Resistencia al fuego de elementos constructivos de cerramiento Las exigencias de comportamiento ante el fuego de un elemento

As mentioned above, the expert validation is based on usability questionnaire designed to assess user satisfaction related to model attributes and model results. For the 26 questions, descriptive statistics were examined including mean, maximum, minimum and standard deviation. Table 3 provides the descriptive statistics for questionnaire responses. In overall, the model was given an average rate of 3.59 with SD = 1.07, which is higher than the score 3.0.

167 Table 9.5 : Questionnaire results.

Mean max min SD

1 - Model Usefulness

Effectiveness

1 I can estimate required spare part using the model. 3.80 5.00 2.00 0.87 2 I am successful in general in finding required data when using _{the model.} 3.60 5.00 1.00 1.00 3 Overall, the model is useful in helping me 3.68 5.00 2.00 0.75 4 I achieve what I want using the model 3.48 5.00 2.00 0.92 5 The results I obtain from the model are useful. 3.60 5.00 2.00 0.87 6 The model covers topics that I need. 3.32 5.00 2.00 0.85 Efficiency

1 It is easy to obtain the results that I need 3.56 5.00 1.00 1.39 2 The model is easy to use in general. 3.88 5.00 2.00 1.13 3 I can obtain the results in adequate time using the model 3.12 5.00 2.00 1.01 4 The model is well designed to achieve what I need 3.48 5.00 2.00 1.00 5 Using the model enhances the quality of my work 3.96 5.00 2.00 1.21 Satisfaction

1 Do the results obtained by the model look logic for me 4.20 5.00 3.00 0.58 2 Can the results obtained by the model be applied 3.28 5.00 2.00 1.21 3 Do the results differ largely from those E138 3.48 5.00 2.00 1.05 4 Does not take a great deal of effort to become familiar with the _model 3.52 5.00 2.00 1.16 5 The terminologies used on the model are easily understandable. 3.40 5.00 2.00 1.22 6 Using the model makes it easier to do my work 3.12 5.00 2.00 0.97 7 It was easy to learn to use the model 4.12 5.00 2.00 1.05 8 I feel optimistic that the model will be successful 3.60 5.00 2.00 1.22

2 - Model Adaptability to environments

1 Required input data are easily obtained from organisation _{resources and archives} 3.84 5.00 2.00 0.85 2 Usefulness of the model output by the organisation 3.56 5.00 2.00 1.00 3 Do missing data at your level could be easily estimated? 3.40 5.00 2.00 0.91 4 Applicability at different whole life phases 3.76 5.00 2.00 0.83 5 How important to you are the benefits provided by to your _{organisation?} 3.72 5.00 2.00 0.98

3 - Model Adaptability to culture of users

1 Familiarity with the theory 2.12 3.00 1.00 0.60 2 Ease to manipulate the model 3.60 5.00 2.00 1.26 3 It meets my needs. 3.72 5.00 2.00 1.06 4 I quickly became skilful with it. 3.72 5.00 2.00 1.34

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When investigating questionnaire items, the assessment showed a range of averages between 4.20 and 2.12. Only two panellists, however, gave an overall score less than 3.0 (which could be considered as ‘insufficient’) with mean values of 2.96 and 2.9, respectively. This expert survey revealed that the usability and the overall quality of the model were rated as sufficient. The model was valued to offer a more theoretical and valuable methodology to the maintenance-support problems. Panellists also found that they could recommend the use of the model since it links supportability aspects with system availability and readiness.

Table 9.6 : Model satisfaction results.

Model Usefulness _{Model Adaptability}

to environments Model Adaptability to culture of users Effectiveness Efficiency Satisfaction

Mean 3.58 3.60 3.59 3.66 3.29

max 5 5 5 5 5

min 1 1 2 2 1

SD 0.88 1.18 1.12 0.92 1.28

According to the mean values of each assessment group (Table 9.6), it can be concluded that all experts have high expectations on the model ability of solving problem and its helpfulness. They find it easy to carry out their tasks of using the model. However, the underlying theory seemed to be the least valuated by the participants. The following points summarise the comments that arose from questionnaire answers, and specific suggestions and ideas.

1. At first impression, the model offered a package which could be used for repair location optimisation, spare part optimisation for a given repair configuration and joint optimisation of spare part and repair location. The model encouraged a better integration of procurement teams and maintenance staff.

2. There is a significant difference between the model approach and the actual used support provision method which is based only on manufacturer guidelines and recommendations. The thoroughness in terms of the underlying theory and the inclusion of LORA analysis indicate that the model is different indeed, offering more advantages for decision making.

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3. It was also found that the use of LORA analysis in the early stages of system installation might help to achieve cost effective decisions. The need for immediate repair actions is the main target since the Petroleum industry operates in a large environment.

4. A common idea from the questionnaire is that of component criticality analysis. This is identified as a key point when dealing with procuring and storing spare for all petroleum equipment. It was felt that critical-part procurement is prescribed by safety stocks and operation requirement. Certainly, there is a need to demonstrate how the model addresses part criticality within pilot cases. Eleven panellists, who emphasized this issue, found that spare part mix delivered by the model based on system availability is a good way to deal with this problem. Therefore, this would become a strong rationale for the model adoption and use.

5. Input data was regarded as being fundamental to the way in which the system might operate. Those responsible for using the model might have sufficient technical expertise to analyse the data before running the model. This issue may make the model outcome inconclusive if there is some missing data which must be estimated. That is, the input data will be obtained from system historical database and the model should be designed to accommodate this point.

6. There was a comment regarding the description of the team that should use the model, in addition whom might be concerned by taking supportability decisions. Since the model is based on Integrated Logistics Support ILS, different players may be involved in using its output such as maintenance representatives, procurement engineers, operation managers, etc. Phone discussions with the panellist who arose this issue focused on the model usability and the definition of the model users. This point was extended to the other panellists and they felt that the model could be equally useful to all actors involved in procurement and maintenance and it might be a good solution for conflicting issues related to spare part procurement.

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7. Finally, there were some comments regarding to the user training on the theoretical background of the model, i.e., level of repair analysis and genetic algorithm optimisation technique. Training of the users in integrated logistics support ILS is considered a necessity by the group of panellists. Some of them have suggested developing a training program on the topic and using the model as part of the training.