Dr Riccardo Mogre riccardo.mogre@durham.ac.uk
Associate Professor
Mitigating supply and production uncertainties with dynamic scheduling using real-time transport information
Mogre, R.; Wong, C.Y.; Lalwani, C.S.
Authors
C.Y. Wong
C.S. Lalwani
Abstract
Supply and production uncertainties can affect the scheduling and inventory performance of final production systems. Facing such uncertainties, production managers normally choose to maintain the original production schedule, or follow the first-in-first-out policy. This paper develops a new, dynamic algorithm policy that considers scheduling and inventory problems, by taking advantage of real-time shipping information enabled by today’s advanced technology. Simulation models based on the industrial example of a chemical company and the Taguchi’s method are used to test these three policies under 81 experiments with varying supply and production lead times and uncertainties. Simulation results show that the proposed dynamic algorithm outperforms the other two policies for supply chain cost. Results from Taguchi’s method show that companies should focus their long-term effort on the reduction of supply lead times, which positively affects the mitigation of supply uncertainty.
Citation
Mogre, R., Wong, C., & Lalwani, C. (2014). Mitigating supply and production uncertainties with dynamic scheduling using real-time transport information. International Journal of Production Research, 52(17), 5223-5235. https://doi.org/10.1080/00207543.2014.900201
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 19, 2014 |
Online Publication Date | Mar 24, 2014 |
Publication Date | Sep 2, 2014 |
Deposit Date | Sep 3, 2015 |
Publicly Available Date | Sep 14, 2015 |
Journal | International Journal of Production Research |
Print ISSN | 0020-7543 |
Electronic ISSN | 1366-588X |
Publisher | Taylor and Francis Group |
Peer Reviewed | Peer Reviewed |
Volume | 52 |
Issue | 17 |
Pages | 5223-5235 |
DOI | https://doi.org/10.1080/00207543.2014.900201 |
Keywords | Simulation, Supply uncertainty, Production uncertainty, Dynamic scheduling, Information sharing. |
Public URL | https://durham-repository.worktribe.com/output/1403326 |
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Copyright Statement
This is an Accepted Manuscript of an article published by Taylor & Francis Group in International Journal of Production Research on 24/03/2014, available online at: http://www.tandfonline.com/10.1080/00207543.2014.900201.
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