Question

Question 2Forecastinga.

Name and discuss three qualitative forecasting methods.Give examples of situations in which each qualitative forecasting method may be used.[6 marks]

b.Olivia Ltd. Sells cars to local companies and private owners. The company tracked the sales for the last 6 years. The Table 3shows the sales level for those 6 years.

Table 2

Year        Sales

1              80

2             90

3            95

4           95

5           101

6          110

i.Calculate the slope[9 marks

]ii.Calculate the intercept[4 marks]

iii.Formulate the linear equation for this problem[2 marks]

iv.Develop a forecast for the firm’s annual sales for the next two years.[4 marks

Answer 1

FORECASTING MODELS AND ITS USES

Moving average = fairly consistent demand with no random fluctuation. For example, base wage increase.

Weighted moving average = when certain periods of previous demands need to be emphasized. For example, demand forecast based on a robust last month but a weaker and less consistent month before the last month.

Exponential smoothing = using carefully calculated adjustment factor based on market research. For example, deciding the right compensation package based on market data.

Trend adjusted exponential smoothing = where demand shows a positive or negative trend. For example, forecasting seasonal sales.

Linear regression = used in long term forecasting where we do not have the necessary future data but the past data is significant and shows a consistent increase or decrease with some random fluctuations allowed. For example a company wants to know how their product would perform in the coming 7 months based on the previous month's data.

LINEAR REGRESSION

PERIOD	DEMAND	X	Y	XY	X^2
1	80	1	80	80	1
2	90	2	90	180	4
3	95	3	95	285	9
4	95	4	95	380	16
5	101	5	101	505	25
6	110	6	110	660	36
SIGMA		21	571	2090	91

INTERCEPT(B0) = (SIGMA(Y) * SIGMA(X^2) - SIGMA(X) * SIGMA(XY)) / (N * SIGMA(X^2) - SIGMA(X)^2)

INTERCEPT = (571 * 91) - (21 * 2090) / ((6 * 91) - 21^2) = 76.87

SLOPE(B1) = ((N * SIGMA(XY)) - (SIGMA(X) * SIGMA(Y))) - (N * SIGMA(X^2) - SIGMA(X)^2)

SLOPE = ((6 * 2090) - (21 * 571) / ((6 * 91) - 21^2) = 5.23

EQUATION

Y = INTERCEPT + SLOPE(X)

Y = 76.87 + (5.23 * (X))

FOR THE NEXT TWO YEARS

FOR X = 7 FORECAST = 76.87 + (5.23 * 7) = 113.48

FOR X = 8 FORECAST = 76.87 + (5.23 * 8) = 118.71