Ahmad Albqowr, Malek Alsharairi and Abdelrahim Alsoussi
The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of…
Abstract
Purpose
The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.
Design/methodology/approach
This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.
Findings
This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.
Research limitations/implications
The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.
Originality/value
This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.
Details
Keywords
Rasoul Mehdikhani, Changiz Valmohammadi and Roghayeh Taraz
The main purpose of this study is to assess the influence of business analytics (BA) on supply chain ambidexterity (SCA) and market learning (ML) in the context of Iran as a…
Abstract
Purpose
The main purpose of this study is to assess the influence of business analytics (BA) on supply chain ambidexterity (SCA) and market learning (ML) in the context of Iran as a developing country.
Design/methodology/approach
The study population encompasses a range of key positions such as senior managers, supply chain managers, senior IT managers and senior marketing and marketing research managers in Iran. Through a survey, a questionnaire was designed to gather data from these individuals. The data collected from a total of 214 participants underwent rigorous analysis using structural equation modeling.
Findings
Findings revealed BA has a positive influence on SCA and ML. Furthermore, the study found that distinct facets of ML, namely, exploratory and exploitative learning, exerted a positive influence on SCA. Additionally, the investigation uncovered that the mechanisms of exploratory ML and exploitative ML play a partially mediating role in the relationship between BA and SCA.
Research limitations/implications
It is prudent to acknowledge that the study’s sampled entities were exclusively Iranian companies, potentially curtailing the extent of generalizability of our findings.
Originality/value
This research contributes valuable theoretical insights and practical implications to policymakers and top managers of organizations, particularly the surveyed organizations to formulate and implement an appropriate strategy to avail of BA techniques toward enhancing SCA. Also, this study provides significant insights into the determinants of SCA and demonstrates how organizations can leverage data analytics and ML to attain sustained growth and ambidexterity within the supply chain context.