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Prescriptive analytics for sustainable supply chain operations: The PASO framework for Industry 5.0

Year: 2025

Type: article

Abstract: Today, in supply chain operations, achieving the goal of sustainability with the use of data is critical. In the data analytics era, both companies and non-profit making organizations are extensively using data to improve decision making. As a critical stage of data analytics, to improve decision making for the unforeseeable future, prescriptive analytics aims to provide the advanced data-driven scientific decision supporting models for real world applications in supply chain operations. In this paper, we propose a novel “prescriptive analytics for sustainable operations” (PASO) framework for Industry 5.0 to provide academics and industrialists with the guidance on how to make the proper use of prescriptive analytics for sustainable supply chain operations. We start by defining the role of prescriptive analytics as the ultimate stage of data analytics. Then, we select a few important prior studies and critically examine how prescriptive analytics has been implemented. Combining the findings from the literature and some observed real-world practices, we propose and construct the PASO framework, which highlights the importance of people welfare, in addition to enjoying efficiency improvement with technologies. Finally, we establish a future research agenda.

Source: Transportation Research Part E Logistics and Transportation Review

DOI: 10.1016/j.tre.2025.104206

Authors: Tsan-Ming Choi, Xuting Sun

Institutions: University of Liverpool, Shanghai University

Language: English

Topic: Sustainable Supply Chain Management

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Responsible consumption and production, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,240