Artificial intelligence for sense making in survival supply chains
本文针对新冠疫情中人类无法理解大数据环境的问题，提出了一个包含扫描、存储、解释、执行和学习五个环节的认知模型，并设计了基于AI的解决方案，帮助供应链管理者在灾难中做出有效决策。
A defining feature of the response to the COVID-19 pandemic was a pervasive and palpable collapse of sense-making in individuals and organisations. A key factor underlying this chaos was the inability of humans to make sense of the big and noisy data environment. Such big data environments are especially amenable to interpretation by Artificial intelligence. In this study, a theoretical framework has been developed for the use of AI for sense-making in survival supply chains. The proposed framework, ‘The Cognitive Model of Survival Supply Chains’, is articulated as consisting of scan, store, interpret, execute, and learn as its purposive components. This framework is presented as a scaffolding to organise extant knowledge, identify gaps, synthesise new knowledge, and guide future research and practice. Research questions have been identified to develop a systematic research agenda. Implications for research, policy and practice for managing pandemics have been drawn out for researchers, policy makers, and practitioners, respectively. The paper also presents the design and architecture of an AI based solution for sense-making in survival supply chains. Such a solution is urgently needed for organising the efforts of diverse stakeholders in presenting a concerted and effective response to disasters that may unfold in the future.