Focus and Technique

Technique, can be reviewed through literature both in a commercial and academic sense, some COTS tools are available to manage the more common problems typically faced by suppliers for aeronautic performance contracting. The survey conducted did not find any reference to the evaluation of a contracting framework or performance contract, including relevant processes such as the scoring process or any risk evaluation of the framework (or contract) together with the predicted performance of a fleet of aircraft operating under such an agreement.

The tools and techniques that are currently available within the scope of this research activity generally fall within the category of simulation and optimisation for decision-support. The tools within the simulation category commonly utilise techniques such as Monte Carlo or DES.

Additionally, heuristic technics are generally employed on the side of optimisation however

55 the objective function usually is in relation to the availability of parts, logistics turnaround time, repair turnaround time et cetera, and therefore the focus of the sampled literature is within these domains.

Highly specialised systems covered in literature such as DES systems also tend to focus on the ability to predict the performance of a system, or specific aspects of a system such as the ability of a logistics supply chain to meet the demands of scheduled and unscheduled part replacement.

(34), (35, 36), (37), (38), (39) Currently the literature is not covering the use of simulation to predict the performance of a fleet of aircraft together with the contract performance given a set of contract requirements or contracting framework. This is a focal point for this research as outlined in the research questions and formulates part of the first research question.

Continuing the theme, the technique or methodology engaged by those research works is also limited in scope. For example, Mirzahosseinian (164) sought to answer the following; “How do inventory management, component reliability and repair facility efficiency influence the availability of systems under the PBL contract?” and “How can customers monitor the supplier’s performance and ensure that the supplier provides the desired performance level?”

To achieve this, Mirzahosseinian (164) modelled the PBL system as a queuing network coded in Matlab, which is a type of DES. Mirzahosseinian achieved this by enhancing the classical repairable parts inventory model, further suggesting that the model improves upon classical models by relaxing the restrictive assumptions such as: fixed failure rates, fixed repair rates, and infinite capacity at repair facilities.

Mirzahosseinian (164) concluded they formulated two metrics that facilitate monitoring (and control) of the suppliers actual performance during a PBC – Mean Time Between Failure (MTBF) and Mean Time To Recovery (MTTRe), furthermore they recommend concentrating on the component reliability and repair system efficiency to improve the availability of the system with repairable spare parts. A point to note is that the MTBF and MTTR are already well established in previous PBC Frameworks such as the ASD PBC Framework (90) and the

56 US DoD Performance Contracting guidelines. Alternatively Mirzahosseinian (164) did not consider the cost benefit of such activities as it may not be cost effective to improve the reliability of specific systems when considering the time and effort in system development, further analysis is certainly required to ascertain cost benefit of such system improvements.

Additionally, and with applicability to highly complex systems such as aircraft, vendors and suppliers of particular systems could take even longer to implement reliability improvements and may come at a greater cost. The practicality of this application under the typical contractual structure of a PBS may not be realistic.

However, Mirzahosseinian (164) identified that optimising the cost for a repairable inventory system in order to discover the optimal failure rate, server repair rate and the number of servers.

(19) provides a relatively recent survey of available tools in literature and industry, segregates them into the manufacturing phase and support life-cycle phase, the same phases as the proposed research questions for this research work.

In the manufacturing phase of an acquisition typically involves costs such as equipment costs, and are associated as non-recurring tasks for unit production. Simulation tools are often aggregated at a high level and use techniques such as Monte Carlo or 'commercial off the shelf' (COTS) such as RiskHive suite(40), Predict! suite(41), Crystal Ball (42), and others. The purpose being for the management of risks associated with this phase of the contract which are generally relating to potential time and schedule impacts to program costs.

During the support life-cycle phase, tools are used to simulate the rate of occurrence of repeating in-service support costs. These are generally focused within the domains of scheduled/unscheduled maintenance and/or related logistics needs. Typical examples include Vari-Metric (38), Opus Suite (43), Siemens PLM software such as Tecnomatrix (44), Enovia by Dassault Systemes (45), amongst others.

57 Erkoyuncu, (19) also finds that practitioners within both the supplier and customer communities tend to prefer COTS tools due to the simplification of the verification and validation process during contract tenders. These COTS tools however, are not always able to cope with specific circumstances that often arise due to the unique nature of an aeronautic PBC, and therefore it is often reported that special-to-purpose models are commonly utilised in Microsoft Excel or other specifically developed software packages (46), (39) many of which could be considered as 'black box' software as they tend to include corporate intellectual property and consequently are not described in full within academic literature.