Demand Forecasting

DEMAND FORECASTING

GROUP 1 SHWETHA RAGHAVAN SWATI MEHTA DEEPTI MALLELA ABHISHEK BANSAL SANJEEV MOHIT NARULA

DEMAND FORECASTING Demand forecasting refers to the prediction or estimation of a future situation under given constraints. TYPES OF FORECASTING: 4.Short Term 5.Medium Term 6.Long Term

OBJECTIVES OF DEMAND FORECASTING 1. 2. 3. 4. 5.

Helping for continuous production Regular supply of commodities Formulation of price policy Arrangement of finance Labor requirement

FACTORS INVOLVED IN DEMAND FORECASTING 1. Time period 2. Levels of forecasting -- International level -- Macro level -- Industry level -- Firm level 3. Purpose - General or Specific 4. Methods Of Forecasting 5. Nature Of Commodity 6. Nature Of Competition

DETERMINANTS FOR DEMAND FORECASTING 1.

Capital goods – goods required for further production of goods Demand for capital goods is derived demand - Replacement demand - New demand

•

Durable consumer goods— goods used continuously for a period of time - Replacement demand - New demand

•

Non-durable consumer goods— commodities which are used in a single act of consumption Demand for these goods is influenced by - Disposable income of people - Price of the commodity - Size and characteristics of population

CRITERIA FOR GOOD DEMAND FORECASTING 2. 3. 4. 5. 6.

Accuracy Plausibility Durability Availability Economy

METHODS OF FORECASTING SURVEY METHOD

STATISTICAL METHOD

1.Survey of buyer’s intentions

1.Trend projection method

2.Expert opinion method or Delphi Method

2.Moving averages method

3.Controlled Experiments

3.Regression analysis

4.Simulated market situations

4.Barometric method

SURVEY OF BUYER’S INTENTIONS

• Least sophisticated method • Customers are directly contacted to find out their intentions to buy commodities in the near future • Intentions recorded through personal interviews, mail or post service,telephone interviews and questionnaires. • Two types of Consumer Survey – Complete enumeration Method – Sample survey Method

DELPHI METHOD • The forecasters are given the forecasts and assumptions of other experts, and a final report is compiled with the combined consensus of the experts.

MARKET SURVEY METHOD • CONTROLLED EXPERIMENTS Different determinants of demand are varied and price quantity relationships are established at different points of time in the same market or different markets. Only one determinant varied ; others kept constant.

• SIMULATED MARKET SITUATION An artificial market situation is created and “consumer clinics” selected. Consumers are asked to spend time in an artificial departmental store and different prices are set for different buyer groups.

TREND PROJECTION METHOD • Based on analysis of past sales patterns • Shows effective demand for the product for a specified time period • The trend can be estimated by using the Least Square Method

A producer of soaps decides to forecast the next years sales of his product. The data for the last five years is as follows: YEARS

SALES IN Rs.LAKHS

1996

45

1997

52

1998

48

1999

55

2000

60

The data is plotted on a graph:

• The equation for the straight line trend is

Y = a + bx a-intercept b-shows impact of independent variable The Y intercept and the slope of the line are found by making substitutions in the following normal equations:

∑Y = na + b ∑ x

YEARS

SALES Rs. LAKHS (Y)

X

X2

XY

1996

45

1

1

45

1997

52

2

4

104

1998

48

3

9

144

1999

55

4

16

220

2000

60

5

25

300

N=5

∑Y=260

∑X=15

∑X2=55

∑XY=813

Substituting the above values in the normal equations: 260=5a +15b (Eq.3) 813=15a + 55b (Eq.4) solving the two equations, a = 42.1 , b = 3.3

Therefore, the equation for the straight line trend is Y=42.1 + 3.3X Using this equation we can find the trend values for the previous years and estimate the sales for the year 2001 as Y 1996 42.1+3.3(1) 45.4 follows: = Y 1997 =Y 1998

= 42.1+3.3(2) = 42.1+3.3(3)

48.7

= Y 1999 = Y 2000

= 42.1+3.3(4) = 42.1+3.3(5)

55.3

= Y 2001 =

= 42.1+3.3(6) =

61.9

52.0 58.6

Thus, the forecast sales for year 2001 is Rs.61.9 lakhs.

MOVING AVERAGES METHOD Moving averages method can be used when the forecast period is either oddYEAR or even.

SALES IN Rs.LAKHS

1993

12

1994

15

These are the annual sales of goods 1995 during the period of 1993-2003. 1996 We have to find out the trend of the 1997 sales using (1) 3 yearly moving averages 1998 and (2) 4 yearly moving averages 1999 and forecast the value for 2005.

14 16 18 17 19

2000

20

2001

22

2002

25

2003

24

3 yearly period: The value of 1993 + 1994 +1995 12 +15+14 = 41 written at the capital period 1994 of the years 1993, 1994 and 1995 YEAR

SALES (Rs. LAKHS)

3 YEARLY MOVING TOTAL

1993

12

-

’94

15

41

3 YEARLY MOVING AVG. TREND VALUES 41/3= 13.7

’95

14

45

45/3= 15

’96

16

48

48/3 =16

’97

18

51

51/3 =17

’98

17

54

54/3 = 18

’99

19

56

56/3 = 18.7

2000

20

61

61/3 = 20.2

’01

22

67

67/3 = 22.3

’02

25

71

71/3 = 23.7

’03

24

-

-

4 YEARLY MOVING AVERAGES YEAR.

SALES (Rs. LAKHS)

4 YEARLY MOVING TOTAL

’93

12

-

’94

15

-

’95

14

’96

16

’97

18

’98

17

’99

19

’00

20

’01

22

’02

25

’03

24

57 63 65 70 74 78 86 91

-

MOVING TOTAL OF PAIRS OF YEARLY -TOTAL

4 YEARLY MOVING AVG. TREND VALUES

-

-

120

120/8 = 15

128

128/8 = 16

135 144

135/8 = 16.9 144/8 = 18

152

152/8 = 19

164 -

164/8 = 20.5 177/8 = 22.1 -

-

-

177

-

57 = ‘93 + ‘94 +’95 + ‘96 = 12 + 15 + 14 + 16 120= 57 +63, 128 = 16 +65 and so on. 120 is total of 8 years and so the avg. is calculated by dividing 120

The trend values from the previous tables can be plotted on a graph as follows:

REGRESSION METHOD “Method of Least Squares” YEAR

1998

1999

2000

2001

2002

SALES

240

280

240

300

340

(Rs. In crores)

From the above data we can project the sales for ‘03, ‘04, ‘05. First we calculate the required values which are (i) Time Deviation, (ii)YEAR Deviation Squares, (iii) Product of time deviation and sales. (n) SALES (RS. TIME TD SQUARED PRODUCT OF CRORE) (y)

DEVIATION FROM MIDDLE YEAR 2000 (x)

(x2)

TIME DEVIATION & SALES(xy)

’98

240

-2

4

-480

’99

280

-1

1

-280

’00

240

0

0

0

’01

300

+1

1

+300

’02

340

+2

4

+680

∑y = 1400

∑x = 0

∑x2 = 10

∑xy = 220

X=5

The equation is

Y = a + bx ‘a’ – independent variable ‘b’ – exhibits rate of growth a & b can be found out as follows:

a = ∑y / n = 1400 / 5 = 280 b = ∑xy / ∑ x2 = 220/10 = 22 Now, applying values to the regression equation, Y = 280 + 22x Hence, sales projection from 2003-2005 can be ascertained. 2003 = 280 + 22(3) = Rs.346 crores 2004 = 280 + 22(4) = Rs.368 crores 2005 = 280 + 22 (5) = Rs.390 crores

“Method of Simple linear Regression” The linear trend can be fitted in the equation Sales = a + b (Price) i.e. S = a + bP where in, a and b are constants. b = n∑Si Pi- (∑Si)(∑Pi) n∑Pi2 – (∑Pi) 2 a = ∑Si - b ∑ Pi n

e.g. fit a linear regression line to the following data & estimate the demand at price Rs.30

YEAR PRICE (Pi) SALES (Si) in 1000 units

’8 ’82 1 15 15

’83

’84

’85

’86

’87

’88

’89

’90

’91

‘92

12

26

18

12

8

38

26

19

29

22

52 46

38

37

37

37

34

25

22

22

20

14

To find the values of a and b the following table is constituted: 2 2 Pi

Si

Pi

Si

Si Pi

15

52

225

2704

780

15

46

225

2116

690

12

38

144

1444

456

26

37

676

1369

962

18

37

324

1369

666

12

37

144

1369

444

8

34

64

1156

272

38

25

1444

625

950

26

22

676

484

572

19

22

361

484

418

29

20

841

400

580

22

14

484

196

308

∑Pi2 = 5708

∑Si2 = 13716

∑Si Pi = 7098

∑Pi = 240 ∑Si = 384

b = n∑Si Pi- (∑Si)(∑Pi) = 12(7098)-(240)(384) = 0.641 n∑Pi2 – (∑Pi) 2

2 12 (5708)-(240)

a = ∑Si - b ∑ Pi = [384-(240)(-0.641)] = 44.82 n 12 Thus the regression line is S= 44.82 - 0.641P By assigning value 30 to P, The corresponding sales level is S = 44.82 – 0.641 (30) = 25.29 thousand units

BAROMETRIC METHOD • Improvement over trend projection method • Events of the present are used to predict future demand • Basic approach- constructing an index of relevant economic indicators  Leading indicators  Coincident indicators  Diffusion indices

IMPORTANCE OF DEMAND FORECASTING • • • •

Planning and scheduling production Budgeting of costs and sales revenue Controlling inventories Making policies for long term investment • Helps in achieving targets of the firm