DE LA SALLE UNIVERSITYRamon V. Del Rosario College of Business

NORTH-SOUTH AIRLINEA Group Report

 

In Partial Fulfillment ofthe Requirements for

MSC530M 

Presented by:GROUP 5

Arniebelle DoriaMarvin Francisco

Gee ManaloBernadette ReyesJohn Robinson Uy

 

Presented to:Dr. Dennis Berino

&Group 6

07 November 2013

I. Background and Summary of the Case

North-South Airline is a merger of the Northern Airlines and Southeast Airlines in January 2008, making it the fourth largest U.S. carrier. An aging fleet of Boeing 727-300 aircraft and its President, Stephen Ruth, who was the former Secretary of the Navy and the chairman of the board, was inherited by the new airline.

For an airline company, maintenance cost is one of the major expenses that is inevitable. It is a given already that the maintenance costs rise with the age of the aircraft. So for Stephen, this is a major concern that might get in the way for him to create a financially stable airline company. Based on his observations on historical data, there was a difference in the documented maintenance costs in both the airframe and engine areas of the Northern Airlines and Southeast Airlines, where Southeast Airlines have newer fleets.

The vice president for operations and maintenance, Peg Jones, was tasked by the president to look carefully on the report and present his findings quantitatively and graphically. In order to come up with this report, Jones asked her staff to prepare the average age of Northern and Southeast B727-300 fleets, and available cost data, both of the airframe costs and engine costs. The president wanted to know if there is a correlation between the average fleet age and direct airframe maintenance cost; and if there is a strong relationship between average fleet age and direct engine maintenance cost.

Such data are shown in the succeeding tables below:

NORTHERN AIRLINE

Year Airframe cost per Aircraft ($) Engine cost per Aircraft ($) Average Age (Hours)

2001 51.80 43.49 6,512

2002 54.92 38.58 8,404

2003 69.70 51.48 11,077

2004 68.90 58.72 11,717

2005 63.72 45.47 13,275

2006 84.73 50.26 15,215

2007 78.74 79.60 18,390

SOUTHERN AIRLINE

Year Airframe cost per Aircraft ($) Engine cost per Aircraft ($) Average Age (Hours)

2001 13.29 18.86 5,107

2002 25.15 31.55 8,145

2003 32.18 40.43 7,360

2004 31.78 22.10 5,773

2005 25.34 19.69 7,150

2006 32.78 32.58 9,364

2007 35.56 38.07 8.259

II. Statement of the Problem  The airframe cost per aircraft and the engine maintenance costs between Northern Airlines and Southeast Airlines significantly differ. Given that fact, is there a relationship between the 1) direct airframe maintenance costs and average fleet age, and 2) direct engine maintenance costs and average fleet age? If there are, what are the relationships of average fleet age to airframe and engine maintenance costs of North-South Airline?

III. Objective/sa. To be able to determine if there is a significant relationship between the average fleet age to direct

airframe and to direct engine maintenance costs. b. To be able to identify the relationship between the average fleet age to direct airframe and to

direct engine maintenance costs of the two airlines.c. To be able to use appropriate quantitative and graphical descriptions of the relationship to support

the analysis/answers.

IV. Facts of the Case, Limitations, and Constraints

Facts of the case:A. The two airlines that merged are Northern and Southeast Airlines. Both inherited an aging fleet of

Boeing 727-300 aircrafts and Stephen Ruth. Southeast have newer fleet. B. The president, Stephen Ruth views a financially sound company by lowering maintenance costs as it

is common in the airlines industry to have high maintenance costs as the fleet grows older.C. Ruth noticed a difference in the maintenance costs in the airframe and engine between Northern and

Southeast Airlines and ordered for a study on the correlation of average fleet age to direct airframe maintenance costs and relationship between average fleet age and direct engine maintenance costs.

D.   Average age of each fleet is computed as:Ave. age (hrs) = (# of calendar days the aircraft in service   x   ave. daily utilization)

                                     total fleet hours flown /total number of aircraft in serviceAverage daily utilization is 8.7 hours a day for Northern and 8.3 hours for Southeast.

Limitations and Constraints: A. It is commonly surmised in the airline industry that maintenance cost rise with the age of the aircraft B. Historical data from Northern and Southeast airline are not parallel as Southeast have newer fleet C. The correlation can only be based on the seven-year data presented. D.   The independent variable presented in the case was fleet age and dependent variable was only maintenance cost (both airframe and engine)

V. Alternative Approaches to Solve the Problem/s

Considering the objectives of the case study which is to determine relationships between two variables (the average age of the aircraft and its engine and/or airframe costs), different types of regression models will be used in our case analysis quantitatively.

A. Regression analysis is a forecasting procedure employed to understand the relationship between variables and to predict the value of one based on the other. It uses the least square approach on one or more independent variable to develop as a forecasting model.

B. A scatter diagram or scatter plot is the diagram of the variable forecasted that is helpful to determine whether there is a relationship between variables.

C. Coefficient of Determination (r2) is the percent of the variability in the dependent variable (Y) that is explained by the regression equation.

D.   Coefficient of Correlation (r) is the measure of the strength of the relationship between two variables. E.    Simple Linear Regression Model is a regression model using one independent variable. F.    Multiple Regression Model is a regression model that has more than one independent variable.    

VI. Quantitative Methods/Usage of Management Science Models

The group identified the variables present in the case. These are the direct airframe and engine maintenance costs which are the dependent variables and the average fleet age which is the independent variable.

Given one independent variable and the requirement of identifying correlation and testing relationship among variables, the group used the simple linear regression model.

In QM for Windows, under the Module Forecasting, the Least Squares – Simple and Multiple Regression is utilized.

Four situations are analyzed to respond to the need of North-South Airlines. These are:1. Northern Airlines Airframe Cost and Average Age2. Northern Airlines Engine Cost and Average Age Relationship3. Southeast Airlines Airframe Cost and Average Age4. Southeast Airlines Engine Cost and Average Age Relationship

VII. Analysis of Results

In statistics, dependence refers to any statistical relationship between two random variables or two sets of data. Correlation refers to any of a broad class of statistical relationships involving dependence. Correlation is also defined as the measure of the degree of relationship between two variables which is represented with ‘r,’ called Pearson product. This representation can be any number between -1 to +1 which explains that if the slope is on the right side or is positive, then there is a positive relationship between the two variables that are being tested; and if the slope is on the left side or is negative, then there is a negative relationship between the two variables being tested.

To resolve one of our stated problems if there is a relationship between the average fleet age to airframe maintenance cost and to direct engine maintenance costs per airline, the group will be using QM for windows after identifying the dependent and independent variables.

We will be using ‘direct airframe maintenance cost’ and ‘direct engine maintenance cost’ as the dependent variable (Y) per airline and ‘average fleet age’ as the independent variable (X).

1) NORTHERN AIRLINES:

A. Airframe Costs vs. Average Fleet Age

Table 1. Using QM for Windows, Airframe Costs as dependent variable (Y) and Average Fleet Age as independent variable (X)

Table 2. Forecasting Results of Table 1

   

INTERPRETATION of RESULTS: Airframe Costs vs. Average Fleet AgeThe airframe maintenance costs increase by 0.0026 as average age of the aircraft increase by

a unit. Only 77% of the changes in the direct airframe maintenance costs are explained by aircraft age. The 23% remaining attributes to other variables not mentioned in the case.

The regression equation is ‘Airframe Cost =  36.098 + 0.0026 * Average Age.’ There is a positive relationship between the ‘airframe cost’ and the ‘average age’ because the coefficient of correlation is 0.8772.

Test for Significance:

i. H0 : ß1 = 0 (There is no linear relationship between airframe costs and average age)    H1 : ß1 ≠ 0 (There is linear relationship ingbetween airframe costs and average age)ii. Select α = 0.05iii. Calculate for F statistic:    From the ANOVA summary         MSR = 653.2601        MSE = 39.1517    F statistic = 16.6831    Using the df1 = 1 and df2 = 5 at 5% level of significance, F value is 6.6078 (please see Annex A). Since F value is 6.6078. Since Fstatistics> Fvalue, (16.6831 > 6.6078) H1 will be accepted. Therefore, there is significant relationship between the airframe maintenance costs and average age.

            B. Engine Cost vs. Average Age

Table 3. Using QM for Windows, Engine Cost as dependent variable (Y) and Average Fleet Age as independent variable (X)

   

Table 4. Forecasting Results of Table 3

INTERPRETATION of RESULTS: Engine Costs vs. Average Fleet AgeThe engine maintenance costs increase by 0.0026 as average age of the aircraft increase by a

unit. There is 61% of the changes in engine maintenance costs that can be explained by aircraft age. The 39% remaining attributes to other variables not mentioned in the case.

The regression equation is ‘Engine Cost = 20.5744 + 0.0026 * Average Age.’ There is a positive relationship between the ‘engine cost’ and the ‘average age’ because the coefficient of correlation is 0.7826.

Test for Significance:

i. H0 : ß1 = 0 (There is no linear relationship between engine cost and average age)    H1 : ß1 ≠ 0 (There is linear relationship existing between engine costs and average age)ii. Select α = 0.05iii. Calculate for F statistic:    From the ANOVA summary         MSR = 675.8532        MSE = 85.5338    F statistic = 7.9016    Using the df1 = 1 and df2 = 5 at 5% level of significance, F value is 6.6078 (please see Annex A). Since F value is 6.6078. Since Fstatistics> Fvalue, (7.9016 > 6.6078) H1 will be accepted. Therefore, there is significant relationship between the engine costs and average age.

2) SOUTHEAST AIRLINES:A. Airframe Costs vs. Average Fleet Age

Table 1.  Using QM for Windows, Airframe Costs as dependent variable (Y) and Average Fleet Age as independent variable (X)

Table 2.  Forecasting Results of Table 1

INTERPRETATION of RESULTS: Airframe Costs vs. Average Fleet AgeAirframe cost per aircraft increases by 0.0032 as the airline increase average age of aircraft

by a unit. There is only 39% of the changes in airframe cost that can be explained by aircraft age. The 61% remaining attributes to other variables not mentioned in the case.

The regression equation is ‘Airframe Cost = 4.5966 + 0.0032 * Average Age.’ There is a positive relationship between the ‘average cost’ and the ‘average age’ because the coefficient of correlation is 0.6249.

    Test for Significance:

i. H0 : ß1 = 0 (There is no linear relationship between airframe cost and average age)    H1 : ß1 ≠ 0 (There is linear relationship existing between airframe costs and average age)ii. Select α = 0.05iii. Calculate for F statistic:    From the ANOVA summary         MSR = 134.0621        MSE = 41.8564    F statistic = 3.2029

    Using the df1 = 1 and df2 = 5 at 5% level of significance, F value is 6.6078 (please see Annex A). Since F value is 6.6078. Since Fstatistics< Fvalue, (3.2029 < 6.6078) H0 will be accepted. Therefore, there is no significant relationship between the airframe maintenance costs and average age.

B. Engine Cost vs. Average AgeTable 3.  Using QM for Windows, Engine Costs as dependent variable (Y) and Average Fleet

Age as independent variable (X)

Table 4.  Forecasting Results of Table 3

INTERPRETATION of RESULTS: Engine Costs vs. Average Fleet AgeEngine cost per aircraft increases by 0.0041 1as the airline increase average age of aircraft

by a unit. There is only 46% of the changes in engine cost that can be explained by aircraft age. The 54% remaining attributes to other variables not mentioned in the case.

The regression equation is ‘Engine Cost = -0.6709 + 0.0041 * Average Age.’ There is a positive relationship between the ‘engine cost’ and the ‘average age’ because the coefficient of correlation is 0.6782.

    Test for Significance:

i. H0 : ß1 = 0 (There is no linear relationship between engine cost and average age)    H1 : ß1 ≠ 0 (There is linear relationship exists between engine costs and average age)ii. Select α = 0.05iii. Calculate for F statistic:    From the ANOVA summary         MSR = 215.8518        MSE = 50.6943    F statistic = 4.2579    Using the df1 = 1 and df2 = 5 at 5% level of significance, F value is 6.6078 (please see Annex A). Since F value is 6.6078. Since Fstatistics< Fvalue, (4.2579 < 6.6078) H0 will be accepted. Therefore, there is no significant relationship between the engine maintenance costs and average age.

VIII. Ethical Considerations

1. As per request of Stephen Ruth, it seems that the direction of the company would be on the reduction of maintenance costs. If this would be the case, there would be a foreseeable crisis on labor relations particularly among those who are regularly employed in the airline company. This would give rise to layoffs and new contracts for prospective cheaper service partners. General welfare of the employees and service partners should not be compromised with the merger. An option would be, instead of laying off, it is better if these ‘would-be-laid-off’ employees be assigned to new services/department under the new merged airline company for their sustentation. As included in the case, before the merger took place, both airline companies (Northern and Southeast) have separate sets of personnel (directors, managers, and supervisors). Consequently, after the merge, the new airline company (North-South Airlines) will have to appoint its new own directors, managers, and supervisors in an ethical way in order to maintain company’s operation.

2. With the pressure to continually meet market demands and to reduce maintenance costs, North-South Airlines may implement strategies and actions that tend to hamper the quality and safety of their airline service. Since their now-merged fleet is comprised of both old and new aircraft, North-South Airlines may have a difficulty of balancing cost minimization and the maintenance of safe and quality services.

3. Since North-South Airlines is a merger of two organizations managed by a different group of individuals, cultural differences should also be considered. Cultural differences between employees of these pre-merged organizations largely affect the way these individuals think, and the same largely affects how they perceive ethical or non-ethical conducts. These differences should be resolved so that there will be unity of thought and practice within North-South Airlines.

4. Sooner than later, the fleet of aircrafts that North-South Airlines currently has will become outdated. Aside from the fact that these would entail higher maintenance costs, these aircrafts would also pose a grave threat to the environment because older fuel-consuming machines produce more environment-hazardous gases and other by-products. North-South Airlines should be sensitive on this light and advocate for a healthy and livable ecology not only for their immediate community but also for the upcoming generations.

5. Passenger Safety. Looking into another view point, the president of North - South Airline maybe is not just after the difference in the maintenance costs of the aircrafts from both airlines. Existing aircrafts will still be used with the merger. Since these aircrafts have been used for quite,  it is still safe to fly them?

6. Passenger Satisfaction. Passengers who have tried both airlines surely know of what both can offer. WIth the merger being North - South Airline, passengers should expect a new experience while flying to their destination and this should be served by the new North - South Airline. It is possible to apply what each airlines had did in their previous voyages, but they need to establish a new brand or mark because they are now a new airline company as one.

IX. Conclusion

  Maximixing profit and minimixing cost are among the goals of all managers to be able to succeed financially.  However, in order to achieve such, a lot of considerations in terms of the business operation must be taken into account.  Considering the merging of Northern and Southeast airlines, changes in the practices and functions of each must be expected.  Changes and improvement on the current practices will initially happen after the review and analysis of both companies’ historical data.  With new President Stephen Ruth’s attempt to create a financially solid company, significant differences in the numbers/data of each company were noticed specifically the maintenance costs that lead to our group’s quantitative analysis.

Both airframe costs and engine costs in Northern Airline have a significant correlation with age as both accepted the alternative hypothesis in the tests for significance. The tests showed that airframe costs have a higher correlation to aircraft age as compared to engine costs and age.

For Southeast Airline, neither airframe nor engine shows a correlation between costs and average age per aircraft. There is failure in tests for significance and there is low correlation coefficients and coefficient of determination. Obviously, engine costs have higher correlation coefficients and coefficient of determination while the costs of airframe have lower MSE.

With the foregoing, we can conclude that there are other factors that affect the movement of airframe and engine costs in Southeast Airlines aside from aircraft age.

X. Annex

A. F-distribution Chart: α = 0.05    df1 = 1    df2 = 5    F-statistic = 6.6078

    B. Northern Airline: Airframe Costs vs. Average Fleet Age Graph

    C. Northern Airline: Engine Costs vs. Average Fleet Age Graph

    D. Southeast Airline: Airframe Costs vs Average Fleet Age Graph

    E. Southeast Airline: Engine Costs vs. Average Fleet Age Graph