Benchmark Assignment. Excel and Word.

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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.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are required to submit this assignment to Turnitin. Please refer to the directions in the Student Success Center

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Forecasting Moving averages - 4 period moving average Data Period Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 7 Period 8 Period 9 Period 10 Period 11 Next period 4 Demand Forecasts and Error Analysis Forecast Error Absolute #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Total Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Bias #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! MAD SE Squared Abs Pct Err #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! MSE MAPE 1 #DIV/0! 0.9 Not enough data to compute the standard error 0.8 0.7 Value Num pds 0.6 0.5 0.4 0.3 0.2 0.1 0 1 Forecasting 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 3 4 5 6 7 Time Demand Forecast 8 9 10 11 Forecasting Data Period Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 7 Period 8 Period 9 Period 10 Period 11 Weighted moving averages - 2 period moving average Demand Weights 0.15 0.3 Forecasts and Error Analysis Forecast Error Absolute 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total Average Bias Next period 0 Squared 0 0 0 0 0 0 0 0 0 0 0 MAD SE Abs Pct Err 0 0 0 0 0 0 0 0 0 0 0 MSE 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! MAPE Forecasting 1 0.9 0.8 Value 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 3 4 5 6 7 Time Demand Forecast 8 9 10 11 Forecasting Next period 0.05 Forecasts and Error Analysis Forecast Error Absolute Squared Abs Pct Err 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! Total 0 0 0 #DIV/0! Average 0 0 0 #DIV/0! Bias MAD MSE MAPE SE 0 Demand 1 0.9 0 0.8 0.7 Value Alpha Data Period Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 7 Period 8 Period 9 Period 10 Period 11 Exponential smoothing 0.6 0.5 0.4 0.3 0.2 0.1 0 1 Forecasting 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 3 4 5 6 Time 0 7 8 9 10 Dine In (1)/Take Out (2) 1 1 1 1 2 2 2 1 2 1 2 2 1 1 1 1 1 2 2 1 2 1 2 2 1 2 2 1 2 1 2 2 1 2 2 1 1 1 2 2 1 2 2 1 2 1 Satisfaction with Service Satisfaction with Food 4 2 3 5 3 2 3 4 3 2 1 2 5 4 4 3 4 3 3 4 4 2 3 3 3 4 3 4 3 4 2 2 4 3 3 3 3 4 3 3 4 3 2 3 4 2 Overall Satisfaction 4 3 3 5 4 4 4 3 3 3 3 2 4 5 5 4 3 4 4 5 5 3 5 4 4 5 3 4 4 5 3 2 4 2 3 3 3 5 3 4 4 3 3 3 4 3 4 3 3 5 3 3 3 3 3 2 2 2 4 4 4 3 4 3 3 4 4 3 4 3 3 4 3 4 4 4 2 2 4 3 3 3 3 4 3 3 4 3 2 3 4 2 2 1 1 4 4 5 5 5 5 4 4 5 Driving Distance to Restaurant 5 5 10 12 10 15 10 16 2 10 15 10 12 16 18 20 18 20 16 7 9 10 6 10 9 8 10 6 10 10 15 16 18 16 14 20 16 17 16 5 10 6 10 6 7 6 Total Bill 10 15 10 15 25 25 26 27 25 26 20 20 20 20 20 27 28 28 28 12 20 24 26 28 27 24 22 23 25 20 20 20 20 20 25 22 23 28 23 15 28 24 27 26 28 24 8 6 8 22 23 20 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 identifying 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 in an attempt to better forecast restaurant customer volume. MONTH January February March April May June July August September October # OF CUSTOMERS 650 725 850 825 865 915 900 930 950 899 © 2018. Grand Canyon University. All Rights Reserved. November December 935 ? 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 1 2 3 4 5 Time 10:00 a.m. – 1:00 p.m. 1:00 p.m. – 4:00 p.m. 4:00 p.m. – 7:00 p.m. 7:00 p.m. – 10:00 p.m. 10:00 p.m. – 1:00 a.m. # of Staff Required 3 4 6 7 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.” 2 LP_min 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 x1 Minimize 10 AM-1 PM 1:00 PM - 4:00 PM 4:00 PM- 7:00 PM 7:00 PM- 10:00 PM 10:00 PM- 1:00 AM Results Variables Objective x2 1 0 x3 1 x4 1 x5 1 1 sign > > > > > RHS 3 4 6 7 4 0 0 Page 1 LP_min Results LHS Problem setup area Slack/Surplus 0 0 0 0 0 0 3 4 6 7 4 < constraints 0 0 0 0 0 0 0 0 0 0 > constraints 0 0 0 0 0 Page 2 3 4 6 7 4
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