1. Forecasting

2. Planning
Forecast
Customer
Production
Process
Finished
Goods
Inputs

3. Forecasting Marketing: forecasts sales for new and existing products.
Production: uses sales forecasts to plan
production and operations; sometimes
involved in generating sales forecasts.

4. Characteristics of Forecasts
They are usually wrong
A good forecast is usually more than a single number
Aggregate forecast are more accurate
The longer the forecasting horizon, the less accurate the forecasts will be
Forecasts should not be used to the exclusion of known information

5. Forecasting Horizon Short term Intermediate term Long term
(inventory management, production plans..)
Intermediate term
(sales patterns for product families..)
Long term
(long term planning of capacity needs)

6. Forecasting Techniques
Judgmental
Models
Time Series
Methods
Causal Methods
Delphi
Method
Moving
Average
Regression
Analysis
Exponential
Smoothing
Seasonality
Models

7. Types of forecasting Methods
Subjective methods
FREE HAND METHOD
Objective methods
SEMI AVERAGE
EVEN DATA
ODD DATA
LEAST SQUARE
TREND MOMENT

8. FREE HAND METHOD

9. SEMI AVERAGE EVEN DATA

10. Y = a + bX No. Year Sales (Y-axis) Base time (X-axis) 1 1988 1850
∑ 1-6 =
11520 2
1989
1800 Y1
1920 3
1990
1900 X1
2.5 4
1991
2000 5
1992
1950 6
1993
2020
a= and b= 7
1994
1980
∑ 7-12 =
11979 8
1995
1960 Y2
1996.5 9
1996 X2
8.5 10
1997
2200 11
1998
2240 12
1999
2220

11. SEMI AVERAGE ODD DATA

12. Y = a + bX No. Year Sales (Y-axis) Base time (X-axis) 1 1988 1850
∑ 1-5 =
9500 2
1989
1800 Y1
1900 3
1990 X1 4
1991
2000 5
1992
1950 6
1993
2020
a= 1868 and b= 16 7
1994
1980
∑ 7-11 =
9980 8
1995
1960 Y2
1996 9 X2 10
1997
2200 11
1998
2240

13. TREND MOMENT METHOD

15. LEAST SQUARE METHOD

16. EVEN DATA CASE