5 325 315
|At – Ft|
5
5
20
10
= 550
4
=137.5
(At – Ft)2
25
25
400
100
= 550
 A - F

n
MSE =
n
t=1
2
 t t
=√137.5 =11.73
n = 4
RMSE = MSE
11

12. Application
Supply Chain Management
Inventory Control
Personal Investment
12

13. 13