Table 1.

Literature review of information processing capabilities of Artificial Intelligence for supply chain

IPC levels	AI-driven IPC for supply chain	AI techniques	References
Level 1: exploiting	Process much more massive amounts of information and knowledge Support supply chain problems detection Overcome cognitive information processing constraints	Machine learning and big data	Priore et al. (2019), Cavalcante et al. (2019), Min et al. (2019), Choi et al. (2018)
		Robust optimization	Baryannis et al. (2019), Choi et al. (2018)
		fuzzy logic and programming	Leung et al. (2019), Baryannis et al. (2019)
		Stochastic programming	Sabet et al. (2020), Baryannis et al. (2019)
		Knowledge, representation Reasoning	Baryannis et al. (2019)
Level 2: expanding	Generate new ideas within the supply chain innovation process Support supply chain problems analysis Strengthen the interaction between human and machine	Network-based algorithms	Elhoone et al. (2020), Hosseini and Ivanov (2020), Baryannis et al. (2019), Bottani et al. (2019)
		Rough set theory	Mehdizadeh (2020), Li et al. (2018), Shidpour et al. (2016)
		Tree-based clustering	Zanjani et al. (2016), Thomassey (2010)
Level 3: exploring	Explore new ways of identifying problems Explore new innovative solutions Prototype and evaluate the effectiveness of the innovation	Agent-based systems	Muravev et al. (2020), Baryannis et al. (2019), Giannakis and Louis (2016)
		Model Predictive Control	Belhadi et al. (2019), Nawaz et al. (2019), Zhang et al. (2019)
		Robotic Process Automation	Schniederjans et al. (2020)
		Computer vision	Grover et al. (2020), Dhamija and Bag (2020)