• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. Integrating Domain Expertise and Artificial Intelligence for Effective Supply Chain Management Planning Tasks: A Collaborative Approach
 
  • Details
  • Full
Options
November 2023
Conference Paper
Title

Integrating Domain Expertise and Artificial Intelligence for Effective Supply Chain Management Planning Tasks: A Collaborative Approach

Abstract
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.
Author(s)
Lick, Jonas
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Schreckenberg, Felix  
Fraunhofer-Institut für Materialfluss und Logistik IML  
Sahrhage, Philipp
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Wohlers, Benedict
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Klöcker, Susanne  
Fraunhofer-Institut für Materialfluss und Logistik IML  
Enzberg, Nikolaus Sebastian von
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Kühn, Arno  
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Dumitrescu, Roman  
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Mainwork
Artificial Intelligence, Social Computing and Wearable Technologies  
Conference
International Conference on Human Factors in Design, Engineering, and Computing 2023  
File(s)
Download (838.88 KB)
Rights
Use according to copyright law
DOI
10.54941/ahfe1004185
10.24406/publica-2426
Language
English
Fraunhofer-Institut für Materialfluss und Logistik IML  
Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM  
Keyword(s)
  • Artificial intelligence

  • Social computing

  • Supply chain management

  • Process model

  • Business-IT-alignment

  • Interdisciplinary work

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024