The document provides information about analyzing potential locations for a new Culver's franchise restaurant. A student consulting group analyzed historical data from 89 existing Culver's locations to build a statistical model for predicting gross sales at new locations. Their model includes variables like traffic count, population within a mile radius, number of automobiles, and other demographic data. Using regression analysis, they identified a best-fit model including traffic, population within a mile, automobiles within 5 miles, and population within 5 miles to predict sales. They will apply this model to 3 potential sites and consider startup costs to make a site recommendation.
Customer Service and Queuing Analysis - Lizard's ThicketJacob Sims
The document provides an audit of three Lizard's Thicket restaurant locations conducted by a group of students. They analyzed wait times, conducted customer surveys, and interviewed servers. Key findings include:
- Forest Drive had the fastest wait times while Knox Abbott was slowest, contrary to management expectations.
- Customer surveys found Elmwood had the highest satisfaction overall despite longer wait times.
- Surveys identified a need for more healthy menu options and opportunities to attract more student customers.
- Server interviews provided additional feedback on opportunities like attitude and food temperature.
The group recommends addressing healthy options on the menu and promotions targeting student customers to improve performance. Overall wait times were not an issue but servers could maintain attentiveness during
This document discusses disaster management in India. It begins by outlining India's vulnerability to natural disasters and the large human and economic toll they often take. It then describes India's institutional and policy framework for disaster management, including the establishment of new authorities at the national, state, district, block and village levels to shift the focus from post-disaster response to also emphasize prevention, mitigation and preparedness. Key agencies coordinate response efforts, and funding mechanisms support relief activities.
Jheickson Romario Noguera Torin is a name. The name consists of a first name, middle name, and last names. This document provides an individual's full name in a single line.
O documento resume:
1) A experiência da especialista em higiene ocupacional com agentes químicos e cancerígenos no trabalho;
2) Os conceitos de perigo, risco, dose e limites de tolerância para agentes cancerígenos como o amianto;
3) A possibilidade de uso seguro do amianto através do controle da exposição abaixo dos limites de tolerância estabelecidos.
Tres especies de delfines, el delfín común, el delfín mular y el delfín calderón, viven permanentemente en el Estrecho, variando sus tamaños de casi 2 metros y 100 kilos el común, hasta más de 5 metros y 3500 kilos el calderón; el mular alcanza los 4 metros y 600 kilos. Las orcas y otros cetáceos son animales sociables y juguetones aunque salvajes a veces, que se comunican mediante sonidos y usan la ecolocalización para orientarse.
El documento habla sobre las propiedades del sonido y las ondas sonoras. Explica que el sonido es una onda longitudinal que requiere un medio material para propagarse y que la velocidad del sonido depende de la temperatura y el material. También cubre temas como la intensidad, frecuencia y longitud de onda del sonido, así como cómo el oído humano percibe el sonido.
Customer Service and Queuing Analysis - Lizard's ThicketJacob Sims
The document provides an audit of three Lizard's Thicket restaurant locations conducted by a group of students. They analyzed wait times, conducted customer surveys, and interviewed servers. Key findings include:
- Forest Drive had the fastest wait times while Knox Abbott was slowest, contrary to management expectations.
- Customer surveys found Elmwood had the highest satisfaction overall despite longer wait times.
- Surveys identified a need for more healthy menu options and opportunities to attract more student customers.
- Server interviews provided additional feedback on opportunities like attitude and food temperature.
The group recommends addressing healthy options on the menu and promotions targeting student customers to improve performance. Overall wait times were not an issue but servers could maintain attentiveness during
This document discusses disaster management in India. It begins by outlining India's vulnerability to natural disasters and the large human and economic toll they often take. It then describes India's institutional and policy framework for disaster management, including the establishment of new authorities at the national, state, district, block and village levels to shift the focus from post-disaster response to also emphasize prevention, mitigation and preparedness. Key agencies coordinate response efforts, and funding mechanisms support relief activities.
Jheickson Romario Noguera Torin is a name. The name consists of a first name, middle name, and last names. This document provides an individual's full name in a single line.
O documento resume:
1) A experiência da especialista em higiene ocupacional com agentes químicos e cancerígenos no trabalho;
2) Os conceitos de perigo, risco, dose e limites de tolerância para agentes cancerígenos como o amianto;
3) A possibilidade de uso seguro do amianto através do controle da exposição abaixo dos limites de tolerância estabelecidos.
Tres especies de delfines, el delfín común, el delfín mular y el delfín calderón, viven permanentemente en el Estrecho, variando sus tamaños de casi 2 metros y 100 kilos el común, hasta más de 5 metros y 3500 kilos el calderón; el mular alcanza los 4 metros y 600 kilos. Las orcas y otros cetáceos son animales sociables y juguetones aunque salvajes a veces, que se comunican mediante sonidos y usan la ecolocalización para orientarse.
El documento habla sobre las propiedades del sonido y las ondas sonoras. Explica que el sonido es una onda longitudinal que requiere un medio material para propagarse y que la velocidad del sonido depende de la temperatura y el material. También cubre temas como la intensidad, frecuencia y longitud de onda del sonido, así como cómo el oído humano percibe el sonido.
Fall 2016 PM007 Team 04 P2 Final ReportAlexis Marino
The document is a feasibility report for a new restaurant concept called Foodies & Movies that combines movies and dining by providing private movie screening pods where customers can watch newly released films while enjoying a meal. The report analyzes the restaurant industry, provides details on the Foodies & Movies business model and concept, forecasts customer demand through primary and secondary research, and establishes strategic and financial plans to introduce the new business. The team believes Foodies & Movies offers a unique experience that will attract customers and be profitable in its first year of operation.
This business plan proposes setting up a food cart within a school campus. It includes an analysis of the food cart industry, competitors, target market, and environmental factors. The plan outlines products/services like burgers and salads to be offered at competitive prices. A social media and website based marketing strategy is proposed. The plan involves 3 personnel and obtaining licenses to set up the cart and operate legally within the school. Financial projections and milestones are included to track progress.
Kraft Foods is a large global food and beverage company with over 113,000 employees worldwide. To maintain its market leadership, Kraft must efficiently plan and manage its complex global supply chain. In the early 2000s, Kraft established a Supply Chain department and implemented a management intelligence project with three key modules to improve supply chain performance: a customer service level module to monitor order fulfillment, a cost to serve module to analyze and optimize logistic costs, and a logistics cost budgeting module to facilitate budgeting and scenario planning.
Empower Mobile Restaurant Operations Analytics with Oracle business Intellige...Perficient, Inc.
Multi-unit, multi-concept restaurant companies face challenging reporting requirements.
How should they compare promotion, holiday, and labor performance data across concepts? How should they maximize fraud detection capabilities? How should they arm restaurant operators with the data they need to react to changes affecting day-to-day operations as well as over-time goals?
An industry-leading data model, integrated metadata, and prebuilt reports and dashboards deliver the answers to these questions and more. Deliver relevant, actionable mobile analytics for the restaurant industry with an integrated solution of Oracle Business Intelligence and Oracle Endeca Information Discovery.
The management team for the new Noodles & Company restaurant will consist of 6 roles, with the 2 lowest roles being entry-level positions that do not require prior experience. The general manager and shift managers will handle most administrative duties and hiring. Employees will receive training on the company's methods and culture. Pay will depend on the position and experience level.
This document provides a technology plan and consultation for Cab Cuisine, a food delivery service with 10 locations near college campuses. It analyzes Cab Cuisine's competitiveness using Porter's Five Forces model. It identifies strategies of operational effectiveness, differentiation, and customer orientation. It recommends the disruptive technology of autonomous vehicles to improve efficiency. It discusses using social media and a rewards program to build network effects. It determines Cab Cuisine should purchase an existing restaurant delivery software instead of developing a custom one due to lack of IT resources.
Business PlanBADM 332-50Ms. Ima StudentExecutive Summary.docxhumphrieskalyn
Business Plan
BADM 332-50
Ms. Ima Student
Executive Summary
Trackside Cafe is a mobile food provider and eatery based in the greater Indianapolis area. With the constant stream of events such as the Indy 500, Brickyard 400, the NCAA Final Four, Indianapolis Colts, Indiana Pacers, Indy 1500 gun shows, as well as numerous conventions and conferences, food trucks are able to provide food services for attendees. Trackside Cafe provides the freshest ingredients to provide the best tasting burger in town. Trackside Cafe will use signs and social media campaigns to advertise.
Company Description
Trackside Cafe is a mobile eating establishment providing the highest quality food for people on the go. Trackside Cafe is a food truck supplying the finest hamburgers, fries, and classic American cuisine. Trackside Cafe is set to open February 20, 2015. Trackside Cafe is operated as a Limited Liability Partnership. Normal business will be conducted in the greater Indianapolis metro area. Trackside Cafe meets the needs of potential customers who have very little time to leave work to get lunch, and those who simply enjoy quick, easy, high quality hamburgers.
Market Analysis
Trackside Cafe operates in of the most vibrant cities in America. Indianapolis is home to numerous sporting events, conventions, and businesses such as the Indy 500, Brickyard 400, the NCAA Final Four, Indianapolis Colts, Indiana Pacers, Indy 1500 gun shows, as well as numerous conventions and conferences Events include Trackside Cafe will be in competition with other food trucks such as Neighborhood Pizza, Dashboard Diner, and Mac Genie. Each of these competing food trucks have similar service and products and are established as successful food trucks in the Indianapolis Metro area. The primary target consumer is the event attendee or the local worker out for lunch. These two groups create a large sustainable group of consumers and patrons. With a high quality product, Trackside Cafe will be able differentiate itself from the competition and carve out a sizable portion of the market share. Outside of normal licensing and permissions from the City of Indianapolis, there are no major barriers to impede the rapid growth and establishment of Trackside Café. (Forbes Magazine, 2012)
Organization
Trackside Cafe is owned equally among the six partners who have set up the company as a Limited Liability Partnership. Trackside Cafe is organized into an LLP to ensure that the partners are protected in the event of legal dispute while maintaining maximum control over the company. The six partners Mr. Jones, Ms. Doe, Mr. Rascal, Ms. Student, Mr. Al-Y, and Mr. X operate as the owning partners and primary division making body of Trackside Cafe. Each of the six owners and partners equally are involved in the management of the company. Ms. Doe’s specialty is in the bookkeeping and accounting. Mr. Rascal is the marketing manager. Mr. Jones is the operations manager and administrator. Mr. Al-Y is th ...
This document provides a service audit report for Pediatric Dentistry of the Rockies. It summarizes the results of a customer survey, identifies gaps in service through analyzing the customer and provider gaps, and provides recommendations to improve the patient experience. The key recommendations are to create a new listening device to better understand customer feedback, compile and analyze survey data, and improve the waiting room experience for both children and parents.
The document analyzes customer data from QWE Inc. to predict customer churn. Logistic regression analysis identified three key factors for predicting churn probability: Customer Happiness Index in December, change in Customer Happiness Index from November to December, and change in days since last login between the two months. The analysis calculates churn probabilities for three sample customers based on these factors. It also provides a list of the top 10 customers most likely to churn. While decision tree analysis validates the logistic regression findings, logistic regression is recommended as the best model for QWE to use going forward to identify at-risk customers and reduce churn.
Supply Chain Metrics That Matter: A Closer Look at the Cash-To-Cash Cycle (20...Lora Cecere
Executive Overview
When it comes to metrics that matter, the cash-to-cash cycle is one of the top metrics cited by supply chain professionals. It is among the best financial metrics to provide a comprehensive picture of a company’s supply chain and the management of working capital.
The supply chain is a complex system. Successful management requires both orchestration and balance. To drive supply chain excellence, companies are required to balance four competing priorities: growth, profitability, cycle management and complexity. Several popular metrics, including the cash-to-cash cycle, for a variety of industries are presented in table 1.
Mountain High Balloons is proposing a hot air balloon tour business in Gatlinburg, Tennessee to take advantage of the large number of tourists visiting the Great Smoky Mountains National Park each year. The document provides an overview of the tourism industry, analyzes the local competition and market, and details the business concept of Mountain High Balloons. It discusses the target market, location, services, pricing, equipment, technology, and employees. Financial projections and demand scenarios indicate the business would be feasible and receive a significant number of customers.
www.evidek.com
Introduction to lean six sigma. Principles of Lean Six Sigma presented through case studies illustrating how top companies use this approach to improve processes and gain competitive advantage. This is chapter one from a new book that will be available soon. Contact us at info@evidek.com for more information.
- The document analyzes demand and production data from Mrs. Smyth Pies over 72 months in Chicago and Minneapolis to develop demand and production models.
- For demand, separate regression models were developed for each city using price, competitor's price, population, and seasonal dummy variables. These models explained around 80-75% of variation in monthly sales.
- For production, a Cobb-Douglas production function was estimated using labor hours, management hours, and capital hours, explaining around 99.95% of variation in sales quantity. This showed constant returns to scale.
I enjoy helping large enterprises like Intel and Microsoft stretch their leads even farther. I also enjoy helping scrappy small companies turn into thriving larger companies.
I enjoy helping large enterprises like Intel and Microsoft stretch their leads even farther. I also enjoy helping scrappy small companies turn into thriving larger companies.
What do you need to win customers in 2016? Experts agree that delivering a personalized customer experience will be the determining factor in who wins (and retains) customers in 2016. How do you go about doing this? Data. But not just any data. Clean data. Find out how to master the customer experience in 2016 and the role clean data plays.
The Complete Clean Club is launching an online subscription service that delivers toiletry products to customers in Los Angeles. It will offer customizable packages of popular brands of shampoo, body wash, deodorant, dental/facial care, delivered monthly. The company is headquartered in Las Vegas and targets college students in LA for convenience. It faces competition from Amazon and Walmart but sees opportunities in its focus on timely delivery and customer value. Financial projections estimate increasing revenue over three years but an overall loss due to thin profit margins in the health/beauty industry.
Supply Chain Metrics That Matter: A Focus on Hospitals - 6 JAN 2013Lora Cecere
Executive Overview
Growth is slowing. Profitability is tougher. Compliance mandates are growing. How do companies balance the goals for value-based outcomes for health and wellness with costs? In this environment, supply chain matters more than ever.
In this report, we share insights on healthcare supply chains. Grown out of the offices of procurement, the supply chain function of the hospital is still in its infancy; but based on increasing business requirements and complexity, it is only beginning to show its true potential.
Growth: Futures are Uncertain.
The hospital administrator is facing many challenges. Growth is slowing and pressures are growing. The question is how to balance patient outcomes with better cost management. How does a hospital move forward with an uncertain future? And, what will be the impact of healthcare reform?
In table 1, we list top line growth over the past decade for four companies operating within the hospital industry. While there are ups and downs, the general trend is downward.
The document outlines research conducted for the design of a new point-of-sale system, including background research on the restaurant industry and POS competitors, interviews with restaurant professionals to understand user needs, and consideration of alternative POS features and options. The research identified a need for a system that can track individual customers through open social spaces using technologies like GPS and location-based apps. Key functions for the table layout component include managing tables, completing orders, and displaying staff organization.
Fall 2016 PM007 Team 04 P2 Final ReportAlexis Marino
The document is a feasibility report for a new restaurant concept called Foodies & Movies that combines movies and dining by providing private movie screening pods where customers can watch newly released films while enjoying a meal. The report analyzes the restaurant industry, provides details on the Foodies & Movies business model and concept, forecasts customer demand through primary and secondary research, and establishes strategic and financial plans to introduce the new business. The team believes Foodies & Movies offers a unique experience that will attract customers and be profitable in its first year of operation.
This business plan proposes setting up a food cart within a school campus. It includes an analysis of the food cart industry, competitors, target market, and environmental factors. The plan outlines products/services like burgers and salads to be offered at competitive prices. A social media and website based marketing strategy is proposed. The plan involves 3 personnel and obtaining licenses to set up the cart and operate legally within the school. Financial projections and milestones are included to track progress.
Kraft Foods is a large global food and beverage company with over 113,000 employees worldwide. To maintain its market leadership, Kraft must efficiently plan and manage its complex global supply chain. In the early 2000s, Kraft established a Supply Chain department and implemented a management intelligence project with three key modules to improve supply chain performance: a customer service level module to monitor order fulfillment, a cost to serve module to analyze and optimize logistic costs, and a logistics cost budgeting module to facilitate budgeting and scenario planning.
Empower Mobile Restaurant Operations Analytics with Oracle business Intellige...Perficient, Inc.
Multi-unit, multi-concept restaurant companies face challenging reporting requirements.
How should they compare promotion, holiday, and labor performance data across concepts? How should they maximize fraud detection capabilities? How should they arm restaurant operators with the data they need to react to changes affecting day-to-day operations as well as over-time goals?
An industry-leading data model, integrated metadata, and prebuilt reports and dashboards deliver the answers to these questions and more. Deliver relevant, actionable mobile analytics for the restaurant industry with an integrated solution of Oracle Business Intelligence and Oracle Endeca Information Discovery.
The management team for the new Noodles & Company restaurant will consist of 6 roles, with the 2 lowest roles being entry-level positions that do not require prior experience. The general manager and shift managers will handle most administrative duties and hiring. Employees will receive training on the company's methods and culture. Pay will depend on the position and experience level.
This document provides a technology plan and consultation for Cab Cuisine, a food delivery service with 10 locations near college campuses. It analyzes Cab Cuisine's competitiveness using Porter's Five Forces model. It identifies strategies of operational effectiveness, differentiation, and customer orientation. It recommends the disruptive technology of autonomous vehicles to improve efficiency. It discusses using social media and a rewards program to build network effects. It determines Cab Cuisine should purchase an existing restaurant delivery software instead of developing a custom one due to lack of IT resources.
Business PlanBADM 332-50Ms. Ima StudentExecutive Summary.docxhumphrieskalyn
Business Plan
BADM 332-50
Ms. Ima Student
Executive Summary
Trackside Cafe is a mobile food provider and eatery based in the greater Indianapolis area. With the constant stream of events such as the Indy 500, Brickyard 400, the NCAA Final Four, Indianapolis Colts, Indiana Pacers, Indy 1500 gun shows, as well as numerous conventions and conferences, food trucks are able to provide food services for attendees. Trackside Cafe provides the freshest ingredients to provide the best tasting burger in town. Trackside Cafe will use signs and social media campaigns to advertise.
Company Description
Trackside Cafe is a mobile eating establishment providing the highest quality food for people on the go. Trackside Cafe is a food truck supplying the finest hamburgers, fries, and classic American cuisine. Trackside Cafe is set to open February 20, 2015. Trackside Cafe is operated as a Limited Liability Partnership. Normal business will be conducted in the greater Indianapolis metro area. Trackside Cafe meets the needs of potential customers who have very little time to leave work to get lunch, and those who simply enjoy quick, easy, high quality hamburgers.
Market Analysis
Trackside Cafe operates in of the most vibrant cities in America. Indianapolis is home to numerous sporting events, conventions, and businesses such as the Indy 500, Brickyard 400, the NCAA Final Four, Indianapolis Colts, Indiana Pacers, Indy 1500 gun shows, as well as numerous conventions and conferences Events include Trackside Cafe will be in competition with other food trucks such as Neighborhood Pizza, Dashboard Diner, and Mac Genie. Each of these competing food trucks have similar service and products and are established as successful food trucks in the Indianapolis Metro area. The primary target consumer is the event attendee or the local worker out for lunch. These two groups create a large sustainable group of consumers and patrons. With a high quality product, Trackside Cafe will be able differentiate itself from the competition and carve out a sizable portion of the market share. Outside of normal licensing and permissions from the City of Indianapolis, there are no major barriers to impede the rapid growth and establishment of Trackside Café. (Forbes Magazine, 2012)
Organization
Trackside Cafe is owned equally among the six partners who have set up the company as a Limited Liability Partnership. Trackside Cafe is organized into an LLP to ensure that the partners are protected in the event of legal dispute while maintaining maximum control over the company. The six partners Mr. Jones, Ms. Doe, Mr. Rascal, Ms. Student, Mr. Al-Y, and Mr. X operate as the owning partners and primary division making body of Trackside Cafe. Each of the six owners and partners equally are involved in the management of the company. Ms. Doe’s specialty is in the bookkeeping and accounting. Mr. Rascal is the marketing manager. Mr. Jones is the operations manager and administrator. Mr. Al-Y is th ...
This document provides a service audit report for Pediatric Dentistry of the Rockies. It summarizes the results of a customer survey, identifies gaps in service through analyzing the customer and provider gaps, and provides recommendations to improve the patient experience. The key recommendations are to create a new listening device to better understand customer feedback, compile and analyze survey data, and improve the waiting room experience for both children and parents.
The document analyzes customer data from QWE Inc. to predict customer churn. Logistic regression analysis identified three key factors for predicting churn probability: Customer Happiness Index in December, change in Customer Happiness Index from November to December, and change in days since last login between the two months. The analysis calculates churn probabilities for three sample customers based on these factors. It also provides a list of the top 10 customers most likely to churn. While decision tree analysis validates the logistic regression findings, logistic regression is recommended as the best model for QWE to use going forward to identify at-risk customers and reduce churn.
Supply Chain Metrics That Matter: A Closer Look at the Cash-To-Cash Cycle (20...Lora Cecere
Executive Overview
When it comes to metrics that matter, the cash-to-cash cycle is one of the top metrics cited by supply chain professionals. It is among the best financial metrics to provide a comprehensive picture of a company’s supply chain and the management of working capital.
The supply chain is a complex system. Successful management requires both orchestration and balance. To drive supply chain excellence, companies are required to balance four competing priorities: growth, profitability, cycle management and complexity. Several popular metrics, including the cash-to-cash cycle, for a variety of industries are presented in table 1.
Mountain High Balloons is proposing a hot air balloon tour business in Gatlinburg, Tennessee to take advantage of the large number of tourists visiting the Great Smoky Mountains National Park each year. The document provides an overview of the tourism industry, analyzes the local competition and market, and details the business concept of Mountain High Balloons. It discusses the target market, location, services, pricing, equipment, technology, and employees. Financial projections and demand scenarios indicate the business would be feasible and receive a significant number of customers.
www.evidek.com
Introduction to lean six sigma. Principles of Lean Six Sigma presented through case studies illustrating how top companies use this approach to improve processes and gain competitive advantage. This is chapter one from a new book that will be available soon. Contact us at info@evidek.com for more information.
- The document analyzes demand and production data from Mrs. Smyth Pies over 72 months in Chicago and Minneapolis to develop demand and production models.
- For demand, separate regression models were developed for each city using price, competitor's price, population, and seasonal dummy variables. These models explained around 80-75% of variation in monthly sales.
- For production, a Cobb-Douglas production function was estimated using labor hours, management hours, and capital hours, explaining around 99.95% of variation in sales quantity. This showed constant returns to scale.
I enjoy helping large enterprises like Intel and Microsoft stretch their leads even farther. I also enjoy helping scrappy small companies turn into thriving larger companies.
I enjoy helping large enterprises like Intel and Microsoft stretch their leads even farther. I also enjoy helping scrappy small companies turn into thriving larger companies.
What do you need to win customers in 2016? Experts agree that delivering a personalized customer experience will be the determining factor in who wins (and retains) customers in 2016. How do you go about doing this? Data. But not just any data. Clean data. Find out how to master the customer experience in 2016 and the role clean data plays.
The Complete Clean Club is launching an online subscription service that delivers toiletry products to customers in Los Angeles. It will offer customizable packages of popular brands of shampoo, body wash, deodorant, dental/facial care, delivered monthly. The company is headquartered in Las Vegas and targets college students in LA for convenience. It faces competition from Amazon and Walmart but sees opportunities in its focus on timely delivery and customer value. Financial projections estimate increasing revenue over three years but an overall loss due to thin profit margins in the health/beauty industry.
Supply Chain Metrics That Matter: A Focus on Hospitals - 6 JAN 2013Lora Cecere
Executive Overview
Growth is slowing. Profitability is tougher. Compliance mandates are growing. How do companies balance the goals for value-based outcomes for health and wellness with costs? In this environment, supply chain matters more than ever.
In this report, we share insights on healthcare supply chains. Grown out of the offices of procurement, the supply chain function of the hospital is still in its infancy; but based on increasing business requirements and complexity, it is only beginning to show its true potential.
Growth: Futures are Uncertain.
The hospital administrator is facing many challenges. Growth is slowing and pressures are growing. The question is how to balance patient outcomes with better cost management. How does a hospital move forward with an uncertain future? And, what will be the impact of healthcare reform?
In table 1, we list top line growth over the past decade for four companies operating within the hospital industry. While there are ups and downs, the general trend is downward.
The document outlines research conducted for the design of a new point-of-sale system, including background research on the restaurant industry and POS competitors, interviews with restaurant professionals to understand user needs, and consideration of alternative POS features and options. The research identified a need for a system that can track individual customers through open social spaces using technologies like GPS and location-based apps. Key functions for the table layout component include managing tables, completing orders, and displaying staff organization.
Similar to GB304 Final Project Report Final 2 (20)
1. GENERAL BUSINESS 304
The Culver’s Case
Abstract
The major purpose for this business report is to help our clients, Russ and Vicky, choose
the best location for a new Culver’s franchise restaurant in Carbondale, Illinois, basing on
comprehensive statistical analysis
RenaHuang, YutingYang, Yaoyao Chen,
Zichun He, Tina Zhou, & Pack Zhao
[Email address]May, 10th, 2015
2. i
CONTENTS
Executive Summary.......................................................................................................................................ii
Introduction........................................................................................................................................................1
Building Business Model.............................................................................................................................4
Financial Analysis.........................................................................................................................................29
Recommendation.........................................................................................................................................33
Conclusion.........................................................................................................................................................35
Appendix...................................................................................................................................................38/-1-
Figure 1 - Handling Missing Data.................................................................................................................6
Figure 2 - Normal probability plot of the residuals for the gross sales ....................................26
Figure 3 - Plot of residuals against dummy variable 2 of CFSI suitability rating of the
Culver's restaurant...........................................................................................................................................26
Figure 4 - Plot of residuals against dummy variable 1 of CFSI suitability rating of the
Culver's restaurant...........................................................................................................................................26
Figure 5 - Plot of residuals against the traffic count around the Culver's restaurant ........26
Figure 6 - Plot of residuals against the population within a one-mile radius of the
Culver's restaurant...........................................................................................................................................26
Figure 7 - Predicted gross sales..................................................................................................................29
Figure 8 - Startup expenses..........................................................................................................................30
Figure 9 - Location Information.................................................................................................................33
3. EXECUTIVE SUMMARY
SCOPE AND OBJECTIVE
As a student-run consulting firm, we received a request from our clients, Russ
and Vicky, to make a decision of choosing a site location for a Culver's franchise
restaurant by analyzing a set of data from Culver’s restaurants and relative
financial information.
IMPORTANCE OF THE ANALYSIS
Location is one of the key factors of a restaurant’s success, but it is hard to decide
which location is the best by just looking at demographic
characteristics. Therefore we did statistical analysis of the past data and financial
analysis of other relevant information with the intention to provide the most
accurate and reliable recommendations for our clients.
PREDICTION AND RECOMMENDATION
Along with analysis of the sample data, we utilized our financial knowledge and
business acumen to do further comparisons. Based on our business prediction
model, Site C is the most profitable one, but its startup expenses exceed our
clients' budget. Considering their financial ability, we recommend our clients to
choose the second most profitable location, Site A, instead.
4. 1
INTRODUCTION
Culver's is one of the various food chains in the Midwest, which provides
franchise options for individuals. Our clients want to seize this business
opportunity, but they have difficulties selecting the best site for the new
franchise restaurant among three potential locations. To help our clients choose
the most appropriate site, we analyzed the sample data retrieved from 89
current Culver’s restaurants. Besides statistical analysis, we also took other
associated costs into account for site recommendations.
CLIENTS’ PROFILES
Name: Russ (age: 64) and Vicky (age: 60)
Goal: open a Culver's restaurant
Location: Carbondale, Illinois
Workforce Preference: Young students
Post-retirement Plan: actively manage an operation for a decade or more
Financial Ability: $1.2 million to $1.5 million
Acceptable minimum annual sales: $1,000,000
5. 2
GENERAL COMPANY DESCRIPTION
Culver's Frozen Custard Restaurant is a family business founded in 1984 in Sauk
City, Wisconsin. It is a fast-food restaurant that cooks meal to order individually.
After the first opening’s great success, Culver’s began to franchise restaurants
through Culver Franchising System, Inc. (CFSI). Following is the information we
found useful for our clients to consider.
Mission Statement: Every guest who chooses Culver's leaves happy.
Founding principles: Freshness and quality, hospitality and service to the
community.
Business Hours: 10:00am-10:00pm (with exceptions serving breakfast as well)
Typical food service areas:
Indoor seating
Outdoor seating
Drive-up service
Competitive Advantages:
A wide variety of entrees
ButterBurgers® that are made from fresh ground chuck and serve on a
buttered toasted bun
6. 3
Three daily flavors of custard: vanilla, chocolate, and the "flavor of the
day"
Qualities Culver’s are looking for of their franchise partners:
Leadership skills to take a team of people and operate a Culver's
according to the high standards.
Energy and enthusiasm
Willing to work hard
Love people and believe that having a great heart is also good business
STATISTICAL METHODS
Our statistical methods have two parts: 1) build the business prediction model;
and 2) analyze the profitability of each site. First we chose indicating variables,
and then filled out the missing data. Next we ran regressions to find the
relationship between gross sales and those relevant variables, and ultimately
picked out the best model. Then we applied this model to predicting gross
revenues of Site A, Site B and Site C. Furthermore, we took startup costs and
operating costs into consideration to find the most appropriate location for the
new Culver's restaurant. More details are in the “Building Business Model” section
and the “Financial Analysis” section.
7. 4
BUILDING BUSINESS MODEL
CHOOSINGINDICATING VARIABLES
We have collected the historical data of 89 Culver's Frozen Custard
restaurants from the company, which might be useful for us to build the best
model to predict future sales in the three potential locations. These independent
variables include:
Operation years (AGE)
Cost of food (FOOD)
Cost of paper (PAPER)
Labor expenses (LABOR)
Other operating expenses (OTHER OP)
Gross revenue from sales (GSALES)
Traffic count (TRAFFIC)
Population within a one-mile radius (POP1M)
Population within a five-miles radius (POP5M)
Per capita income within a one-mile radius (PERCAP1)
Per capita income within a five-mile radius (PERCAP5)
Average number of autos per household within a one-mile radius
(AUTO1)
Average number of autos per household within a five-mile radius
(AUTO5)
8. 5
Percentage of adults married within a one-mile radius (MARRIED1)
Percentage of adults married within a five-mile radius (MARRIED5)
CFSI suitability rating (SUIT)
However, not all of these variables are relevant to predict gross sales. We
decided to discard variables PAPER, LABOR, FOOD, AGE and OTHER OP first
because they were irrelevant to the location of a new restaurant.
Therefore, the variables that we decided to keep to further analyze are:
TRAFFIC, POP1M, POP5M, PERCAP1, PERCAP5, AUTO1, AUTO5, MARRIED1,
MARRIED5, and SUIT.
HANDLINGMISSING DATA
The raw data we have contained several missing data, so we decided to handle
missing data first. For Store 11 and 71, the data for POP1M, POP5M, PERCAP1,
PERCAP5, AUTO5, AUTO1, MARRIED1, MARRIED5, and SUIT were all missing.
Considering too many data were missing for these two stores and the whole data
set was relatively big, we decided to eliminate the data of these two stores as a
whole.
Then we moved on to deal with the missing data of TRAFFIC for Store 19, 23, 39,
42, 65, 75, 82, and 83. According to the information we had, this kind of missing
data was called “missing at random”. This means the cases with incomplete data
were different from those with complete data, but the pattern of the missing data
9. 6
is traceable and predictable from the known data. Therefore, we decided to deal
with these missing data using hot deck imputation method.
Hot deck imputation is a method to identify the most similar case to the case
with a missing value and substitute the most similar case's Y value for the
missing case's Y value. Based on our intuition, we figured traffic count would be
related to gross sales, population and the number of automobiles, so we sorted
the data from smallest to largest based on TRAFFIC, POP1M and AUTO5. Then
we substituted the missing TRAFFIC data with those from the cases having the
most similar values of GSALES, POP1M and AUTO5.
Figure1 - Handling MissingData
(The cells highlighted in yellow are missing data and those in blue are reference data.)
10. 7
BUILDINGTHE MODEL FOR PREDICTION
So far we have identified the 10 relevant variables to perform further data
analysis and have handled with missing data. The relevant explanatory variables
are: TRAFFIC, POP1M, POP5M, PERCAP1, PERCAP5, AUTO1, AUTO5, MARRIED1,
MARRIED5, and SUIT.
Then we used Forward Selection Method and Backward Elimination Method to
obtain the best-fit model. Then based on the best-fit model, we would decide on
the most appropriate model in this scenario and use it to make gross sales
prediction for each site.
Forward Selection Method involves starting with no variables in the model,
testing the addition of each variable using a chosen model comparison criterion
(P-value, R Square/adjusted R Square and Standard Error in our analysis),
adding the variable (if any) that improves the model the most, and repeating this
process until none improves the model.
Backward Elimination Method involves starting with all candidate variables,
testing the deletion of each variable using a chosen model comparison criterion
(P-value, R Square/adjusted R Square and Standard Error in our analysis),
deleting the variable (if any) that improves the model the most by being deleted,
and repeating this process until no further improvement is possible.
11. 8
SCATTERPLOTS WITH INTERPRETATION
Here we conducted data analysis to figure out the best model: We plotted scatter
plots between every independent variable and the dependent variable, gross
sales. Scatter plot is a type of chart that displays values for two variables for a set
of data using X and Y axes and coordinates. From a scatter plot, one can get a
basic idea of the relationship between the two variables (See the scatter plots in
Appendix).
From those scatter plots, no distinct outliers could be observed, so we would not
consider the influence of outliers in further analysis. By observing the trend of
each scatter plot, we concluded that, a positive relationship existed between
gross sales and independent variables including TRAFFIC, POP1M, POP5M,
PERCAP1, PERCAP5, AUTO1, and AUTO5. Meanwhile, both MARRIED1 and
MARRIED5 variables had a negative relationship with gross sales. As for variable
SUIT, because it is categorical data, we could not conclude its relationship with
gross sales from the scatter plot.
FINDING THE BEST FIT MODEL WITH TWO METHODS
Forward Selection Method
After looking at the general relationships between each x-variable and gross
sales, we used Forward Selection to build the Model first.
12. 9
1) Simple Linear Regression between Each Independent Variable and
Dependent Variable.
By doing simple linear regression of single variables, we got the following
results:
The p-values of variables MARRIED1 and MARRIED5 are both greater
than significance level of 0.05, which indicates that these two variables have no
significant influence on GSALES. Therefore we excluded them from the relevant
independent variables. The rest eight variables all have p-values less
than 0.05, which means that they have significant influence on GSALES.
Therefore, we would take them into the next step’s analysis.
Table 1 - Simple linear regression results for single variables
13. 10
Among these eight independent variables, variable TRAFFIC has the highest R
Square and the lowest standard error. Therefore, it was selected as the first
independent variable of our model.
2) Multiple Regression with Two Independent Variables.
After choosing TRAFFIC as the first variable, we combined TRAFFIC with each of
the other independent variables and ran linear regressions between the two
x-variables and gross sales. The results are shown below:
Regression with an Interaction Terms:
According to the results, the p-values of all interaction terms are greater than
0.05, which means that these interactions are not significant to explain the
dependent variable. Therefore we did not need to include these
interaction terms in our model.
Table 2 - P-values of the interaction terms of two x-variables
14. 11
Regression Model without Interaction Term:
Based on the results, the regression model with independent variables
TRAFFIC and POP1M is significant and has the highest adjusted R square and
the lowest standard error, so we added the second variable POP1M to
our model.
3) Multiple Regression with Three Independent Variables.
After choosing TRAFFIC and POP1M as variables, we combined TRAFFIC
and POP1M with each of the other independent variables and ran linear
regressions between the three x-variables and gross sales. The results are shown
below:
Table 3 - Regression results for two x-variables
15. 12
Regression with an Interaction Term
According to the results, the p-values of all interaction terms are greater than
0.05, which means that all these interactions between each two terms are
not significant to explain the dependent variable. Therefore, we did not need
to include these interaction terms in our model.
Regression Model without Interaction Terms:
Table 4 - P-values of the interaction terms of three x-variables
Table 5 - Regressionresults for three x-variables
16. 13
According to the results, the regression model with independent variables
TRAFFIC, POP1M and AUTO5 is significant and has the highest adjust R
square and the lowest standard error, so we added the third variable, AUTO5,
to our model.
4) Multiple Regression with Four Independent Variables.
After choosing TRAFFIC, POP1M and AUTO5 as our variables, we combined
TRAFFIC, POP1M and AUTO5 with each of the other independent variables
and ran linear regressions between the four x-variables and gross sales.
The results are shown below:
Regression Model with Interaction Terms:
According to the results, the p-values of all interaction terms are greater than
0.05, which means that these interactions are not significant to explain the
dependent variable. Therefore, we did not need to include these interaction
terms in our model.
Table 6 - P-values of the interaction terms of four x-variables
17. 14
Regression Model Without Interaction Terms:
Based on the results, the regression model with independent variables
TRAFFIC, POP1M, AUTO5 and POP5M is significant and has the highest adjust
R square and the lowest standard error, so we added the
fourth variable POP5M to our model.
5) Multiple Regression with Five Independent Variables.
After choosing TRAFFIC, POP1M, AUTO5 and POP5M as our variables,
we combined TRAFFIC, POP1M, AUTO5 and POP5M with each of the other
independent variable and ran linear regressions between the five x-variables and
gross sales. The results are shown below:
Table 7 - Regressionresults for four x-variables
Table 8 - P-values of the interaction terms of five x-variables
18. 15
Regression Model with Interaction Terms:
According to the results, the p-values of all interaction terms are greater than
0.05, which means that all these interactions between each two terms are
not significant to explain the dependent variable. Therefore, we did not need
to include these interaction terms in our model.
Regression Model without Interaction Terms:
According to the results, we found that each model had one or two
x-variables with p-values greater than 0.05, so no additional x-variable could
be added to our model to help explain the dependent variable. We ended our
forward selection process here.
6) Conclusion
Through the forward selection process, we were able to find a model with
independent variables TRAFFIC, POP1M, AUTO5 and POP5M to best explain the
Table 9 - Regressionresults for five x-variables
19. 16
variation in gross sales, with adjusted R square 57.02% and standard error
101764.9321.
Backward Elimination Method
We then used Backward Elimination Method to validate our model.
1) Multiple Regression with Nine Independent Variables
In the regression model with interaction terms, all the p-values of interaction
terms are greater than alpha 0.05, which indicates that there is no evidence of
interaction. We then moved on to consider model without interaction terms and
got the following results:
Based on the result, the p-values of SUIT, PERCAP1, PERCAP5 and AUTO1 are
greater than the significance level of 0.05, which means that they are
not significant in explaining the variations in the dependent variable.
Table 10 - Regressionresults for nine x-variables
20. 17
As PERCAP5 has the highest p-value, being the most insignificant
predictor among these four, we excluded it from our model first.
2) Multiple Regression with Eight Independent Variables
After eliminating PERCAP5, we ran regression with the rest predictors with
interaction terms, all the p -values of interaction terms were greater than the
significance level of 0.05, which indicated there was no evidence of interaction.
We then moved on to consider model without interaction and got the following
results:
Based on the result, the p-values of SUIT, PERCAP1, POP5M and AUTO1 were
greater than 0.05, which meant that they were not significant to explain the
variations in dependent variable. As AUTO1 had the highest p-value, being the
most insignificant predictor, we then excluded it from our model.
Table 11 - Regressionresults for eight x-variables
21. 18
3) Multiple Regression with Seven Independent Variables
After eliminating PERCAP5 and AUTO1, we ran regression with the
rest predictors with interaction terms, all the p-values of interaction terms are
greater than the significance level of 0.05, which indicates there is no evidence of
interaction. Then we considered model without interaction and got the following
results:
Based on the result, the p-values of SUIT, PERCAP1 and POP5M are greater than
0.05, which means that they are not significant to explain the variations in
dependent variable. Although SUIT has the highest p value, we could not exclude
this variable because only one of its dummy variable's p-value is greater than
0.05. As PERCAP1 has the second-highest p-value, being the most insignificant
predictor, we then excluded it from our model.
Table 12 - Regressionresults for seven x-variables
22. 19
4) Multiple Regression with Six Independent Variables
After eliminating PERCAP5, AUTO1 and PERCAP1, we ran regression with the
rest predictors with interaction terms. All the p-values of interaction terms are
greater than alpha 0.05, which indicates there is no evidence of interaction. We
then considered model without interaction and got the following results:
Based on the result, the p-values of two dummy variables of SUIT are
both greater than 0.05, which means that they are not significant to explain the
variations in dependent variable. Therefore, we excluded it from our model.
Table 13 - Regressionresults for sixx-variables
23. 20
5) Multiple Regression with Four Independent Variables
After eliminating PERCAP5, AUTO1, PERCAP1 and SUIT, we ran regression
with the rest predictors with interaction terms.
According to this result, all the p-values of interaction terms are greater than
alpha 0.05, which indicates there is no evidence of interaction. We then moved
on to consider model without interaction and got following results:
Based on the result, the significance F for the overall model and the p-value of
each independent variable are all less than 0.05, which indicates that this is our
Table 15 - Regressionresults for four x-variables
Table 14 - Regressionresults for four x-variables with interaction terms
24. 21
best model with all predictors being significant. So we ended our
Backward Elimination process here.
6) Conclusion
Through the backward elimination process, we were able to find a model with
independent variables TRAFFIC, POP1M, AUTO5 and POP5M to best explain the
variation in gross sales, with adjusted R square 57.02% and standard error
101764.9321.
FINDING THE MOST APPROPRIATE MODEL AND JUSTIFICATION
Using the Forward Selection and Backward Elimination method, we have
found the best-fit regression model with independent variables TRAFFIC, POP1M,
AUTO5 and POP5M. Then we tried to find the most appropriate model to select
location and predict gross sales based on the information we know.
Because the data of AUTO5 and POP5M of these three sites are unavailable,
we need to predict AUTO5 and POP5M for each site based on other variables in
order to use the best-fit model on these sites.
Make Predictions for AUTO5 and POP5M:
25. 22
We considered POP5M as a dependent variable and used other known
variables as independent variables to find the best regression model to predict
POP5M.
After examining the factors including significance F<0.05, individual p
value<0.05, the highest R Square or adj R Square, and the lowest standard error,
we found that it is best to use POP1M to predict POP5M.
Table 16 - Regressionresults for finding the best x-variables to predict POP5M
26. 23
Then we need to predict AUTO5, we did the same process as above:
After examining the factors including significance F<0.05, individual p
value<0.05, the highest R Square or adj R Sqaure, and the lowest standard error,
we found that it is best to use TRAFFIC to predict AUTO5.
Problems if we use other known variables to predict POP5M and AUTO5:
We noticed that the R Square of the regression model between TRAFFIC and
AUTO5 is only 21.72%, which means that only 21.72% of total variations in
dependent variable AUTO 5 is explained by TRAFFIC. Due to the low R Square,
the predicted value of AUTO5 will not be very accurate. The similar situation
applied to POP5M too. In addition, the adjusted R Square for the regression
Table 17 - Regressionresults for finding the best x-variables to predict AUTO5
27. 24
model without these two variables is 51.32%, which is only 5.7% lower than the
adjusted R Square for the best-fit model.
We thought using inaccurate data to gain only such small percentage increase in
adjusted R Square was not worthy. Last but not least, there is a parsimony rule of
selecting variables in model building: use as few X variables as
possible. Therefore, we decided to exclude the two unknown independent
variable AUTO5 and POP5M from the best-fit model we got from the model
building process, in order to be align to the rule and to avoid getting an
over-specified model.
This left us with the model that includes two variables, TRAFFIC and
POP1M. Reviewing information about variables and considering that we also
have information of SUIT of each site, which is the CFSI suitability rating about
the comprehensive site analysis, we were wondering if we could add SUIT as an
explanatory variable into this model. Therefore, we ran regression analyses with
and without the two dummy variables of SUIT to see which model would be
better. The regression models are shown below.
Table 18 - Regressionresults using TRAFFIC and POP1M as explanatory variables of GSALES
28. 25
Because significance F and p-values of two model are both less than 0.05 (only
one p-value of SUIT is greater than 0.05. In this case, we still treated it as a
significant variable and kept it in the model), these two models are both
appropriate to make predictions for gross sales. However, considering the higher
adjusted R square and the lower standard error, the second model with SUIT as a
variable is more appropriate than the first model.
Therefore, we decided that the regression model with independent variables of
TRAFFIC, POP1M and SUIT is the most appropriate model to predict gross sales
for each site.
CHECKING RESIDUALS
After selecting the best model, we performed a residual analysis to see if the
model violated any assumptions. If any assumption was violated, we might want
to use other methods to build model, such as log transformation. These
assumptions include:
Linearity
Table 19 - Regressionresults using TRAFFIC, POP1M and SUIT as explanatory variables of GSALES
29. 26
Independence of errors
Normality of errors
Equal variances of errors
Figure6 - Plot of residuals against the population withina
one-mile radius of the Culver's restaurant
Figure5 - Plot of residuals against the traffic count
around the Culver's restaurant
Figure4 - Plot of residuals against dummyvariable 1 of CFSI
suitability rating of the Culver's restaurant
Figure3 - Plot of residuals against dummyvariable 2 of
CFSI suitability rating of the Culver's restaurant
Figure2 - Normal probability plotof the residuals for the
gross sales
30. 27
From these residual plots, both POP1 and TRAFFIC satisfied the four
assumptions of regression:
1) There was no apparent pattern in the residual plots; the residuals
appeared to be evenly spread above and below 0. Therefore, this
assumption was not violated.
2) When data collected over periods of time sometimes exhibit an
autocorrelation effect among successive observations. In these instances,
there is a relationship between consecutive residuals. Because the
Culver's data were collected during the same time period for each
variable, we did not need to evaluate the independence assumption.
3) According to the normal probability plot of the residuals, the data did not
appear to depart substantially from a normal distribution.
4) There did not appear to be major differences in the variability of the
residuals for different Xi values. Thus, there is no apparent violation in
the assumption of equal variance at each level of X.
Note that the residual plots for dummy variables of variable SUIT cannot be
interpreted.
CHECKING COLLINEARITY
Here we checked if multicollinearity existed. Multicollinearity is a phenomenon
in which two or more predictor variables in a multiple regression model are
highly correlated, meaning that one can be linearly predicted from the
31. 28
others. When multicollinearity exists, some x-variables might do a good job at
predicting the Y variable, but these variables do not bring new information to the
regression model, therefore we want to exclude them from the model. We can
detect (Multi)Collinearity when high correlation exists between predictor
variables, when absolute value of r > 0.95.
From the table, we could see that all correlations were less than 0.95 or greater
than -0.95.Therefore we concluded that there was no collinearity between these
variables, and all of them provided new information to the regression model.
LOCATION PREDICTION
Using the model we chose, we predicted the gross revenue of each location. The
results are as following:
Gross Sales Revenue = 493758.5 + 31971.99*Better + 80487.16*Best +
36.28567*TRAFFIC + 64.3183*POP1M
Table 20 - Multicollinearity results for ten relevant variables in predicting gross sales
Table 21 - Predicted gross sales for Site A, B and C
32. 29
FINANCIAL ANALYSIS
This section focuses on financial analysis of opening the Culver's Frozen Custard
restaurant at each site. The primary goal is to find the most promising location
among Site A, Site B, and Site C by the financial estimation. To simplify our
analysis, we did not consider the time value of money and the potential growth
rate of annual sales. Additionally, we took our clients' financial ability and
profitability requirements into consideration.
GROSS SALES
We predicted the gross sales of all three sites based on the traffic count, the
population within a one-mile radius, and the CFSI suitability rating: $1,391,607
for Site A, $1,279,961 for Site B, and $1,448,375 for Site C. Our clients only
consider the site with annual sales more than $1 million. Based on our results, all
sites were favorable to our clients.
Figure7 - Predicted gross sales
33. 30
START-UP SUMMARY
The Culver's Frozen Custard restaurant has the following start-up costs:
Initial Lease Payments and Deposits
Structure and Improvements
Equipment & Signage
Other miscellaneous Costs
Franchise Fee (15-year agreement)
Note that the items mentioned above are depreciable. The difference of the
startup expenses is only due to the initial lease payments and deposits. The
startup costs of all sites are reasonable, which is less than the maximum of
typical initial investment costs ($3,046,000). However, our clients are only able
to obtain $1.2 to $1.55 million from the local bank. Site C ($1,815,000) requires
Figure8 - Startup expenses
34. 31
more than $1.55 million to start-up, which is beyond the clients' financial
abilities.
OPERATING COSTS & ANNUAL PROFITS
The operating costs for the Culver's Frozen Custard restaurant include food costs,
paper cost, labor cost, and other operating costs. To eliminate the differences of
operating costs caused by suitability, we used the comparable analysis to
estimate each site's operating costs. Specifically, we concluded each expense as
the percentage of gross sales, and then sort the data by its suitability. For the
suitability rating of 1, the total average operating costs are 94% of the gross sales:
FOOD-31%, PAPER-4%, LABOR-30%, and OTHER OP-29%. For the suitability
rating of 2, the total average operating costs are 91% of the gross sales:
FOOD-30%, PAPER-4%, LABOR-29%, and OTHER OP-28%. As results, annual
profits of each site are as followings: $80,155 for Site A, $73,725 for Site B, and
$132,410 for Site C.
OTHER CONSIDERATIONS
We also compared those three sites using their payback periods. By dividing the
initial investment by the annual profits, we got the results: Site C (6.91 years) has
the shortest payback period. Note that the payback period of Site A (7.05 years)
is similar to Site C. Even though Site C has the highest annual profit, it has the
highest startup expenses as well.
35. 32
Our clients have options to renew their franchise agreement every 10 years after
the first 15 years. We recommend that they consider the renew options if the
restaurant operates well in the first 15 years, because the more year the
restaurant operates, the less annual allocation of the start-up expenses will be.
RISKS
There are also risks for the restaurant to generate profits:
1) Workforce: if the local workforce is weak, the restaurant will probably
have higher labor expense, which will lower the annual profits.
2) Competitor: there will probably be a price competition, which will lower
the annual profits.
3) Macroeconomic: if the macroeconomics got better, costumers would
probably spend less money on fast foods, which would lower the annual
profits.
In general, based on our financial analysis mentioned above, we recommend our
clients to choose Site A. If the restaurant at Site A operates well, they can
consider either to renew the franchise agreement or to open another restaurant
at Site C. Note that our clients should pay attention to the changing
circumstances to modify their financial strategies.
36. 33
RECOMMENDATION
Each site has different characteristics. The CFSI rates those three sites based on
their unique characteristics: Site A - better, Site B - better, and Site C - best. We
noticed that the suitability rates provided by the CFSI did not take our clients'
preference and goal into account. Thus, we reevaluated those three sites listed
below based on our clients' needs.
Customer Group: All sites have big potential customer bases due to their
location description. The followings shows neighboring groups for each site:
Site A: 1) high school students; 2) hospital patients and employees
Site B: drivers
Site C: 1) middle school students; 2) business office residents; 3)
shoppers
Figure9 - LocationInformation
37. 34
Our clients prefer to interact with young students, so both Site A and Site C are
favorable to our clients. Note that the size of each site's customer group is
unknown based on our data.
Workforce: The neighboring groups mentioned above are also indications of the
potential workforce for each site. The major workforce of each site is listed as
followings:
Site A - students
Site B - adult employees
Site C - adult employees
Our clients indicated their preference for labor force - young students. Based on
their preference, Site A is the suitable location to hire young students.
Competitor Pressure: Both Site B and Site C will face intense competitions from
other restaurants: Site B - other 3 fast food restaurants (i.e. McDonald's, A&W,
and Pizza Hut); and Site C - other restaurants at the food court. As results, Site A
has the biggest business viability.
Accessibility: One of the Culvers' competitive advantages is "a wide variety of
entrees." Site C is located within a shopping center, which reduces the likelihood
for providing the out-door seating and the drive-up service. Unlike Site C, both
Site A and Site B are able to provide in-door seating, out-door seating, and
drive-up service.
38. 35
Visibility: Site A is the most visible location. Its speed limit (i.e. 30 mph) will also
help draw drivers' attentions. Site B is the second visible location. Site C is the
least.
In general, Site A is most favorable place for our clients based on the location
descriptions. Both the result from the financial analysis and the result from the
location analysis have demonstrated that Site A is the best.
CONCLUSION
In conclusion, we suggest our client to locate the new Culver's restaurant at Site
A. According to the prediction of gross sale, all of the three location are profitable.
Although Site C has the highest predicted gross sale, after taking other start-up
expenses, such as franchise fee and venue purchase fee, into consideration, we
found that locating at site C was beyond our clients' financial abilities. On the
other hand, Site A has the second highest predicted gross sale, and it is affordable
to our clients. In addition, Site A is the location that suits best for our clients'
interest. Our clients enjoy interacting with young students and they expect to
recruit students as their main labor force. Because Site A is close to a school, this
location is in our clients' favor.
39. 36
For further recommendation, if the restaurant at Site A operates well, our clients
can consider either to renew the franchise agreement or to open another
restaurant.
59. - 20 -
Site Information
Site Price Location
Posted
Speed Limit
Nearby Notes
A 410,000$
in a residental area on a main
street
30 mph
Within Walking Distance:
- a high school
- a small grocery store
- a hospital
B 480,000$
off a freeway ramp on the
edge of Carbondale
45 mph
A Frontage Road
- 2 gas stations w/ convenience
stores
- a McDonald's
- an A&W
- a Pizza Hut
1) the frontage road runs to the
county road leading to the
freeway ramp
2) the frontage road has a
spotlight to make access easier
C 760,000$
at the far end of Carbondale's
new shopping center
N/A
A Shopping Center:
- a food court (including a
Baskin-Robbins ice cream
shop)
Within Waling Distance:
- a middle school
- business offices housing
(about 100 employees)
access from:
1) the street that runs past the
shopping cenrer and dead ends
about a block beyond Site C
2) the end of the shopping
center itself
61. - 22 -
Startup Funding
Startup Costs
A B C
Initial Lease Payments and Deposits 410,000$ 480,000$ 760,000$
Structure and Improvements 600,000 600,000 600,000
Equipment & Signage 300,000 300,000 300,000
Miscellaneous Costs 100,000 100,000 100,000
Franchise Fee (15 yr) 55,000 55,000 55,000
Total 1,465,000 1,535,000 1,815,000
Renew Franchise Agreement (10 yr) 30,000 30,000 30,000
62. - 23 -
Organization Budget - Example
Numbers of Personnel
A B C
Owners 2 2 2
General Manager (1) 1 1 1
Assistant Manager (2) 2 2 2
Team Member (3) 52 52 52
Total (4) 57 57 57
Site
Personnel Plan -Yearly
A B C
Owners - - -
General Manager (1) 37,158 37,158 37,158 37,158
Assistant Manager (2) 69,278 69,278 69,278 34,639
Team Member (3) - - -
Total (4) 106,436 106,436 106,436
Site Salaries per
person
64. - 25 -
Annual Profit
Initial Investment
A B C
Franchise Fee 55,000$ 55,000$ 55,000$
Start-up Costs 100,000 100,000 100,000
Site 410,000 480,000 760,000
Total 565,000$ 635,000$ 915,000$
Renew Franchise Agreement (10 yr) 30,000$
Annual Profit
A B C
Predicted GSales (1) 1,391,607$ 1,279,961$ 1,448,375$
Food (2) (430,900) (396,330) (436,847)
% of Gsales 31% 31% 30%
Paper (3) (53,004.49) (48,752.04) (51,822.65)
% of Gsales 4% 4% 4%
Labor (420,519) (386,782) (417,426)
% of Gsales 30% 30% 29%
Other Operating Costs (4) (407,028) (374,373) (409,870)
% of Gsales 29% 29% 28%
Total Annual Profit 80,155 73,725 132,410
65. - 26 -
Total Profits
Total Profits after () yr A B C
10 236,554 102,246 409,100
15 637,330 470,870 1,071,150
25 1,408,884 1,178,116 2,365,250
35 2,210,438 1,915,363 3,689,350