本次讲座将向参与者介绍运营和供应链管理中的人工智能领域。我们将首先探讨当前供应链的现状和主要驱动因素，以阐明我们所处的数据驱动时代。随后，我们将介绍人工智能的多种定义，以涵盖人工智能的定义以及更重要的人工智能之外的概念。我们将介绍主要应用于供应链管理的人工智能和数据科学子领域。接下来，我们将深入探讨一些“另类”的供应链人工智能应用，特意选择这些应用是为了强调以往无法实现的功能。
之后，我们将介绍网络分析、数字化供应链监控、集体学习以及分布式决策和自动化等领域的最新研究案例。我们的目标是打破运营管理领域的学科壁垒，鼓励大家就如何评估人工智能展开讨论。最后，我们将讨论潜在的陷阱和挑战，例如数据可追溯性丧失、自满情绪、缺乏问责制以及认知衰退。本次演讲的结论是，鉴于供应链管理面临的挑战如此多样且意义重大，它必须成为一个不可逆转的跨学科领域。
This talk will introduce participants to the field of Artificial Intelligence in Operations and Supply Chain Management. We will first talk about the state of affairs and major driving forces shaping supply chain today, to motivate the data driven era we are in. Then AI is introduced with multiple definitions, to cover what is AI and importantly, what is not AI. We introduce sub-fields of AI and data science primarily used in supply chain management. We then delve deeper into an “exotic” selection of supply chain AI, deliberately so, in order to emphasise, that which could not have been done before.
This then brings us to state of the art research examples in network analytics, digital supply chain surveillance, collective-learning and distributed decision making and automation. Our aim is to encourage debate on how AI should be evaluated by breaking disciplinary siloes in the OM community. We will then discuss the potential pitfalls and challenges, such as loss of data traceability, complacency, lack of accountability, and cognitive atrophy. The talk concludes with supply chain management needing to become an irrevocably interdisciplinary field with challenges so varied and significant.
