Enhancing B2B supply chain traceability using smart contracts and IoT

Mohamed Ahmed, Chantal Taconet, Mohamed Ould, Sophie Chabridon and Amel Bouzeghoub

A chapter in Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain, 2020, pp 559-589 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management

Abstract: Purpose: The management of B2B supply chains that involve many stakeholders re-quires traceability processes. Those processes need to be secured. Furthermore, quality traceability data has to be transparently shared among the stakeholders. In order to improve the traceability process, we propose to enhance blockchain based traceability architectures with the capability to detect and record well-qualified inci-dents. Methodology: To achieve this goal, we propose a generic smart contract for B2B traceability data management, including transport constraints such as temperature, delay and allowing automatic incident detection and recording. We propose an ar-chitecture where data are collected by connected objects and verified and qualified before being sent to the smart contract. This proposition has been validated with medical equipment transport use cases. Findings: As results, the proposed generic template contract can be used in various traceability use cases, well qualified incidents are transparently shared among stakeholders, and secured, qualified and verified traceability data can be used in case of claims or litigation and can facilitate also the automation of invoicing pro-cess. Originality: The originality of this work arises from the automated B2B traceability management system based on qualified IoT data, contractual milestones and pro-cess coded in a generic smart contract, and also from the fact that traceability related data and incidents are verified and qualified in order to increase the integrated data quality.

Keywords: Logistics; Industry 4.0; Digitalization; Innovation; Supply Chain Management; Artificial Intelligence; Data Science (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hiclch:228933

DOI: 10.15480/882.3110

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