Question

 

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 10 locations 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 a year 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 identify the strengths and weaknesses of a business. Michael confides his concerns to you and asks 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 did not hurt.

First Evaluation

The first evaluation required an understanding of the factors that contribute 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 to better forecast restaurant customer volume.

Month	# of Customers
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 p.m. – 4:00 p.m.	4
3	4:00 p.m. – 7:00 p.m.	6
4	7:00 p.m. – 10:00 p.m.	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.

1-Based on these factors, recommend the staff for each shift to accommodate the minimum requirements for customer service.

 

Answer 1

 

1)2)3)

Third Evaluation

Assumption: Each employee does 9 hour shift.

Linear programming model:

	x 1	x 2	x 3	x 4	x 5
Minimize	1	1	1	1	1	sign	RHS
10 AM-1 PM	1	0	0	0	0	>	3
1:00 PM - 4:00 PM	1	1	0	0	0	>	4
4:00 PM- 7:00 PM	1	1	1	0	0	>	6
7:00 PM- 10:00 PM	0	1	1	1	0	>	7
10:00 PM- 1:00 AM	0	0	1	1	1	>	4

1. Given: The shift starting at 10 a.M will end at 7 P.M as it is a 9 hour shift. This indicates that employees of shift one will be present for shift 1 shift 2 as well as Shift 3. In a similar pattern, I can say that employee in the shift 2 will be present for shift 2, 3, and 4. Also employees who belong to shift 3 will be present for Shift 3, 4, and 5. But employees which are in the shift or will be present for shift 4 and 5 only. However employees of shift 5 will be present for shift 5 only.
2. Dependent variable: number of employees starting their work at the beginning of each shift.
3. Objective function: minimize total number of employees starting the shifts.
4. Constraint: have minimum support staff in each shift desired by customer service requirements.

Output:

x 1	x 2	x 3	x 4	x 5
3	3	3	1	0

           

I want to achieve the optimal output which is to minimize the total number of employees in the starting shift. The application of linear programming problems tell that, I need to start the shift with 3 employees corresponding to shift 1 2 and 3. And shift PO should be started with one employee.

5) Second Evaluation

WMA has lowest values for MAD, MSE and MAPE. Hence the best method of forecasting is Weighted Moving Average. The worst method of forecasting is Exponential Smoothing as it has highest value of MAD, MSE and MAPE.

The forecasted values are given below.

It is recommended to business owner to use Weighted Moving Average method as it yields lowest error. The forecast for December for volume of customers is 923 (using WMA)

Output:

MA (4):

Num pds	4
Data			Forecasts and Error Analysis
Period	Demand		Forecast	Error	Absolute	Squared	Abs Pct Err
Period 1	650
Period 2	725
Period 3	850
Period 4	825
Period 5	865		762.5	102.5	102.5	10506.25	11.85%
Period 6	915		816.25	98.75	98.75	9751.563	10.79%
Period 7	900		863.75	36.25	36.25	1314.063	04.03%
Period 8	930		876.25	53.75	53.75	2889.063	05.78%
Period 9	950		902.5	47.5	47.5	2256.25	05.00%
Period 10	899		923.75	-24.75	24.75	612.5625	02.75%
Period 11	935		919.75	15.25	15.25	232.5625	01.63%
			Total	329.25	378.75	27562.31	41.83%
			Average	47.03571	54.10714	3937.473	05.98%
				Bias	MAD	MSE	MAPE
					SE	74.24596
Next period	928.5

WMA:

Data				Forecasts and Error Analysis
Period	Demand	Weights		Forecast	Error	Absolute	Squared	Abs Pct Err
Period 1	650	0.15
Period 2	725	0.3
Period 3	850			700	150	150	22500	17.65%
Period 4	825			808.3333	16.66667	16.66667	277.7778	02.02%
Period 5	865			833.3333	31.66667	31.66667	1002.778	03.66%
Period 6	915			851.6667	63.33333	63.33333	4011.111	06.92%
Period 7	900			898.3333	1.666667	1.666667	2.777778	00.19%
Period 8	930			905	25	25	625	02.69%
Period 9	950			920	30	30	900	03.16%
Period 10	899			943.3333	-44.3333	44.33333	1965.444	04.93%
Period 11	935			916	19	19	361	02.03%
				Total	293	381.6667	31645.89	43.24%
				Average	32.55556	42.40741	3516.21	04.80%
					Bias	MAD	MSE	MAPE
						SE	67.2372
Next period	923

ES (a=0.05):

Alpha	0.05
Data			Forecasts and Error Analysis
Period	Demand		Forecast	Error	Absolute	Squared	Abs Pct Err
Period 1	650		650	0	0	0	00.00%
Period 2	725		650	75	75	5625	10.34%
Period 3	850		653.75	196.25	196.25	38514.06	23.09%
Period 4	825		663.5625	161.4375	161.4375	26062.07	19.57%
Period 5	865		671.6344	193.3656	193.3656	37390.26	22.35%
Period 6	915		681.3027	233.6973	233.6973	54614.45	25.54%
Period 7	900		692.9875	207.0125	207.0125	42854.17	23.00%
Period 8	930		703.3381	226.6619	226.6619	51375.6	24.37%
Period 9	950		714.6712	235.3288	235.3288	55379.63	24.77%
Period 10	899		726.4377	172.5623	172.5623	29777.76	19.19%
Period 11	935		735.0658	199.9342	199.9342	39973.69	0.213833
			Total	1901.25	1901.25	381566.7	213.62%
			Average	172.8409	172.8409	34687.88	19.42%
				Bias	MAD	MSE	MAPE
					SE	205.9036
Next period	745.062504