Towards Explainable Artificial Intelligence (XAI) in Supply Chain Management: A Typology and Research Agenda

被引:11
作者
Mugurusi, Godfrey [1 ]
Oluka, Pross Nagitta [2 ]
机构
[1] Norwegian Univ Sci & Technol, Dept Ind Econ & Technol Management Gjovik, Trondheim, Norway
[2] Uganda Management Inst, Dept Econ & Managerial Sci, Kampala, Uganda
来源
ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT IV | 2021年 / 633卷
关键词
Artificial intelligence; Explainable AI; Supply chain management; Supply chains; Integrative literature review; SYSTEMS;
D O I
10.1007/978-3-030-85910-7_4
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The potential for artificial intelligence (AI) to drive digital supply chain transformation today is beyond question. However, its full potential to address more complex supply chain management (SCM) problems is still unclear partly due to AI's black-box problem both in practice and in literature. This paper attempts to highlight the significance of explainable AI (XAI) in SCM and shades light on SCM areas where AI's black-box problem remains problematic. The goal of this integrative literature review paper is to provide new insight into the status of XAI as a solution to AI's black-box problem in SCM where AI techniques have made rapid in-roads. The AI techniques in SCM literature and the significance of XAI in SCM are contrasted. We present an integrative research typology for XAI in SCM to better align how SCM literature has conceived AI deployment in SCM this far. The typology should help us understand the gap between what we know about AI deployment in practice, AI maturity in SCM, and the extent of XAI in SCM.
引用
收藏
页码:32 / 38
页数:7
相关论文
共 27 条
[1]  
Aguezzoul A., 2019, J. Syst. Cybern. Inform., V17, P10
[3]   Challenges of Explaining the Behavior of Black-Box AI Systems [J].
Asatiani, Aleksandre ;
Malo, Pekka ;
Nagb, Per Radberg ;
Penttinen, Esko ;
Rinta-Kahila, Tapani ;
Salovaara, Antti .
MIS QUARTERLY EXECUTIVE, 2020, 19 (04) :259-278
[4]   Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI [J].
Barredo Arrieta, Alejandro ;
Diaz-Rodriguez, Natalia ;
Del Ser, Javier ;
Bennetot, Adrien ;
Tabik, Siham ;
Barbado, Alberto ;
Garcia, Salvador ;
Gil-Lopez, Sergio ;
Molina, Daniel ;
Benjamins, Richard ;
Chatila, Raja ;
Herrera, Francisco .
INFORMATION FUSION, 2020, 58 :82-115
[5]   Supply chain risk management and artificial intelligence: state of the art and future research directions [J].
Baryannis, George ;
Validi, Sahar ;
Dani, Samir ;
Antoniou, Grigoris .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2019, 57 (07) :2179-2202
[6]   Digital Supply Chain: Literature review and a proposed framework for future research [J].
Buyukozkan, Gulcin ;
Gocer, Fethullah .
COMPUTERS IN INDUSTRY, 2018, 97 :157-177
[7]   Performance Evaluation for a Sustainable Supply Chain Management System in the Automotive Industry Using Artificial Intelligence [J].
Dumitrascu, Oana ;
Dumitrascu, Manuel ;
Dobrota, Dan .
PROCESSES, 2020, 8 (11) :1-20
[8]   An intelligent decision support system for production planning based on machine learning [J].
Gonzalez Rodriguez, German ;
Gonzalez-Cava, Jose M. ;
Mendez Perez, Juan Albino .
JOURNAL OF INTELLIGENT MANUFACTURING, 2020, 31 (05) :1257-1273
[9]   Explanations from intelligent systems: Theoretical foundations and implications for practice [J].
Gregor, S ;
Benbasat, I .
MIS QUARTERLY, 1999, 23 (04) :497-530
[10]   Expert systems and artificial intelligence in the 21st century logistics and supply chain management [J].
Gunasekaran, Angappa ;
Ngai, Eric W. T. .
EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (01) :1-4