DATA COLLECTION

Data for case studies may be grouped into two classes: qualitative, in the form of words, or quantitative in the form of numbers (Neuman, 2003). Yin (1994) presented a quite exhaustive list for data sources that comprises archival records, interviews, direct observations and documents. In addition, he provides an analysis of advantages and limitations for each source with regard to different settings of use. This research focuses on quantitative approach to study an event within its real-life environment by collecting evidence (Yin, 1994 and Robson, 1993). Archival records, documentation, direct observation and interview will be therefore quantitative data collection methods used in this research. Accordingly, this research uses secondary data collection methods (organisation documents, maintenance manuals and reports, spare part supplier documents, etc.).

Stake (1995) and Yin (1994) claimed that data sources should be multiple to ensure the reliability of the study. They considered the following list as exhaustive primary sources of evidence. Besides, they specified that not all sources are required in every case study

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and the use of each source relies heavily on researcher skills and research questions. The data sources categorised by Yin (1994) are:

ƒ documentation, ƒ archival records, ƒ direct observation, ƒ participant observation, ƒ interviews, and ƒ physical artifacts.

Table (5.2): Research data sources (Source: Yin, 1994)

Data Sources strengths Limitations

Documentation stable - repeated review

unobtrusive - exist prior to case study

exact - names etc.

broad coverage - extended time span

retrievability - difficult biased selectivity

reporting bias - reflects author bias

access - may be blocked

Archival Records

Same as above

precise and quantitative

Same as above

privacy might inhibit access Interviews targeted - focuses on case study

topic

insightful - provides perceived causal inferences

bias due to poor questions response bias

incomplete recollection

reflexivity - interviewee expresses what interviewer wants to hear

Direct Observation

reality - covers events in real time

contextual - covers event context

time-consuming

selectivity - might miss facts reflexivity - observer's presence might cause change

cost - observers need time Participant

Observation

Same as above

insightful into interpersonal behaviour

Same as above

bias due to investigator's actions Physical

Artifacts

insightful into cultural features

insightful into technical operations

selectivity availability

No single source has a complete advantage over the others; rather, they might be complementary and could be used in tandem. Thus a case study should use as many

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sources as are relevant to the study. Table 1 indicates the strengths and weaknesses of each type:

Since this study is conducting an integrated logistics support ILS analysis requires a broad quantity of information and a large amount of this information is available neither in adequate format nor in organisation documents. In general, ILS models deal with the following aspects: a description of a technical system, a modelling of the deterioration and its effect on system operational output, a definition of the available information about the system, a designation of the objective function and the optimisation methods which determine the best trade-off. The data inherent to these ILS aspects consist mostly of failure frequencies, repair time, costs, maintenance capabilities and procedures, spare procurement time, installed repair shops and how ease these shops are interconnected. Beyond these aspects, this study explores also the effect of the operating environment on the research questions through direct observation, questionnaires and the examination of reports and documents. Maintenance data which may be used for integrated logistics support are generally gathered from the following sources:

ƒ Engineering drawings;

ƒ Product data for design and manufacturing; ƒ Technical specifications and standards; ƒ Technical publications and handbooks; ƒ Training materials for maintainers; ƒ Spare parts descriptions;

ƒ Maintenance plans; ƒ Maintenance reports;

ƒ Maintenance crew interviews etc.

5.4.2 DATA REQUIREMENTS

The validation of any model outputs dependent mainly upon its modelling tools and the quality of the input data. It is necessary, therefore, that the accuracy of input data is established. In setting up data requirements, the methodology was to ascertain a trade-off between a realistic level of rigor and standardisation, flexible recording database that could be adapted to any specific use and scientific technique requirements used as modelling

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tools. A thorough analysis of the integrated logistics support ILS techniques has shown that the ILS data requirements could be classified as follows:

ƒ The system level data: a hierarchical system structure should containing all the components and sub-components that are replaceable or to be repaired at the all system breakdown levels. The predicted or observed failure, repair and supply characteristics of these components are necessary. Additionally, a set of other core data should be defined and it may contain general information about the system as capacity, physical dimensions and weight. This information is vital for logistics considerations.

ƒ The operational data: this category may encompass event data (detailed information about the outages or maintenance activities that occur), counter data (cumulative functioning hours since the beginning of operation), environmental data (information about environmental conditions observed at the site), and the required level of availability of the system.

ƒ The support data : may include stock positions and their costs, procurement mean time for each components and are also required, as is deployment a ƒ The repair facility data: details of the repair shops (their positions,

characteristics, and interactions).

ƒ The economic data: the economic data required include the discount rate, inflation rate, direct and common costs and the analysis period (or the life cycle).