Tombro Industries is in the process of automating one of its plants and developing a flexible manufacturing system. The company is finding it necessary to make many changes in operating procedures. Progress has been slow, particularly in trying to develop new performance measures for the factory.

In an effort to evaluate performance and determine where improvements can be made, management has gathered the following data relating to activities over the last four months:

The president has read in industry journals that throughput time, MCE, and delivery cycle time are important measures of performance, but no one is sure how they are computed. You have been asked to assist the company, and you have gathered the following data relating to these measures:

Required:

1-a. Compute the throughput time for each month.

1-b. Compute the manufacturing cycle efficiency (MCE) for each month.

1-c. Compute the delivery cycle time for each month.

3-a. Refer to the inspection time, process time, and so forth, given for month 4. Assume that in month 5 the inspection time, process time, and so forth, are the same as for month 4, except that the company is able to completely eliminate the queue time during production using Lean Production. Compute the new throughput time and MCE.

3-b. Refer to the inspection time, process time, and so forth, given for month 4. Assume that in month 6 the inspection time, process time, and so forth, are the same as in month 4, except that the company is able to eliminate both the queue time during production and the inspection time using Lean Production. Compute the new throughput time and MCE.

Accurate and Efficient Reporting
Case Study- 1: Delta Lloyd Group Ensures Accuracy and Efficiency in Financial Reporting
Case Study- 2: Flood of Paper Ends at FEMA
 
Learning Objectives:
• How to improve accuracy in reporting
• Compliance with industry regulations
• How to improve efficiency in reporting
Case Study- 1: Delta Lloyd Group Ensures Accuracy and Efficiency in Financial Reporting
Delta Lloyd Group is a financial services provider based in the Netherlands. It offers insurance, pensions, investing, and banking se1vices to its private and corporate clients through its three strong brands: Delta Lloyd, OHRA, and ABN AMRO Insurance. Since its founding in 1807, the company has grown in the Netherlands, Germany, and Belgium, and now employs around 5,400 pe1manent staff. Its 2011 full-year financial reports show €5.5 billion in gross written premiums, with shareholders' funds amounting to €3 .9 billion and investments under management worth nearly €74 billion.
Challenges:
Since Delta Lloyd Group is publicly listed on the NYSE Euronext Amsterdam, it is obliged to produce annual and half-year repo1ts. Various subsidiaries in Delta Lloyd Group must also produce reports to fulfill local legal requirements: for example, banking and insurance reports are obligatory in the Netherlands. In addition, Delta Lloyd Group must provide reports
to meet international requirements, such as the IFRS (International Financial Reporting Standards) for accounting and the EU Solvency I Directive for insurance companies. The data for these reports is gathered by the group's finance department, which is divided into small teams in several locations, and then converted into XML so that it can be published on the corporate Web site.
Importance for Accuracy:
The most challenging part of the reporting process is the "last mile"-the stage, at which the consolidated figures are cited, formatted, and described to form the final text of the report. Delta Lloyd Group was using Microsoft Excel for the last-mile stage of the reporting process. To minimize the risk of errors, the finance team needed to manually check all the data in its reports for accuracy. These manual checks were very time-consuming. Arnold Honig,
team leader for reporting at Delta Lloyd Group, comments: "Accuracy is essential in financial
reporting, since errors could lead to penalties, reputational damage, and even a negative impact on the company's stock price. We needed a new solution that would automate some of the last mile processes and reduce the risk of manual error. "

Solution
The group decided to implement IBM Cognos Financial Statement Reporting (FSR). The implementation of the software was completed in just 6 weeks during the late summer. This rapid implementation gave the finance department enough time to prepare a trial draft of the annual report in FSR, based on figures from the third financial quarter. The successful creation of this draft gave Delta Lloyd Group enough confidence to use Cognos FSR for the final version of the annual report, which was published shortly after the end of the year.

Results
Employees are delighted with the IBM Cognos FSR solution. Delta Lloyd Group has divided the annual report into chapters, and each member of the reporting team is responsible for one chapter. Arnold Honig says, "Since employees can work on documents simultaneously, they can share the huge workload involved in report generation. Before, the reporting process was inefficient, because only one person could work on the report at a time."
Since the workload can be divided up, staff can complete the report with less overtime. Arnold Honig comments, "Previously, employees were putting in 2 weeks of overtime during the 8 weeks required to generate a report. This year, the 10 members of staff involved in the report generation process worked 25 percent less overtime, even though they were still getting used to the new software. This is a big win for Delta Lloyd Group and its staff. " The group is expecting further reductions in employee overtime in the future as staff becomes more familiar with the software.
Accurate Reports
The IBM Cognos FSR solution automates key stages in the report-writing process by populating the final report with accurate, up-to-date financial data. Wherever the text of the report needs to mention a specific financial figure, the finance team simply inserts a "variable "- a tag that is linked to an underlying data source. Wherever the variable appears in the document, FSR will pull the figure through from the source into the report. If the value of the figure needs to be changed, the team can simply update it in the source, and the new value will automatically flow through into the text, maintaining accuracy and consistency of data throughout the report.
Arnold Honig comments, "The ability to update figures automatically across the whole report
reduces the scope for manual error inherent in spreadsheet-based processes and activities. Since we have full control of our reporting processes, we can produce better quality reports more efficiently and reduce our business risk. “IBM Cognos FSR also provides a comparison feature, which highlights any changes made to reports. This feature makes it quicker and easier for users to review new versions of documents and ensure the accuracy of their reports.
Adhering to Industry Regulations
In the future, Delta Lloyd Group is planning to extend its use of IBM Cognos FSR to generate internal management reports. It will also help Delta Lloyd Group to meet industry regulatory standards, which are becoming stricter. Arnold Honig comments, "The EU Solvency II Directive will come into effect soon, and our Solvency II reports will need to be tagged with extensible Business Reporting Language [XBRL]. By implementing IBM Cognos FSR, which fully suppo1ts
XBRL tagging, we have equipped ourselves to meet both current and future regulatory requirements. "
 
Case Study- 2: Flood of Paper Ends at FEMA
Staff at the Federal Emergency Management Agency (FEMA), a U.S. federal agency that coordinates disaster response when the President declares a national disaster, always got two floods at once. First, water covered the land. Next, a flood of paper, required to administer the National Flood Insurance Program (NFIP), covered their desks-pallets and pallets of green-striped reports poured off a mainframe printer and into their offices. Individual reports were sometimes 18 inches thick, with a nugget of information about insurance claims, premiums, or payments buried in them somewhere. Bill Barton and Mike Miles don't claim to be able to do anything about the weather, but the project manager and computer scientist, respectively,
from Computer Sciences Corporation (CSC) have used WebFOCUS software from Information
Builders to turn back the flood of paper generated by the NFIP. The program allows the government to work together with national insurance companies to collect flood insurance premiums and pay claims for flooding in communities that adopt flood control measures. As a result of CSC's work, FEMA staff no longer leaf through paper reports to find the data they need. Instead, they browse insurance data posted on NFIP's BureauNet intranet site, select just
the information n they want to see, and get an onscreen report or download the data as a spreadsheet.
And that is only the start of the savings that WebFOCUS has provided. The number of times that
NFIP staff asks CSC for special reports has dropped in half, because NFIP staff can generate many of the special reports they need without calling on a programmer to develop them. Then there is the cost of creating BureauNet in the first place. Barton estimates that using conventional Web and database software to export data from FEMA's mainframe,
store it in a new database, and link that to a Web server would have cost about 100 times as much more than $500,000- and taken about two years to complete, compared with the few months Miles spent on the WebFOCUS solution .
When Tropical Storm Allison, a huge slug of sodden, swirling clouds, moved out of the Gulf of
Mexico onto the Texas and Louisiana coastline in June 2001, it killed 34 people, most from drowning; damaged or destroyed 16,000 homes and businesses; and displaced more than 10,000 families. President George W. Bush declared 28 Texas counties disaster areas, and FEMA moved in to help. This was the first serious test for BureauNet, and it delivered. This first comprehensive use of BureauNet resulted in FEMA field staff readily accessing what they needed and when they needed it, and asking for many new types of reports. Fortunately, Miles and WebFOCUS were up to the task. In some cases, Barton says, "FEMA would ask for
a new type of report one day, and Miles would have it on BureauNet the next day, thanks to the speed with which he could create new reports in WebFOCUS.”
The sudden demand on the system had little impact on its performance, notes Barton. "It handled the demand just fine,” he says. "We had no problems with it at all." "And it made a huge difference to FEMA and the job they had to do. They had never had that level of access before, never had been able to just click on their desktop and generate such detailed and specific reports. "
 
Simulation
Case Study- 1: Agent-based simulation helps Analyze Spread of a Pandemic Outbreak
 
Learning Objectives:
• Know the concepts behind and application of genetic algorithm
Agent-based simulation helps Analyze Spread of a Pandemic Outbreak
Knowledge about the spread of a disease plays an important role in both preparing for and responding to a pandemic outbreak. Previous models for such analyses are mostly homogenous and make use of simplistic assumptions about transmission and the infection rates. These models assume that each individual in the population is identical and typically has the same number of potential contacts with an infected individual in the same time period.
Also each infected individual is assumed to have the same probability to transmit the disease. Using these models, implementing any mitigation strategies to vaccinate the susceptible individuals and treating the infected individuals become extremely difficult under limited resources.
In order to effectively choose and implement a mitigation strategy, modeling of the disease spread has to be done across the specific set of individuals, which enables researchers to prioritize the selection of individuals to be treated first and also gauge the effectiveness of mitigation strategy.
Although nonhomogenous models for spread of a disease can be built based on individual characteristics using the interactions in a contact network, such individual levels of infectivity and vulnerability require complex mathematics to obtain the information needed for such models.
Simulation techniques can be used to generate hypothetical outcomes of disease spread by simulating events on the basis of hourly, daily, or other periods and tallying the outcomes throughout the simulation. A nonhomogenous agent-based simulation approach allows each member of the population to be simulated individually, considering the unique individual characteristics that affect the transmission and infection probabilities. Furthermore, individual behaviors that affect the type and length of contact between individuals, and the possibility of infected individuals recovering and becoming immune, can also be simulated via agent-based models.
One such simulation model, built for the Ontario Agency for Health Protection and Promotion
(OAHPP) following the global outbreak of severe acute respiratory syndrome (SARS) in 2002-2003, simulated the spread of disease by applying various mitigation strategies. The simulation models each state of an individual in each time unit, based on the individual probabilities to transition from susceptible state to infected stage and then to recovered state and back to susceptible state. The simulation model also uses an individual's duration of contact with infected individuals. The model also accounts for the rate of disease transmission per time unit based on the type of contact between individuals and for behavioral changes of individuals in a disease progression (being quarantined or treated or recovered). It is flexible enough to consider several factors affecting the mitigation strategy, such as an individual's age, residence, level of general interaction with other members of population, number of individuals in each household, distribution of households, and behavioral aspects involving daily commutes, attendance at schools, and asymptotic time period of disease.
The simulation model was tested to measure the effectiveness of a mitigation strategy involving an advertising campaign that urged individuals who have symptoms of disease to stay at home rather than commute to work or school. The model was based on a pandemic influenza outbreak in the greater Toronto area. Each individual agent, generated from the population, was sequentially assigned to households. Individuals were also assigned to different ages based on census age distribution; all other pertinent demographic and behavioral attributes were assigned to the individuals.
The model considered two types of contact: close contact, which involved members of the same household or commuters on the public transport; and causal contact, which involved random individuals among the same census tract. Influenza pandemic records provided past disease transmission data, including transmission rates and contact time for both close and causal contacts. The effect of public transportation was simplified with an assumption that every individual of working age used the nearest subway line to travel. An initial outbreak of infection was fed into the model. A total of 1,000 such simulations were conducted.
The results from the simulation indicated that there was a significant decrease in the levels of infected and deceased persons as an increasing number of infected individuals followed the mitigation strategy of staying at home. The results were also analyzed by answering questions that sought to verify issues such as the impact of 20 percent of infected individuals staying at home versus 10 percent staying at home. The results from each of the simulation outputs were fed into geographic information system software, ESRI ArcGIS, and detailed shaded maps of the greater Toronto area, showing the spread of disease based on the average number of cumulative infected individuals. This helped to determine the effectiveness of a particular mitigation strategy. This agent-based simulation model provides a what-if analysis too l that can be used to compare relative outcomes of different disease scenarios and mitigation strategies and help in choosing the effective mitigation strategy.

Case Study- 1: Delta Lloyd Group Ensures Accuracy and Efficiency in Financial Reporting
Case Study- 2: Flood of Paper Ends at FEMA
Activity
Based on the above case studies discussion answer the following questions:
1. How did Delta Lloyd Group improve accuracy and efficiency in financial reporting?

2. What were the challenges, the proposed solution, and the obtained results?

3. Why is it important for any organization to comply with industry regulations?

4. What are the main challenges that FEMA faces?

5. How did FEMA improve its inefficient reporting practices?


Simulation
Case Study- 1: Agent-based simulation helps Analyze Spread of a Pandemic Outbreak
Learning Objectives:
• Know the concepts behind and application of genetic algorithm
___________________________________________________
Activity
Based on the above case studies discussion answer the following questions:
1. What are the characteristics of an agent-based simulation model?

2. List the various factors that were fed into the agent based simulation model described in the case.

3. Elaborate on the benefits of using agent-based simulation models.

4. Besides disease prevention, in which other situations could agent-based simulation be employed?

Please, short answers, she does not want to copy paste, she wants in my style

Case study and the questions underneath that you want an answer, Please, the duty closes ten

Write Matlab programs implementing the algorithms based on bisection,Newton, and secant method for numerical solution of scalar nonlinear equa-tions. Use these programs to compute approximations to real roots of the

following equations:

exp(x)−3x^2=0, (1)

x^3=x^2+x+1, (2)

exp(x) =1/(0.1 +x^2), (3)

and

x= 1 + 0.3 cos(x). (4)

Use an error tolerance tol=10^(−12). Display the obtained approximations to the roots of these equations, and compare the number of iterations, starting with the same initial values x0 for bisection and Newton methods, and with x0 and x1, where x1 was computed by bisection starting with x0, for secant method.

1. The following table presents shear strengths (in kN/mm) and weld diameters (in mm) for a sample of spot welds.

1. Construct a scatterplot of strength (y) versus diameter (x). Verify that a linear model is appropriate.
2. Compute the correlation coefficient.
3. Compute the least-squares line for predicting strength from diameter.
4. Compute the fitted value and the residual for each point.
5. If the diameter is increased by 0.3 mm, by how much would you predict the strength to increase or decrease?
6. Predict the strength for a diameter of 5.5 mm.

How do you solve this?

A thin-wall pressure vessel is shown below. Internal diameter=d, wall thickness=t.

In addition to the internal pressure p (p=100 psi), a torque T is applied. Assume d=20 inch (r=10 in), t=0.1 inch, T=502,640 in-lb. Determine the wall stress components. Assume that the longitudinal direction is labeled as x and the circumferential (hoop) direction is label as y.

Use Mohr's circle to determine the principle strains and maximum shear strain. Assume that E=10,000 ksi and Poisson's ratio v=0.3 and plane stress case.

Find epsilon x, y, and z. Find gamma xy

A block has mass M=2kg and is initially at rest, then slides a distance L = 1.25m down a theta = 30degrees frictionless incline plane until it is momentarily brought to rest by a spring. The spring is compressed a distance X= 3cm .

a) Write down expressions for the initial and final energy of the system

b) Find an expression for the spring constant K in terms of L, X, theta and M and calculate K

c)The experiment is repeated but the spring is removed. This time the block slides a distance L = 1.4m to the bottom of the frictionless incline and at the bottom of the incline the block slides an additional distance d along a horizontal surface with coefficient of kinetic friction = 0.3, before coming to rest. Use Work and energy to find d.

A machine is designed to fill containers with 16 ounces of coffee. A consumer suspects that the machine is not filling the containers completely and the amount of coffee is less than 16 ounces when filled. A sample of 8 containers has a mean of 15.6 ounces of coffee and a standard deviation of 0.3 ounces of coffee.  Assume that the distribution of data is normal. At 10% significance level, is there enough evidence to support the consumer’s suspicion that the machine is not filling the containers completely and the amount of coffee in a container is less than 16 ounces when filled?

Question: Which of the following is the correct calculator command to use?

2-SampTTest

2-SampTInt

ANOVA

Z-Test

2-SampZTest

T-Interval

Z-Interval

2-SampZint

T-Test

A battery pack used in a medical device needs to be recharged about every 5 hours. A random sample of 20 battery packs is selected and subjected to a life test. The average life of these batteries is 5.05 hours with standard deviation ? = 0.3 hours. Assume that battery life is normally distributed. Is there evidence to support the claim that mean battery life is more than 5 hours? Use ? = 0.01.

a. Write the appropriate hypothesis.

b. Use P-value approach to test the hypothesis.

c. Use t-test to test the hypothesis.

d. Use confidence interval to test the hypothesis.

e. What is your conclusion?

f. If the true mean life is 5.1 hours, what is the type II error?

Assume firm A wants to borrow float while firm B wants to borrow fixed. Firm A is offered 10% fixed borrowing cost and LIBOR + 0.3% float borrowing cost while firm B is offered 11.2% fixed borrowing cost and LIBOR + 1% float borrowing cost.

1. Show how the two firms can reduce their borrowing costs equally by entering into an interest rate plain vanilla swap? Use a table to demonstrate your solution
2. If the agreed-upon notional amount is $100m (no need to know the swap tenor), how much each party annually pays the other in absolute amounts? (assume that the LIBOR = 5%). Show your answer in a table similar to that in (a) solution