SALES FORECASTING

SLMT3

Group 6

Exercise 1: MRF

Particulars

Past Data ProjectionsYear 1

Year 2

Year 3

Year 4

Year 5

Year 6

Year 7

Year 8

Sales2500

001500

001500

003000

0025000

025000

025000

025000

0Number of tyres per car 5 5 5 5 5 5 5 5

Tyre demand for new cars        12500

0012500

0012500

0012500

00

Change every 4 years (50%)      1250

00 75000 7500015000

012500

0Number of tyres per car       4 4 4 4 4

Tyre demand every 4 years      5000

0030000

030000

060000

050000

0Change of stepney every 8 years (50%)              

125000

Change every 6 years (50%)          12500

0 75000 75000Number of tyres per car           5 5 5

Tyre demand every 6 years          62500

037500

037500

0                 

Demand for Tyres        18000

0024250

0024750

0025000

00

Exercise 2: Surf

(Note: Assuming product to be detergent)

Quantitative Data:

Size of detergent category in India, growth prospects, CAGR Current market share of Surf Past sales data showing customer preference for branded versus

non-branded detergent Disposable income of the target segment, price elasticity Financial ratios indicating monetary health of the brand

Other Information:

Company’s future plans and strategies to enhance product features, introduce new variants and expand into new markets

Macro-economic variables such as GDP, inflation, liquidity, trade Extent of import and export of detergent internationally Growth prospects of alternatives such soap Existing competition from other detergents Customer perception and satisfaction surveys

Assumptions:

Detergent is an essential and customers cannot afford to be too price sensitive

Setting up a multi-regression model, making assumptions as to the independent variables.

Surf Sales = B0 + B1 (Sales growth) + B2 (Disposable income) + B3 (CAGR of Toiletries Industry) + u

Where B0 is the intercept, and beta measures the impact of change caused by the independent variable on sales of Surf

Employing multicollinearity tests, we can find out which of the variables is most statistically significant in influencing Surf sales. This can be used to forecast sales with better accuracy.