Demand Estimation for Britannia Biscuit Industry 1. Introduction: Our objective was to study the demand variation of Britannia Biscuits in India with respect to certain variables like, Price, Price of the substitute (Parle Biscuits), Income of Consumer and Population. To achieve this we assume all other factors like Tastes of Consumer, Advertising etc are constant. The biscuit industry is taken as a whole without differentiating between various segmented brands. To arrive at the demand function of Britannia, multivariate regression analysis has been used over non linear model.
All data has been taken from the Centre for Monitoring Indian Economy (CMIE) database. 1. 1 Biscuit Industry in India: Indian Biscuit Industry is the largest among all the food industries and has a turnover of around Rs. 3000 crores. India is known to be the second largest manufacturer of biscuits, the first being USA. However, the per capita consumption of biscuits in India at 2. 1 kg is considerably lower than other countries. It is classified under two sectors: organized and unorganized. Bread and biscuits are the major part of the bakery industry and covers around 80 percent of the total bakery products in India.
Biscuits stand at a higher value and production level than bread. This belongs to the unorganized sector of the bakery Industry and covers over 70% of the total production. India Biscuits Industry came into limelight and started gaining a sound status in the bakery industry in the later part of 20th century when the urbanized society called for readymade food products at a tenable cost. Biscuits were assumed as sick-man’s diet in earlier days. Now, it has become one of the most loved fast food products for every age group.
Biscuits are easy to carry, tasty to eat, cholesterol free and reasonable at cost. States that have the larger intake of biscuits are Maharashtra, West Bengal, Andhra Pradesh, Karnataka, and Uttar Pradesh. Maharashtra and West Bengal, the most industrially developed states, hold the maximum amount of consumption of biscuits. Even, the rural sector consumes around 55 percent of the biscuits in the bakery products. The total production of bakery products have risen from 5. 19 lakh tonnes in 1975 to 18. 95 lakh tonnes in 1990.
Biscuits contributes to over 33 percent of the total production of bakery and above 79 percent of the biscuits are manufactured by the small scale sector of bakery industry comprising both factory and non-factory units. The production capacity of wafer biscuits is 60 MT and the cost is Rs. 56,78,400 with a motive power of 25 K. W. Indian biscuit industry has occupied around 55-60 percent of the entire bakery production. Few years back, large scale bakery manufacturers like Cadbury, Nestle, and Brooke Bond tried to trade in the biscuit industry but couldn’t hit the market because of the local companies that produced only biscuits.
The biscuit industry has Herfindahl Index of around 0. 25 indiacting it is of the monopolistic competition type. Also the entry and exit of firms in the industry is not very tough. The Federation of Biscuit Manufacturers of India (FBMI) has confirmed a bright future of India Biscuits Industry. According to FBMI, a steady growth of 15 percent per annum in the next 10 years will be achieved by the biscuit industry of India. Besides, the export of biscuits will also surpass the target and hit the global market successfully. In 2003 the Biscuit market in India was estimated to be above 0. million tonnes with a vlue of about Rs. 45 billion. The share of the organised sector in the biscuit market was estimated to be above 50%. Britannia and Parle held more than 80% of the organised sector share in value terms. The market was going at a rate of 5% to 8%, however with the introduction of ITC by 2007 Sunfeast had become a Rs 5 billion brand growing at 15% per annum. 1. 2 Britannia Industries: Britannia Industries Limited is an Indian company based in Kolkata that is famous for its Britannia and Tiger brands of biscuit, which are highly recognized throughout the country.
Britannia is India’s biscuit firm, with an estimated 38% market share. The Company’s principal activity is the manufacture and sale of biscuits, bread, rusk, cakes and dairy products. The Britannia’s fame is largely acknowledged through the colorful Britannia logos that Indian cricketers such as Virender Sehwag and Rahul Dravid wear on their bats. 1. 2. 1 History: It was started way back in 1892 with an investment of Rs. 295. Initially, biscuits were manufactured in a small house in central Kolkata. Later, the business was acquired by the Gupta brothers and operated under the name of V.
S. Brothers. In 1918, C H Holmes, an English businessman in Kolkata was taken as a partner and The Britannia Biscuit Company Limited (BBCo) was launched. The Mumbai factory was setup in 1924 and Peak Freans, UK acquired a controlling interest in BBCo. Biscuits were in big demand during World War II, which gave a fillip to the company’s sales. The company name was changed to the current Britannia Industries Limited in 1979. In 1982 Nabisco Brands Inc. , USA became a major foreign shareholder. Kerala businessman K.
Rajan Pillai secured control of the group in the late 1980s, becoming known in India as the ‘Biscuit King’. In 1993, the Wadia Group acquired a stake in ABIL, UK and became an equal partner with Groupe Danone in Britannia Industries Limited. In what the Economic Times referred to as one of [India’s] most dramatic corporate sagas, Pillai ceded control to Wadia and Danone after a bitter boardroom struggle, then fled his Singapore base to India in 1995 after accusations of defrauding Britannia, and died the same year in Tihar Jail. 1. 2. 2 Growth and profitability: The company is a growing and profitable one.
Between 1998 and 2001, the company’s sales grew at a compound annual rate of 16 per cent against the market, and operating profits reached 18 per cent. More recently, the company has been growing at 27 per cent a year, compared to the industry’s growth rate of 20 per cent. At present, 90 per cent of Britannia’s annual revenue of Rs2,200 crores comes from biscuits 2. Demand Estimation by Regression Analysis: This consists of five steps namely: 1)Model Specification 2)Collecting Data on variables 3)Specifying the form of Demand Equation 4)Testing the econometric results 5)Correction of model if needed
These are explained with respect to the project in the coming sections 2. 1 Model Specification: In regression analysis and related fields such as econometrics, specification is the process of converting a theory into a regression model. This process consists of selecting an appropriate functional form for the model and choosing which variables to include. Model specification is one of the first steps in regression analysis. If an estimated model is wrongly specified, it will be biased and inconsistent. For our study of Britannia we assume the following model: QX = f(PX, N, I, PY) Where, QX = Quantity demanded of commodity X.
PX = Price per unit of commodity X. N = Numbers of consumers in the market. PY = Price of related (substitute or complementary) commodity. Ideally the demand for a commodity changes because of taste of the consumer, marketing strategies etc. But the taste parameter is qualitative and hence difficult to analyse the data over years, we assume these factors to be constant throughout our analysis time period. Also including too many explanatory variables (more than 5 or 6) may lead to econometric difficulties such as having too few degrees of freedom and multi-collinearity. Hence we restrict our model to four variables. . 2 Collecting Data on Variables: This is the second step in regression analysis where to estimate the demand of a particular commodity we collect data for the variables. There can be two approaches of data collection: •Cross Sectional Data Analysis •Time Series Data Analysis For our study we have selected time series data. The study window is from the year 1994 to 2008. All data has been collected from CMIE database and used Prowess & Economic Intelligence Survey (EIS). Qx = Quantity of Britannia biscuits sold in tonnes (Prowess) Px = Price of Britannia biscuits in Rs per ton (Prowess).
Py = Price of Parle biscuits in Rs per ton (Prowess). I = Private Final Consumption Expenditure on Other food items in Crores. (EIS) N* = Midyear Population of India in millions. (EIS) P. S:- Refer the appendix to see the collected Data. 2. 3 Specify the Form of Demand Equation: The third step in estimating demand by regression analysis is to form the functional model to be estimated. The simplest model is the linear model i. e. QX = a0 + a1PX + a2N + a3I + a4 PY + e * Under the assumption that each and every household consumes biscuit Where a’s are the parameter (Coefficients) to be estimated and e is the error term.
There are cases when nonlinear relationship fit the data better than the linear form. The most common form of the non-linear specification of the demand function is the Power function i. e. QX = a0 (PX)a1(PY)a2 Ia3 Na4 Taking Natural Logarithm both sides we get ln(QX) = ln(a0) + a1 ln(PX) + a2 ln(PY) + a3 ln(I) + a4ln(N) We have used the power function so as to fit the data properly and to minimise the effect of Auto Correlation. Also in this form the individual coefficients i. e. a1 , a2 etc are the respective elasticity. 2. 4 Testing the Econometric Results:
The fourth step of estimation of demand by regression analysis by evaluating the regression results first the sign of each slope coefficient must be checked to see if it conforms to what is postulated on theoretical grounds. Second T-tests must be conducted on statistical significance of the estimated parameters to determine the degree of confidence that we can have in each of the slope coefficients. The adjusted coefficient of determination will indicate the proportion of total variation in the demand for the commodity that is explained by the independent or explanatory variable included in the demand equation.
Finally the estimated demand equation should yield no auto-correlation. Following is the result obtained with the help of SPSS Software. ModelUnstandardized CoefficientsStandardized Coefficients BStd. ErrorBetatSig. a0 -40. 1884. 199-9. 571. 000 a1-1. 532. 425-. 376-3. 605. 005 a21. 575. 533. 2182. 956. 014 a3. 033. 058. 019. 566. 584 a47. 534. 4281. 24117. 588. 000 Hence the demand equation becomes: ln(QX) = – 40. 188 – 1. 532 ln(PX) + 1. 575ln(PY) + . 033ln(I) + 7. 534ln(N) The following table contains information for data accuracy and other econometric test such as autocorrelation
ModelRR SquareAdjusted R SquareStd. Error of the EstimateDurbin-Watson 1. 998a. 996. 994. 036271. 915 2. 5 Correction of model if needed 2. 5. 1 Analysis of the Obtained Result: Constant term (a0): The value of constant term obtained is -40. 188. This value means that if every value is zero then the quantity of Britannia biscuit sold will be equal to -40. 188. This of course is a wrong assumption; hence the constant term is only for the better fitting of the curve. Also the constant term imposes a limitation on the validity of the model very near to zero values in the prediction.
Coefficient of price of Britannia (a1): The value of coefficient obtained is -1. 532. This means that the price elasticity of Britannia (Ep) biscuit is -1. 532. Hence for every 1 percent increase in the price of biscuit the quantity sold is expected to go down by 1. 532 percent. This confirms to the expected theoretical result also. In the market the demand function is given by Qx = a – bPx. Also the level of significance is . 005 which indicates a highly significant result. Coefficient of price of Parle (a2): The value of coefficient obtained is 1. 575. This means that the cross price elasticity of Britannia biscuit w. r. . to Parle (Exy) is 1. 575. Hence for every 1 percent increase in the price of Parle biscuit the quantity sold is expected to go up by 1. 575percent. This confirms to the expected theoretical result also. In the market the demand of any commodity is positively linked to the price of the substitute and negatively linked to the demand of complementary good. Hence this proves our initial assumption that Britannia and Parle biscuits are substitutes of each other. Also the level of significance for t –test is . 014, which indicates a good amount of significance. Coefficient of Income (a3): The value of coefficient obtained is 0. 33. This means that the Private Final Consumption Expenditure on other food items has not much significance on the amount of biscuit consumed. Its value is as low as 0. 033, which means that it is a normal commodity. The Income elasticity EI is 0. 033. One reason of such a low value can be the data. We were able to get the data on the PFCE value of Other food items. The other food items includes biscuits and a host of other items as well which may act as substitute of biscuit. The various items which may fall under this section can be potato chips, wafers, bakery products, etc.
The consumer might have switched from one product to another to maximize his utility. Since this data was not available we cannot analyze much about this coefficient. Also the level of significance of t test is . 584 Coefficient of Income (a3): The value of coefficient obtained is 0. 033. This means that the Private Final Consumption Expenditure on other food items has not much significance on the amount of biscuit consumed. Its value is as low as 0. 033, which means that it is a normal commodity. The Income elasticity EI is 0. 033. One reason of such a low value can be the data.
We were able to get the data on the PFCE value of Other food items. The other food items includes biscuits and a host of other items as well which may act as substitute of biscuit. The various items which may fall under this section can be potato chips, wafers, bakery products, etc. The consumer might have switched from one product to another to maximize his utility. Since this data was not available we cannot analyze much about this coefficient. Also the level of significance of t test is . 584. Coefficient of Population (a4): The value of coefficient obtained is 7. 534.
This means that with increase in population the demand of Britannia biscuit is also increasing. This is quite true to the theoretical assumption as well. Also over the years the per capita income of people have increased, hence more people can afford biscuits now. The high value of this coefficient can be explained because of these two reasons. Adjusted R Square (R2): The value of this is . 994. It means that 99. 4 % of the variation can be explained with the variation of the independent variables. This indicates a good degree of explanation and model can be used to predict well within the range.
However in the case of extrapolation we need to be cautious before predicting. In extrapolating many other factors need to be considered. Autocorrelation: Autocorrelation is the cross-correlation of a signal with itself. It is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal which has been buried under noise, or identifying the missing fundamental frequency in a signal implied by its harmonic frequencies. It is used frequently in signal processing for analyzing functions or series of values, such as time domain signals.
Informally, it is the similarity between observations as a function of the time separation between them. The Durbin-Watson coefficient obtained is 1. 915. It indicates very meagre amount of autocorrelation among the variables and hence the econometric result has less problems. This is one of the reasons we went for the power model. In the linear model the autocorrelation coefficient had a value of 1. 5 which indicated positive correlation among the variables. 3. Conclusion: The demand Equation obtained using multivariate regression analysis is: ln(QX) = – 40. 188 – 1. 532 ln(PX) + 1. 75ln(PY) + . 033ln(I) + 7. 534ln(N) The various elasticities and econometric test results are mentioned below: •Price Elasticity (Ep) = -1. 532 •Income Elasticity (EI) = 1. 575 •Cross-price Elasticity (EXY) = 0. 033 •Durbin Watson Index = 1. 91 •Correlation coefficient (R-square) = 0. 994 •Herfindahl Index = 0. 34 (Monopolistic Competition) Interpretation of Results: •Negative price elasticity indicates increase in price as quantity demanded decreases •Positive cross-price elasticity indicates the product is a substitute •Positive income elasticity indicates that increase in income ncreases the demand Demand Forecasting: The following demand equation can be used to predict future demand of the Britannia biscuit industry. However, if some major economic change or regulation comes in between the model is liable to inaccurate predictions. Assuming other things to be constant we will try to predict the demand of the Britannia Biscuits for the year 2010. Assumptions made for the year 2010: 1)Inflation rate to be 6%, hence the price of the Britannia Biscuits(Px) and Parle(Py) is expected to rise by 6% 2)GDP to be 6. 5% and hence the per capita Income or Expenditure is expected to increase by 6. 75% 3)Population growth rate to be 1. 578, so population of india is expected to rise by 1. 578 times. Therefore the values for independent variables in our demand equation are: Px = Rs 55870 per ton Py = Rs 38070 per ton I = Rs 11256 crores N = 1172 million Thus calculating the demand for Britannia Biscuits after substituting these values in our demand equation is: 565984 tonnes. Hence Britannia should increase its production by almost 13 % APPENDIX A. 1 Data
YearQuantity of Brittania Biscuits(Q)Price of Brittania(Px)Price of Parle(Py)Income of Customer(I)Number of people(N) Mar 1994 10219034558. 1831011. 664151 892 11403938959. 4832671. 996550910 Mar 1996 12189443199. 0135595. 174888928 Mar 1997 13601647772. 3238101. 096551946 Mar 1998 14421350258. 9937737. 739141964 Mar 1999 16746751482. 3838408. 45468983 Mar 2000 19264650785. 937926. 58112261001 Mar 2001 21421451691. 337131. 4283401019 Mar 2002 22844751133. 9636076. 2396271040 Mar 2003 25092049838. 5934127. 2687721056 Mar 2004 28004448630. 334049. 0483511072 Mar 2005 31185347865. 1833982. 3481101089 Mar 2006 34686348316. 7732545. 1383041106 Mar 2007 42998948631. 0133850. 1689131122 Mar 2008 44204152708. 6935915. 2192441138 A. 2 Graphs A. 3 Other Information about Britannia Britannia Industries Limited Type Private Founded1892 HeadquartersKolkata and Bangalore; R Chennai, India Number of locations300 stores (2000) Area servedIndia Key peopleNusli Wadia, Chairman Ms. Vinita Bali, (Managing Director) Industry Food Products Biscuits Tiger, Britannia, milk Revenue ^ Rs 2,200 crore Owner(s)
Danone, Kalabakan Investments Parent Wadia Group, Associated Biscuits Intl. Holdings Website www. britannia. co. in B. References •Managerial Economics Principles and Worldwide Applications, Sixth Edition, Dominick Salvatore •Marketing Management , A south Asian Perspective,13th Edition, Philip Kotler •Quantitative Methods for Business, 10th Edition, David Anderson •http://business. mapsofindia. com/india-industry/biscuits. html •http://en. wikipedia. org/wiki/Britannia_Industries •https://www. cia. gov/library/publications/the-world-factbook/print/in. html