Review “Benchmark Assignment – Data Analysis Case Study” and “Benchmark Assignment – Data Analysis Case Study Data” for this topic’s case study, evaluating operations for a local restaurant.
Although your friend and restauranteur Michael Tanaglia offered to go over your findings in person, you believe it would be appropriate to also prepare a report and document your findings in writing. In a 1,000-1,250-word report, explain your approach for each evaluation and the rationale for the methods you used. Include any recommendations based on customer satisfaction, forecasting, and staff scheduling data.
Use an Excel spreadsheet file for the calculations and explanations. Cells should contain the formulas (i.e., if a formula was used to calculate the entry in that cell). Students are highly encouraged to use the “Benchmark Assignment – Data Analysis Case Study Template” and “Benchmark Assignment – Data Analysis Case Study Linear Programming Template” to complete this assignment.
Mac users can use StatPlus:mac LE, free of charge, from AnalystSoft.
Prepare the assignment according to the guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required.
Benchmark Assignment – Data Analysis
Case Study
The Cicero Italian Restaurant was
founded by Anthony Tanaglia in 1947 in Cicero, Illinois, a suburb of Chicago. He
built the business with his family from a small pizza and pasta restaurant to 10locations
in the Chicago area. Michael Tanaglia, Anthony’s grandson, moved to Arizona to
escape the cold Chicago winters and opened a restaurant in the Chandler area. The
Arizona restaurant gained momentum thanks to the Chicago-style pizza and
quality Italian dishes. Anthony decided to expand operations in Arizona, adding
a second location in Glendale. The Glendale location was managed by Michael’s
son Tony.
After ayear of operations, Michael had
some concerns with the Glendale location. Michael does not want his family’s
business to fail, and he wants his grandfather’s legacy to last. Michael also
understands how important an operational evaluation can be to identifying the strengths
and weaknesses of a business. Michael confides his concerns to you andasks if
you will do him a favor and use your quantitative analytic expertise to help
him evaluate the Glendale location’s operations in three key areas: customer
satisfaction, customer forecasting, and staff scheduling. As his friend, you
agree – though his offer to treat you to the large pizza of your choice didnot
hurt.
First
Evaluation
The first evaluation required an
understanding of the factors thatcontribute to customer satisfaction and
spending. Refer to the data Michael provided in the Excel spreadsheet “Benchmark
Assignment – Data Analysis Case Study Data.” Identify which variables are
significant to predicting overall satisfaction. Develop and interpret the
prediction equation and the coefficient of determination. Based upon the data
in this evaluation, what areas should Michael and Tony Tanaglia focus on to
improve customer satisfaction?
Second
Evaluation
The second evaluation requires a
forecast of customers based upon demand. Michael reviewed data for the previous
11 months in an attempt to better forecast restaurant customer volume.
|
Month |
# of |
|
January |
650 |
|
February |
725 |
|
March |
850 |
|
April |
825 |
|
May |
865 |
|
June |
915 |
|
July |
900 |
|
August |
930 |
|
September |
950 |
|
October |
899 |
|
November |
935 |
|
December |
? |
Which method should the business owner
use to yield the lowest amount of error and what would be the forecast for
December?Refer to the Excel spreadsheet “Benchmark Assignment – Data Analysis
Case Study Template.”
Third
Evaluation
The third evaluation concerns staff
scheduling.Some of the customers have complained that service is slow. The
restaurant is open from 11:00 a.m.to midnight every day of the week. Tony
divided the workday into five shifts. The table below shows the minimum number
of workers needed during the five shifts of time into which the workday is
divided.
|
Shift |
Time |
# of Staff Required |
|
1 |
10:00 a.m.– 1:00 p.m. |
3 |
|
2 |
1:00 |
4 |
|
3 |
4:00 p.m. – 7:00 p.m. |
6 |
|
4 |
7:00 |
7 |
|
5 |
10:00 p.m. – 1:00 a.m. |
4 |
The owners must find the right number of
staff to report at each start time to ensure that there is sufficient coverage.
The organization is trying to keep costs low and balance the number of staff
with the size of the restaurant, so the total number of workers is constrained
to 15.
Based on these factors, recommend the
staff for each shift to accommodate the minimum requirements for customer
service.Refer to the Excel spreadsheet “Benchmark Assignment – Data Analysis
Case Study Linear Programming Template.”
|
Linear, Integer and Mixed Integer Programming |
|
|||||||||||||||
| Signs | ||||||||||||||||
| < | less than or equal to | |||||||||||||||
| = | equals (You need to enter an apostrophe first.) |
|||||||||||||||
| > | greater than or equal to | |||||||||||||||
| Data | Results | Problem setup area | ||||||||||||||
| x 1 | x 2 | x 3 | x 4 | x 5 | LHS | Slack/Surplus | ||||||||||
| Minimize | 1 | 1 | 1 | 1 | 1 | sign | RHS | 0 | < constraints |
> constraints | ||||||
| 10 AM-1 PM | > | 3 | 0 | 3 | 0 | 0 | 0 | 3 | ||||||||
| 1:00 PM – 4:00 PM | > | 4 | 0 | 4 | 0 | 0 | 0 | 4 | ||||||||
| 4:00 PM- 7:00 PM | > | 6 | 0 | 6 | 0 | 0 | 0 | 6 | ||||||||
| 7:00 PM- 10:00 PM | > | 7 | 0 | 7 | 0 | 0 | 0 | 7 | ||||||||
| 10:00 PM- 1:00 AM | > | 4 | 0 | 4 | 0 | 0 | 0 | 4 | ||||||||
| Results | ||||||||||||||||
| Variables | 0 | 0 | ||||||||||||||
| Objective | 0 | |||||||||||||||
| Dine In (1)/Take Out (2) |
Satisfaction with Service | Satisfaction with Food | Overall Satisfaction | Driving Distance to Restaurant | Total Bill | |
| 1 | 4 | 4 | 4 | 5 | 10 | |
| 1 | 2 | 3 | 3 | 5 | 15 | |
| 1 | 3 | 3 | 3 | 10 | 10 | |
| 1 | 5 | 5 | 5 | 12 | 15 | |
| 2 | 3 | 4 | 3 | 10 | 25 | |
| 2 | 2 | 4 | 3 | 15 | 25 | |
| 2 | 3 | 4 | 3 | 10 | 26 | |
| 1 | 4 | 3 | 3 | 16 | 27 | |
| 2 | 3 | 3 | 3 | 2 | 25 | |
| 1 | 2 | 3 | 2 | 10 | 26 | |
| 2 | 1 | 3 | 2 | 15 | 20 | |
| 2 | 2 | 2 | 2 | 10 | 20 | |
| 1 | 5 | 4 | 4 | 12 | 20 | |
| 1 | 4 | 5 | 4 | 16 | 20 | |
| 1 | 4 | 5 | 4 | 18 | 20 | |
| 1 | 3 | 4 | 3 | 20 | 27 | |
| 1 | 4 | 3 | 4 | 18 | 28 | |
| 2 | 3 | 4 | 3 | 20 | 28 | |
| 2 | 3 | 4 | 3 | 16 | 28 | |
| 1 | 4 | 5 | 4 | 7 | 12 | |
| 2 | 4 | 5 | 4 | 9 | 20 | |
| 1 | 2 | 3 | 3 | 10 | 24 | |
| 2 | 3 | 5 | 4 | 6 | 26 | |
| 2 | 3 | 4 | 3 | 10 | 28 | |
| 1 | 3 | 4 | 3 | 9 | 27 | |
| 2 | 4 | 5 | 4 | 8 | 24 | |
| 2 | 3 | 3 | 3 | 10 | 22 | |
| 1 | 4 | 4 | 4 | 6 | 23 | |
| 2 | 3 | 4 | 4 | 10 | 25 | |
| 1 | 4 | 5 | 4 | 10 | 20 | |
| 2 | 2 | 3 | 2 | 15 | 20 | |
| 2 | 2 | 2 | 2 | 16 | 20 | |
| 1 | 4 | 4 | 4 | 18 | 20 | |
| 2 | 3 | 2 | 3 | 16 | 20 | |
| 2 | 3 | 3 | 3 | 14 | 25 | |
| 1 | 3 | 3 | 3 | 20 | 22 | |
| 1 | 3 | 3 | 3 | 16 | 23 | |
| 1 | 4 | 5 | 4 | 17 | 28 | |
| 2 | 3 | 3 | 3 | 16 | 23 | |
| 2 | 3 | 4 | 3 | 5 | 15 | |
| 1 | 4 | 4 | 4 | 10 | 28 | |
| 2 | 3 | 3 | 3 | 6 | 24 | |
| 2 | 2 | 3 | 2 | 10 | 27 | |
| 1 | 3 | 3 | 3 | 6 | 26 | |
| 2 | 4 | 4 | 4 | 7 | 28 | |
| 1 | 2 | 3 | 2 | 6 | 24 | |
| 2 | 4 | 5 | 4 | 8 | 22 | |
| 1 | 4 | 5 | 4 | 6 | 23 | |
| 1 | 5 | 5 | 5 | 8 | 20 | |
|
Moving averages – 4 period moving average |
||||||||||
| Num pds | 4 |
|
|||||||||
| Data | Forecasts and Error Analysis |
||||||||||
| Period | Demand | Forecast | Error | Absolute | Squared | Abs Pct Err | |||||
| Period 1 | |||||||||||
| Period 2 | |||||||||||
| Period 3 | |||||||||||
| Period 4 | |||||||||||
| Period 5 | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | ||||||
| Period 6 | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | ||||||
| Period 7 | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | ||||||
| Period 8 | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | ||||||
| Period 9 | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | ||||||
| Period 10 | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | ||||||
| Period 11 | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | ||||||
| Total | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | |||||||
| Average | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | |||||||
| Bias | MAD | MSE | MAPE | ||||||||
| SE | #DIV/0! | ||||||||||
| Next period | #DIV/0! | Not enough data to compute the standard error |
|||||||||
|
Weighted moving averages – 2 period moving average |
||||||||
| Data | Forecasts and Error Analysis |
||||||||
| Period | Demand | Weights | Forecast | Error | Absolute | Squared | Abs Pct Err | ||
| Period 1 | 0.15 | ||||||||
| Period 2 | 0.3 | ||||||||
| Period 3 | 0 | 0 | 0 | 0 | #DIV/0! | ||||
| Period 4 | 0 | 0 | 0 | 0 | #DIV/0! | ||||
| Period 5 | 0 | 0 | 0 | 0 | #DIV/0! | ||||
| Period 6 | 0 | 0 | 0 | 0 | #DIV/0! | ||||
| Period 7 | 0 | 0 | 0 | 0 | #DIV/0! | ||||
| Period 8 | 0 | 0 | 0 | 0 | #DIV/0! | ||||
| Period 9 | 0 | 0 | 0 | 0 | #DIV/0! | ||||
| Period 10 | 0 | 0 | 0 | 0 | #DIV/0! | ||||
| Period 11 | 0 | 0 | 0 | 0 | #DIV/0! | ||||
| Total | 0 | 0 | 0 | #DIV/0! | |||||
| Average | 0 | 0 | 0 | #DIV/0! | |||||
| Bias | MAD | MSE | MAPE | ||||||
| SE | 0 | ||||||||
| Next period | 0 | ||||||||
|
Exponential smoothing |
||||||
| Alpha | 0.05 | ||||||
| Data | Forecasts and Error Analysis |
||||||
| Period | Demand | Forecast | Error | Absolute | Squared | Abs Pct Err | |
| Period 1 | 0 | 0 | 0 | 0 | #DIV/0! | ||
| Period 2 | 0 | 0 | 0 | 0 | #DIV/0! | ||
| Period 3 | 0 | 0 | 0 | 0 | #DIV/0! | ||
| Period 4 | 0 | 0 | 0 | 0 | #DIV/0! | ||
| Period 5 | 0 | 0 | 0 | 0 | #DIV/0! | ||
| Period 6 | 0 | 0 | 0 | 0 | #DIV/0! | ||
| Period 7 | 0 | 0 | 0 | 0 | #DIV/0! | ||
| Period 8 | 0 | 0 | 0 | 0 | #DIV/0! | ||
| Period 9 | 0 | 0 | 0 | 0 | #DIV/0! | ||
| Period 10 | 0 | 0 | 0 | 0 | #DIV/0! | ||
| Period 11 | 0 | 0 | 0 | 0 | #DIV/0! | ||
| Total | 0 | 0 | 0 | #DIV/0! | |||
| Average | 0 | 0 | 0 | #DIV/0! | |||
| Bias | MAD | MSE | MAPE | ||||
| SE | 0 | ||||||
| Next period | 0 | ||||||

