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The purpose of this paper is to explore how intra-industry strategic alliances (SAs) seek to assess supplier risk related to sustainability, what motivation drives single members to form or join such an SA, and how such a joint endeavor affects supplier risk management.

An embedded single case study with multiple units of analysis was conducted. The main data were collected through semi-structured interviews with key respondents from seven leading chemical companies, three of which were founding members of the SA, while four were new members.

This paper shows that forming/joining an SA concerning sustainability-related supplier risk assessment, results in the reduction of task uncertainty and equivocality as well as the increase of information processing capacities. Based on the implemented sharing routines, a higher overall efficiency can be achieved. Moreover, the members benefit from an enhanced identification of varying stakeholder expectations, a facilitated capability building and a more comprehensive supplier risk assessment. In particular, the joint endeavors result in assessment processes of higher robustness, which provide outcomes of higher quality.

This paper is the first to investigate companies’ efforts toward improving their supplier risk management in the area of sustainability by establishing/joining an intra-industry SA. By providing insights into the motivation to form or join such a collaborative platform and illustrating the effects that arise from the SA’s work from an organizational information processing perspective, it provides a contribution to both academics and managerial practice.

Particularly in volatile, uncertain, complex and ambiguous (VUCA) business conditions, staff in supply chain management (SCM) look to real-time (RT) data processing to reduce uncertainties. However, based on the premise that data processing can be perfectly mastered, such expectations do not reflect reality. The purpose of this paper is to investigate whether RT data processing reduces SCM uncertainties under real-world conditions.

Aiming to facilitate communication on the research question, a Delphi expert survey was conducted to identify challenges of RT data processing in SCM operations and to assess whether it does influence the reduction of SCM uncertainty. In total, 14 prospective statements concerning RT data processing in SCM operations were developed and evaluated by 68 SCM and data-science experts.

RT data processing was found to have an ambivalent influence on the reduction of SCM complexity and associated uncertainty. Analysis of the data collected from the study participants revealed a new type of uncertainty related to SCM data itself.

This paper discusses the challenges of gathering relevant, timely and accurate data sets in VUCA environments and creates awareness of the relationship between data-related uncertainty and SCM uncertainty. Thus, it provides valuable insights for practitioners and the basis for further research on this subject.