Dr Manish Shukla manish.shukla@durham.ac.uk
Assistant Professor
Big-Data Analytics Framework For Incorporating Smallholders In Sustainable Palm Oil Production
Shukla, M.; Tiwari, M.K.
Authors
M.K. Tiwari
Abstract
This paper aims to address the constraints faced in incorporating smallholders in sustainable palm oil production. There exists literature that acknowledges the need for incorporating smallholders in the production of sustainable palm oil but none has proposed a solution beyond ‘Roundtable on Sustainable Palm Oil’ (RSPO) certification. In the current business scenario, several organizations are struggling to procure RSPO certified palm oil even after committing huge resources. RSPO, though a good first step, has a major process and capacity constraints resulting in long processing times, delays, and lack of traceability for the customers. This paper proposes a Big Data Analytics framework enabled by cutting-edge technologies to incorporate smallholders in the RSPO certification process. The data used was collected through farm visits, stakeholder meetings, key stakeholder interviews, and, secondary sources. The proposed framework not only addresses the limitation of the current certification process but also converts it from being punitive to preventive. The outcomes of this research will be extremely useful for all the stakeholders in the palm oil supply chain.
Citation
Shukla, M., & Tiwari, M. (2017). Big-Data Analytics Framework For Incorporating Smallholders In Sustainable Palm Oil Production. Production Planning and Control, 28(16), 1365-1377. https://doi.org/10.1080/09537287.2017.1375145
Journal Article Type | Article |
---|---|
Acceptance Date | May 11, 2017 |
Online Publication Date | Oct 10, 2017 |
Publication Date | Oct 10, 2017 |
Deposit Date | Jul 25, 2017 |
Publicly Available Date | Oct 10, 2018 |
Journal | Production Planning and Control |
Print ISSN | 0953-7287 |
Electronic ISSN | 1366-5871 |
Publisher | Taylor and Francis Group |
Peer Reviewed | Peer Reviewed |
Volume | 28 |
Issue | 16 |
Pages | 1365-1377 |
DOI | https://doi.org/10.1080/09537287.2017.1375145 |
Public URL | https://durham-repository.worktribe.com/output/1372976 |
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Copyright Statement
This is an Accepted Manuscript of an article published by Taylor & Francis in Production Planning and Control on 10 Oct 2017, available online: http://www.tandfonline.com/10.1080/09537287.2017.1375145.
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