Learning from Incidents: A Supply Chain Management Perspective in Military Environments
Raffaele Cantelmi,
Giulio Di Gravio and
Riccardo Patriarca
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Raffaele Cantelmi: Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18-00184 Rome, Italy
Giulio Di Gravio: Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18-00184 Rome, Italy
Riccardo Patriarca: Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18-00184 Rome, Italy
Sustainability, 2020, vol. 12, issue 14, 1-19
Abstract:
Supply chain management (SCM) represents a crucial role in the military sector to ensure operation sustainability. Starting from the NATO handbook for military organizational learning, this paper aims at investigating the link between technical inconveniences and sustainable supply chain operations. Taking advantage of the learning from incidents (LFI) models traditionally used in the risk and safety management area, this paper proposes an information management system to support organizational learning from technical inconveniences in a military supply chain. The approach is discussed with reference to the Italian context, in line with international and national standards for technical inconvenience reporting. The results of the paper show the benefits of adopting a systematic LFI system for technical inconveniences, providing related exemplar business intelligence dashboards. Further implications for the generalization of the proposed information management system are presented to foster a healthy and effective reporting environment in military scenarios.
Keywords: incidents learning system; technical inconveniences; military supply chain; information management; business intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:14:p:5750-:d:385758
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