Browsing by Author "McNaught, K. R."
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Item Open Access Investigating the applicability of Bayesian networks to demand forecasting during the final phase of support operations(2019-03) Boutselis, Petros; McNaught, K. R.; Zagorecki, AdamA challenge faced by businesses that provide logistical support to systems is when the provision of those support services is no longer required. A typical example of such a situation is when military operations come to an end. In such cases, those companies that have a contract with the Armed Forces to provide maintenance support for the deployed systems, need to maintain those systems at minimum cost during that final phase, that is from the time the decision to stop the operations is announced until their very end. During the final phase, a challenging problem is forecasting the demand for spare parts, corresponding to equipment failures within the system. This is because the support context, the number of supported systems, the support equipment or even the operational demand can change during that period, and also because there can be very limited opportunities to place orders to cover demand. This thesis suggests that these types of problems can take advantage of the data that have been collected during the support operations prior to the initiation of the closing down process. Moreover, the thesis investigates the exploitation of these data by the use of Bayesian Networks to forecast the demand for spares that will be required for the provision of maintenance during the final phase. The research uses stochastically simulated Support Chain scenarios to generate data and also to evaluate different methods of constructing Bayesian Networks. The simulated scenarios differ in the demand context as well as in the complexity of the Equipment Breakdown Structure of the supported systems. The Bayesian Networks’ structure development methods that are tested include unsupervised machine learning, eliciting the structure from Subject Matter Experts, and two hybrid approaches that combine expert elicitation and machine learning. These models are compared to respective logistic regression models, as well as subject matter experts-adjusted single exponential smoothing forecasts. The comparison of the models is made using both accuracy metrics and accuracy implication metrics. These forecast models’ comparison methods are analysed in order to evaluate their appropriateness. The analyses have provided a number of novel outputs. The algebraic analysis of the accuracy metrics theoretically proves empirical problems that have been discussed in the literature but also reveals others. Regarding the accuracy implication metrics, the analysis shows that for the particular type of problems examined in this thesis –final phase problems – the accuracy implication metrics commonly applied are not enough to inform decision making, and a number of additional ones are required.The research shows that for the scenarios examined, the Bayesian Networks that had their structure learned using an unsupervised algorithm performed better in the accuracy metric than any of the other models. However, even though these Bayesian Networks also did well with the accuracy implication metrics, neither they, nor any of the others was consistently dominant. The reason for the discrepancy in the results between the accuracy and the accuracy implication metrics is that the latter are not only driven by how accurate the forecast model’s prediction is, but also by the model of the residual error and the bias.Item Open Access Supporting operational decision making concerning aircraft structural integrity damage identified during maintenance.(2021-06-10) Green, Richard; McNaught, K. R.; Saddington, A.Military aircraft operations balance delivery pressures and engineering risks. Aircraft structural damage incurred in-service creates complex risk decision problems for managers deliberating maintenance activity such as delaying rectification to continue operations, or grounding an aircraft or entire fleet. In many operational settings, aircraft availability demands restrict the time, information, or resources to analyse structural risks, making formal risk or decision analysis intractable. Exact solutions are information intensive and require specialist knowledge or machinery beyond the capabilities of generalist engineering managers, often compelling decision-makers to use their subjective judgement in an unsupported way. For actors deliberating aircraft maintenance structural risks in such circumstances, a novel approach based upon heuristics, argument and bounded rationality is proposed, which was informed by the results from a survey of engineering practitioners and case study analyses. Testing of the approach was carried-out with 21 aircraft engineering decision-makers with experience of structural integrity risks, split into three groups, using realistic but fictional textual simulations of aircraft maintenance. One group used existing decision justification approaches and were compared with a second group who provided decision justifications using the novel approach. Users of the novel approach felt supported and were very confident in their justifications. The third group of raters comparing the two sets of decision justifications indicated preferences using Likert scales against the criteria: which is easier to understand, which is more transparent, and which gives the better justification. Analysis of the comparative results using ANOVA provided evidence that the novel approach enabled better decision justification and transparency compared to existing approaches. The novel approach aids decision-makers compelled to use their unsupported subjective judgement, improving organisational resilience by improving robustness and stretching system process to handle surprises, and providing a clear record of the decision basis for post hoc review.