Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82773
Title: Managing Supply Systems with Partial Information on Shipment Locations
Authors: Bryan, Nana
Srinivasan, Mandyam M.
Viswanathan, S.
Keywords: Stochastic processes
Markov chain
Stochastic lead time
Shipment tracking
Value of information
Issue Date: 2016
Source: Bryan, N., Srinivasan, M. M., & Viswanathan, S. (2016). Managing supply systems with partial information on shipment locations. International Journal of Production Research.
Series/Report no.: International Journal of Production Research
Abstract: This paper studies a supply system for a retailer who orders a single product from one manufacturer. Orders filled by the manufacturer pass through multiple transportation stages before reaching the retailer. Each stage represents either a physical location or a step in the delivery process. The lead time for a new order depends on the location of shipments against prior orders in transit. Shipments are not allowed to cross over in time. Thus, the movement of each shipment depends on the movements of shipments ahead of it and the resulting congestion. The retailer is able to track shipments as they move through the transportation channel. The retailer adopts an ordering policy that minimises the sum of his one-period holding and shortage costs, using available status information of shipments already in transit. The case where practical constraints prevent the retailer from obtaining a complete status of shipments at all stages in the transportation channel is considered. The methodology developed evaluates the value of partial shipment tracking information, and uses it to determine the optimal placement of a limited number of tracking devices. The methodology can also be used to evaluate the cost–benefit of placing additional tracking devices in the supply system.
URI: https://hdl.handle.net/10356/82773
http://hdl.handle.net/10220/40308
ISSN: 0020-7543
DOI: 10.1080/00207543.2016.1142132
Schools: Nanyang Business School 
Rights: © 2016 Taylor & Francis. This is the author created version of a work that has been peer reviewed and accepted for publication by International Journal of Production Research, Taylor & Francis. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1080/00207543.2016.1142132].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:NBS Journal Articles

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