DF Limited is a manufacturing company that carries out jobs to customers requirements in three production departments. The following data is available for Jobs A, B and C.
A B C
Bought in components £248 £76 -
Direct material from stores £312 £32 £96
Direct labour hours:
Machining 12 18 15
Assembling 6 9 4
Finishing and Spraying 3 2 2
Hourly wage rates:
Machining £12.00
Assembling £8.00
Finishing and Spraying £6.00
Production overhead absorption rates per direct labour hour:
Machining £26.00
Assembling £21.00
Finishing and Spraying £25
Your cost estimates should include allowances for:
In: Accounting
El toro grande restaurant advertises that customers will have their orders taken with three minutes of being seated. Management wants to monitor average times, as it is such an important guarantee for business. Construct x? - and s-charts for the data given in the worksheet pro07-05 in the ch07data.xlsx file for this chapter.
a. compute the sample means and the average standard deviation, calculate the control limits, and plot them on control charts.
b. does the process appear to be in statistical control? Why or why not?
c. calculate the process capability statistics, using three minutes as the upper tolerance limit and zero as the lower tolerance limit. What recommendation would you make to management concerning the process, based on your findings?
| El Toro Grande Restaurante | |||||
| 1 | 1.22 | 1.54 | 1.53 | 1.86 | 1.49 |
| 2 | 1.48 | 1.18 | 1.41 | 1.29 | 1.61 |
| 3 | 2.12 | 1.76 | 1.29 | 1.78 | 1.74 |
| 4 | 1.34 | 1.22 | 1.29 | 1.69 | 1.42 |
| 5 | 1.11 | 1.21 | 1.34 | 1.95 | 1.22 |
| 6 | 0.73 | 1.97 | 1.51 | 1.67 | 1.77 |
| 7 | 1.20 | 1.46 | 1.05 | 1.14 | 1.80 |
| 8 | 1.72 | 1.58 | 1.79 | 1.95 | 0.83 |
| 9 | 1.23 | 1.39 | 1.57 | 1.49 | 1.58 |
| 10 | 0.70 | 0.94 | 1.14 | 1.54 | 1.81 |
| 11 | 1.50 | 1.83 | 1.60 | 1.15 | 1.79 |
| 12 | 1.72 | 1.61 | 1.63 | 1.84 | 1.95 |
| 13 | 1.64 | 1.13 | 1.60 | 1.87 | 1.36 |
| 14 | 0.73 | 1.39 | 1.39 | 1.85 | 1.86 |
| 15 | 1.72 | 1.42 | 1.59 | 0.70 | 1.55 |
| 16 | 1.91 | 2.08 | 1.64 | 1.77 | 1.60 |
| 17 | 1.63 | 1.57 | 0.95 | 2.02 | 1.69 |
| 18 | 1.53 | 1.47 | 2.05 | 1.19 | 1.52 |
| 19 | 1.18 | 1.78 | 1.37 | 1.53 | 1.30 |
| 20 | 1.74 | 2.14 | 1.24 | 0.92 | 1.34 |
| 21 | 1.47 | 1.89 | 1.53 | 2.28 | 1.84 |
| 22 | 1.68 | 1.35 | 1.26 | 1.58 | 1.63 |
| 23 | 0.99 | 1.57 | 1.45 | 1.50 | 1.98 |
| 24 | 1.92 | 1.01 | 0.93 | 1.68 | 1.96 |
| 25 | 2.15 | 1.57 | 1.75 | 1.72 | 1.63 |
| 26 | 1.13 | 0.99 | 1.27 | 1.35 | 1.37 |
| 27 | 1.87 | 1.74 | 0.89 | 1.61 | 1.77 |
| 28 | 0.99 | 1.36 | 0.89 | 1.54 | 2.01 |
| 29 | 1.75 | 1.96 | 1.57 | 1.67 | 2.31 |
| 30 | 1.59 | 2.15 | 1.68 | 1.42 | 1.50 |
| 31 | 0.93 | 1.65 | 1.29 | 1.02 | 1.48 |
| 32 | 1.40 | 1.98 | 1.54 | 0.97 | 1.62 |
| 33 | 1.69 | 1.62 | 1.47 | 1.81 | 0.97 |
| 34 | 1.98 | 1.26 | 1.32 | 1.17 | 1.39 |
| 35 | 1.73 | 1.42 | 2.06 | 1.27 | 1.34 |
| 36 | 1.45 | 1.57 | 1.70 | 1.32 | 1.26 |
| 37 | 1.98 | 1.61 | 1.45 | 1.46 | 2.19 |
| 38 | 1.46 | 1.46 | 1.70 | 1.56 | 1.93 |
| 39 | 1.80 | 1.34 | 1.46 | 1.91 | 1.10 |
| 40 | 1.04 | 1.29 | 1.30 | 1.77 | 1.13 |
In: Operations Management
The distribution system for the Herman Company consists of three plants, two warehouses, and four customers. Plant capacities and shipping costs per unit (in $) from each plant to each warehouse are as follows:
Warehouse
Plant 1 2 Capacity
1 4 7 450
2 8 5 600
3 5 6 380
Customer
Warehouse 1 2 3 4
1 6 4 8 4
2 3 6 7 7
Demand 300 300 300 400
Formulate the linear programming model to minimize the cost of shipping for this transshipment problem.
A-at the optimal solution how much is shipped from Plant 3 to Warehouse 1?
B-what is the range of optimality of coefficient of cost from Plant 3 to Warehouse 1 and what does this
mean?
C-what is the range of feasibility for the supply amount for Plant 2 and what does it mean?
D-what is the range of feasibility for the demand amount for customer 2 and what does it mean?
In: Operations Management
The distribution system for the Herman Company consists of three plants, two warehouses, and four customers. Plant capacities and shipping costs per unit (in $) from each plant to each warehouse are shown below along with customer demand and shipping costs per unit (in $) from each warehouse to each customer
Formulate the linear programming model to minimize the cost of shipping for this transshipment problem.
A-at the optimal solution how much is shipped from Plant 3 to Warehouse 1?
B-what is the range of optimality of coefficient of cost from Plant 3 to Warehouse 1 and what does this mean?
C-what is the range of feasibility for the supply amount for Plant 2 and what does it mean?
d-what is the range of feasibility for the demand amount for customer 2 and what does it mean?
|
Warehouse 1 |
Warehouse 2 |
Plant Capacity |
||
|
Plant 1 |
4 |
7 |
450 |
|
|
Plant 2 |
8 |
5 |
600 |
|
|
Plant 3 |
5 |
6 |
380 |
|
|
Customer 1 |
Customer 2 |
Customer 3 |
Customer4 |
|
|
Warehouse 1 |
6 |
4 |
8 |
4 |
|
Warehouse 2 |
3 |
6 |
7 |
7 |
|
Demand |
300 |
300 |
300 |
400 |
In: Operations Management
(a) draw and label a sketch of the normal curve
(b) identify and shade the area of interest
(c) identify any formulas and values substituted
(d) identify the calculator command used and values entered into the calculator
(e) write your response as a decimal rounded to three places
A greenhouse in a tri-county area has kept track of its customers for the last several years and has determined that 28% of its customers plant a vegetable garden in the spring.
Use the Central Limit Theorem.
a. In a random sample of 1000 customers, what is the probability that at most 300 customers plant a vegetable garden in the spring.
b. In a random sample of 1000 customers, what is the probability that at least 225 customers plant a vegetable garden in the spring?
c. In a random sample of 1000 customers, what is the probability that 275 to 325 customers plant a vegetable garden in the spring?
In: Statistics and Probability
Scenario: You are an information technology (IT) intern working for Health Network, Inc. (Health Network), a fictitious health services organization headquartered in Minneapolis, Minnesota. Health Network has over 600 employees throughout the organization and generates $500 million USD in annual revenue. The company has two additional locations in Portland, Oregon and Arlington, Virginia, which support a mix of corporate operations. Each corporate facility is located near a colocation data center, where production systems are located and managed by third-party data center hosting vendors.
Company Products Health Network has three main products: HNetExchange, HNetPay, and HNetConnect.
HNetExchange is the primary source of revenue for the company. The service handles secure electronic medical messages that originate from its customers, such as large hospitals, which are then routed to receiving customers such as clinics.
HNetPay is a Web portal used by many of the company’s HNetExchange customers to support the management of secure payments and billing. The HNetPay Web portal, hosted at Health Network production sites, accepts various forms of payments and interacts with credit-card processing organizations much like a Web commerce shopping cart.
HNetConnect is an online directory that lists doctors, clinics, and other medical facilities to allow Health Network customers to find the right type of care at the right locations. It contains doctors’ personal information, work addresses, medical certifications, and types of services that the doctors and clinics offer. Doctors are given credentials and are able to update the information in their profile. Health Network customers, which are the hospitals and clinics, connect to all three of the company’s products using HTTPS connections. Doctors and potential patients are able to make payments and update their profiles using Internet-accessible HTTPS Web sites.
Information Technology Infrastructure Overview
Health Network operates in three production data centers that provide high availability across the company’s products. The data centers host about 1,000 production servers, and Health Network maintains 650 corporate laptops and company-issued mobile devices for its employees. Threats Identified Upon review of the current risk management plan, the following threats were identified:
*) Loss of company data due to hardware being removed from production systems ? Loss of company information on lost or stolen company-owned assets, such as mobile devices and laptops
*) Loss of customers due to production outages caused by various events, such as natural disasters, change management, unstable software, and so on
*) Internet threats due to company products being accessible on the Internet
*) Insider threats
*) Changes in regulatory landscape that may impact operations Management Request
Senior management at Health Network has determined that the existing risk management plan for the organization is out of date and a new risk management plan must be developed. Because of the importance of risk management to the organization, senior management is committed to and supportive of the project to develop a new plan. You have been assigned to develop this new plan.
Additional threats other than those described previously may be discovered when re-evaluating the current threat landscape during the risk assessment phase.
The budget for this project has not been defined due to senior management’s desire to react to any and all material risks that are identified within the new plan. Given the company’s annual revenue, reasonable expectations can be determined.
Project Part 2 Task 3: Disaster Recovery Plan (DRP)
Your project on risk management, the BIA, and the BCP have been well received by senior management at Health Network. They now want you to develop a DRP in order to overcome any mishaps that might occur in the future. You may research and use National Institute of Standards and Technology (NIST) templates to develop a DRP plan for the company.
Evaluation Criteria and Rubrics (Ask these questions to yourself)
In: Operations Management
Read the below article and answer the following questions, in regards to Operations and Supply Chain management:
1) A comparative analysis of two systems (One with a single line and tree servers and a second with 3 waiting lines and three servers) found that the first is approximately three times more effective. Provide and illustrate three reasons for this difference in performance (Keep in mind service rate variability, customers and employers behavior.
2) Amusement park priority in term of customer satisfaction is to decrease the customers waiting time. Provide and explain two approaches that Amusement parks use to improve the customers experience from a waiting perspectives.
Article: Why You Always Seem to Get Stuck in the Slowest Line
Liz Klimas (http://www.theblaze.com/author/lizklimas/) July 16, 2014 10:27 am
You’ve taken a peek into nearly every line at the grocery store and selected the one that you think will get you checked out the fastest. Then you see someone in the next line over, who queued up two minutes after you, heading out the door well before all of your goods have even been bagged.
Feel like this happens
to you every time you pick a line? There’s probably a reason. "When
you’re selecting among several lines at the grocery store, the odds
are not in your favor. Chances are, the other line really is
faster,” Adam Mann for Wired wrote
(http://www.wired.com/2014/07/whatsupwiththeotherlineisalwaysfaster/).
“Mathematicians who study the behavior of lines are called queueing
theorists, and they’ve got the numbers to prove this. Their models
also underlie a diverse set of modern problems, including traffic
engineering, factory design, and Internet infrastructure. At the
same time, queueing theory provides a fairer way to checkout at the
store. The only problem is that many customers don’t like it.”
Based on queuing theory, which Mann goes into detail about
(http://www.wired.com/2014/07/whatsupwiththeotherlineisalwaysfaster/),
there is no special trick to ensure you will always be in the
fastest line possible. "A grocery store tries to have enough
employees at the checkout lines to get all their customers through
with minimum delay. But sometimes, like on a Sunday afternoon, they
get super busy. Because most grocery stores don’t have the physical
space to add more checkout lines, their system becomes
overwhelmed,” he wrote. “Some small interruption — a price check, a
particularly talkative customer — will have downstream effects,
holding up the entire line behind them. "If there are three lines
at the store, these delays will happen randomly at different
registers,” Mann continued. “Think about the probability. The
chances of your line being that fastest one are only one in three.
Which means you have a twothirds chance of not being in the
fastest line. So it’s not just in your mind: Another line is
probably moving faster than yours.”
To take care of at least part of this problem, queuing theorists
suggest having all customers stand in a single long line and then
each clerk serves the next person as they become available. This is
similar to the method employed at several Trader Joe’s and T.J.
Maxx stores, as well as many fastfood chains, for example. "With a
serpentine line, a long delay at one register won’t unfairly punish
the people who lined up behind it. Instead, it will slow everyone
down a little bit,” Mann wrote. Unfortunately, Mann noted, many
customers actually prefer to test their luck rather than stand in
one long line. Traffic lanes come with a host of other issues that
can make one seem slower than the other. One of them, Tom Stafford
for the BBC wrote, is the “universevictim theory.”
“When my lane is moving along I’m focusing on where I’m going,
ignoring the traffic I’m overtaking. When my lane is stuck I’m
thinking about me and my hard luck, looking at the other lane. No
wonder the association between me and being overtaken sticks in
memory more,” he said, explaining the one of the psychological
aspects
(http://www.bbc.com/future/story/20130827whyotherqueuesmovefaster)
that plays into lines.
Tom Vanderbilt, author of “Traffic: Why We Drive the Way We Do” who
wrote of the “other lane” issue, among other observations of how
traffic has shaped us, agreed with this psychological aspect in a
Q&A
(http://freakonomics.com/2008/06/05/howsmydrivingaqawiththeauthoroftraffic/).
“Given the general findings that humans are more sensitive to losses than gains, it doesn’t seem a stretch to imagine that this sense of being passed — of the other lane being faster — would stick out in our brains. All you have to do is pick out a benchmark car in the adjoining lane to see how often we fall for this illusion,” he said (http://freakonomics.com/2008/06/05/howsmydrivingaqawiththeauthoroftraffic/). “I’ve seen these cars pass well out of vision, only to find myself passing them again minutes later. Part of the reason this seesaw effect is happening in the first place is because of all the drivers ahead thought they could get a better deal, and basically ended up just shifting the equilibrium around temporarily.”
In: Operations Management
A model rocket is fired vertically upward from rest. Its acceleration for the first three seconds is a(t)=96t, at which time the fuel is exhausted and it becomes a freely "falling" body. 1919 seconds later, the rocket's parachute opens, and the (downward) velocity slows linearly to −16 ft/s in 5 s. The rocket then "floats" to the ground at that rate.
(a) Determine the position function s and the velocity function v(for all times t).
| v(t)= |
|
| s(t)= |
|
(b) At what time does the rocket reach its maximum height? (Round your answer to two decimal places.)
What is that height? (Round your answer to the nearest integer.)
(c) At what time does the rocket land? (Round your answer to one decimal place.)
In: Physics
Transactions; Financial Statements
Bev’s Dry Cleaners is owned and operated by Beverly Zahn. A building and equipment are currently being rented, pending expansion to new facilities. The
actual work of dry cleaning is done by another company for a fee. The assets and the liabilities of the business on November 1, 2019, are as follows: Cash,
$14,280; Accounts Receivable, $29,240; Supplies, $2,720; Land, $34,000; Accounts payable, $12,240. Business transactions during November are
summarized as follows:
a. Beverly Zahn invested additional cash in the business with a deposit of $27,000 in the business bank account.
b. Purchased land adjacent to land currently owned by Bev’s Dry Cleaners to use in the future as a parking lot, paying cash of $13,400.
c. Paid rent for the month, $16,320.
d. Charged customers for dry cleaning revenue on account, $4,900.
e. Paid creditors on account, $2,280.
f. Purchased supplies on account, $12,080.
g. Received cash from cash customers for dry cleaning revenue, $26,110.
h. Received cash from customers on account, $32,640.
i. Received monthly invoice for dry cleaning expense for November (to be paid on December 10), $13,060.
j. Paid the following: wages expense, $7,180; truck expense, $2,610; utilities expense, $2,770; miscellaneous expense, $1,240.
k. Determined that the cost of supplies on hand was $1,800; therefore, the cost of supplies used during the month was $3,200.
l. Withdrew $7,500 cash for personal use; Financial Statements
Bev’s Dry Cleaners is owned and operated by Beverly Zahn. A building and equipment are currently being rented, pending expansion to new facilities. The
actual work of dry cleaning is done by another company for a fee. The assets and the liabilities of the business on November 1, 2019, are as follows: Cash,
$14,280; Accounts Receivable, $29,240; Supplies, $2,720; Land, $34,000; Accounts payable, $12,240. Business transactions during November are
summarized as follows:
a. Beverly Zahn invested additional cash in the business with a deposit of $27,000 in the business bank account.
b. Purchased land adjacent to land currently owned by Bev’s Dry Cleaners to use in the future as a parking lot, paying cash of $13,400.
c. Paid rent for the month, $16,320.
d. Charged customers for dry cleaning revenue on account, $4,900.
e. Paid creditors on account, $2,280.
f. Purchased supplies on account, $12,080.
g. Received cash from cash customers for dry cleaning revenue, $26,110.
h. Received cash from customers on account, $32,640.
i. Received monthly invoice for dry cleaning expense for November (to be paid on December 10), $13,060.
j. Paid the following: wages expense, $7,180; truck expense, $2,610; utilities expense, $2,770; miscellaneous expense, $1,240.
k. Determined that the cost of supplies on hand was $1,800; therefore, the cost of supplies used during the month was $3,200.
l. Withdrew $7,500 cash for personal use; Financial Statements
Bev’s Dry Cleaners is owned and operated by Beverly Zahn. A building and equipment are currently being rented, pending expansion to new facilities. The
actual work of dry cleaning is done by another company for a fee. The assets and the liabilities of the business on November 1, 2019, are as follows: Cash,
$14,280; Accounts Receivable, $29,240; Supplies, $2,720; Land, $34,000; Accounts payable, $12,240. Business transactions during November are
summarized as follows:
a. Beverly Zahn invested additional cash in the business with a deposit of $27,000 in the business bank account.
b. Purchased land adjacent to land currently owned by Bev’s Dry Cleaners to use in the future as a parking lot, paying cash of $13,400.
c. Paid rent for the month, $16,320.
d. Charged customers for dry cleaning revenue on account, $4,900.
e. Paid creditors on account, $2,280.
f. Purchased supplies on account, $12,080.
g. Received cash from cash customers for dry cleaning revenue, $26,110.
h. Received cash from customers on account, $32,640.
i. Received monthly invoice for dry cleaning expense for November (to be paid on December 10), $13,060.
j. Paid the following: wages expense, $7,180; truck expense, $2,610; utilities expense, $2,770; miscellaneous expense, $1,240.
k. Determined that the cost of supplies on hand was $1,800; therefore, the cost of supplies used during the month was $3,200.
l. Withdrew $7,500 cash for personal use
In: Accounting
Explain in 200 words the relationship between Openness and economic development by calculating the correlation coefficient between GDP per capita (proxy for economic development) and Openness for Paraguay and Poland, respectively.
| Country Name | Country Code | Series Name | Series Code | 2001 [YR2001] | 2002 [YR2002] | 2003 [YR2003] | 2004 [YR2004] | 2005 [YR2005] | 2006 [YR2006] | 2007 [YR2007] | 2008 [YR2008] | 2009 [YR2009] | 2010 [YR2010] | 2011 [YR2011] | 2012 [YR2012] | 2013 [YR2013] | 2014 [YR2014] |
| Paraguay | PRY | Exports of goods and services (current US$) | NE.EXP.GNFS.CD | 3459319570 | 3402825624 | 3625989129 | 4371893087 | 5083809323 | 6252319090 | 7818347667 | 9993980610 | 8210295841 | 11036468064 | 13186264509 | 12278348692 | 14356651476 | 13954911448 |
| Paraguay | PRY | GDP (current US$) | NY.GDP.MKTP.CD | 7662595076 | 6325151760 | 6588103836 | 8033877360 | 8734653809 | 10646157920 | 13794910634 | 18504130753 | 15929902138 | 20030528043 | 25099681461 | 24595319574 | 28965906502 | 30881166852 |
| Paraguay | PRY | GDP per capita (current US$) | NY.GDP.PCAP.CD | 1417 | 1148 | 1175 | 1409 | 1507 | 1810 | 2312 | 3060 | 2600 | 3226 | 3988 | 3856 | 4480 | 4713 |
| Paraguay | PRY | GINI index (World Bank estimate) | SI.POV.GINI | 55 | 57 | 56 | 53 | 51 | 54 | 52 | 51 | 50 | 52 | 53 | 48 | 48 | 52 |
| Paraguay | PRY | Imports of goods and services (current US$) | NE.IMP.GNFS.CD | 2727373823 | 2298406126 | 2623501714 | 3307792347 | 4018039423 | 5221045741 | 6461917817 | 9166237324 | 7130137358 | 10313046052 | 12621883682 | 11979621541 | 12983600420 | 13242370791 |
| Poland | POL | Exports of goods and services (current US$) | NE.EXP.GNFS.CD | 51878648721 | 57137009804 | 72632296220 | 87410323710 | 105952277925 | 130565028203 | 165538367008 | 202086584758 | 163740453116 | 191967370760 | 225042181278 | 222344181762 | 242809098962 | 259386390289 |
| Poland | POL | GDP (current US$) | NY.GDP.MKTP.CD | 190521263343 | 198680637255 | 217518642325 | 255102252843 | 306134635594 | 344826430298 | 429249647595 | 533815789474 | 440346575958 | 479257883742 | 528725113046 | 500284003684 | 524201151607 | 545075908846 |
| Poland | POL | GDP per capita (current US$) | NY.GDP.PCAP.CD | 4981 | 5197 | 5694 | 6681 | 8021 | 9041 | 11260 | 14001 | 11542 | 12598 | 13891 | 13144 | 13780 | 14340 |
| Poland | POL | GINI index (World Bank estimate) | SI.POV.GINI | 33 | 34 | 35 | 35 | 35 | 34 | 34 | 34 | 34 | 33 | 33 | 32 | 33 | 32 |
| Poland | POL | Imports of goods and services (current US$) | NE.IMP.GNFS.CD | 58766945944 | 63908088235 | 78406788377 | 94256069554 | 109183717624 | 137680257857 | 180703003578 | 228993441806 | 167514280213 | 201543256955 | 235386043059 | 224546822229 | 232598709188 | 251529270071 |
In: Economics