Abstract
Today, "data science" is positioned as the best tool strong enough for revolutionizing supply chain and logistics (SC&L) companies. Different sources of data generate very large diverse data sets that demand separate specific data science technologies in line with big data analysis. The fast growing research on data science and big data analysis (DS&BDA) encouraged us to conduct a systematic comprehensive literature review on DS&BDA applications/techniques in the SC&L fields. Investigation on the recent relevant review studies
around the topic illustrated several gaps in reviewing former studies and assisted us to develop a conceptual framework for our proposed reviewing process. Our keyword search found 13724 papers published from 2000
to 2019. With the help of a systematic research methodology, we focused on 227 prosperous publications from high-ranked journals and applied a detailed coding system according to our conceptual framework. We will provide some insights from the results section here. Finally, significant research gaps are introduced to be investigated as future studies by researchers in the domain of SC&L.
around the topic illustrated several gaps in reviewing former studies and assisted us to develop a conceptual framework for our proposed reviewing process. Our keyword search found 13724 papers published from 2000
to 2019. With the help of a systematic research methodology, we focused on 227 prosperous publications from high-ranked journals and applied a detailed coding system according to our conceptual framework. We will provide some insights from the results section here. Finally, significant research gaps are introduced to be investigated as future studies by researchers in the domain of SC&L.
| Original language | English |
|---|---|
| Number of pages | 19 |
| Publication status | In preparation - 2022 |
Keywords
- Data Analytics
- Logistics
- predictive analytics
- Data Science
- Big Data
- Data Mining
- Machine Learning
- Supply chain