netrashetty
Netra Shetty
Delta Air Lines, Inc. (NYSE: DAL) is a major airline based in the United States[9] headquartered in Atlanta. Delta is the world's largest airline operating under a single certificate, operating flights on six continents across the globe. Delta operates an extensive domestic and international network, spanning North America, South America, Europe, Asia, Africa, the Middle East, the Caribbean and Australia. Delta and its subsidiary Delta Connection operate over 4,000 flights every day.[10] Delta and the Delta Connection carriers fly to 348 destinations in 64 countries. (excluding codeshare)[8] Delta operates the world's largest and busiest hub at Hartsfield-Jackson Atlanta International Airport. It has been the world's busiest airport by passenger traffic and number of landings and take-offs since 1999, serving 88 million passengers per year. Delta is a founding member of the SkyTeam alliance.
On October 29, 2008, Delta completed its merger with Northwest Airlines to form the world's largest commercial carrier. In February 2009, the airline began consolidating gates and ticket counters at airports where both Delta and Northwest operate. The consolidation was completed February 2010.[11] On December 31, 2009, the Federal Aviation Administration granted Delta's request to allow Delta and Northwest to operate under a single operating certificate.
In Q16, the survey revealed that 46% of the visitors are dissatisfied while 16% are very dissatisfied as compared with only 15% satisfied and 5% very satisfied visitors. 18% have neutral perception. In line with the price value worthiness in Q17, only 7% out of 985 respondents were on the high and 3% have perceived that the price is very high. 18% of the total respondents find it to be average and 58% gives low or negative feedback.
In terms of the demographic information, the research have found that most of the visitors (54%) considered as at the age of 26-40 years old, married female who became housewives in their secondary education, with a family income over HK$20,001 or above.
IX. CONCLUSION
Based on the result of the survey conducted, this study have drawn into the following conclusions. In this, the major findings of the research shows that 817 visitors are first time visitors with only 168 people who came for the second or third time. This shows that the re-visit percentage for HK Disneyland is extremely low. Among the 1,000 visitors, more than half of them came from China. And the reasons for visiting is because the visit is pre-scheduled or tour package inclusive. Most of the respondents have no knowledge or even never heard names and the characters of Disneyland.
It has been revealed that the main reason why local residents wanted to visit the park is because of the interest of their children. The feedback on the questionnaire have shown that most visitors have noted that the ticket and the restaurant prices are too costly, In terms of attractiveness, souvenirs, rides and environments and services, the overall rating from the visitors are negative, except on the show performance and parade. Most of the visitors have been hesitant in recommending the park to their friends, and are wondering if they will be visiting again in the future or rather visit other Disneyland in other nations.
From the demographic profile, most of the respondents are young adults whose age ranges from 26-40, married female, housewives in their secondary education with a monthly family income of HK$20,000. The visitors have noted that their main concerns in the theme park is to add more facilities which will give benefits to their kids.
X. RECOMMENDATIONS
Based on the conclusion provided, it has been found that 80% of the respondents are visiting with their families. Such kind of date is very essential for the marketing department to have better strategies. Thus, the following marketing measures will be recommended to Senior Management for consideration:-
Since most of the people in Mainland China are unfamiliar with the Disney cartooned characters, the management should be able to actively educate these people by showing more Disney cartoons in terms of Media through television channels. Further, the management can also consider the sponsorship of some Children program. This is to help Chinese people in recognizing Disney characters and enhance their interests to seem them live. In addition, more print ads, and visual media advertisements and promotions can also be considered.
For local Hong Kong residents, majority of the visitors have considered that the price of the ticket are too high, hence this should be considered to have some promotional approaches to provide discounted tickets for the Hong Kong people, like free visit for birthday boys and girls on their birth month excluding their family members. In this way, the revenue and sales can be boosted. IN addition, another discount can be given to birthday boys and girls if they choose to celebrate at all Disneyland restaurants.
he final stage in the marketing research process is to report the findings. For marketers doing small-scale research for their own purposes, communication may be quite informal. The marketer may simply draw conclusions from what he or she gleans from the data analysis.
For more serious marketing research projects, those conducting the research will prepare a written report outlining what was researched and offer results. Additionally, an oral presentation may be required in which the research is explained within a slide presentation
quires a decision. In addition, it is assumed that the smaller problems are not independent of one another given they contribute to the larger question.
Dynamic programming can be utilized in the areas of capital budgeting, inventory control, resource allocation, production scheduling, and equipment replacement. These applications generally begin with a longer time horizon, such as a year, and then break down the problem into smaller time units such as months or weeks. For example, it may be necessary to determine an optimal production schedule for a twelve-month period.
Dynamic programming would first find a solution for smaller time periods, for example, monthly production schedules. By answering such questions, dynamic programming can identify solutions to a problem that are most efficient or that best serve other business needs given various constraints.
GOAL PROGRAMMING
Goal programming is a technique for solving multi-criteria rather than single-criteria decision problems, usually within the framework of linear programming. For example, in a location decision a bank would use not just one criterion, but several. The bank would consider cost of construction, land cost, and customer attractiveness, among other factors.
Goal programming establishes primary and secondary goals. The primary goal is generally referred to as a priority level 1 goal. Secondary goals are often labeled level 2, priority level 3, and so on. It should be noted that trade-offs are not allowed between higher and lower level goals.
Assume a bank is searching for a site to locate a new branch. The primary goal is to be located in a five-mile proximity to a population of 40,000 consumers. A secondary goal might be to be located at least two miles from a competitor. Given the no trade-off rule, we would first search for a target solution of locating close to 40,000 consumers.
BLENDING PROBLEM.
The XYZ Company mixes three raw materials to produce two products: a fuel additive and a solvent. Each ton of fuel additive is a mixture of 2/5 ton of material A and 3/5 ton of material C. A ton of solvent base is a mixture of 1/2 ton of material A, 1/5 ton of material B, and 3/10 ton of material C. Production is constrained by a limited availability of the three raw materials. For the current production period XYZ has the following quantities of each raw material: 20 tons of material A, 5 tons of material B, and 21 tons of material C. Management would like to achieve the following priority level goals:
Goal 1. Produce at least 30 tons of fuel additive.
Goal 2. Produce at least 15 tons of solvent.
Goal programming would provide directions for production.
INTEGER PROGRAMMING
Integer programming is useful when values of one or more decision variables are limited to integer values. This is particularly useful when modeling production processes for which fractional amounts of products cannot be produced. Integer variables are often limited to two values—zero or one. Such variables are particularly useful in modeling either/or decisions.
Areas of business that use integer linear programming include capital budgeting and physical distribution. For example, faced with limited capital a firm needs to select capital projects in which to invest. This type of problem is represented in Table 1.
As can be seen in the table, capital requirements exceed the available funds for each year. Consequently, decisions to accept or reject regarding each of the projects must be made and integer programming would require the following integer definitions for each of the projects.
x1 = 1 if the new office project is accepted; 0 if rejected
x2 = 1 if the new warehouse project is accepted; 0 if rejected
x3 = 1 if the new branch project is accepted; 0 if rejected
A set of equations is developed from the definitions to provide an optimal solution.
NONLINEAR PROGRAMMING
Nonlinear programming is useful when the objective function or at least one of the constraints is not linear with respect to values of at least one decision variable. For example, the per-unit cost of a product may increase at a decreasing rate as the number of units produced increases because of economies of scale.
STOCHASTIC PROGRAMMING
Stochastic programming is useful when the value of a coefficient in the objective function or one of the constraints is not know with certainty but has a known probability distribution. For instance, the exact demand for a product may not be known, but its probability distribution may be understood. For such a problem, random values from this distribution can be substituted into the problem formulation. The optimal objective function values associated with these formulations provide the basis of the probability distribution of the objective function.
MARKOV PROCESS MODELS
Markov process models are used to predict the future of systems given repeated use. For example, Markov models are used to predict the probability that production machinery will function properly given its past performance in any one period. Markov process models are also used to predict future market share given any specific period's market share.
COMPUTER FACILITY PROBLEM.
The computing center at a state university has been experiencing computer downtime. Assume that the trials of an associated Markov Process are defined as one-hour periods and that the probability of the system being in a running state or a down state is based on the state of the
Table 1
Integer Programming Example
Project Estimated Net Return Year 1 Year 2 Year 3
New office 25,000 10,000 10,000 10,000
New warehouse 85,000 35,000 25,000 25,000
New branch 40,000 15,000 15,000 15,000
Available funds 50,000 45,000 45,000
system in the previous period. Historical data in Table 2 show the transition probabilities.
Table 2
To
Running Down
From Running .9 .1
Down .3 .7
The Markov process would then solve for the following: if the system is running, what is the probability of the system being down in the next hour of operation?
QUEUING THEORY/WAITING
LINE THEORY
Queuing theory is often referred to as waiting line theory. Both terms refer to decision making regarding the management of waiting lines (or queues). This area of management science deals with operating characteristics of waiting lines, such as:
the probability that there are no units in the system
the mean number of units in the queue
the mean number of units in the system (the number of units in the waiting line plus the number of units being served)
the mean time a unit spends in the waiting line
the mean time a unit spends in the system (the waiting time plus the service time)
the probability that an arriving unit has to wait for service
the probability of n units in the system
Given the above information, programs are developed that balance costs and service delivery levels. Typical applications involve supermarket checkout lines and waiting times in banks, hospitals, and restaurants.
BANK LINE PROBLEM.
XYZ State Bank operates a drive-in-teller window, which allows customers to complete bank transactions without getting out of their cars. On weekday mornings arrivals to the drive-in-teller window occur at random, with a mean arrival rate of twenty-four customers per hour or 0.4 customers per minute.
Delay problems are expected if more than three customers arrive during any five-minute period. Waiting line models can determine the probability that delay problems will occur.
TRANSPORTATION METHOD
The transportation method is a specific application of the simplex method that finds an initial solution and then uses iteration to develop an optimal solution. As the name implies, this method is utilized in transportation problems.
TRANSPORTATION PROBLEM.
A company must plan its distribution of goods to several destinations from several warehouses. The quantity available at each warehouse is limited. The goal is to minimize the cost of shipping the goods. An example of production capacity can be found in Table 3. The forecast for demand is shown in Table 4.
The transportation method will determine the optimal amount to be shipped from each warehouse and determine the optimal destination.
Table 3
Origin Warehouse Location 3 month capacity
1 Houston 2,000
2 Dallas 2,500
3 San Antonio 2,800
Total 7,300
Table 4
Destination Location 3 month forecast
1 New Orleans 1,800
2 Little Rock 3,200
3 Las Vegas 2,300
Total 7,300
SIMULATION
Simulation is used to analyze complex systems by modeling complex relationships between variables with known probability distributions. Random values from these probability distributions are substituted into the model and the behavior of the system is observed. Repeated executions of the simulation model provide insight into the behavior of the system that is being modeled.
On October 29, 2008, Delta completed its merger with Northwest Airlines to form the world's largest commercial carrier. In February 2009, the airline began consolidating gates and ticket counters at airports where both Delta and Northwest operate. The consolidation was completed February 2010.[11] On December 31, 2009, the Federal Aviation Administration granted Delta's request to allow Delta and Northwest to operate under a single operating certificate.
In Q16, the survey revealed that 46% of the visitors are dissatisfied while 16% are very dissatisfied as compared with only 15% satisfied and 5% very satisfied visitors. 18% have neutral perception. In line with the price value worthiness in Q17, only 7% out of 985 respondents were on the high and 3% have perceived that the price is very high. 18% of the total respondents find it to be average and 58% gives low or negative feedback.
In terms of the demographic information, the research have found that most of the visitors (54%) considered as at the age of 26-40 years old, married female who became housewives in their secondary education, with a family income over HK$20,001 or above.
IX. CONCLUSION
Based on the result of the survey conducted, this study have drawn into the following conclusions. In this, the major findings of the research shows that 817 visitors are first time visitors with only 168 people who came for the second or third time. This shows that the re-visit percentage for HK Disneyland is extremely low. Among the 1,000 visitors, more than half of them came from China. And the reasons for visiting is because the visit is pre-scheduled or tour package inclusive. Most of the respondents have no knowledge or even never heard names and the characters of Disneyland.
It has been revealed that the main reason why local residents wanted to visit the park is because of the interest of their children. The feedback on the questionnaire have shown that most visitors have noted that the ticket and the restaurant prices are too costly, In terms of attractiveness, souvenirs, rides and environments and services, the overall rating from the visitors are negative, except on the show performance and parade. Most of the visitors have been hesitant in recommending the park to their friends, and are wondering if they will be visiting again in the future or rather visit other Disneyland in other nations.
From the demographic profile, most of the respondents are young adults whose age ranges from 26-40, married female, housewives in their secondary education with a monthly family income of HK$20,000. The visitors have noted that their main concerns in the theme park is to add more facilities which will give benefits to their kids.
X. RECOMMENDATIONS
Based on the conclusion provided, it has been found that 80% of the respondents are visiting with their families. Such kind of date is very essential for the marketing department to have better strategies. Thus, the following marketing measures will be recommended to Senior Management for consideration:-
Since most of the people in Mainland China are unfamiliar with the Disney cartooned characters, the management should be able to actively educate these people by showing more Disney cartoons in terms of Media through television channels. Further, the management can also consider the sponsorship of some Children program. This is to help Chinese people in recognizing Disney characters and enhance their interests to seem them live. In addition, more print ads, and visual media advertisements and promotions can also be considered.
For local Hong Kong residents, majority of the visitors have considered that the price of the ticket are too high, hence this should be considered to have some promotional approaches to provide discounted tickets for the Hong Kong people, like free visit for birthday boys and girls on their birth month excluding their family members. In this way, the revenue and sales can be boosted. IN addition, another discount can be given to birthday boys and girls if they choose to celebrate at all Disneyland restaurants.
he final stage in the marketing research process is to report the findings. For marketers doing small-scale research for their own purposes, communication may be quite informal. The marketer may simply draw conclusions from what he or she gleans from the data analysis.
For more serious marketing research projects, those conducting the research will prepare a written report outlining what was researched and offer results. Additionally, an oral presentation may be required in which the research is explained within a slide presentation
quires a decision. In addition, it is assumed that the smaller problems are not independent of one another given they contribute to the larger question.
Dynamic programming can be utilized in the areas of capital budgeting, inventory control, resource allocation, production scheduling, and equipment replacement. These applications generally begin with a longer time horizon, such as a year, and then break down the problem into smaller time units such as months or weeks. For example, it may be necessary to determine an optimal production schedule for a twelve-month period.
Dynamic programming would first find a solution for smaller time periods, for example, monthly production schedules. By answering such questions, dynamic programming can identify solutions to a problem that are most efficient or that best serve other business needs given various constraints.
GOAL PROGRAMMING
Goal programming is a technique for solving multi-criteria rather than single-criteria decision problems, usually within the framework of linear programming. For example, in a location decision a bank would use not just one criterion, but several. The bank would consider cost of construction, land cost, and customer attractiveness, among other factors.
Goal programming establishes primary and secondary goals. The primary goal is generally referred to as a priority level 1 goal. Secondary goals are often labeled level 2, priority level 3, and so on. It should be noted that trade-offs are not allowed between higher and lower level goals.
Assume a bank is searching for a site to locate a new branch. The primary goal is to be located in a five-mile proximity to a population of 40,000 consumers. A secondary goal might be to be located at least two miles from a competitor. Given the no trade-off rule, we would first search for a target solution of locating close to 40,000 consumers.
BLENDING PROBLEM.
The XYZ Company mixes three raw materials to produce two products: a fuel additive and a solvent. Each ton of fuel additive is a mixture of 2/5 ton of material A and 3/5 ton of material C. A ton of solvent base is a mixture of 1/2 ton of material A, 1/5 ton of material B, and 3/10 ton of material C. Production is constrained by a limited availability of the three raw materials. For the current production period XYZ has the following quantities of each raw material: 20 tons of material A, 5 tons of material B, and 21 tons of material C. Management would like to achieve the following priority level goals:
Goal 1. Produce at least 30 tons of fuel additive.
Goal 2. Produce at least 15 tons of solvent.
Goal programming would provide directions for production.
INTEGER PROGRAMMING
Integer programming is useful when values of one or more decision variables are limited to integer values. This is particularly useful when modeling production processes for which fractional amounts of products cannot be produced. Integer variables are often limited to two values—zero or one. Such variables are particularly useful in modeling either/or decisions.
Areas of business that use integer linear programming include capital budgeting and physical distribution. For example, faced with limited capital a firm needs to select capital projects in which to invest. This type of problem is represented in Table 1.
As can be seen in the table, capital requirements exceed the available funds for each year. Consequently, decisions to accept or reject regarding each of the projects must be made and integer programming would require the following integer definitions for each of the projects.
x1 = 1 if the new office project is accepted; 0 if rejected
x2 = 1 if the new warehouse project is accepted; 0 if rejected
x3 = 1 if the new branch project is accepted; 0 if rejected
A set of equations is developed from the definitions to provide an optimal solution.
NONLINEAR PROGRAMMING
Nonlinear programming is useful when the objective function or at least one of the constraints is not linear with respect to values of at least one decision variable. For example, the per-unit cost of a product may increase at a decreasing rate as the number of units produced increases because of economies of scale.
STOCHASTIC PROGRAMMING
Stochastic programming is useful when the value of a coefficient in the objective function or one of the constraints is not know with certainty but has a known probability distribution. For instance, the exact demand for a product may not be known, but its probability distribution may be understood. For such a problem, random values from this distribution can be substituted into the problem formulation. The optimal objective function values associated with these formulations provide the basis of the probability distribution of the objective function.
MARKOV PROCESS MODELS
Markov process models are used to predict the future of systems given repeated use. For example, Markov models are used to predict the probability that production machinery will function properly given its past performance in any one period. Markov process models are also used to predict future market share given any specific period's market share.
COMPUTER FACILITY PROBLEM.
The computing center at a state university has been experiencing computer downtime. Assume that the trials of an associated Markov Process are defined as one-hour periods and that the probability of the system being in a running state or a down state is based on the state of the
Table 1
Integer Programming Example
Project Estimated Net Return Year 1 Year 2 Year 3
New office 25,000 10,000 10,000 10,000
New warehouse 85,000 35,000 25,000 25,000
New branch 40,000 15,000 15,000 15,000
Available funds 50,000 45,000 45,000
system in the previous period. Historical data in Table 2 show the transition probabilities.
Table 2
To
Running Down
From Running .9 .1
Down .3 .7
The Markov process would then solve for the following: if the system is running, what is the probability of the system being down in the next hour of operation?
QUEUING THEORY/WAITING
LINE THEORY
Queuing theory is often referred to as waiting line theory. Both terms refer to decision making regarding the management of waiting lines (or queues). This area of management science deals with operating characteristics of waiting lines, such as:
the probability that there are no units in the system
the mean number of units in the queue
the mean number of units in the system (the number of units in the waiting line plus the number of units being served)
the mean time a unit spends in the waiting line
the mean time a unit spends in the system (the waiting time plus the service time)
the probability that an arriving unit has to wait for service
the probability of n units in the system
Given the above information, programs are developed that balance costs and service delivery levels. Typical applications involve supermarket checkout lines and waiting times in banks, hospitals, and restaurants.
BANK LINE PROBLEM.
XYZ State Bank operates a drive-in-teller window, which allows customers to complete bank transactions without getting out of their cars. On weekday mornings arrivals to the drive-in-teller window occur at random, with a mean arrival rate of twenty-four customers per hour or 0.4 customers per minute.
Delay problems are expected if more than three customers arrive during any five-minute period. Waiting line models can determine the probability that delay problems will occur.
TRANSPORTATION METHOD
The transportation method is a specific application of the simplex method that finds an initial solution and then uses iteration to develop an optimal solution. As the name implies, this method is utilized in transportation problems.
TRANSPORTATION PROBLEM.
A company must plan its distribution of goods to several destinations from several warehouses. The quantity available at each warehouse is limited. The goal is to minimize the cost of shipping the goods. An example of production capacity can be found in Table 3. The forecast for demand is shown in Table 4.
The transportation method will determine the optimal amount to be shipped from each warehouse and determine the optimal destination.
Table 3
Origin Warehouse Location 3 month capacity
1 Houston 2,000
2 Dallas 2,500
3 San Antonio 2,800
Total 7,300
Table 4
Destination Location 3 month forecast
1 New Orleans 1,800
2 Little Rock 3,200
3 Las Vegas 2,300
Total 7,300
SIMULATION
Simulation is used to analyze complex systems by modeling complex relationships between variables with known probability distributions. Random values from these probability distributions are substituted into the model and the behavior of the system is observed. Repeated executions of the simulation model provide insight into the behavior of the system that is being modeled.
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