Coffee Grain Case Study

Executive Summary

This report summarizes the statistical data of a coffee shop, Coffee Grain; and uses analytical breakdown of the daily operations resulting in sales revenue and productivity of employees. The objective of this report works on a managerial context to look at room for improvement. The need to study the operation of the cafe came about when sales revenue was stagnant and feedbacks from customer was not impressive. Due to this, management decision was brought in to discuss on the road for improvement. Data was collected prior to the decision-making. With the data collated from the cafe, it seeks to assist management to decide on action plan.

During the analytical studies, descriptive statistic was used to summarize and describe

This means that the baristas preparation times were collected and some baristas may contribute more data as compared to the other. Base on the raw data collected, it is observed that on certain days, the staff may take longer than usual to prepare the food items and this may be due to high volume of customers or shortage of manpower. Therefore, 2 types of data are presented based on population data and sample data. The purpose of this report is to find out if the complaint by customer that preparation time is taking longer than expected is true.

Food Item

Recommended Preparation Time

Latte

1.5mins – 2.10mins

Hot Chocolate

1min – 1.5mins

Panini

1.9mins – 2.4mins

Diagram 1: Recommended Preparation Time during OJT

1.2 Mean

In order to find out if the population preparation time is within the recommended time, we can find the population mean to get an overview of the average time taken.

Diagram 2: Graph of Population mean of preparation time

For this section, we would only be focusing on the sample data. To do so, 3 random baristas are selected and their data are used. After which we focused on baristas with staff identification (ID) of AB, EF and IJ. We will be using this sample data throughout the whole report.

Mean

Staff ID

Latte

Hot Chocolate

Panini

AB

1.88

1.60

2.37

EF

1.87

2.3 Cumulative Probability Distribution

Cumulative probability alludes to the likelihood that the estimation of a random variable falls within a predefined range. As the problem statement asked, suppose that 30 customer comes in to order, what is the probability that customers will purchase 1 or more food items in each order? This means that when an order comes in, there is a possibility that more than 10 orders are of 1 food item or no item at all. The Cumulative probability could be derived based on the following equation:

P(X ≤ 1) = P(X = 0) + P(X = 1) = 0.1 + 0.47 = 0.57

The calculation is done accordingly for the other probability and is listed in the cumulative table in diagram 11.

Number of orders: x

Probability: P(X = x)

Cumulative Probability: P(X ≤ x)

0

0.1

0.1

1

0.47

0.57

2

0.43

1

Diagram 11: Cumulative Probability distribution