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MIT Airline Data Project

The Airline Data Project (ADP) was established by the MIT Global Airline Industry Program to better understand the opportunities, risks and challenges facing this vital industry. The ADP is designed to support the goals of the MIT Airline Industry Consortium. It is a unique repository of data and analysis that will allow individuals – from academia to the financial community to the news media – to monitor the evolution of the U.S. commercial airline industry.

Alle Datensätze:  E F R T
  • E
    • April 2018
      Quelle: MIT Airline Data Project
      Hochgeladen von: Knoema
      Zugriff am: 24 Januar, 2019
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      The U.S. airline industry focused significant attention on total compensation of employees - the total cost to an airline for salary and wages, pension, benefits, payroll taxes and other items affected by terms included in a collective bargaining agreement. These labor costs, which are interdependent, are key to calculating an airline's fixed cost of its respective work forces.
    • April 2018
      Quelle: MIT Airline Data Project
      Hochgeladen von: Knoema
      Zugriff am: 24 Januar, 2019
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      Historically, benchmarking productivity between airlines has been limited to comparing each airline to a select group of carriers that often are arbitrarily chosen to be the comparator group (e.g., "low-cost" airlines). The ADP makes a concerted effort to provide the data and analysis to make meaningful comparisons between companies as well as sectors of the industry and to address a lack of consistent metrics. To align with unit costs, we assess ASMs (output) per employee (input) for all employees and flight employees. We then take that analysis a step further and assess the number of ASMs produced per dollar of labor compensation. Calculating the output per employee is only one part of the equation. It is equally important to understand the cost of that output. Often the two produce very different results. We do not exhaust all of the measurements that could be used to gauge productivity. Instead, metrics within the data-set allow users to make adjustments for average aircraft size and stage-length in addition to the analysis we provide for the carriers included in the industry average. For example, an adjustment for average aircraft size is relevant when analyzing the number of ground employees an airline employs per aircraft.
    • Juni 2018
      Quelle: MIT Airline Data Project
      Hochgeladen von: Knoema
      Zugriff am: 24 Januar, 2019
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      The ADP expense section, like the revenue section, examines the system performance of each airline. Since 2000, the components which comprise the total operating expense for each airline have undergone significant change. There has been increased focus on labor costs, fuel, maintenance and the outsourcing of some services and departments. The ADP provides unit costs with each of these expenses either included or excluded. Furthermore, each relevant metric has been stage-length adjusted with the formula displayed. The data-set concludes with a look at labor versus non-labor costs per available seat mile. The relationship between these two measures is a key factor in any assessment of the cost-competitiveness of individual airlines. In addition, we have calculated labor costs, excluding executive salaries, to provide insight on this key component of airline costs. While this measure is not perfect, it does help to put some context around this much publicized issue. All of the major items that go into calculating total operating expense are aligned, as reported to the DOT and the SEC. We encourage users to relate changes in expenses to changes in revenue.
  • F
    • April 2018
      Quelle: MIT Airline Data Project
      Hochgeladen von: Knoema
      Zugriff am: 28 Januar, 2019
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      The ADP provides a basic set of data and analysis highlighting earnings and the impact on a company’s cash flow, as well as the impact of earnings and cash flow on liquidity and capitalization positions. The ADP will perform analyses of each U.S. airline that files with the SEC and provide the ratios most commonly used in the analysis of a company’s financial position. These ratios can be used for basic analysis of an individual company, as well as for comparison purposes with other companies examined by the Airline Data Project.
    • April 2018
      Quelle: MIT Airline Data Project
      Hochgeladen von: Knoema
      Zugriff am: 25 Januar, 2019
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      Efficient fleet utilization is one of the key factors in an airline's success and profitability. In this section, the Airline Data Project (ADP) demonstrates how mainline carriers have improved the use of their fleets and how the hub-and-spoke carriers typically achieve lower fleet utilization than their low-cost counterparts. While network architecture explains some of the differences, the age of a carrier's fleet also is a significant factor. Details on carrier operating fleets found in this section include:The key measurements of aircraft productivity, including aircraft utilization per day, stage-length and average number of seatsA carrier-by-carrier profile of costs associated with operating their fleets and sub fleets as defined by the ADP Carrier profiles with supporting data for further analysis of other productivity and cost measurements that are not provided for in the summary charts In keeping with the ADP’s efforts to aggregate data to ensure meaningful comparisons, fleets are broken down into the following categories:Small narrowbody aircraft: Typically 150 seats or less in a two-class configuration (e.g. Boeing 737-700, Airbus A320)Large narrowbody aircraft: Typically 151 seats or more in a two-class configuration (e.g. Boeing 737-800, Boeing 737-900, Boeing 757, Airbus A321)Widebody aircraft: Two-aisle configuration
  • R
    • April 2018
      Quelle: MIT Airline Data Project
      Hochgeladen von: Knoema
      Zugriff am: 23 Januar, 2019
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      Dramatic changes to airline industry revenues have forced management at most U.S. airlines to review many long-standing business approaches. The rapid growth of low cost carriers (LCCs) and shifts to Internet distribution channels put downward pressure on airfares and, in turn, airline revenues. Many of these changes accelerated as airline revenues began falling behind their historic relationship with the Gross Domestic Product since late 2000. Ancillary revenues have become a critical source to individual airlines and the publicly available data can be found in the ADP. The ADP focuses on key revenue metrics to highlight the role of revenue in airline profitability and to provide context when expenses, finances and operating characteristics are analyzed. The data-set includes a simple and transparent stage-length adjustment for each carrier by year. Visitors should take into account each carrier's industry revenue position at different points in time when making conclusions about a carrier's network structure, cost position and other operational decisions. This section includes comprehensive industry statistics; while the individual airline data includes more specific network information.
  • T
    • Juli 2018
      Quelle: MIT Airline Data Project
      Hochgeladen von: Knoema
      Zugriff am: 29 Januar, 2019
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      The most fundamental data to any analysis of the airline industry are traffic, capacity and the relationship of one to the other. Traffic, measured by revenue passenger miles (RPMs), and capacity, measured by available seat miles (ASMs), are together used to determine Average Load Factor (ALF) – the proportion of airline output that is actually sold. In turn, these metrics provide the basis for measures of unit revenue and unit costs.  Given that other public sources for origin and destination (O&D) traffic are available, this section provides less detail in this area. Like other aggregate data included on this site, traffic and capacity information is taken from airline Form 41 filings with the U.S. Department of Transportation and filings with the Securities and Exchange Commission. An examination of traffic is not complete without an analysis of average fares and revenue.