Coffee Time: Research And Design

Introduction

As we continue to explore India's market for coffee we will discuss the major research concern in this simulation. As Coffee Time begins to attract customers it will be very important to create product awareness in India. The product has had good market response and to help continue awareness, Coffee Time has decided to participate in the South Asia shopping festival in Mumbai, India. Coffee Time will set up two stalls at this festival and will attempt to encourage patrons to buy Coffee Time's products. The simulation had us compare data from previous festivals to predict the average attendance of the event. We used the information to decide whether we would make enough profits to set up our own stalls or to franchise these two stalls out. This simulation provided us with a company, Forefront, to give an accurate estimate for the South Asia's market and help to target the daily attendance and how much supplies we will need throughout the shopping festival. We will need to study charts and decide an optimal stocking level the expected monetary values or expected opportunity loses. This will help to decide the approximate sales figures during the duration of the shopping festival whether Coffee Time should over stock their supply or under stock of cups, basically whether we make a profit.

Major Research Concern

The major research concern of the simulation would be different methods of how Coffee Time would develop their product awareness and their market demand in India. This is important because it will have a direct affect on the profitability of Coffee Time. The critical decisions will have an impact on the business and will have an affect on Coffee Time's profits. It will be important to revise the probabilities in each scenario during the simulation using the Bayes' theory. It will be important to decide on estimating demand and calculating stock levels for certain coffee beans to minimize losses and ultimately maximizing profits. It was important to follow the decision tree throughout the simulation because it broke down the decision making and listed the alternate decisions and I was able to see my mistakes.

Characteristics of the Sample or Population

A population is the entire collection of data you are gong to study and a sample is some specific subset of your population. The simulation used a population which was the number of people who attended the shopping festival daily. We would consider this a sample if we took the daily attendance and placed them into subsets of what stalls they attended throughout the shopping festival.

The population used to estimate the daily attendance of the shopping festival was a chart comparing the past 15 years. The daily attendance according to the charts from the simulation has been an average of over 100,000 people in attendance in the past couple of years. The attendance average in 1998 was 131, 980 and in 1997 was 126,728. According to the simulation the average attendance has began to decline but has surpassed the average of over 100,000 people in attendance for the 21 days the shopping festival is in town. The population will be a very important factor because the actual daily attendance will affect Coffee Time's expected monetary value throughout the shopping festival. I would consider using Forefront's forecast and help me to look ahead and determine how to estimate my daily profits. We are expecting attendance to exceed the 100,000 market as it had the past couple years but it will not change Coffee Time's positioning if the results exceed the overall average of 100,000. I think it would be important to research the year 2000, when it was recorded that the daily attendance was only 67,236. It might be important to search and find what factors caused the drop in attendance especially since in 1999 and 2001, the daily average was over 100,000 people.

Difference between Probability and Non-Probability Sampling

The difference between probability and non-probability sampling has to do with a basic assumption about the nature of the population under study. In probability sampling, every item has a chance of being selected. In non-probability sampling, there is an assumption that there is an even distribution of characteristics within the population. The simulation used probability sampling when gathering information on the attendance of the shopping festival. The probability of attendance has a possibility of exceeding 100,000. The number of people who attended the shopping festival had an opportunity of being selected and the average daily number is an approximate number.

Application of Bayes'