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Blockchain Traceability Valuation for Perishable Agricultural Products Under Demand Uncertainty

Blockchain Traceability Valuation for Perishable Agricultural Products Under Demand Uncertainty

Zhuoyi Zhao, K. Jo Min
Copyright: © 2020 |Volume: 11 |Issue: 4 |Pages: 32
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781799806561|DOI: 10.4018/IJORIS.2020100101
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MLA

Zhao, Zhuoyi, and K. Jo Min. "Blockchain Traceability Valuation for Perishable Agricultural Products Under Demand Uncertainty." IJORIS vol.11, no.4 2020: pp.1-32. http://doi.org/10.4018/IJORIS.2020100101

APA

Zhao, Z. & Min, K. J. (2020). Blockchain Traceability Valuation for Perishable Agricultural Products Under Demand Uncertainty. International Journal of Operations Research and Information Systems (IJORIS), 11(4), 1-32. http://doi.org/10.4018/IJORIS.2020100101

Chicago

Zhao, Zhuoyi, and K. Jo Min. "Blockchain Traceability Valuation for Perishable Agricultural Products Under Demand Uncertainty," International Journal of Operations Research and Information Systems (IJORIS) 11, no.4: 1-32. http://doi.org/10.4018/IJORIS.2020100101

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Abstract

Various perishable agricultural products are recalled due to harmful health risks. Blockchain has been used to reduce the amount of such products wasted and disposed. Specifically, a supply chain with a wholesaler, a retailer, and customers is considered where the retailer decides when to switch from a conventional supply chain information management system (SCIMS) to a blockchain-based SCIMS. This article models the uncertain customers' demand as a geometric Brownian motion process and shows how to obtain the optimal demand threshold above which the switch occurs and the corresponding expected time. Next, the model is extended by incorporating two types of government subsidies (i.e., a fixed subsidy on the switching cost and a variable subsidy per unit demand). Through sensitivity analysis and numerical studies, the impacts of key parameters on the optimal demand threshold and expected time of switching are presented. Finally, managerial insights and policy implications are derived.