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The paper presents the results from a study in collaboration with an airline that looked at modeling the relationship of maintenance and fuel burn costs relative to minimizing the life cycle cost relative to schedule. The work has verified that the bucket theory presented in the paper is a correct and has a direct impact on the scheduling interval. Ultimately, it was found that the maintenance schedule at the collaborating company was overly long and could be reduced by 15-20%, to reduce total costs in the longer term. The Genetic-Causal Approach was used in the cost modelling process and incorporated into the Value Operations Methodology. Consequently, the generic relevance of both these theories has been validated through the work presented.

The aviation industry contributes about 2% to the total global manmade CO 2 emissions, which is seen as the main (manmade) greenhouse gas inducing climate change. This paper focuses on the design of a CO 2 rating system which makes it possible to make a fair comparison of the environmental performance of airlines with respect to CO 2 on the basis of public available data. It is argued that airlines can be best compared on the amount of CO 2 emitted per revenue ton kilometer (CO 2 / RTK) on the basis of distance sectors. Therefore, an airline is rated on various distance sectors. The CO 2 efficiency scores of an airline within a distance sector can then be compared with other airlines. For nine airlines the CO 2 efficiency is modeled, and the distance sector boundaries are determined. It is shown that the relative positions of airlines may change when choosing a different boundary, since the CO 2 efficiency changes with distance. It is also shown that on the basis of public available information it is difficult to accurately determine the CO 2 / RTK of an airline, which is due to lack of detail in public available data. A sensitivity analysis has been performed to show on which parameters information in greater detail is needed.

This paper describes the development of the Airside Value Model. In the field of airport operations and airport performance measurement, there is much focus on 'economical performance'. Additionally, Key Performance Indicators to assess airside operations are used, but these sets of KPIs are very diverse and the reasons for measuring them are not always clear. Moreover, these KPIs are not used to actually drive the operations at the airport's airside. The Airside Value Model seeks to expand this limited domain by allowing airport managers to assess the Value created in their airside operations and use this information to optimize them. This Value measurement goes beyond just economical considerations, but also includes operational performance, environmental aspects et cetera. It has been shown that the Airside Value Model is able to measure these different aspects of Value and link them to operational processes. However, more work is required to enhance its effectiveness.

Theories on value creation, co-innovation and co-development and lean enterprise have gained in popularity in recent times. This research has taken aim at extending the investigation on how to quantify companies' capabilities in creating value for their stakeholders. A theoretical framework was adopted to build the performance measurement method on. This framework identifies five performance indicators of company performance: competition performance, financial performance, manufacturing capability, innovation capability and supply chain relationships. Due to the limited availability of data in aviation industry, use was made of data from the automotive industry. Data from 33 automotive OEMs was collected from which a set of variables was constructed. The behavior and relations of these variables were investigated and eventually five variables were selected, one for each performance indicator. Using multiple regression techniques weight factors were determined for each variable and a linear model was constructed, expressing a company performance index. This linear model allows assessing and comparing the performance of different companies over an arbitrary period of time. For the automotive OEMs this was qualitatively shown to work. The model was then adapted to fit the aerospace OEMs and the weight factors were recalculated. Unfortunately, due to the limited availability of data for aerospace OEMs, it was not possible to obtain great insights into the behavior and relations of the variables for these aerospace companies. Moreover, the weight factors of the linear model could not be determined with much accuracy. To solve this, it is recommended that for future research data collection continues and that in some years the research is redone with more data, allowing statistical analysis to be able to detect smaller effects. NomenclatureI p = performance index T/C Norm = normalized turnover per employee SP Norm = normalized share price T Norm = normalized turnover V/C Norm = normalized number of verhicles produced per employee R&D/C Norm = normalized R&D expenditure per employee