Data and Predictive Analytics Use for Logistics and Supply Chain Management

Authors

Anníbal C. Sodero, The Ohio State UniversityFollow
Yao Henry Jin, Miami University - Oxford
Mark A. Barratt, Marquette UniversityFollow

Document Type

Article

Language

eng

Publication Date

2019

Publisher

Emerald Publishing Limited

Source Publication

International Journal of Physical Distribution & Logistics Management

Source ISSN

0960-0035

Abstract

Purpose

The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations.

Design/methodology/approach

The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC.

Findings

Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations.

Practical implications

This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place.

Originality/value

The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area.

Comments

Accepted version. International Journal of Physical Distribution & Logistics Management, Vol. 49, No. 7 (2019): 706-726. DOI. © 2019 Emerald Publishing Limited. Used with permission.

Recommended Citation

Sodero, Anníbal C.; Jin, Yao Henry; and Barratt, Mark A., "Data and Predictive Analytics Use for Logistics and Supply Chain Management" (2019). Management Faculty Research and Publications. 336.
https://epublications.marquette.edu/mgmt_fac/336