Teoría y práctica de la composición por edad de las parejas

The acquisition and through-life operation of military aircraft can be one of the most expensive acquisitions for many nations, and to answer the research questions outlined above this thesis (a) examines the existing tools available for PBC, (b) considers the potential for improved modelling, and (c) develops a model – a DSS – which will allow for more effective management of PBCs. The approach proposed is to utilise a multi-disciplinary methodology together with an existing PBC framework as a case study. The multi-disciplinary methodology utilises the ASD PBC framework as a case study to design and integrate a DSS.

To answer the two research questions proposed in the previous section, a novel model of a DSS is developed for use with PBCs in this research work. The model incorporates a Data Warehouse, a DES System and three FISs the architecture of which is represented in Fig. 2 below. The model developed uses an existing PBC framework as a case study for a FIS, a computational intelligence and artificial intelligence technique, which has a benefit of automation for the calculation process and decisions within the PBC framework. The DES system is used to simulate the PBC framework over the lifecycle of the contract for a given fleet of aircraft. Finally a data warehouse is used for the collection, storage and reporting of data in relation to the PBC.

12 Figure 2 – Decision-Support System Architecture

The second research question is answered through the DWS proposed in this thesis. The DWS is developed using proven design methodologies, and captures the data relating to the three main Outcomes as defined by the PBC framework. Additional benefits of this design are anticipated by lowering the number resources required to analyse and report on metrics supplied from the various sources of data and for the management of the overall contract performance. A more simplified management process is anticipated along with the identification of any key problem areas within the sustainment and support side of the Aerospace and Aeronautic industries. This system also provides a single source of information, which then enables a consistent and streamlined approach for contract simulation in addition to providing an effective means for management of data uncertainty.

The DWS provides an interface to an Analytics System, which is used for the reporting of, calculation of and other analytical methods such as ‘drill down’ of KPIs/KSHIs, Availability Status, Performance Evaluation etc. or as recommended by the PBC framework. An interface

13 to the Simulation Model is also provided, allowing for the transfer and recording of data from both systems.

The first research question is answered through the contribution of the Simulation Model proposed. The Simulation Model is comprised of both the combination of a Fuzzy Logic model together with a DES System. The Fuzzy Logic model is used to model the performance agreements set in a typical PBC and is comprised of three Fuzzy Inference Systems (FIS), with this thesis using the ASD PBC Framework as a case study example to model and the methodology described. The Fuzzy Logic model is expected to be an enabler for decision support during contract performance calculation, and therefore can be considered a Decision-Support System (DSS). The Fuzzy Logic model can be applied within in two aspects of this novel DSS.

a) In a Simulation layer, to determine potential contract performance for hypothetical scenario analysis or future potential contract analysis, and

b) Within the Extraction, Transformation and Loading layer for the automatic calculation of contract performance when loading raw data into the system.

The DES system is used to simulate the PBC framework over the lifecycle of the contract for a given fleet of aircraft. The DES system works together with the Fuzzy Logic model to simulate and predict the performance of a fleet of aircraft and the impact that performance has to the contract performance, whether it is perceived to be good performance or bad. A demonstrated benefit of this aspect of the system is that it allows users to identify and quantify risk and/or opportunities with the design of their contract as well as the performance of the aeronautic system operating under that contract.

The novelty of this research work provides the following key contributions:

• A formalisation of an aeronautic PBC framework as set of fuzzy subsets.

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• Evaluation methodology and formalisation for comparison of an aeronautic system operating versus the same system operating under a PBC.

• A DES system that simulates a PBC framework over the support life-cycle of the contract for fleet of aircraft, with a dynamic fleet and contract length.

• The DES system utilises a Fuzzy Logic model to simulate and predict the performance of a fleet of aircraft and the impact fleet and aircraft performance has to the contracted performance.

• The ability to modify and simulate perceived good performance or bad performance periods, and to identify and quantify limitations both in the platform and the design of the performance contract.

• A methodology for decision support incorporating performance contract requirements and the platform requirements for the through-life support phase of a contract.

• A methodology for risk analysis of performance contract requirements together with platform performance prediction

• A methodology for verification and management of continued through-life support for suppliers.

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