Dominos balance sheet
| Fiscal year is January-December. All values USD millions. | 2014 | 2015 | 2016 | 2017 | 2018 | 5-year trend |
|---|---|---|---|---|---|---|
| Cash & Short Term Investments | 151.81M | 314.39M | 169.31M | 227.53M | 237.42M | |
| Cash Only | 151.81M | 314.39M | 169.31M | 227.53M | 237.42M | |
| Short-Term Investments | - | - | - | - | - | |
| Total Accounts Receivable | 118.4M | 131.58M | 150.37M | 173.68M | 190.09M | |
| Accounts Receivables, Net | 118.4M | 131.58M | 150.37M | 173.68M | 190.09M | |
| Accounts Receivables, Gross | 121.76M | 134.24M | 152.71M | 175.1M | 191.97M | |
| Bad Debt/Doubtful Accounts | (3.36M) | (2.66M) | (2.34M) | (1.42M) | (1.88M) | |
| Other Receivables | - | - | - | - | - | |
| Inventories | 37.94M | 36.86M | 40.18M | 39.96M | 45.98M | |
| Finished Goods | 31.63M | 30.17M | 36.64M | 36.65M | 42.92M | |
| Work in Progress | - | - | - | - | - | |
| Raw Materials | 6.32M | 6.69M | 3.54M | 3.32M | 3.05M | |
| Progress Payments & Other | - | - | - | - | - | |
| Other Current Assets | 120.21M | 119.81M | 136.01M | 138.61M | 93.47M | |
| Miscellaneous Current Assets | 120.21M | 119.81M | 136.01M | 138.61M | 93.47M | |
| Total Current Assets | 428.36M | 602.64M | 495.87M | 579.78M | 566.95M |
| 2014 | 2015 | 2016 | 2017 | 2018 | 5-year trend | |
|---|---|---|---|---|---|---|
| Net Property, Plant & Equipment | 114.05M | 131.89M | 138.53M | 169.59M | 234.94M | |
| Property, Plant & Equipment - Gross | 310.22M | 336.17M | 359.5M | 406.87M | 487.12M | |
| Buildings | 25.86M | 18.56M | 18.63M | 18.67M | 12.25M | |
| Land & Improvements | - | - | - | - | - | |
| Computer Software and Equipment | - | - | - | - | - | |
| Other Property, Plant & Equipment | 99.8M | 111.07M | 120.73M | 128.61M | 170.5M | |
| Accumulated Depreciation | 196.17M | 204.28M | 220.96M | 237.28M | 252.18M | |
| Total Investments and Advances | 4.59M | 6.05M | 7.26M | 8.12M | 8.72M | |
| Other Long-Term Investments | 4.59M | 6.05M | 7.26M | 8.12M | 8.72M | |
| Long-Term Note Receivable | - | - | - | - | - | |
| Intangible Assets | 36.86M | 44.6M | 56.31M | 68.25M | 78.73M | |
| Net Goodwill | 16.3M | 16.1M | 16.06M | 15.42M | 14.92M | |
| Net Other Intangibles | 20.56M | 28.51M | 40.26M | 52.82M | 63.81M | |
| Other Assets | 10.01M | 8.8M | 9.38M | 8.27M | 12.52M | |
| Tangible Other Assets | 10.01M | 8.8M | 9.38M | 8.27M | 12.52M | |
| Total Assets | 596.33M | 799.85M | 716.3M | 836.75M | 943.08M |
Liabilities & Shareholders' Equity
| 2014 | 2015 | 2016 | 2017 | 2018 | 5-year trend | |
|---|---|---|---|---|---|---|
| ST Debt & Current Portion LT Debt | 565,000 | 59.33M | 38.89M | 32.32M | 35.89M | |
| Short Term Debt | - | - | - | - | - | |
| Current Portion of Long Term Debt | 565,000 | 59.33M | 38.89M | 32.32M | 35.89M | |
| Accounts Payable | 86.55M | 106.93M | 111.51M | 106.89M | 92.55M | |
| Income Tax Payable | - | - | - | - | - | |
| Other Current Liabilities | 178.49M | 209.72M | 253.3M | 259.07M | 251.3M | |
| Dividends Payable | 14.35M | 557,000 | - | - | - | |
| Accrued Payroll | 23.62M | 33M | 42.09M | 37.42M | 40.96M | |
| Miscellaneous Current Liabilities | 140.52M | 176.17M | 211.21M | 221.65M | 210.34M | |
| Total Current Liabilities | 265.61M | 375.98M | 403.7M | 398.29M | 379.74M | |
| Long-Term Debt | 1.5B | 2.18B | 2.15B | 3.12B | 3.5B | |
| Long-Term Debt excl. Capitalized Leases | 1.5B | 2.18B | 2.15B | 3.12B | 3.48B | |
| Non-Convertible Debt | 1.5B | 2.18B | 2.15B | 3.12B | 3.48B | |
| Convertible Debt | - | - | - | - | - | |
| Capitalized Lease Obligations | - | - | - | 4.61M | 14.61M | |
| Provision for Risks & Charges | 26.95M | 23.31M | 27.14M | 30.61M | 31.07M | |
| Deferred Taxes | 3.11M | (5.87M) | (8.94M) | (2.75M) | (5.53M) | |
| Deferred Taxes - Credit | 5.59M | - | - | - | 35.7M | |
| Deferred Taxes - Debit | 2.48M | 5.87M | 8.94M | 2.75M | 41.22M | |
| Other Liabilities | 17.05M | 19.34M | 19.61M | 21.75M | 5.11M | |
| Other Liabilities (excl. Deferred Income) | 17.05M | 19.34M | 19.61M | 21.75M | 5.11M | |
| Deferred Income | - | - | - | - | - | |
| Total Liabilities | 1.82B | 2.6B | 2.6B | 3.57B | 3.95B | |
| Non-Equity Reserves | - | - | - | - | - | |
| Preferred Stock (Carrying Value) | - | - | - | - | - | |
| Redeemable Preferred Stock | - | - | - | - | - | |
| Non-Redeemable Preferred Stock | - | - | - | - | - | |
| Common Equity (Total) | (1.22B) | (1.8B) | (1.88B) | (2.74B) | (3.04B) | |
| Common Stock Par/Carry Value | 556,000 | 498,000 | 481,000 | 429,000 | 410,000 | |
| Retained Earnings | (1.25B) | (1.8B) | (1.88B) | (2.74B) | (3.04B) | |
| ESOP Debt Guarantee | - | - | - | - | - | |
| Cumulative Translation Adjustment/Unrealized For. Exch. Gain | (2.66M) | (3.55M) | (3.11M) | (2.03M) | (4.43M) | |
| Unrealized Gain/Loss Marketable Securities | - | - | - | - | - | |
| Revaluation Reserves | - | - | - | - | - | |
| Treasury Stock | - | - | - | - | - | |
| Total Shareholders' Equity | (1.22B) | (1.8B) | (1.88B) | (2.74B) | (3.04B) | |
| Accumulated Minority Interest | - | - | - | - | - | |
| Total Equity | (1.22B) | (1.8B) | (1.88B) | (2.74B) | (3.04B) | |
| Liabilities & Shareholders' Equity | 596.33M | 799.85M | 716.3M | 836.75M | 943.08M |
*Question* for years ((**2015-2018**)) please show work , please and thank you :)
What is Dominos 1) current ratio 2) debt-equity ratio 3) profit margin 4) return on assets (ROA) 5) return on equity (ROE) 6) Use the Dupont identity to calculate total assets turnover (TAT). For years (2015-2018)
In: Finance
On January 1, 2018,
Splash City issues $470,000 of 9% bonds, due in 20 years, with
interest payable semiannually on June 30 and December 31 each
year.
Assuming the market interest rate on the issue date is 10%, the
bonds will issue at $429,678.
2. Record the bond issue on January 1, 2018, and the first two semiannual interest payments on June 30, 2018, and December 31, 2018
|
In: Accounting
Kane Candy Company offers a coffee mug as a premium for every ten $1 candy bar wrappers presented by customers together with $2. The purchase price of each mug to the company is $1.80; in addition it costs $1.20 to mail each mug. The results of the premium plan for the years 2017 and 2018 are as follows (assume all purchases and sales are for cash):
| 2017 | 2018 | |||
| Coffee mugs purchased | 730,000 | 820,000 | ||
| Candy bars sold | 5,700,000 | 6,720,000 | ||
| Wrappers redeemed | 2,800,000 | 4,190,000 | ||
| 2017 wrappers expected to be redeemed in 2018 | 2,100,000 | |||
| 2018 wrappers expected to be redeemed in 2019 | 2,660,000 |
Prepare the general journal entries that should be made in 2017 and 2018 related to the above plan by Kane Candy. (Credit account titles are automatically indented when the amount is entered. Do not indent manually.)
|
Date |
Account Titles and Explanation |
Debit |
Credit |
|
2017 |
|||
| (To record purchase of coffee mugs) | |||
|
2017 |
|||
| (To record sale of candy bars) | |||
|
2017 |
|||
| (To record coffee mugs offered for wrappers redeemed) | |||
|
2017 |
|||
| (To record liability against expected redemption of wrappers in 2018) | |||
|
2018 |
|||
| (To record purchase of coffee mugs) | |||
|
2018 |
|||
| (To record sale of candy bars) | |||
|
2018 |
|||
| (To record coffee mugs offered for wrappers redeemed) | |||
|
2018 |
|||
| (To record liability against expected redemption of wrappers in 2019) |
In: Accounting
Stock Inc. has two sites in Pittsburgh that are four miles apart. Each site consists of a large factory with office space for 25 users at the front of the factory and up to 50 workstations in two work cells on each factory floor. All office users need access to an inventory database that runs on a server at the Allegheny Street location; they also need access to a billing application with data residing on a server at the Monongahela site. All factory floor users also need access to the inventory database at the Allegheny Street location. Office space is permanently configured, but the manufacturing space must be reconfigured before each new manufacturing run begins. Wiring closets are available in the office space. Nothing but a concrete floor and overhead girders stay the same in the work cell areas. The computers must share sensitive data and control access to files. Aside from the two databases, which run on the two servers, office computers must run standard word-processing and spreadsheet programs. Work cell machines are used strictly for updating inventory and quality control information for the Allegheny Street inventory database. Workstations in the manufacturing cells are switched on only when they’re in use, which might occur during different phases of a manufacturing run. Seldom is a machine in use constantly on the factory floor. Use the following write-on lines to evaluate the requirements for this network. After you finish, determine the best network topology or topology combination for the company. On a blank piece of paper, sketch the network design you think best suits ENorm, Inc.’s needs.
● Will the network be peer to peer or server-based?
● How many computers will be attached to the network?
● What topology works best for the offices, given the availability of wiring closets? What topology works best for the factory floor, given its need for constant reconfiguration?
can i also have the network design please
In: Computer Science
A retailer, Continental Palms Retail (CPR), plans to create a database system to keep track of the information about its inventory.
CPR has several warehouses across the country. Each warehouse is uniquely named. CPR also wants to record the location, city, state, zip, and space (in cubic meters) of each warehouse. There are several warehouses in any single city.
CPR stores its products in the warehouses. A product may be stored in multiple warehouses. A warehouse may store multiple products. The quantity of a product in a warehouse needs to be recorded.
Every product has a unique UPC number. Other information about a product includes a name, a buying price, an approximate selling price, and a size (in cubic meters).
CPR also keeps track of the information about the manufacturers of products. Every product has a single manufacturer, but a manufacturer may manufacture multiple products. Each manufacturer has a unique name, an address (street, city, state, zip), and a contact phone number.
The requirements of CPR also indicate that there are the following full Functional Dependencies:
• UPC -> Name, Buying_Price, Selling_Price, Size, Manufacturer_Name, MStreet, MCity, MState, MZip, MPhone • Manufacturer_Name -> MStreet, MCity, MState, MZip, MPhone • Warehouse_Name -> WLocation, WCity, WState, WZip, WSpace • UPC, Warehouse_Name -> Quantity
A consulting company named Database Experts has designed the following relation data model for CPR. Product (UPC, Name, Buying_Price, Selling_Price, Size, Manufacturer_Name, MStreet, MCity, MState, MZip, MPhone, Warehouse_Name, WLocation, WCity, WState, WZip, WSpace, Quantity)
Although the designers at Database Experts claim that their design is superior in all aspects, CPR gives you a fair chance to justify your position. Now it’s your time to do the following.
(1) Show them what normal form their relation is in and why.
(2) Rescue their “bad” design using normalization. Decompose their relation Product into multiple smaller relations that are all in 3NF. Underline the primary key of each of your relations.
In: Computer Science
Part I – Build a simple Servlet called MyServlet using NetBeans. Add this Servlet to you “ChattBank” Project. This MyServlet will display a message like “Go Braves” in a simple <h1> tag. Run this servlet from a Browser window by typing in the servlet name in the URL line.
(ie. http://localhost:8080/ChattBank/MyServlet). Make sure that your Server is up and running before you test this Servlet. The best way to do this is just Run your “ChattBank” Project once before you test the Servlet. Running the Project will start the Server.
Part II – Next, build a simple Servlet called LoginServlet in your “ChattBank” Project. Now make it so that when the Customer logs in, the LoginServlet will get called and will validate the user id and password.
<form action=”http://localhost:8080/ChattBank/LoginServlet” method=”post”>
Part III – Now, modify the LoginServlet.
Use : request.getParameter() to get these items. At first just read in these 2 strings and display them to the Server Log.
2.) If the id = “admin” and the Password = “123”, return an HTML page that says “Valid Login”.
3.) If not return an HTML page that says “InValid Login”. Use out.println() to send these HTML messages.
4.) Test out your WebApp.
Part IV– Lastly, modify the LoginServlet. This time we are going to go to the database to verify the user login. First look at the ChattBank database. There is a Customers table. In this table there is a UserID and a Passwd. Write the database code, in your LoginServlet to let anyone of these customers login, using their own ids and passwords.
In: Computer Science
2. Demand for hotel rooms in Tallahassee takes two possible values: on game days, demand is described by the demand curve q = 100−p, while on non-game-days demand is described by the demand curve q = 60 − 2p.
(a) Suppose that the hotel price on game days is ph = 80. What quantity is demanded at this price?
(b) Find the inverse demand curve on non-game-days. Assuming that the price on game days is ph = 80 as above, what price would induce the same quantity demanded on non-game-days as on game days?
(c) Plot the demand curves on game days and on non-game-days. Pay careful attention to the price and quantity intercepts for both curves.
(d) Assuming the price on non-game-days is as you found in (ii), what is consumer surplus in this market on non-game-days? What is consumer surplus on game days?
(e) Suppose that you encounter the following claim: “Because the hotel price is higher on game days than on non-game-days, consumer surplus in the hotel market must be lower on game days.” What is wrong with this claim?
In: Economics
Demand for hotel rooms in Tallahassee takes two possible values: on game days, demand is described by the demand curve q = 100−p, while on non-game-days demand is described by the demand curve q = 60 − 2p.
(a) Suppose that the hotel price on game days is ph = 80. What quantity is demanded at this price?
(b) Find the inverse demand curve on non-game-days. Assuming that the price on game days is ph = 80 as above, what price would induce the same quantity demanded on non-game-days as on game days?
(c) Plot the demand curves on game days and on non-game-days. Pay careful attention to the price and quantity intercepts for both curves.
(d) Assuming the price on non-game-days is as you found in (ii), what is consumer surplus in this market on non-game-days? What is consumer surplus on game days?
(e) Suppose that you encounter the following claim: “Because the hotel price is higher on game days than on non-game-days, consumer surplus in the hotel market must be lower on game days.” What is wrong with this claim?
In: Economics
To study bonding between mothers and infants, a researcher places each mother and her infant in a playroom and has the mother leave for 10 minutes. The researcher records crying time in the sample of infants during this time that the mother was not present and finds that crying time is normally distributed with
M = 8
and
SD = 1.1.
Based on the empirical rule, state the range of crying times within 68% of infants cried, 95% of infants cried, and 99.7% of infants cried.
(a) 68% of infants cried
__________ to __________ min
(b) 95% of infants cried
_______ to ________min
(c) 99.7% of infants cried
_________ to _________ min
In: Statistics and Probability
To study bonding between mothers and infants, a researcher places each mother and her infant in a playroom and has the mother leave for 10 minutes. The researcher records crying time in the sample of infants during this time that the mother was not present and finds that crying time is normally distributed with
M = 8
and
SD = 1.1.
Based on the empirical rule, state the range of crying times within 68% of infants cried, 95% of infants cried, and 99.7% of infants cried.
(a) 68% of infants cried
__________ to __________ min
(b) 95% of infants cried
_______ to ________min
(c) 99.7% of infants cried
_________ to _________ min
In: Statistics and Probability