The Governments general index of Retail Price, which has been existence since 1914, measures the change in the price of the goods and services brought by the average family from one month to the next. The actual method of calculation of the Retail Price Index is closely related the base weighted system. The information of the prices each month is generated by investigators through out the country, who covers a wide range of types of retail outlet from supermarket to small grocery suppliers. Some items can not be collected over the country but they can be collected centrally. This includes such things as Postage and Telephone charges. When all the information has been gathered the index can be calculated, but as you can imagine this is quite a substantial task. By the time all the collection and computation has been done, the index is about four and half weeks out of date when it is announced. The standing advisory panel guides the Government that what item should go into the index, what base year should be used, when it should be changed and so on is an important part of producing the index. Retail Price Index is some kind of overall figure highlighting rapid change in the market prices pertaining to the cost of living. It would not be fair to leave the topic of the Retail Price Index without mentioning some of the criticisms. The monthly announcement of the latest change in the Retail Price Index attained an almost mystical significance during period of high inflation. Some items which have a very significant effect on people’s standard of living are excluded from its calculation. In recognition of some of those deficiencies in the RPI, several alternatives have been proposed. There are TPI and CPI which is an internationally agreed measure. The difference between these measures are largely in terms of which items are and are not included though the methods of calculation remains similar to that we have discussed above. Different measures leads to different values of the current level of inflation. Number of Words for Part a : 378

Introduction of part -b

Produce an application, based on your experience from the organization that you work for or one that you are familiar with, to show how RPI aids the accuracy of forecasting Retail Price Index is based on the principle that if there is an increase in the general level of prices which is reflected through Retail Price Index by say an X% over a period of Y period then also it would not affect the invested capital amount at the time one had invested since one is likely to gain a minimum return of the same % on the savings made. Whenever the situation of inflation arises we could make use of the Retail Price Index to compare the financial crisis in terms of income & profit being paid at various period of time. Example: I have prepared an example, where an engineer’s rate per hour is 18 pounds in the year 2003. To keep pace with the inflation his rate in the next year 2004 would be as follows:

YEAR

RETAIL PRICE INDEX

EARNED AMOUNT (POUNDS)

2003 181.3 18 2004 186.6

?

Here we are increasing the value of an amount of money in line with increasing price and this method is inflation. Earned amount of 2004 = Earned amount of 2003 x Retail Price Index of 2004

—————————————————————-

Retail Price Index of 2003 = 18 x 186.6 / 181.3 = 18.53

Earned amount of 2004 = 18.53

2. Comet Electronics is a switch producer organization and the respective year data is available. Here the annual average Retail Price Index values are taken in to consideration for a successful period of five years all based upon January 1987 data and the base year for the calculation is 2001. This has been represented in the form of a table as under:

ANNUAL SALES

YEAR

RPI

(MILLIONS OF POUNDS)

(Average)

11.0 2001 173.3 13.0 2002 176.2 14.5 2003 181.3 16.1 2004 186.6 18.5 2005 192.0 Here 2001 year data remains unchanged. For the year 2002 ——– 173.3 / 176.2 x 13 = 12.79 For the year 2003 ——– 173.3 / 181.3 x 14.5 = 13.86 For the year 2004 ——– 173.3 / 186.6 x 16.1 = 14.95 For the year 2005 ——– 173.3 / 192.0 x 18.5 = 16.87 Here in this case the percentage changes in the annual sales of the organization from year to year increases from 11 to 12 % per year but at the same time there is an increase in the value of RPI by about 1.25% per year. Thus one can conclude that the firms value of money in terms of sales from the past period is increasing which is clearly seen when it is compared to the original date values. Thus, increase in the firms sale is accountable by an increase in the prices and inflation. A graph for the above example has been represented by plotting year vs annual sales which is shown as under.

. SR.NO

YEAR

MONTH

ANNUAL SALES

R.P.I

A

(MILLION OF POUNDS) 1 2001 JAN

A —

171.1

A

A

MARCH 11.0 172.2 2 2002 JAN

A —

173.3

A

A

MARCH 13.0 174.5 3 2003 JAN

A —

178.4

A

A

MARCH 14.5 179.9 4 2004 JAN

A —

183.1

A

A

MARCH 16.1 184.6 5 2005 JAN

A —

188.9

A

A

MARCH 18.5 190.5 6 2006 JAN

A —

193.4

A

MARCH

A

?

For the example no:2 the complete data of the annual sales & the Retail Price Index corresponding with respect to the year 2001 to 2006 has been given above: Assume a linear relation between the year & the annual sales without considering the Retail Price Index for month of march 2006 the sale forecast would be as follows: From the straight line equation, y=a+bx where b= nA¢Ë†‘xy- A¢Ë†‘xA¢Ë†‘y / nA¢Ë†‘x2 -(A¢Ë†‘x)2 Here y is the total sales, x is the year, a is the constant cost, b is the gradient, n is the number of years. The turnover has to be evaluated so it would be the y-variable & the year is the x variable.

SR. NO

ANNUAL SALES

(MILLION OF POUNDS)

YEAR

A¢Ë†‘x2

A¢Ë†‘xy

(y)

(x)

1 11.0 1 1 11.0 2 13.0 2 4 26.0 3 14.5 3 9 43.5 4 16.1 4 16 64.4 5 18.5 5 25 92.5 A¢Ë†‘y = 73.1 A¢Ë†‘x=15 55 237.4 From y=a+bx a=y-bx, y=A¢Ë†‘y / n = 73.1 / 5 = 14.62. x= A¢Ë†‘x / n = 15 / 5 = 3 b = 5 x 237..4 – (15) (73.1) / 5 x 55 – (15) (15) = 90.50 / 50 = 1.81 a= 14.62 – 1.81 x 3 = 9.19

:

y = 9.19 + 1.81 x 6 ( x = 6, the sixth year whose turnover to be calculated ) y = 20.05 millions of pounds.

The forecasted sale for the year 2006 without using the RPI is 20.05 millions of Pound.

3. If we plot the data of example no.2 the growth of the sales is not far from a straight line pattern. We will therefore fit regression line to get a forecast of the Retail Price Index for the sixth year i.e. 2006 which become the variable and the year is x-variable from straight line equation. y = a + bx y = Total Cost a = Constant Cost b = Gradient x = Units Total Cost = Constant Cost + Gradient = Constant Cost + Cost / Unit x No. of Units Assuming linear relation between the Retail Price Index and the number of years the best of the line fit is, y = a + bx y = mean of y x = mean of x a = y – bx From the equation b = nA¢Ë†‘xy- A¢Ë†‘xA¢Ë†‘y / nA¢Ë†‘x2 -(A¢Ë†‘x)2

S. NO

YEAR

R.P.I

A¢Ë†‘x2

A¢Ë†‘xy

(x)

(y)

1 1 172.2 1 172.2 2 2 174.5 4 349 3 3 179.9 9 539.7 4 4 184.6 16 738.4 5 5 190.5 25 952.5 A¢Ë†‘x=15 A¢Ë†‘y=901.7 55 2751.8 n = 5 5 x 2751.8 – (15) x 901.70 b = ——————————– 5 x 55 – (15) b = 4.67 a = y – bx y = A¢Ë†‘y / n = 901.70 / 5 y = 180.34 x = A¢Ë†‘x / n = 15 / 5 x = 3 a = 180.34 – 4.67 x 3 a = 166.33 y = a + bx = 166.33 +4.67 x 6 y = 194.35

Therefore the RPI for the 6th year is 194.35

In this chart we can clearly see the increase in the RPI by year to year. Using the RPI value to calculate the % change in the index between the years is as under. % change = Later date RPI x Earlier date RPI —————————————– – 100 Earlier date RPI For the year 2000 – 2001 = 172.2 x 171.1 / 168.40 – 100 = 74.96 % For the year 2001 – 2002 = 174.5 x 173.3 / 172.2 – 100 = 75.61 % For the year 2002 – 2003 = 179.9 x 178.4 / 174.5 – 100 = 83.92 % For the year 2003 – 2004 = 184.6 x 183.1 / 179.9 – 100 = 87.88 % For the year 2004 – 2005 = 190.50 x 188.90 / 184.60 – 100 = 94.93 % For the year 2005 – 2006 = 193.40 x 194.35 / 190.50 – 100 = 97.30 % Now taking the average of the % we get 74.96 + 75.61 + 83.92 + 87.88 + 94.93 + 97.30 ——————————————————– = 85.76 % 6 With the help of above chart we can clearly see that it is not in a straight line as we shown in the first chart. Forecasting the turnover for the year 2006 by taking the base year data of RPI. The increase in the % we get = 11 x 85.76% + 12 = 21.43 millions of pounds.

By using the RPI we can forecast that the annual sale for the year 2006 is 21.43 millions of pound.

Conclusion: As above shown in both example number 2 and 3 we can clearly see that the forecast of the year 2006 with the help of RPI is more than that of other. Using the RPI would be better to plan a proper growth of the sales. But, the only problem is that, it is based on assumption.

Reference: Clare Morris, Year 2008 7th edition, “Quantitative approaches in business studies”, England (Essex) & UK Retail Price Index from internet.

Total number of words: 1743