In April 1993, Dr. Nancy Olivieri, head of the hemoglobinopathy program at the Hospital for Sick Children (HSC), the teaching hospital for the University of Toronto in Canada, signed an agreement with the Canadian drug company Apotex to undertake clinical trials on a drug called deferiprone (referred to as L1 during the study). The drug was designed to help children with thalassemia, an inherited blood disorder that can cause the fatal buildup of iron in the blood. The agreement that Olivieri signed with Apotex included a clause (later referred to as a “gag clause”) that specifically prevented the unauthorized release of any findings in the trial for a period of three years: As you now [sic], paragraph 7 of the LA-02 Contract provides that all information whether written or not, obtained or generated by you during the term of the LA-02 Contract and for a period of three years thereafter, shall be and remain secret and confidential and shall not be disclosed in any manner to any third party except with the prior written consent of Apotex. Please be aware that Apotex will take all possible steps to ensure that these obligations of confidentiality are met and will vigorously pursue all legal remedies in the event that there is any breach of these obligations. The existence of this clause was to prove significant to the relationship between Olivieri and Apotex. After reporting some initial positive findings in the trial in April 1995, Olivieri reported in December 1996 that long-term use of the drug appeared to result in the toxic buildup of iron in the liver of a large number of her pediatric patients—a condition known as hepatic fibrosis. When she reported the findings to Apotex, the company determined that her interpretation of the data was incorrect. Olivieri then contacted the hospital’s Research Ethics Board (REB), which instructed her to change the consent form for participation in the trial to ensure that patients were made aware of the risks of long-term use of the drug. After copying Apotex on the revised form, the company notified Olivieri that the Toronto trials were being terminated effective immediately and that she was being removed as chair of the steering committee of the global trial that included patients in Philadelphia and Italy. When Olivieri notified Apotex that she and her research partners, including Dr. Gary Brittenham of Case Western Reserve University in Cleveland, were planning to publish their findings in the August 1998

issue of the New England Journal of Medicine, Apotex Vice President Michael Spino threatened legal action for breaching the confidentiality clause in her agreement with the company. Olivieri then asked the HSC administration for legal support in her forthcoming battle with Apotex. The administrators declined. She then approached the University of Toronto, where the dean of the Faculty of Medicine declined to get involved on the grounds that her contract with Apotex had been signed without university oversight and that the university would never have agreed to the confidentiality clause in the first place. Olivieri forged ahead with the publication despite this [lack of support] and instantly became celebrated as a courageous whistle-blower in the face of corporate greed. The situation was further clouded by reports that the University of Toronto and HSC were, at the time, in the process of negotiating a $20 million donation from Bernard Sherman, the CEO and founder of Apotex. The bitter relationship with her employers was to continue for several years, during which time she was referred to the Canadian College of Physicians and Surgeons for research misconduct and dismissed from her post at HSC, only to be reinstated following the aggressive support of several of her academic colleagues, including Dr. Brenda Gallie of the Division of Immunology and Cancer at HSC, who led a petition drive that succeeded in garnering 140 signatures in support of a formal enquiry into Dr. Olivieri’s case. That enquiry was undertaken by both the Canadian College of Physicians and Surgeons, which found her conduct to be “exemplary,” and by the Canadian Association of University Teachers, whose 540-page report concluded that Dr. Olivieri’s academic freedom had been violated when Apotex stopped the trials and threatened legal action against her. The two-and-a-half-year battle ended in January 1999 when an agreement was brokered between the university, HSC, and Olivieri thanks to the efforts of two world-renowned experts in blood disorders—Dr. David Nathan of Harvard and Dr. David Weatherall of Oxford who intervened on the basis of the international importance of Dr. Olivieri’s research. Working with the president of the University of Toronto, Robert Pritchard, and lawyers for both parties, a compromise settlement was reached that reinstated Olivieri as head of the hemoglobinopathy program at HSC, covered her legal expenses up to $150,000, and withdrew all letters and written complaints about her from her employment file. As part of the agreement, a joint working group appointed by the University of Toronto and the university’s Faculty Association was chartered with the task of making “recommendations on changes to university policies on the dissemination of research publications and conflict of interest and the relationship of these issues to academic freedom.”

1. Was it ethical for Apotex to include a three-year gag clause in the agreement with Dr. Olivieri?

2. Even though Dr. Olivieri later admitted that she should never have signed the agreement with Apotex that included a confidentiality clause, does the fact that she did sign it have any bearing on her actions here? Why or why not?

3. Was Olivieri’s decision to publish her findings about the trial an example of universalism or utilitarianism? Explain your answer.

4. If we identify the key players in this case as Dr. Olivieri, Apotex, the Hospital for Sick Children, and the University of Toronto, what are the conflicts of interest between them all?

5. What do you think would have happened if Dr. Olivieri’s fellow academics had not supported her in her fight?

6. How could this situation have been handled differently to avoid such a lengthy and bitter battle?

Jack, Jills and the Buffalo Bills

Before the 2014 season, Cailin Ferrari had conflicting thoughts about continuing her dream of being a member of the Buffalo Jills, (the Buffalo Bill’s cheerleading team) or to seek employment elsewhere. For the past 48 years, the Jills were an important part of the Bills organization, entertaining fans both on and off the playing field. However, after some careful research, the Jills found themselves wondering if they should continue to entertain fans under tense circumstances.

Buffalo Jills

Established in 1967, the Jills began as a permanent replacement for the cheerleaders from Buffalo State College who previously cheered from the Buffalo Bills sidelines. The Jills cheerleaders recognized for their high spirit, dedication, and humanitarian nature, had become a favorite for the city of Buffalo. After 42 seasons of entertaining Bills fans, the Jills established the Buffalo Jills Alumni Association.

Buffalo Bills

The Buffalo Bills, located in Buffalo NY, is currently owned by Terrence and Kim Pegula. In 2016, Forbes reported the team value at one billion, five-hundred million dollars (see exhibit 1). New Era Field, formally Rich Stadium and later Ralph Stadium, has been the home for the Buffalo Bills since 1973. The stadium has a capacity seating for 71,870 Bills fans. NEF is currently within the top 15 in capacity in the National Football League.

Exhibit 1: Bills Value Breakdown

Legal Issues

In April 2014, five former Bills cheerleaders sued the team over a pay system that had them working hundreds of hours for free at games and at mandatory public appearances. Soon after, management suspended the dance team.

The class action lawsuit claimed the Jills cheerleaders were paid below minimum wage and were required to attend unpaid events. The former cheerleaders also alleged that the Jills were wrongly classified as independent contractors and were subjected to policies that violate the state's $8 per hour minimum wage law and other workplace rules (Rodak, 2014). The Jills were not paid for games or practices and had to make 20 to 35 community and charity events each season.

The Jills stated that at some of these sponsored events, they were made to feel uncomfortable by male attendees. They were forced to adhere to strict dress codes and behavioral guidelines set by the team. According to the Jills, the Buffalo Bills controlled everything from their physical appearance to music selection (Garcia, 2016). The Bills organization claimed the Jills were not traditional employees but independent contractors.

In a 1995 ruling by the National Labor Relations Board, the Jills were classified as non-exempt employees. A former employee of Cumulus Broadcasting Co. (formally Citadel Broadcasting Co), named Stephanie E. Mateczun, managed the Jills. The contracts gave Citadel/Cumulus the exclusive rights to run the Jills, and required each member of the cheerleading squad to sign independent contractor agreements that the Jills would not be paid for working Bills games (Davis, 2017).

National Football Association

Currently, only six teams in the National Football Association (NFL) do not have a cheerleading team, either by personal choice or in the Jills case, suspension: Buffalo Bills, Cleveland Browns, New York Giant, Pittsburgh Steelers, Green Bay Packers, and Chicago Bears.

The NFL has remained quiet with this issue. Rodger Goodell, the commissioner of the NFL stated, he had no knowledge of the Jills’ selection, training, compensation and/or pay practices. According to the NFLPA (National Football League Players Association), the NFL protects its players but has no mention of its cheerleader teams. As reported by the NFLPA website, the National Football League Players Association:

Represents all players in matters about wages, hours and working conditions.

Protects their rights as professional football players

Assures that all the terms of the Collective Bargaining Agreement are met.

Decision

New York State Supreme Court Justice Mark A. Montour decided the cheerleaders' 2005 agreement they signed were unenforceable, and that the plaintiffs were non-exempt employees and they were misclassified as independent contractors.

In response to the lawsuit, the Cheerleaders' Fair Pay Act would force team owners to treat the Jills as employees rather than independent contractors. The change would mean teams like the Buffalo Bills would have to comply with much stricter New York labor laws when it comes to cheerleaders' wages and workplace protections. Was the contract negotiable between both parties? Was the contract by the Jills signed under duress? What employment laws did the Buffalo Bills violate? Should the NFL create a regulated pay scale for all NFL cheerleaders?

Questions to Answer.

1. What employment laws (if any) did the Buffalo bills violate? Please explain your answer thoroughly in either scenario?

2. Do you think the ruling was fair? Was there any ethical concerns in the case? Discuss your view point.

3. Discuss the social responsbility (if any) for the NFL and the Buffalo Bills.

4. Should the NFL creat a regulated pay scale for all NFL Cheerleaders? Or a union for the cheerleading team? Why or why not?

5. Was the contract negotiable between both parties?

Note: This problem is for the 2019 tax year.

Alice J. and Bruce M. Byrd are married taxpayers who file a joint return. Their Social Security numbers are 123-45-6784 and 111-11-1113, respectively. Alice's birthday is September 21, 1972, and Bruce's is June 27, 1971. They live at 473 Revere Avenue, Lowell, MA 01850. Alice is the office manager for Lowell Dental Clinic, 433 Broad Street, Lowell, MA 01850 (Employer Identification Number 98-7654321). Bruce is the manager of a Super Burgers fast-food outlet owned and operated by Plymouth Corporation, 1247 Central Avenue, Hauppauge, NY 11788 (Employer Identification Number 11-1111111).

The following information is shown on their Wage and Tax Statements (Form W–2) for 2019.

The Byrds provide over half of the support of their two children, Cynthia (born January 25, 1995, Social Security number 123-45-6788) and John (born February 7, 1999, Social Security number 123-45-6780). Both children are full-time students and live with the Byrds except when they are away at college. Cynthia earned $6,200 from a summer internship in 2019, and John earned $3,800 from a part-time job.

During 2019, the Byrds provided 60% of the total support of Bruce's widower father, Sam Byrd (born March 6, 1943, Social Security number 123-45-6787). Sam lived alone and covered the rest of his support with his Social Security benefits. Sam died in November, and Bruce, the beneficiary of a policy on Sam's life, received life insurance proceeds of $1,600,000 on December 28.

The Byrds had the following expenses relating to their personal residence during 2019:

The Byrds had the following medical expenses for 2019:

The medical expenses for Sam represent most of the 60% that Bruce contributed toward his father's support.

Other relevant information follows:

• When they filed their 2018 state return in 2019, the Byrds paid additional state income tax of $900.
• During 2019, Alice and Bruce attended a dinner dance sponsored by the Lowell Police Disability Association (a qualified charitable organization). The Byrds paid $300 for the tickets. The cost of comparable entertainment would normally be $50.
• The Byrds contributed $5,000 to Lowell Presbyterian Church and gave used clothing (cost of $1,200 and fair market value of $350) to the Salvation Army. All donations are supported by receipts, and the clothing is in very good condition.
• Via a crowdfunding site (gofundme.com), Alice and Bruce made a gift to a needy family who lost their home in a fire ($400). In addition, they made several cash gifts to homeless individuals downtown (estimated to be $65).
• In 2019, the Byrds received interest income of $2,750, which was reported on a Form 1099–INT from Second National Bank, 125 Oak Street, Lowell, MA 01850 (Employer Identification Number 98-7654322).
• The home mortgage interest was reported on Form 1098 by Lowell Commercial Bank, P.O. Box 1000, Lowell, MA 01850 (Employer Identification Number 98-7654323). The mortgage (outstanding balance of $425,000 as of January 1, 2019) was taken out by the Byrds on May 1, 2015.
• Alice's employer requires that all employees wear uniforms to work. During 2019, Alice spent $850 on new uniforms and $566 on laundry charges.
• Bruce paid $400 for an annual subscription to the Journal of Franchise Management and $741 for annual membership dues to his professional association.
• Neither Alice's nor Bruce's employer reimburses for employee expenses.
• The Byrds do not keep the receipts for the sales taxes they paid and had no major purchases subject to sales tax.
• This year the Byrds gave each of their children $2,000, which was then deposited into their Roth IRAs.
• Alice and Bruce paid no estimated Federal income tax, and they did not engage in any virtual currency transactions during the year. Neither Alice nor Bruce wants to designate $3 to the Presidential Election Campaign Fund.

Required:

Compute net tax payable or refund due for Alice and Bruce Byrd for 2019. If they have overpaid, they want the amount to be refunded to them.

• Make realistic assumptions about any missing data.
• If an amount box does not require an entry or the answer is zero, enter "0".
• Enter all amounts as positive numbers.
• It may be necessary to complete the tax schedules before completing Form 1040.
• When computing the tax liability, do not round your immediate calculations. If required, round your final answers to the nearest dollar.

You have downloaded the MS_Excel file with data on the prices of homes in two neighborhoods around the City of New York. The data is taken from Staten Island.

Using the MS_Excel, calculate:

a. The Average and the Standard Deviation for Sale Price for houses in the two neighborhoods

and please post the excel chart you come up with thank you

Kindly summarize this Literature Review Section 3.2 Efficient Techniques and Performance Measurement Recently, developed techniques compare the efficiency of similar service organizations by explicitly considering their use of multiple inputs to produce multiple outputs. These new efficiency techniques are often divided into two categories. One broad category consists of the linear programming procedures used in this paper (DEA). The second category is a set of regression-based techniques that derive inefficiency estimates from two-part error terms, and has been called the econometric or stochastic frontier approach. Both techniques use sample firms to construct an efficient production frontier. The frontier is efficient in the sense that a firm operating on the frontier could not increase output without increasing its input utilization, or it could not reduce its input utilization without decreasing output. Deviations from the frontier represent inefficiencies, and are termed X-inefficiencies in the finance and economics literature. Efficient frontier techniques avoid the need to develop a standard cost for each service provided and are more comprehensive and reliable that using a set of operating ratios and profit measures. These techniques permit managers and researchers to service organizations and identify units that are relatively inefficient, determine the magnitude of the inefficiency, suggest alternative strategies to reduce the inefficiencies, all in a composite measure. Moreover, these techniques provide an estimate of the overall efficiency level of the market that is under consideration. We know of only two studies that use efficient frontier techniques in the hotel industry. The first is that of Morey and Ditman (1995) who measure the relative performance of hotel general managers using DEA. The authors gathered input-output data for 54 hotels from a geographically dispersed area. They found that managers were operating 89 percent efficiency. In other words, given their output, managers on average could reduce their inputs by 11 percent. The study reported that the least efficient hotel was 64 percent efficient. These results are relatively high compared to those found in other industry studies that utilize DEA. Large efficiency scores are indicators of High performance and competition (Leibenstein 1966). Thus in an economic context, the market for lodging services appears to be operating efficiently. Anderson et al. (1998) argue for the benefits of using a stochastic frontier methodology in addition to DEA in order to accurately assess performance. Using a classical stochastic frontier model, they also find the hotel industry to be performing relatively efficiently, with efficiency measures above 90 percent. While both of these studies are informative, neither provides any information on the source of the inefficiencies. The source of the inefficiencies, whether technical or allocative in nature, is important information that managers need in order to take proactive positions to increase performance. We re-examine hotel efficiency using a method of DEA that provides significantly more detailed results and we further analyze the inefficiency sources. The following section describes our procedure.

SECTION 4 EFFICIENCY DETERMINATION

Section 4.1 The DEA Technique

Within the DEA framework, performance of an individual firm is evaluated with respect to an efficient frontier, which is constructed by taking linear combinations of existing firms. While there are several DEA approaches, wee use an unput-base approach, assuming that inputs are contracted proportionally with exogenous outputs. The procedure relies on sophisticated mathematics; however, the following simplified graphical example deomstates how th eefficiency measures are computed.

Figure 1 displays tha overall (OE) and (TE), and allocativ (AE) efficiency measures. In this example, we assume two inputs (X1 and X2), one output (Y), and constant returns to scale. Additionally, we assume that technology is fixed and that input prices are represented as PP. Firm A is X-efficient since it produces along output isoquant Y by utilizing the least inputs. Suppose thee is a firm operating at point C and producing an output equivalent of that produced along Y. C is uses more inputs than A to produce the output Y and is classified as inefficient with an overall efficiency score of 0D/0C )or equivalenly and inefficiency score of DC/0C).

Overall inefficiency can be decomposed into its techhnical and allocattive components. Without being able to alter input allocations, the bestt that firmC could have done was to operate at point B. The "extra" input usage that was incurred by firm C as a percentage of total input usage is the technical inefficiency measure and can be dpressed as BC/0C The technical efficiency of firm C is ecpresses as 0B/0C. Allocative inefficiency representts managerial failurd to use the optimal input mix. Here, allocative inefficiencies for firm C can be represented by DB/0B, and allocatvie effficiency is expressed as 0D/0B.

Technical efficiency can be further decomposed into technical (PTE) and scale (SE) efficiency measures. Pure technical inefficiency simply refers to deviations from the efficient frontier that result rom failure to utilize the employed resoures efficiently. Hence, this measure assumes that firms are operating at constant return to scale. Scale ineficiencies, on the other hand are losses due tofailure to operate at constant returns to scale. Figure 2 illustrates these two efficiency measures. In this figure, the Y-axis represents output and the X-axis represents input conbinations that contain an equal amount of both input 1 an dinput 2. The graph shows three observations denoted A, B, and C, respectively. Two frontiers are illustrated, a fronier assuming constant returns to scale instead of decreasing or increasing returns toscale.

After completing this analysis, we examine the SE measure to determine if it equals one. If the SE measure equals one, firms are operating at constant returns to scale. If SE does not equal one, we then determine whether the firms are oeprating at increasing or decreasing returns to scale (see Appendix A for a mathematical treatment of DEA).

Case Study 2: Forecasting Box Office Returns

For years, people in the motion picture industry – critics, film historians, and others – have eagerly awaited the second issue in January of Variety. Long considered the show business bible, Variety is a weekly trade newspaper that reports on all aspects of the entertainment industry; movies, television, recordings, concert tours, and so on. The second issue in January, called the Anniversary Edition, summarizes how the entertainment industry fared in the previous year, both artistically and commercially.

In this issue, Variety publishes its list of All Time Film Rental Champs. This list indicates, in descending order, motion pictures and the amount of money they returned to the studio. Because a movie theater rents a film from a studio for a limited time, the money paid for admission by ticket buyers is split between the studio and theater owner. For example, if a ticket buyer pays $8 to see a particular movie, the theater owner keeps about $4 and the studio receives the other $4. The longer a movie plays in a theater, the greater the percentage of the admission price returned to the studio. A film playing for an entire summer could eventually return as much as 90% of the $8 to the studio. The theater owner also benefits from such a success because although the owner’s percentage of the admission price is small, the sales of concessions (candy, soda and so on) provide greater profits. Thus, both the studio and the theater owner win when a film continues to draw audiences for a long time. Variety lists the rental figures (the actual dollar amounts returned to the studios) that the films have accrued in their domestic releases (United States and Canada).

In addition, Variety provides a monthly Box-Office Barometer of the film industry, which is a profile of the month’s domestic box-office returns. This profile is not measure in dollars, but scaled according to some standard. By the late 1980’s, for example, the scale was based on numbers around 100, with 100 representing the average box-office return of 1980. The figures from 1987 and 1996 are given in the table below and in the file BoxOffice.xlsx in blackboard.

All the figures are scaled around the 1980’s box-office returns, but instead of dollars, artificial numbers are used. Film executives can get a relative indication of the box-office figures compared to the arbitrary 1980 scale. For example, in January 1987 the box-office returns to the film industry were 95% of the average that year, whereas in January 1988 the returns were 104% of the average of 1980 (or, they were 4% above the average of 1980’s figure).

From the time series given in the above table, you will make a forecast for the 12 months of the next year, 1997.

Managerial Report is due on … Thursday, 19 Sept (40 pts)

1. Produce a time series plot of the data. From this graph, do you see a pattern? Can you see any seasonality in the data?
2. Use exponential smoothing to fit the data. Select an appropriate constant a based on the variation you see in the data. Comment on the appropriateness of exponential smoothing on this data set. Plot the predictions from this model on the graph with the original data. How well does this technique fit the data? Make forecasts for 1997.
3. Use regression to build a linear trend model. Comment on the goodness-of-fit of this model to the data (or, how well does R² explain the variance in the data?). Plot the predictions from this model on the graph with the original data.
4. Develop multiplicative seasonal indices for the linear trend model developed in question 3. Use these indices to adjust predictions from the linear trend model from question 3 above for seasonal effects. Plot the predictions from this model on the graph with the original data. How well does this technique fit the data? Make forecasts for the next 12 months of 1997 using this technique.
5. Which forecasting method of those that you tried do you have the most confidence for making accurate forecasts for 1997? Use MAPE (mean absolute percent error) as your criterion to justify your decision.

Enrichment (5 pts): Use Optimization (and Solver in Excel) to find the optimal smoothing constant in problem 2 above (by minimizing the Mean Squared Error or MSE).

It is rare that you will find a gas station these days that only sells gas. It has become more common to find a convenient store that also sells gas. The data named “Convenient Shopping data” the sales over time at a franchise outlet of the major US oil company. Each row summarize sales for one day. This particular station sells gas and has a convenient store and car awash. The column labeled Sales gives the dollar sales of the convenient store and the column Volume gives the number of gallons of gas sold.

1) Draw a scatter plot for Sales on Volume where Sales is dependent on Volume of gas sold. Does there appear to be a linear pattern that relates to these two sequences?

2) Estimate the linear regression model using excel analysis tool I showed you in class. Write the linear model and interpret the slope (b1).

3) Interpret the R² and tell if your linear model is a good fit or not.

4) Estimate the difference in sales at the convenient store (on average) between a day with 3,500 gallons sold and a day with 4,000 gallons sold.

5) With regard to inference statistics, formulate a hypothesis test for the slope (b1) and decide if it is statistically significant or not.

6) Construct a 95% confidence interval for the slope.

Vanguard Method as opposed to the traditional managerial thinking typically found in
many organisations ( Jaaron and Backhouse, 2012).
The Vanguard Method embraces the principle that employees need to think, analyse,
judge, and make decisions on the work on hands. Therefore, team members training is
not the focus in the preparation process for this kind of job, it is actually educating them
on “why” a failure happen and then finding ways to eliminate it from the system. To
accommodate for the requirements of the Vanguard Method, managers’ role shifts from
command-and-control to supporters. This keeps managers very close to their employees
to interact with their work when necessary. Bhat et al. (2012) provide a constructive view
about the interactive leadership style and organisational learning. According to them, the
capacity of an organisation to learn how to learn, to change old ways of doing things, and
to produce original knowledge is positively related to interactive leadership styles. Due to
this type of relationship and due to the whole service processes being owned by team
members, the structure of the organisation changes. The organisation becomes
organically structured ( Jaaron and Backhouse, 2014).
The Vanguard Method in practice
The above philosophy usually follows three main practical steps of “check-plan-do” for
implementation. These steps are summarised in Table II.
Check. This stage aims at understanding the system and why it behaves in such a
way that failure demand is achieved. A specially formed team, called the check team,
from the workplace collates information about what customers expect and want from
the organisation and what matters to them most, they need to be able to use views of
different people involved in the problematic system to build the “real situation”
(Checkland, 1995). Once the team understands the type of demand received and how
capable the system is to respond to it, it can start to map the flow of processes in the
system. For this purpose, a visual representation of each operation carried out in the
workplace is developed as a flow chart. Identification of waste (actions not adding any
value from the customer’s point of view) present in the service operations flow is then

carried out (Seddon, 2008). All processes classified as waste are marked in red on the
process flow chart. While processes that add value from a customer’s point of view are
marked in green.
Plan. This stage starts with redesigning the processes flow charts taking into
account what has been learned by considering the customer “wants” and then mapping
out the new service system design. Typically, this stage is focussed on minimising
non-value adding activities from a customer point of view. The final step in the “plan”
process is to build performance measures and the future system success criterion. This
is usually how good employees are in creating a value demand and the percentage of
value demand out of the total demand received ( Jaaron and Backhouse, 2012).
Do. At this stage the new design is used in an experimental environment with the
check team using the new model after it has been discussed with the people doing
the work. The new processes are induced gradually with careful observation of both
employees’ reaction to it and customers feedback. The processes are tested,
re-designed, and re-tested again to make sure that customers get the best possible
service before going fully live. This is much slower process than the check phase as the
slogan at this stage is to “do it right rather than do it quick” ( Jackson et al., 2008).
The Vanguard Method cycle starts with the “check” stage in order to show business
managers the failings of their current system, and to provide them with a solid evidence
for the need to change the way they think and manage things ( Jackson et al., 2008).
To ensure continuous improvement of the new system, the check-plan-do cycle is a
continuous cycle (Seddon, 2008; Jackson et al., 2008). It is, therefore, a learning system
by itself: the process of acquiring knowledge and taking action to improve the situation
is continuous ( Jackson et al., 2008). In addition to continuously altering business
processes to improve the service offered, the Vanguard Method cycle involves the
identification of new demands coming in to the service department. This is followed by
designing new processes to ensure dealing with new demands as value demands
(Seddon, 2008).
4. Research methodology
A case study approach is adopted in this research inquiry in order to build an
understanding of the nature of the research phenomena (Voss et al., 2002). Case studies
have the advantage of being able to answer questions like “what”, “how”, and “why”
(Yin, 2009). This accommodates the type of question presented at the beginning of this
paper. Two case studies were chosen with the help of “extreme case sampling”
technique (Patton, 2002; Creswell, 2004) that displayed evidence of full employment of
the Vanguard Method in their logistics service operations. An earlier research work
conducted with the help of the Vanguard Method consultant of these two case studies
helped researchers in confirming that the Vanguard Method is fully employed in their
logistics operations, and also ensured easy access to both case studies.
According to Aastrup and Halldórsson (2008), the use of case studies in logistics
management research is an enabler for the causal depth required for understanding the
real domain of logistics operations and its performance. Case study research design
typically has the unique strength in providing a full range of evidence through the use
of multi-sources of data, which can achieve data triangulation (Voss et al., 2002). For
this purpose, the mixed methods design (Tashakkori and Teddlie, 1998) is used as the
technique for conducting the research process. Three different sources of data
collection methods are used in the two case studies; these are semi-structured

What is the particularity of vanguard method? (1point)

Why we need this method in logistics? (1 point)

The Farr-Kroger Classic is a women’s professional golf tournament played each year in Ohio. Listed below are the total purse winnings (the amount of money that is distributed to the top golfers) and the prize for the winner for the 15 years from 1991 through 2005. The operators of this golf tournament believe that there is a relationship between the purse winnings and the prize and the prize is related to the purse winnings. In addition to the data provided, some of the possible linear regression relationships are provided.   These might be of help in your analysis.

Using linear regression relationships, answer the questions a) through c) below and on the following page.

a) Develop a projection for the amount of the prize for the winner for the year 2008 if the purse winnings for that year are projected to be $996,430. As part of your answer, include the independent and dependent variables and the accompanying linear regression relationship.

b) Now let’s suppose that we believe the prize for the winner is a function of time (dependent on time). Given this belief, develop a projection for the amount of the prize for the winner for the year 2008 and discuss your results compared to what you found in part a)

c) Would you recommend using the forecasts you found in parts a) and b) based on the strengths of the relationship? Why?