Every day, the world consumes about 3 billion cups of coffee. Coffee is not only a vital presence in people's daily life but also the Coffee industry is currently seen as a significant market opportunity since individuals' demand for coffee is increasing. According to Carlos Mera Arzeno, Coffee demand is robust and growing at about 1.5 percent annually. Since the demand for coffee increases year by year, new Coffee shops open and the level of the competition is getting higher and higher because of the positive expectations for continued growth in the coffee industry. In recent years, the coffee industry has boomed. While fast food chains are growing at a rate of 2 percent annually, coffee businesses grow more than 10 percent every year. According to Bloomberg, there were about 20,000 coffee shop businesses in the U.S. with combined revenues of $10 billion in 2011. People of different age groups are driving this growth not only in the United States but also in the entire world.
The purpose of this project is to define the demand and its determinants, and these determinants are used to do a good demand forecasting of a local Saudi Arabian Coffee shop, named Beyond Coffee. The demand forecasting plays a major role for the survival of local coffee shops. Instead of waiting for demand to emerge and then react to it, coffee shops owners should anticipate and plan for future demand so that they can react immediately to customer orders as they occur. In the past month, secondary data were collected to apply Multiple regression method of the demand forecasting. Five variables and twenty-seven observations were used in order to obtain a model for demand forecasting of the local café.
In this project, data is obtained directly from a recently opened coffee shop named Beyond Coffee. This café is owned and operated by Amal Alharbi, a 27- year-old Saudi young woman with four years of experience in marketing research. The 3,300-space square foot café is located near King Abdulaziz in Jeddah City where students have many other options for coffee.
The café is serving specialty coffees, espresso, drip coffee, and lattes. Its business plan's primary objective is to increase revenues 5% in Year 2 and 10% by Year 3. It also aims to build a strong brand and a secure future by targeting college students in the area. However, due to the increasing number of competitors in the coffee industry, consumers nowadays have more choices. This graph below shows the number of cafe establishments in Saudi Arabia from 2010 to 2015. The number was approximately 8,800 in 2015, up from about 2244 cafes in 2010.
Figure 1: Number of cafes in Saudi Arabia from 2010 to 2015
Source: Statista - The Statistics Portal. Retrieved November 29, 2018
The Determinants of Demand
It is no secret that sales represent the image of the enterprise in the market. In this case, many factors can cause demand for coffee at Beyond Coffee to change. In order to analyze the demand for coffee, this project will tackle the following questions:
1. What factors might affect the demand of coffee in Beyond Coffee?
2. What is the price elasticity of demand for the coffee?
By using surveys, we choose some variables of demand to help Beyond Coffee decides which variables of demand has effect on their sales. We need to test variables that have short-run effects on coffee demand at the local café. The below listed variables might lead to answer the two questions;
1. The Number of Cups served daily (Input Y): The dependent variable is the unit sales since unit sales holds actual sales data.
2. The Average Price of the Coffee Cup (X1): when the price of a good rises, other things remaining the same, its relative price-its opportunity cost-rises. In our project, the substitute is a nearby bakery and café.
3. The prices of the related goods: another influence on demand is the price of a related goods or substitutes. According to Michael Parkin (1998), when the cost of a good rises, people buy less of that good and more of its substitutes. If the price of specialty coffee increases, people buy less of coffee and more of other drinks. In our case, Juices store near Beyond Coffee and nearby bakeries can be substitutes for specialty coffee.
4. Income: Consumer personal income obviously drive demand. When income increases, consumers buy more of most goods, and when income decreases, they buy less of most goods. In our case they're students in public university where the Saudi government compensates students with monthly wages of 500$ plus other incentives and sources of income.
5. The daily mean temperature: It is related factor and either affect the demand for coffee. We think this effect directly the demand for coffee where students prefer juices or other cold drinks in sunny days. If the daily mean temperature rises, people might prefer Juices or ice cream so the demand for the coffee decreases.
6. Average parking spots available per day: the number of parking spots available might affect demand particularly with today's mobility and drive-through services.
In order for Beyond Coffee to retain their customers and increase their sales, the findings of this project should help to identify the factors that influence sales and demand for coffee. For forecasting sales, we can use secondary data by constructing models based on based sales figures, and through extrapolation (Uma Sekaran, 1993). Surveys and secondary data were acquired from the daily observations for 27 days from November 1, 2018 to November 27, 2018.
Column1 Y X1 X2 X3 X4 X5
Day Number of Cups served daily Average Price Per Cup Averge Monthly Income of students visiting Average Compatittor' pricing Average parking spots availabilie per day Mean Air Temperature
1 175 $ 4.50 $1,800 $ 5.00 2 23.9999
2 169 $ 4.50 $2,200 $ 5.00 - 23.7771
3 171 $ 4.50 $2,400 $ 5.50 - 23.7683
4 177 $ 4.75 $2,000 $ 5.50 1 23.7308
5 176 $ 5.00 $1,800 $ 5.50 2 23.7502
6 178 $ 5.00 $2,080 $ 6.00 2 23.8275
7 180 $ 5.00 $2,200 $ 6.00 - 23.8183
8 175 $ 5.20 $1,600 $ 5.75 1 23.0091
9 192 $ 5.50 $1,800 $ 5.75 - 24.0023
10 173 $ 5.50 $1,900 $ 5.75 2 23.6662
11 172 $ 5.50 $2,130 $ 5.75 1 24.0023
12 163 $ 5.50 $2,070 $ 5.75 2 24.2889
13 161 $ 5.20 $1,330 $ 5.50 5 24.3333
14 161 $ 5.20 $1,630 $ 5.50 2 24.0044
15 160 $ 5.20 $1,800 $ 5.50 4 24.0023
16 159 $ 5.20 $1,830 $ 5.50 - 24.3666
17 148 $ 5.00 $2,250 $ 5.50 - 23.8999
18 115 $ 5.00 $1,300 $ 5.00 2 24.0023
19 102 $ 5.00 $1,324 $ 5.00 1 23.6667
20 99 $ 5.00 $3,066 $ 5.00 3 23.6006
21 136 $ 5.20 $4,010 $ 5.50 4 23.8777
22 140 $ 5.20 $1,860 $ 5.50 2 24.0023
23 126 $ 5.20 $1,800 $ 5.50 2 23.6666
24 123 $ 5.20 $2,200 $ 5.50 3 23.3099
25 126 $ 5.00 $2,000 $ 5.50 1 22.9923
26 151 $ 5.00 $1,800 $ 5.00 2 23.0023
27 152 $ 5.20 $2,200 $ 5.00 - 23.7623
Evaluation of Data
We use multiple or multivariate regression analysis to test data when the dependent variable that we seek to explain is hypothesized to other dependent variables (Dominic Salvatore, 1989). The first method used to evaluate the collected data is performing regression models on regular coffee shop sales data from Beyond Coffee in order to find the price elasticity of demand.
This chapter interprets and summarizes the result gotten from the regression. The project centers on the factors causing demand of coffee to change. Data were gotten on the probable factors causing change in the demand for coffee. The analysis made use of the following variable/factors
• Number of cups served daily (dependent Variable)
• The Average price of the coffee Cup
• Prices of the related goods
• The daily mean temperature
• Average parking spots availability per day.
The highlighted variables were important to measure the change in demand of coffee as the number of cups severed or sold will depend on temperature, average price of the coffee cup (this is the price affordability), price of the close substitute will also be huge factor in the demand for coffee, the parking spots available to consumer will be a key factor in determining whether a customer will stop over or not. Lastly, the income of a consumer will determine the number of cups of coffee he or she will consume on a daily basis.
Regression analysis is one of the great tools for establishing and studying relationship between two or more variables, when the subject involves one dependent and one independent variable, we term this simple linear regression, otherwise, if it involves one dependent and several independent variables, it is termed “multiple linear regression”.
Multiple linear regression is the proposed tool for this work, the model would be deployed among others to establish among other variables that significantly contributes to numbers of cups of coffee served. The researcher is interested in establishing all underlined relationship affecting cups of coffee served in this work.
The sole aim of this research work is to examine whether or not variables such as Average parking spots availability per day, the daily mean temperature, income, the price of the coffee cup and price of related goods predicts number of cups served daily.
We attempt to fit a regression model to establish the relationship between two or more variable, here a multiple linear regression is fitted using a dependent variable (number of cups served daily) and five other independent variable selected for this work. The regression model tries to establish whether or not there is any relationship between the research (dependent) variable and the predictor variables.
All analysis to this work will be carried out on SPSS version 22.0 environment, 95% confidence interval which corresponds to 5% level of significance will be deployed.
DISCUSSION OF RESULTS AND REPORT
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .717a 0.515 0.393 19.932
a Predictors: (Constant), Mean Air Temperature, Average Monthly Income of students visiting, Average substitute good price, Average parking spots available per day, Average Price Per Cup
Multiple linear regression model used to find the predictors of cups of coffee served is the subject of this research work, after carrying out the necessary analysis, we present here some of the findings of the research work.
• The total variability of 71.7% (0.717) in cups of coffee was explained jointly by the 6 predictor variables using the coefficient of determination R. Similarly, shown in the table below is the regression ANOVA, which is used to test the fitness of the regression model, gave F (4.243) = 55.639, p < 0.001, this implies that the proposed regression model is a good fit.
df SS MS F Significance F
Regression 8427.91 5 1685.582 4.243 .009b
Residual 7945.474 20 397.274
Total 16373.39 25
a Dependent Variable: Number of Cups served daily
b Predictors: (Constant), Mean Air Temperature, Averge Monthly Income of students visiting, Average Competitor' pricing, Average parking spots available per day, Average Price Per Cup
Unstandardized Coefficients Standardized Coefficients 95.0% Confidence Interval for B
B Std. Error Beta t Sig. Lower Bound Upper Bound
(Constant) -325.9 263.228 -1.238 0.23 -874.974 223.193
Average Price Per Cup -35.57 17.979 -0.394 -1.979 0.06 -73.079 1.93
Average Monthly Income of students visiting -0.009 0.008 -0.184 -1.141 0.27 -0.024 0.007
The prices of the related goods 61.824 16.449 0.731 3.759 0 27.512 96.135
Average parking spots available per day -2.103 3.281 -0.111 -0.641 0.53 -8.948 4.741
Mean Air Temperature 14.43 11.054 0.208 1.305 0.21 -8.628 37.489
a Dependent Variable: Number of Cups served daily
About the significance of the parameters and variables with the highest contribution to cups of coffee served, we can conclude from the standardized coefficient that The prices of the related goods (ẞ =0.731, t=3.759, p= 0.000) and Mean Air Temperature with (ẞ =0.208, t=1.305, p= 0.21) are the only two variables significantly contributing to the occupational prestige. We obtain that the effect of Mean air temperature is weak because the coef (β) =14.43 while that of the price of the related goods is strong coef (β) =61.824.
• On the contrary, the variables not significantly contributing and had the weakest contribution to cups of coffee served per day include Average Price Per Cup (ẞ = -0.394, t= -1.979, p= 0.06) with weak effect of coef (β) = -35.37, Average parking spot available (ẞ = -0.111, t= -0.641 p= 0.53) with weak effect of coef (β) = -2.103, and Average Monthly Income of students visiting with (ẞ =-0.184, t= -1.141, p= 0.27) with weak effect of coef (β) = -0.009.
Finally, the regression equation formular can be given as
Y = α + β1x1 + β2x2 + β3x3 + β4x4 + β5x5\
Y = -325.91 – 35.57x1 – 0.009x2 + 61.824x3 – 2.103x4 + 14.43x5
Our main findings are that the long-run evolution of coffee consumption per adult is determined by a proxy variable for differences in preferences across generations in combination with population dynamics, while permanent changes in prices only have short-run affects on consumer demand.
Our results thus indicate that there is a high degree of competition in the Swedish market for roasted coffee since prices and consumption are unrelated in the long run. Consequently, a reduction in spreads, due to lower consumer prices, will not permanently improve export revenue for coffee- producing countries.
In this section the demand forecasting process is done step by step. The causal method and its multiple regression technique are used. The SPSS computer package program is also used with the historical data of the predictor variables (X1, X2, X3, Xi, and Xs) and dependent variable (Y).At the end of all, expectations and projected data of the predictor variables are used in order to compute the demand of Ektam Kibns Ltd for Pepsi cola for the last two quarters of 2005.
6.2 Identifythe Purposeof Forecast
The purpose of the forecast is to predict the sales of Ektam Kibns Ltd for the Pepsi cola in TRNC by using the historical data of the variables affecting of the sales of Ektam Kibns Ltd for the Pepsi cola in TRNC. These variables are;
The advertising expenditures of ektam ltd for the sales of Pepsi cola, Mean air temperatures in TRNC, The price of the Pepsi cola, GNP per capita for TRNC, Population of TRNC. These variables are obtained from literature (in section 2) and the sales manager of the Ektam Kibns Ltd.
Ardent. (n.d.). Number of cafes in Saudi Arabia from 2010 to 2015. In Statista - The Statistics Portal. Retrieved November 29, 2018, from https://www-statista-com.arktos.nyit.edu/statistics/720432/saudi-arabia-number-of-cafe-outlets/.
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Suri, S. (n.d.). THE IMPACT OF ORGANIZATIONAL BEHAVIOUR ON EMPLOYEES ... Retrieved from http://www.dypatil.edu/schools/management/wp-content/uploads/2015/11/The-impact-of-organizational-behaviour-on-employees-behaviour-in-Pharmaceutical-companies-in-selected-locations-of-Maharashtra-Viz_-Mumbai-Pune-Nasik-Sapna-Suri.pdf
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