Every customer is referred to as a guest and guest count is used in all functions of the operations. Every 30 minutes management record the number of guests who have placed an order and compares that to labor costs which broken down in 30 minutes segments at $4.00 per hour. One such way was to record data every 30 minutes during what we determine was the peak or rush hours. The question becomes how to best capture a true and complete record of the number of complaints given to management. Below are the results we came up with. By using several research methods, the team discovered that there were potential problems that needed to be assessed to measure the performance of the operation. The strategic goals of the Blue Bowl restaurant must be identified for the workable design and execution of the data analysis.
Additionally, candidate development must be aligned with business development and constructively support the cultural, leadership and communication elements. By nature, the Blue Bowl restaurant will continue to evolve with the business needed over the long term and permeate through all levels of the restaurant. The variable is the number of guests complaints received during the most nights the Blue Bowl has an average guest count of 300 guests from 6pm to 10pm. On Friday and Saturday nights this total can climb to 400-600 which could mean an increase of 50% on both days.
The team has decided that the removal of the obstacles on the staff will increase their productivity measured by the number of guests and income per hour. However, the removal of the obstacles on the staff will not increase their productivity measured by the number of guests and income per hour. Based on the information that was provided, as a group we decided on the above scenario that not only included a number of guests but also included the number of available working employees. The graph defines those segments when guest complaints occurred. In the graph you can see that complaints mainly occurred in the beginning of the increase, in the middle of the increased, and at the end of the increase of guests. The graph below defines those segments when guest complaints occurred.
Above are the results of complaints received by guests to management during this time. You will notice in the first quarter and second quarter complaints are lower compared to the target population this would suggest that performance is good with regards to guest counts. However the team seen an increase in complaints in the third quarter suggesting that performance has fallen. This would suggest to them that with the increase of the work load the staff has lost a measure of performance resulting in complaints. It could also mean that management at the time was trying to cut cost by minimizing staffing levels. With staffing levels lower, more responsibility is put on the shoulders of one worker and that could be a very difficult task to retain high service levels.
In the fourth quarter the team found that complaints have again fallen given the data collected which would indicate that the staff has regained performance and is operation better in this quarter with the same being true in the last quarter. As a result, of the results in the data and given the findings that performance decreased in the later quarter, the team believes that employees that are much better suited for handling the increase. The decrease in complaints resulted from gathered information throughout the quarters. Managers were able to see different trends and were able to gather complaints from diners throughout the quarter. Upon compiling all the information together, the manager of Blue Bowl was able to make significant changes and continue with training thus the reason for the steady decrease.
A random sample over the course of a few weeks produces 91 surveys or customer complaint cards. The observations produced a mean of x= 26.1 and a standard deviation to s= 2.8. Since the sample size is large the standard formula will be used. The equation will be 26.1 + and 1.960 2.8 / the square root of 91. Once the calculations are done we can determine the calculations will be 26.1 + and 0.58. Thus the 95% confidence level for u will be 25.52 and 26.68. This allowed us to determine that out of the surveys we received feedback on we can say that 95% of the data is accurate.
With this data, we will be able to move forward with training and different courses of action to perhaps minimize the complaints that we receive during peak hours. From this data we can now determine what was happening during these peak times if we redefine our research question a bit. We can now ask how many complaints where about incorrect orders and how many complaints where from too long of a wait from placing the order and when it arrived at the table? Below is a chart with these redefined questions and the data which has been separated.
So the inferences that can be made here is that the team sees the sharp increase that was due mainly in part to increased wait times and not incorrect orders being delivered to the guests. With this chart you can say that the statistic is regression and from the data you can say that the majority of complaints where in fact due to waiting periods. You can further say that the data infers that the next weeks guests will experience much of the same.
Overall the team as a group found out from their data that not all concerns about the performance actually where related to employees. The team found that the great numbers of complaints where from guests that were unhappy about the wait to be seated. The wait time was increased by the guest who remained longer than projected by management. Early on in their projects they stated that everything in the restaurant was driven by guest count from the food to be ordered, to the number of staff they have on hand to serve the guest, and even the number of kitchen staff working to fulfill the orders. When guests remained longer than one hour the matrix established failed.
In their terms the null hypothesis was rejected. Management had few choices about decreasing wait time given the number of tables. If space is available in the dining room, staffing could not be increased for this reason as well. Friday and Saturday nights saw the most increases in our population size and affected our data the most. Another variable we determined as the cause was effectiveness of both the order taking and order fulfillment. Fatigue played a factor in the performance of employees as well as the unpreparedness of staff when increased order volume occurred. Ten Research Questions:
1. Were you greeted immediately upon entering the restaurant?
2. Once seated, how long did it take before your server greeted you?
3. Was you food/drink order correct?
4. How was the quality/presentation of the food/beverage that was provided?
5. How did everything taste?
6. Was the check presented in a timely matter?
7. Did you observe manager/supervisor interacting with guest?
8.Was the server/host knowledgeable about the items on the menu?
9. Would you recommend Blue Bowl Restaurant to your friends and family?
10. How would you rate your overall experience on a scale from 1-5?
With 1 being poor and 5 being excellent. The strength was asking or being given responses directly from a guest and recording that data to find the core of the complaints of guests during peak times. This approach revealed wealth of information based on the perceived experiences from the customer, direct interaction and gathered information is far better than relying solely on a random survey which may not yield the same or intended information needed to accomplish the objectives. On the weakness side this information was solely the opinion of a guest and not backed up by data. In that respect the information has a likelihood of including false results based on an opinion. There was not any model in which to compare a base line from previous days and not a system that could eliminate miss information such as failure of management to record complete and accurate details of the complaint which is amplified when more than one member of management recording data. Still this does allow for future studies to be completed to fine tune both the data gathering and interpretation of the results found.
The team has also discovered during the previous weeks that our data began developing more variables such as the space in the waiting area and the number of seating available to seat guests, as well as fatigue of staff during peak times which affect performance. Yet the data collected does provide enough information to identify areas of improvement and those conditions for which the study was conducted. The simple answer could be to increase the waiting area to increase waiting space available as well as redesign the dining area to provide for more seating space. However, this would mean an increase in expenditures to make such changes which may not be financially possible. In conclusion the team has pointed out some of the strengths and weaknesses in their data.
They believed that the process that they were able to come up with was going to allow the Blue Bowl Restaurant to continue high success and growth in the future. As a team they gathered data and performed different hypothesis tests to come up with different results. Charts and graphs showed the data that was collected and the variables that were tested. They thought that the complaints came from customers being unhappy with the employees service. In actuality it was because the customers had to wait longer to be seated. So as a team they found out that it was not the employees performance but prior guests sitting longer than projected. If there was more seating space in the dining room then this problem would not occur. This would mean that the Blue Bowl restaurant would have to expand and might not be enough finances for such renovation. As a team they figured out that they should have included the customers that stayed longer than anticipated as a variable in their tests.