流动性维护规划中的 "买或卖 "决策:多标准方法

Logistics Pub Date : 2024-05-20 DOI:10.3390/logistics8020055
Tommaso Ortalli, Andrea Di Martino, Michela Longo, Dario Zaninelli
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引用次数: 0

摘要

背景:当前的技术革新正全面涉及交通领域,将通常的大众交通系统转变为可持续的交通方式。通常采用 "要么做,要么买 "的决策,从成本效益的角度评估不同的解决方案,从而对内部生产或外部供应商做出战略选择。这也可以反映在维护操作上,从而将类似的方法复制到相关的运输公司。方法:通过多标准分析的决策模型,可以为维护工作做出 "要么做,要么买 "的选择。首先简要介绍了实际的交通环境,评估了所提供的交通解决方案的全球和国家趋势。然后,重点介绍了移动领域的维护方法,并考虑了 "要么做,要么买 "决策过程的必要性。决策路径是通过基于特征向量权衡评估的多标准框架制定的,其中确定了不同的关键绩效指标 (KPI),并利用这些指标来评估所涉及的维护方法。结果:对不同方案的比较有助于确定为运输运营商提供的解决方案。特别是在相关案例研究中,发现维护的具体成本降低了 35%,成本变化率降低了 44%。与参考的内部维护相比,机队的可靠性保持在可接受的水平(≥90%),同时观察到平均故障间隔时间有所增加。结论:对于小公司而言,该方法可以解决选择外包维护的最佳问题。最后,从所做的分析中推断出一个总体趋势,以作为决策指南。从运输运营商的角度来看,进一步分析以测试和验证所选择的方法是否有效,会对研究有所裨益。
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Make-or-Buy Policy Decision in Maintenance Planning for Mobility: A Multi-Criteria Approach
Background: The ongoing technical innovation is fully involving transportation sector, converting the usual mass-transit system toward a sustainable mobility. Make-or-buy decision are usually adopted to assess different solutions in terms of costs-benefits to put in place strategic choices regarding in-house production or from an external supplier. This can also be reflected on maintenance operations, thus replicating a similar approach to transport companies involved. Method: A decision-making model by means of a multi-criteria analysis can lead make-or-buy choices adapted to maintenance. A brief introduction into the actual mobility context is provided, evaluating global and national trends with respect to the mobility solutions offered. Then, a focus is set on maintenance approaches in mobility sector and the need of a make-or-buy decision process is considered. The decision-making path is developed through a multi-criteria framework based on eigenvector weighing assessment, where different Key Performance Indicators (KPIs) are identified and exploited to assess the maintenance approach at stake. Results: A comparison among different scenarios considered helped in identify the solution offered to the transport operator. In particular, for the case study of interest a −35% decrease in maintenance specific cost and −44% in cost variability were found. Reliability of the fleet was kept at an acceptable level compared to the reference in-house maintenance (≥90%) while an increase in the Mean Time Between Failure was observed. Conclusions: For the purposes of a small company, the method can address the choice of outsourcing maintenance as the best. Finally, a general trend is then extrapolated from the analysis performed, in order to constitute a decision guideline. The research can benefit from further analysis to test and validate that the selected approach is effective from the perspective of transport operator.
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