Current practice and challenges towards handling uncertainty for effective outcomes in maintenance
Current practice and challenges towards handling uncertainty for effective outcomes in maintenance
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DOI：
10.1016/j.procir.2020.01.024
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Alex Grenyer;F. Dinmohammadi;J. Erkoyuncu;Yifan Zhao;R. Roy
中科院分区：
文献类型：
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作者：
Alex Grenyer;F. Dinmohammadi;J. Erkoyuncu;Yifan Zhao;R. Roy
可行启发式属性与统计测量相结合，在复杂资产的全寿命服务合同下的工业维护中提出了重大挑战。获取和处理启发式属性的技术引起了许多不确定性，这些不确定性往往是未定义的和无法减轻的。对这些不确定性的整体看法可以提高决策能力，减少维护成本和周转时间。因此，有必要确定影响上述挑战所产生的不确定性的因素并对其进行排序。这一点，再加上谁对这些挑战做出了贡献，以及当前应对这些挑战的做法，确定了本研究的重点。32个分类因素对不确定性的影响是通过由一家领先防务公司的9名经验丰富的维修经理完成的问卷来评估的。根据受访者的经验和工作角色，采用谱系法对受访者的答案进行效度评分，使分数正常化。在与受访者的访谈中讨论了结果以及当前的实践和改进不确定性评估的方法。通过层次分析法（AHP）对评分进行加权，以确定对维修不确定性影响最大的因素。分析显示，这些因素包括：知识产权（IPR）、维护人员绩效、信息质量、变革阻力、利益相关者沟通和技术集成。来自不同行业背景的40名从业人员对此进行了验证。从访谈中，可以认为启发式和统计属性的整体视图最终允许更成功的决策，但需要在资产生命周期的质量和成本之间进行权衡。
The combination of viable heuristic attributes with statistical measurements presents significant challenges in industrial maintenance for complex assets under through-life service contracts. Techniques to obtain and process heuristic attributes raise numerous uncertainties which often go undefined and unmitigated. A holistic view of these uncertainties may improve decision-making capabilities and reduce maintenance costs and turnaround time. It is therefore necessary to identify and rank factors that influence uncertainties originating from challenges in the above context. This, along with an identification of who contributes to such challenges and current practice to handle them, sets the focus for this study.The influence of 32 categorised factors on uncertainty is assessed through a questionnaire completed by nine experienced maintenance managers from a leading defence company. The pedigree approach is applied to score validity of respondents’ answers according to their experience and job role to normalise scores. Results are discussed in interviews with respondents along with current practice in and ways to improve uncertainty assessment. Scores are weighted through the Analytical Hierarchy Process (AHP) in order to identify the most influential factors on uncertainty in maintenance. The analysis revealed that these include: intellectual property rights (IPR), maintainer performance, quality of information, resistance to change, stakeholder communication and technology integration. These are verified with 40 practitioners from various industrial backgrounds. From the interviews, it is deemed that a holistic view of heuristic and statistical attributes ultimately allows for more accomplished decision-making but requires trade-offs between quality and cost over the asset’s life cycle.