How supply chain analytics enables operational supply chain transparency

Abstract

Purpose The global business environment combined with increasing societal expectations of sustainable business practices challenges firms with a host of emerging risk factors. As such, firms seek to increase supply chain transparency, enabling them to monitor operational activities and manage supply chain risks. Drawing on organizational information processing theory, the purpose of this paper is to examine how supply chain analytics (SCA) capabilities support operational supply chain transparency. Design/methodology/approach Using data from 477 survey participants, hypotheses are tested using seemingly unrelated regression. Findings The results reveal that: analytics capability in support of planning functions indirectly affects organizational supply chain transparency (OSCT) via SCA capabilities in source, make, and deliver functions; SCA capabilities in source, make, and deliver positively influence OSCT; and supply uncertainty moderates the relationship between SCA capabilities in make and OSCT. Research limitations/implications This research suffers from limitations inherent in all survey-based research. Nonetheless, the authors found convincing evidence that suggests firms can employ SCA capabilities to meet transparency requirements. Practical implications The findings inform design of SCA systems, noting the importance of linking planning tools with tools that support source, make, and deliver functions. The research also shows how transparency can be increased via employing SCA capabilities. Originality/value This is one of first studies to empirically demonstrate that SCA capabilities can be used to increase supply chain transparency. The research also advances organizational information processing theory by illustrating an analytics capability paradox, where increased levels of certain analytics capabilities can become counterproductive in the face of supplier uncertainty.

Third party social sustainability assessment: Is it a multi-tier supply chain solution?

“…Uncertainty as a key organizational driver for collecting and processing relevant information underpins the theory (Kreye, 2017). In supply chain studies, the typical focus has been on uncertainties related to the market, environment, partnerships and supply, delivery reliability, quality and inventory levels, weather and politics (Premkumar et al, 2005;Fan et al, 2017;Zhu et al, 2018;Srinivasan and Swink, in press). Given our examination of social sustainability in multi-tier global supply chains, we focus on sustainability uncertainty as the source of stakeholder information processing needs.…”

Production logistics and human-computer interaction—state-of-the-art, challenges and requirements for the future

“…Interdisciplinary research approaches and results are highly warranted and required in order to provide necessary insights into the development and successful design of production automation in the form of AI and robotics implementation. This will be a high-profile research field in the decade to come in the light of IoT and Physical Internet developments [22,[113][114][115][116].…”

Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations

“…One study reports time constraints as a primary barrier [12], indicating the lack of time to get familiar with analytics and relevant technologies as well as to execute initiatives in addition to daily business. Zhu et al [37] emphasize the time-consuming effort of getting analytics solutions into production (meaning implemented and used in the value creating process), which might consume time from a variety of employees and hinder parallel initiatives. In addition, some labor requirements for analytics present a challenge for LSCM organizations in themselves.…”

“…Another barrier to the adoption and employment of analytics in LSCM can be the physical process assumed to be supported by the analytics solution. Scholars discuss the reduced impact of analytics if the process has a low level of uncertainty (or volatility) [37,39]. While this takes only a certain range of application areas into account and ignores benefits for complex information inputs or benefits for faster decision-making, it presents the potential flaw of employing analytics to processes that may offer little value in terms of return on the investment.…”