American Airlines Managment American American Airlines Managment Essay, Research Paper American Airlines Managment American Airlines is a corporation that exhibits all of the characteristics of a firm in an industry where good tactical management is the key to long term sucess and survival. The airline industry is a prime example of a market where cutthroat competitive activity is the status quo. Airlines that survive in this environment do so through the understanding and continued improvement of the way in which tactical management tasks are addressed. Success is dependent upon doing all of these tasks well including demand forecasting, logistical programming, marketing and production. The key point to remember is that since American Airlines is a tactical entity, its key area of concentration is equilibrium maintenance. A continual endeavor must be made to match supply closely to demand, especially anticipated demand.
If it is not likely that production can be amended to more closely match demand, then promotion should be used to affect demand. American Airlines dedicates large amounts of time and resources to the types of facilities necessary to support the tactical management tasks noted above. This report is an attempt to illustrate the types of information system requirements of each task in the tactical management sequence, as well as describe some of the systems and methods used by American Airlines. In addition, this report offers some off the shelf alternatives, where they exist, which could handle many of the same requirements, albeit on a smaller scale. Since demand forecasting is one of the key drivers of production, i.
The Term Paper on HRM At American Airlines
This case examines the Human Resource Management system at American Airlines and its role in the airline’s past and continued success. The case takes us through a time line from 1980 to 1992 of the company; of how the growth of the company was followed by changes in the strategy, changes in the HR policies at different time and finally their effect and impact on the employees and the ...
e. how many products a firm should supply, this will be the first management task to receive consideration. All firms engaged in activities as a tactical entity will, in some form or another, attempt to get a handle on expected demand for their products within a certain future time period such as a week, month, quarter or year. The main thing to bear in mind is that this is a tactical environment and, aside from any earth shattering new developments or shocks to the existing environment, forecasts for expected demand / maximum -likelihood share of market may be made with a fair degree of accuracy with little variance.
There are several key points that are important to this process which must be considered when making a next period forecast of demand. These items include, but are not limited to, intelligence concerning activities of competitors, market projections for the industry by industry insiders / analysts , and a great deal of historical data. Competitive intelligence is a parameter which attempts to add subjective background to the environment in which demand forecasting is carried out. Information comes from a variety of sources such as secondary information gathered from written sources, direct observation, and from competitors themselves through press releases, industry gatherings and trade journals.
This information provides some indication of what the competition plans to do as far as pricing, new products, promotions and distribution / sales . This data has a dual purpose since it may also be used within model based contingency planning when management scrutinizes competition in an effort to uncover developing threats and opportunities. Experienced tactical managers have the valuable ability to incorporate this type of information, which is not easily quantifiable, as a complement to the numerical aspects of demand forecasting. However, this is not to say that there is no information system requirement for this input into the demand forecasting process simply because it is difficult to assimilate into an objective, quantifiable form. On the contrary, a database should be set up in the context of an expert system to contain information gathered on competitors. It must be readily accessible, updated and accurate in order to aid tactical management in this process.
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Eggs are one of the popular items of food for non-vegetarians and semi-vegetarians. The present study tries to use regression technique of demad forecasting to estimate the demand fuction of eggs for Raigarh district of Chhatisgarh for various occupational groups in rural and urban areas. In this study we consider variables like size and composition of family, family income, occupation, number of ...
Another input item for demand forecasting comes from aggregate market projections. These types of analyses are readily accessible, mostly in the form of secondary information found in trade journals and economic publications. Airlines and transportation in general comprise a large industrial group within the economy of the United States and, accordingly, there is a large interest in its economic future. Wall Street brokerage firms and other financial firms are resplendent with analysts, some of which are charged with the task of tracking the airline industry s past economic performance, as well as anticipated future projections. All of this knowledge is available from many sources and, again, wise tactical managers will take the time to incorporate it. System facilities required for this type of support for demand forecasting are databases which can contain quantifiable economic information.
Since this input to demand forecasting is quantifiable, a database with analytical utilities for ranking and analyzing stored economic projections and raw data are used. This facility may also be presented to management in the guise of a dressed up expert system containing decision table constructs which will allow them to adjust many demand forecasting parameters in order to make the most accurate forecast. Arguably the most important input into the demand forecasting process is a firm s actual historical data from its own internal records sources. Historical sales data may be thought of as the most dependable and accurate input into demand forecasting since it is derived by the firm itself rather than arriving in a second hand fashion from sources outside of the organization. Historical sales data is helpful not only in developing a demand forecast, but is also used as a check against post production performance when the time arrives to compare actual demand to the forecast. This information will likely come from another massive record keeping database which records sales transactions from the point of sale.
The Term Paper on Evolution Of The Pc Operating System part 1
Evolution of the PC Operating System Outline: 1. Abstract 2. Introduction 3. Some facts about the history of computer and operating systems: software evolution depending on hardware development: 3.1 Batch operating systems; 3.2 time-shared operating systems; 3.3 Personal computers of the middle 70s: CP/M (Control Program/Microcomputer). and DOS operating systems; 3.4 Developing personal computers ...
For American Airlines, as well as the rest of the airline industry in general, this requirement is served through a reservation system of some kind. The reservation system must be capable of handling queries, data inflows and other types of processing from thousands of nodes. Dummy terminals, which simply display data, will not be sufficient to satisfy reservation system requirements, and any implementation will involve connections and terminals designed to carry two-way traffic. Additional discussion of reservation systems, including specifically what American Airlines has installed, will follow later in this paper. After satisfying system requirements for generating and handling inputs into the demand forecasting process, the actual forecast derivation may be viewed as somewhat mechanical.
The main management decision at this point is determining which type of probabilistic instrument to use with which analytical utility to yield the most accurate results. Some tactical managers may even require an expert system that does nothing more than aid them in selecting the proper mathematical tool to address the forecasting process. There is an array of probabilistic techniques that can satisfy this management requirement including least squares regression analysis, weighted scenarios, Markov-based stochastic projections and others. Many tactical managers may use a combination of these facilities to arrive at a forecast with which they feel satisfied. A key point to bear in mind when discussing demand forecasting for a tactical entity is that it is central to two important aspects of the firm. The demand forecast is viewed foremost as the progenitor of the firm s production for which it is the main, direct input.
However, it is also an indicator of the general trend of the firm s revenues over time. A forecast whose extrapolation to the next period indicates a decline in revenues may be an early warning of something novel in the industry or indicative of a paradigm shift toward a new era. This aspect of troubleshooting will be discussed more at length in a later section concerning requirements for process control. The demand forecast sets the stage for the next management task– logistical programming and its accompanying system requirements. Logistical programming is the task charged with accumulating proper amounts of the factors of production in the proper place at the proper time.
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The four factors of production (material, finance, equipment and manpower) have certain input requirements which determine the amounts of each factor to apply to the production process. Each of these inputs will necessitate the use of some type of information system to aid tactical managers in allocation of these factors to production. One of the first inputs into logistical programming is the supply schedule, which is the main determinant of the amount of products or services offered by a firm. For the airline industry, supply schedules manifest themselves in the form of the magnitude of flights offered to the public. A demand forecast is the main force behind the supply schedule, but other normative microeconomic factors play an important role in its composition. One of these factors, optimal scale of plant, exerts a direct relationship against the supply schedule and, for American Airlines, consists of the optimal terminal / gate layout at its busiest hub cities.
The goal of proper terminal design is to optimize the number and size of the complexes which converge on a hub terminal throughout the day. A complex consists of a group of inbound flights which land within minutes of each other and take-off within minutes of each other. This is the very heart of a hub and spoke system which allows a large number of flights due to the number of possible connections in the hub. Inbound passengers from many cities will all arrive at approximately the same time, disembark, and make connections to many outbound flights which leave within minutes of each other.
This occurs many times throughout the day and the system requirement for solving this problem and optimizing the operation is available in the form of CADD design stations. CAD/CAM design workstations may be used to solve terminal optimization problems and allow engineers to simulate the capability of the terminal to handle certain scenarios. This is, in fact, exactly what American Airlines did when it was searching for the optimum design for its $80 million expansion of its main hub in Dallas/Fort Worth in 1983. This simulation model was used by senior management to aid them in their decision on the best design to handle the desired flow of traffic in the narrow operational time constraints necessary for the hub to work. In addition to optimizing the terminal layout, the system was useful in optimizing other related areas. The system / model was used to determine dynamic gate assignments in order to minimize baggage handling costs and passenger delays.
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Assessment • Coursework: 20%; • Examination: 2 hours; attempt 3 from 5 questions; 80% of the ? nal mark. 1. 4. 4 Lecture plan • Overall ? ight envelope • Flight control systems • Modern control design methodology • The introduction of the course– structure, assessment, exercises, references 1. Introduction 2. Response to the controls (a) State space analysis (b) Longitudinal response to elevator ...
Another byproduct of the model was a useful algorithm designed to automatically program and update signs for directing passengers around the terminal. The functional facility was even used to determine the best layout for short-term parking in the face of expected increases in passenger traffic. Though optimal scale of plant through optimal terminal design is an important aspect of American Airlines supply schedule determination, the most important part of the supply schedule lies in determining the number of flights to and from certain destinations. For American Airlines and most of the airline industry, flight scheduling is not a simple matter. Flight scheduling is one of the most important tasks performed by tactical airline managers because it is central to where and how the factors of production are allocated. The technical system requirements are myriad, and they must meet the daunting problem of properly scheduling thousands of flights per day between hundreds of domestic and international destinations using a fleet of over 500 aircraft.
One main requirement is for a system capable of analyzing past flight offerings in search of patterns of overbookings and empty flights in order to adjust schedules to better meet forecasted demand. Technical requirements for an airline scheduling system would include a data base structure to house the body of past and present schedules from which managers could choose when composing a new schedule. The problem is compounded since airline schedules are determined months in advance. In addition to using optimization techniques, the system requires certain expert system facilities such as decision table constructs to aid in schedule development. Diagnostic remedial aids are used in order to spot bottlenecks in the proposed schedules where patterns of frequent overbookings are occurring.
In addition, remedial systems capable of offering solutions by reshuffling proposed schedules provides valuable information to flight scheduling managers. Historical data is fed into the scheduling model from the database containing past schedules and data concerning past parameters which influenced those schedules. The system takes this data and combines it with the demand forecast in order to develop a preliminary schedule. The process requires diagnostic and remedial systems to optimize the schedule so that the expected demand will be met in the most efficient manner possible.
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Even with an optimal schedule in place, there will always be disruptions due to weather and shortages of planes and crews; thus forcing scheduling managers to constantly rearrange flights. Before 1991, this was a complex task for American Airlines since dispatchers had to scan data from many different mainframe databases in order to get a handle on managing daily flights. The schedule was constantly being reconfigured to meet anticipated external obstacles such as delays due to inclement weather. In 1991, however, American Airlines invested in a new system known as Smalltalk which made schedule maintenance easier and more efficient. Smalltalk uses of object-oriented programming techniques in order to keep flights running smoothly. The dispatcher simply clicks on an object representing a flight and, when he changes the flight, the system automatically updates other objects (flights) in the system in order to propagate the change throughout the entire system.
In fact, it only took three programmers eight months to write the program which contained only two errors. Once an optimal schedule has been developed through simulation and optimization techniques, the next step is to arrange the factors of production in order to generate enough products and / or services to meet prospective demand. Since manpower costs equal over one-third of all expenditures for American Airlines, it is the first factor to receive consideration. Manpower for an airline takes on many forms; however, almost all of the employees of American Airlines can be classified into one of three different broad categories. The first category represents the aircraft crew whose duty stations are on the aircraft: pilots, copilots, navigators and flight engineers, as well as the cabin crew or flight attendants. The second category is referred to as maintenance workers, and they are the people that maintain the aircraft, which includes anything from refuel ers to engine mechanics.
The final classification includes all of the ramp workers such as baggage handlers, ticketing personnel and office workers. By far the most difficult category to allocate within the manpower group is the aircraft crews. Manpower requirements for airline crews are derived from the flight schedule. The main goal for crew schedulers is to develop a schedule for the entire following month which will ensure that all of the upcoming flights for the month are properly staffed. Flight crews at most airlines bid by seniority for the flights that they will fly in the next month and crew schedulers develop flight packages for them. The flight packages are known in the industry as bidlines.
The bidlines in turn are composed of flight segments called trip pairings, and they customarily cover a one to three day time frame. Compounding the problem for the schedulers are FAA and union work rules designed to minimize the risk of accidents resulting from crew fatigue. Therefore, the main requirement of a generation and optimization system is that it is able to find the optimal set of bidlines (i. e. the set which yields the lowest cost) which maximize the utilization of each crew member, evenly distributes flying time among the bidlines and covers every scheduled flight. The properties inherent in the crew scheduling dilemma require an expert system design.
The first part of the system uses manpower loading algorithms, the current and previous month s schedules (from various databases) and optimization techniques in order to develop the set of trip pairings, which would adequately cover all scheduled flights for the upcoming month within FAA and union work guidelines. The trip pairing process is made even more onerous because American Airlines operates several fleets of different aircraft and most pilots are trained to fly only one type. The following diagram illustrates the requirements for a crew assignment system. Source: “Recent Advances in Crew -Pairing Optimization Techniques at American Airlines’, Interfaces, Jan-Feb. 1991, V.
21, p. 66. The second part of the system takes trip pairings and bidlines and analyzes them (subject to optimization techniques) in order to constantly search for a solution (schedule) which yields the lowest cost for flight crews possible for a given flight schedule. The system will continually runs through iterations of the optimization routine and, if the set of bidlines it determines is more optimal than the last, replaces the former with the latter. Naturally, the faster the iteration speed of the system, mainframe or LAN, the sooner the system arrives at the optimal solution.
The following flow chart describes the subproblem iteration methodology. Source: “Recent Advances in Crew -Pairing Optimization Techniques at American Airlines’, Interfaces, Jan-Feb. 1991, V. 21, p. 67. American Airlines as well as 9 other airlines and a railroad, makes use of a system of this design and it accounts for an annual cost savings of $20 million.
Scheduling for ramp workers, gate crews and ticket counter personnel is less complex and also dependent on the flight schedule. Scheduling systems for these personnel are less complex but also involve optimization techniques in order to arrive at the lowest cost for labor while ensuring that arrival and departure times at each gate are as close together as possible. Manpower loading algorithms are used to assign more personnel to cover peak times and less personnel in each station for off-peak hours during lulls in the hubs. Office personnel and repair crews usually work regularly assigned hours, in the absence of strikes and / or emergencies, and are quite simple to schedule. It should be noted that Human Resources and Payroll Departments need to maintain a database containing each employee s work record, salary history and personal information in order to keep track of thousands of employees. The next factor of production for consideration is the equipment to be used in production to meet forecasted demand.
As mentioned above, American Airlines operates two large fleets of aircraft, as well as several smaller fleets. The main aircraft types are the McDonnell Douglas 80 and Boeing 727. The smaller fleets are comprised of Douglas Corporation 10, British Aerospace 146, Boeing 737, Boeing 747, Boeing 757/767 and Airbus 300 aircraft. A particular flight or route might lend itself to a particular type of aircraft which best matches characteristics of the flight. All airlines have an extremely high capital / labor ratio which is indicative of the large dollar expenditures made for aircraft. The airline industry is a mature, tactical industry and, therefore, lends itself to a capital intensive posture yielding a high capital / labor ratio.
Fleet assignment problems lend themselves to integer linear programming, which is a good way to arrive at a solution. Unfortunately, the best aircraft for a certain flight may not be available because of maintenance routing, flight schedule disruptions due to inclement weather or even pilot strikes. Objectives that must be maximized include utilization of the most efficient types of aircraft and determining the mix of aircraft to yield the lowest operating costs. Other operational constraint parameters the system will be required to deal with include the fact that certain flights will need to use certain aircraft types, limits on number of aircraft remaining overnight at each station and the number of slots available per airport per day. The decision model uses the linear programming methodology and schedules two or more fleets to a flight schedule simultaneously in order to ensure the availability of aircraft to meet demand.
The flight schedule, availability of aircraft (which aircraft to use on a particular flight) and gate availability, as well as other parameters, are fed into the system. It must be ensured that each flight and its following connection, known as a turn, are served by the same type of aircraft. Equipment continuity is very important to the model s integrity and a turn cannot use two different types of aircraft. Each aircraft must be kept track of and counted within the system so the model will know whether an aircraft is available. An aircraft cannot be assigned to two different flights in different areas at the same time. In addition, a provision or adjustment variable must be made to the model when the station is not balanced.
An unbalanced station occurs when there are more arrivals than departures or there is an imbalance between the aircraft types used. By using decision aids and technical utilities, the model will arrive at the optimal fleet assignment through continuous iteration much the same as the crew bidlines model for flight crew scheduling described above. The third factor of production which tactical managers must develop system requirements for is in the area of finance. Aircraft and other related equipment purchases are a large part of the capital budgeting requirement of an airline the size of American Airlines. An issue which is central to the capital budgeting plan for aircraft is the age-old decision, “Should we lease or buy our aircraft?’ Leasing and buying both have very real advantages and disadvantages over each other, and therefore this type of decision tends to be objective based on whichever method will achieve the least detriment to the bottom line. Accordingly, there are several very well-developed methods employed by financial and accounting managers when evaluating capital budgeting plans.
These popular methods include net present value, internal rate of return, payback period and accounting rate of return. Whether or not to undertake capital budgeting is not an issue for a capital intensive firm such as American Airlines. The key problem to be solved in capital budgeting then becomes which analytical model is the best application for evaluation of various scenarios such as which aircraft to buy, when to buy and whether to purchase them or lease from the manufacturer. A capital budgeting system will has to be a technical and / or analytical utility in the form of an expert system to assist tactical managers in capital budgeting.
One of the main inputs into a capital budgeting system is the forecasted incremental cash flows per time period attributable to the prospective project. Data for this requirement comes from historical revenue records for the aircraft in question. A lease scenario and a buy scenario can be run for each prospective capital budgeting plan in order to determine which project will most increase the profits of the firm. Algorithms to perform the number crunching can be programmed into the system without much trouble since these are well developed models. Again, the main purpose of capital budgeting is to act as a decision aid to indicate which analytical methods / models will prove to be the most evaluators of a project s viability. After evaluating the project, the system should aid management in where and how to obtain the needed funds to proceed with an acceptable capital project.
The final factor of production and its attendant information facility requirements to receive consideration in the report before discussing production is the material aspect of the firm. For an airline, materials for production can include, but are not limited to, items used in delivery of services such as aircraft parts, beverages served on flights, in-flight meals, office supplies and many, many more. The main objective is to effectively determine the correct amount of supplies and where to purchase them at the lowest cost. Another goal is to minimize materials carrying and handling costs through a quick response system between airline and suppliers akin to the type endorsed in The Virtual Corporation.
Inventories of aircraft repair and replacement parts represent a major part of American Airlines supply expenditures. Their parts management system requirements serves as a guideline for the supply system as a whole. Maintaining the correct amount and availability of spare, repairable (rotable) replacement parts to ensure overall optimal inventory cost levels is a major endeavor. Like many other considerations in the tactical context, the objective of an airline s inventory system is a function of optimization. American Airlines operates a fleet of more than 500 aircraft of varying types, thereby increasing the number of different rotable parts that must be stocked to well over 5, 000. American Airlines Decision Technology Department has developed an inventory system known as the Rotable Allocation and Planning System (RAPS).
This system is a PC based decision support system designed to aid material managers in making inventory decisions. RAPS was specifically created to replace an older, less accurate and less efficient mainframe application. This system is much easier to use than its mainframe predecessor since it is operated through the use of nested menu screens. RAPS is written in Dbase III, and it contains three different indexed files full of variable inputs necessary for parts planning algorithms. The first and smallest file has data which is specific to the various aircraft types flown by American Airlines.
This data includes shortage costs for parts and minimum availability tolerances. The second file is comprised of station information such as transit times between stations and the total amount of systemwide demand per station. The final file contains data on parts, including price, serial number, repair time and forecasted demand for each part. Information for the RAPS database files is received on a weekly basis through a download from American Airlines mainframe databases at corporate headquarters in Dallas/Fort Worth.
Each time a spare part is used from a station shop, a message is relayed to the repair facility and a new identical part is issued from the repair facility s inventory to replace the one depleted from the individual station s stock. Simultaneously, at least in theory, the station shop will send the broken (if it is rotable / repairable ) part back to the repair facility for remanufacturing. The repair facilities are charged with the responsibility for keeping enough spare parts in stock to satisfy the forecasted demand of each station systemwide. Input into the RAPS system includes the forecasted demand for spare parts by station and the data from the Dbase III files mentioned earlier. The final result of a RAPS iteration is a solution / allocation which yields a minimum value for the objective function (lowest cost) programmed into the system. Optimal results from RAPS indicates the number of spare parts to keep in inventory, when to order them and where to keep them in order to achieve the lowest costs possible.
RAPS has additional applications for other areas of supply and inventory. RAPS is stochastic in nature since its output is dependent largely on the forecasted demand for parts, and it will optimize allocation and carrying costs in the long-term. However, another system spawned from RAPS called the Dynamic Redistribution System, carries the process one step further. Due to variance in demand, certain stations may find they have an overabundance of certain parts while other stations are experiencing supply outages. DRS will work out least cost transfers of parts between stations within the stipulation that a transfer will not cause further disruptions to the system in the form of additional outages for the transferring stations. Experiments with relatives of these systems are being conducted by American Airlines to test whether they can be used for the allocation of other supplies such as in-flight meals and aircraft fuel.
With systems in place to provide data and decision support to the tactical managers at American Airlines for factor allocation solutions, the state is set for the production to take place. Demand has been properly forecasted, supply and logistical schedules have been created and factors of production have been allocated. If the firm has done its calculations correctly and installed information systems which fulfill the requirements of tactical managers within the first part of the tactical management sequence, then proper performance will be observed.
