The document discusses forecasting as a method of predicting future events based on past data, emphasizing its importance for business decision-making. It explains the weighted moving average (WMA) technique, which prioritizes recent data over older data and provides a formula for calculation. An example is provided using attendance data from amusement and water parks to illustrate how to forecast future attendance using the WMA method.

1. Forecasting & its Technique
KUNDAN S. SANAP
ROLL NO. 202030016
MANAGEMENT PRINCIPLES AND PLANNING
VEERMATA JIJABAI TECHNOLOGICAL INSTITUTE,
MATUNGA

2. Forecasting
 Forecasting is the method of making predictions of the future
based on past data and most commonly by analysis of trends.
 Forecasts help a business to identify appropriate responses to
changes in demand levels, price cutting by the competition,
economic ups & downs and more.
 For future planning, forecasting is the must.

4. Weighted Moving Average
 A Weighted Moving Average puts more weight on recent data and less
on past data. i.e. In this method recent data has given more significance
as compared to the old data.
 The weighted moving average is calculated by multiplying each
observation in the data set by a predetermined weighting factor.
 Because of its unique calculation, WMA follows values more closely
than a corresponding Simple Moving Average.
 Weighted Moving Average method has the ability to give more
importance to what happened recently, without losing the impact of
past.

5. Weighted Moving Average Formula
 Weighted Moving Average is formulated as,
Weighted
Moving average
V1 X W1 + V2 X W2 + V3 X W3 + ………+ Vn X Wn
(M) = W1 + W2 + W3 + ………+ Wn
We can write the
formula in a
summarized form
as,

6. Weighted Moving Average Method of Forecasting-
Example
 Consider a data of 5 amusement parks & 5
water parks. Its combined attendance (in
thousands) for the last 15 years is given in the
following table. Compute a 15-year weighted
moving average and Forecast a data i.e.
Attendance for the next year i.e. 2008.
Year Attendance
1993 5,761
1994 6,148
1995 6,783
1996 7,445
1997 7,405
1998 11,450
1999 11,224
2000 11,703
2001 11,890
2002 12,380
2003 12,181
2004 12,557
2005 12,640
2006 12,890
2007 13,015

7. Weighted Moving Average Method of Forecasting Solution-
Weighting
W(t)
t
________
n(n+1)/2
=
n = Nos. of periods
= 15 years
Weighting factor Calculations: -
Year Attendance (Vt) Weighting
Weighting
(Wt)
1993 5,761 1/120 0.008
1994 6,148 2/120 0.017
1995 6,783 3/120 0.025
1996 7,445 4/120 0.033
1997 7,405 5/120 0.042
1998 11,450 6/120 0.050
1999 11,224 7/120 0.058
2000 11,703 8/120 0.067
2001 11,890 9/120 0.075
2002 12,380 10/120 0.083
2003 12,181 11/120 0.092
2004 12,557 12/120 0.100
2005 12,640 13/120 0.108
2006 12,890 14/120 0.117
2007 13,015 15/120 0.125

8. Weighted Moving Average Method of Forecasting Solution-
Weighted
Moving
Average for
year- 2008
(0.125*13015)+(0.117*12890)+(0.108*12640)+(0.1*12557)
+(0.092*12181)+(0.083*12380)+(0.075*11890)+(0.067*11703)
+(0.058*11224)+(0.050*11450)+(0.042*7405)+(0.033*7445)
+(0.025*6783)+(0.017*6148)+(0.008*5761)
______________________________________________________________
(0.125+0.117+0.108+0.1+0.092+0.083+0.075+0.067+0.058+0.05
+0.042+0.033+0.025+0.017+0.008)
=
= _________
11,680
1.0
= 11,680
F2008
F2008
F2008
So, As per the WMA method of forecasting, the Attendance For year 2008 is 11,680

9. The End