集成领域专业知识和人工智能的有效供应链管理计划任务:一种协作方法

Jonas Lick, Benedict Wohlers, Philipp Sahrhage, Felix Schreckenberg, Susanne Klöckner, Sebastian Von Enzberg, Arno Kühn, Roman Dumitrescu
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引用次数: 0

摘要

人工智能(AI)技术在各个领域的集成已经彻底改变了许多行业,供应链管理(SCM)也不例外。本文讨论了在SCM中遇到的挑战,以及在此背景下开发人工智能解决方案。具体来说,我们专注于人工智能在优化供应链规划任务中的应用。这包括预测需求,客户订单的可用性和可行性检查,供应链网络设计和供应链规划过程中的信息流。然而,人工智能在SCM中的成功实现需要对特定领域的挑战以及人工智能技术的能力和局限性有深刻的理解。因此,本文提出了一种促进SCM领域专家和AI专家之间协作的总体方法,使他们能够共同开发有效的解决方案。本文首先概述了SCM专业人员所面临的主要挑战,包括需求波动、库存管理的复杂性,以及动态的市场条件。随后,它将深入研究与为SCM开发AI解决方案相关的挑战,包括数据质量、可解释性和模型透明性。为了应对这些挑战,建议的方法促进供应链管理和人工智能专家之间的密切合作和知识交流。通过利用SCM专家的领域知识和经验,AI专家可以更好地理解SCM过程的特殊问题,并定制AI技术以适应特定需求。反过来,SCM专家可以深入了解人工智能的能力和局限性,允许他们在供应链计划操作中采用和集成人工智能时做出明智的决定。此外,本文讨论了建立一个由SCM、AI和IT领域的专家组成的多学科团队的重要性。这种基于团队的方法促进了对SCM挑战的全面理解,并确保AI解决方案的开发与业务目标和实际约束保持一致。总之,本文强调了结合SCM和AI的挑战,并提出了一种有效解决这些挑战的协作方法。通过利用领域专家和人工智能专家的专业知识,组织可以开发量身定制的人工智能解决方案,增强供应链规划,改进决策流程,并推动竞争优势。所提出的方法有助于人工智能在供应链管理中的成功集成,最终在人工智能时代实现更高效、更有弹性的供应链。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating Domain Expertise and Artificial Intelligence for Effective Supply Chain Management Planning Tasks: A Collaborative Approach
The integration of Artificial Intelligence (AI) techniques into various domains has revolutionized numerous industries, and Supply Chain Management (SCM) is no exception. This paper addresses the challenges encountered in SCM and the development of AI solutions within this context. Specifically, we focus on the application of AI in optimizing supply chain planning tasks. This includes forecasting demand, availability and feasibility checks for customer orders, supply chain network design and information flow inside the supply chain planning processes. However, the successful implementation of AI in SCM requires a deep understanding of both the domain-specific challenges and the capabilities and limitations of AI technologies. Thus, this paper proposes an overarching approach that facilitates collaboration between domain experts in SCM and AI experts, enabling them to jointly develop effective solutions.The paper begins by outlining the key challenges faced by SCM professionals, including demand volatility, complexities in inventory management, and dynamic market conditions. Subsequently, it delves into the challenges associated with developing AI solutions for SCM, including data quality, interpretability, and model transparency. To address these challenges, the proposed approach promotes close collaboration and knowledge exchange between SCM and AI experts. By leveraging the domain knowledge and experience of SCM experts, AI experts can better understand the special issues of SCM processes and tailor AI techniques to suit specific needs. In turn, SCM experts can gain insights into the capabilities and limitations of AI, allowing them to make informed decisions regarding the adoption and integration of AI in their supply chain planning operations. Furthermore, the paper discusses the importance of establishing a multidisciplinary team comprising experts from the fields of SCM, AI, and IT. This team-based approach fosters a holistic understanding of SCM challenges and ensures the development of AI solutions that align with business goals and practical constraints.In conclusion, this paper highlights the challenges in combining SCM and AI and proposes a collaborative approach to address these challenges effectively. By leveraging the expertise of both domain and AI experts, organizations can develop tailored AI solutions that enhance supply chain planning, improve decision-making processes, and drive competitive advantage. The proposed approach contributes to the successful integration of AI in SCM, ultimately leading to more efficient and resilient supply chains in the era of artificial intelligence.
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