A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research
Big data has become a global phenomenon with companies in almost all industries trying in some way to identify and exploit this is untapped asset. The big data application in supply chain management (SCM) has also caught the management’s attention, and with the high influx of data being generated...
| Main Author: | |
|---|---|
| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/63292/ |
| _version_ | 1848800011566972928 |
|---|---|
| author | Nazir, Affaf |
| author_facet | Nazir, Affaf |
| author_sort | Nazir, Affaf |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Big data has become a global phenomenon with companies in almost all industries trying in some
way to identify and exploit this is untapped asset. The big data application in supply chain
management (SCM) has also caught the management’s attention, and with the high influx of data
being generated at different points in supply chain can be used to stimulate data driven decisions,
build supply chain flexibility, adaptability and agility. With the inception and wide adoption of the
Industry 4.0 technologies like Internet of things (IoT), cloud computing (CC), Smart
manufacturing (SM), Artificial intelligence (AI), the need for integration of big data and analytics
has been felt more than ever.
The purpose of this survey is to investigate the applications of predictive analytics in different
supply chain areas and provide a classification based on the different techniques/algorithm used at
various supply chain areas, detect gaps and propose the future direction for research. The review
also investigates the application of big data analytics (specifically, predictive analytics) along with
these disruptive technologies in the SCM areas.
The survey review indicated that manufacturing and demand forecasting are the two major areas
with the most predictive analytics application, whereas the clustering, regression, and artificial
neural networks are the more commonly used algorithms. The new SCM areas identified for Big
data analytics applications integrated with the emerging technology are smart manufacturing and
intelligent logistics management. Furthermore, the immediate need for future studies in other SCM
areas like product development and inventory management are pointed out due to its immense
potential benefits for the supply chain management. |
| first_indexed | 2025-11-14T20:44:47Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-63292 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:44:47Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-632922021-06-08T11:29:34Z https://eprints.nottingham.ac.uk/63292/ A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research Nazir, Affaf Big data has become a global phenomenon with companies in almost all industries trying in some way to identify and exploit this is untapped asset. The big data application in supply chain management (SCM) has also caught the management’s attention, and with the high influx of data being generated at different points in supply chain can be used to stimulate data driven decisions, build supply chain flexibility, adaptability and agility. With the inception and wide adoption of the Industry 4.0 technologies like Internet of things (IoT), cloud computing (CC), Smart manufacturing (SM), Artificial intelligence (AI), the need for integration of big data and analytics has been felt more than ever. The purpose of this survey is to investigate the applications of predictive analytics in different supply chain areas and provide a classification based on the different techniques/algorithm used at various supply chain areas, detect gaps and propose the future direction for research. The review also investigates the application of big data analytics (specifically, predictive analytics) along with these disruptive technologies in the SCM areas. The survey review indicated that manufacturing and demand forecasting are the two major areas with the most predictive analytics application, whereas the clustering, regression, and artificial neural networks are the more commonly used algorithms. The new SCM areas identified for Big data analytics applications integrated with the emerging technology are smart manufacturing and intelligent logistics management. Furthermore, the immediate need for future studies in other SCM areas like product development and inventory management are pointed out due to its immense potential benefits for the supply chain management. 2020-12-01 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/63292/1/ECapproved_20200755_dissertation_BUSI4043%20UNUK_affafnazir.pdf Nazir, Affaf (2020) A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research. [Dissertation (University of Nottingham only)] supply chain management predictive analytics smart manufacturing IoT Cloud computing |
| spellingShingle | supply chain management predictive analytics smart manufacturing IoT Cloud computing Nazir, Affaf A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research |
| title | A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research |
| title_full | A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research |
| title_fullStr | A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research |
| title_full_unstemmed | A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research |
| title_short | A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research |
| title_sort | review of big data and predictive analytics application in supply chain management; new areas, challenges and future research |
| topic | supply chain management predictive analytics smart manufacturing IoT Cloud computing |
| url | https://eprints.nottingham.ac.uk/63292/ |