Leveraging Blockchain for Maritime Port Supply Chain Management through Multicriteria Decision Making
Abstract
:1. Introduction
- Problem Definition
1.1. Research Gap of the Study
1.2. Contribution of the Study
- What are the primary barriers to implementing blockchain technology in the management of supply chains within an uncertainty-characterized environment?
- How is multi-criteria decision-making utilized to prioritize the barriers impeding blockchain technology adoption in supply chain management?
- What maritime line is most suitable for implementing blockchain technology?
2. Background
2.1. Description of the Port System
2.2. Data System
2.3. Literature Review
2.3.1. Challenges and Barriers of Blockchain in Maritime Port
2.3.2. Multi-Criteria Decision-Making Methods in Blockchain-Operated Port Systems
3. Materials and Methods
3.1. Methods
3.1.1. Logarithm Method of Additive Weights (LMAW)
Stage 1. Construction of the Aggregated X Matrix
Stage 2: Normalization of the Aggregated X Table
Stage 3: Determining the Weights of Decision Criteria
3.1.2. Double Normalization-Based Multiple Aggregation (DNMA) Method
Determining the Weights of Criteria
Determining Aggregation Models through Calculation
The Integration of Values Related to Usefulness
3.2. Reason of Using These MCDA Methods
3.2.1. Fuzzy LMAW
3.2.2. DNMA Method
3.3. Relationship between Blockchain Technology, SCM in Port, and Industry 5.0
4. Results
4.1. Delphi Method
4.2. LMAW Analysis
4.2.1. LMAW Analysis
4.2.2. DNMA Analysis
4.3. Sensitive Analysis
5. Discussion
5.1. Technical Aspects
- Factor Identification and Prioritization: Initially, 22 factors influencing port selection were identified using the Delphi method. Subsequently, five key factors were prioritized based on the opinions of decision-makers, using fuzzy LMAW for the initial rankings.
- Port ranking and strategic importance: Ports were then ranked using DNMA based on the weighted factors derived from fuzzy LMAW. The port of Valparaiso was found to be the highest priority, highlighting its strategic importance to Chile’s MTS modernization efforts.
5.2. Strategic Challenges
6. Conclusions
- Using Fuzzy LMAW, important factors affecting the selection of sea ports were ranked. The results indicated that supply chain best practices, risk identification and contingency management, and enhanced security had the same highest weight among them.
- Subsequently, based on DNMA, these ports were ranked. The results revealed that among Valparaíso, San Antonio, Coquimbo, and Lirquen, the port of Valparaíso had the highest priority, suggesting that blockchain technology in supply chain management should be implemented in this port.
- The outcomes of this paper demonstrated that among the applied hybrid MCDA methods in an uncertain environment, the new literature review on implementing BT in SCM during the Industry 5.0 era expanded and highlighted the most important ports and factors.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Alternatives | 2 | … | ||
---|---|---|---|---|
… | ||||
… | … | … | … | … |
Factors | DM1 | DM2 | DM3 | DM4 | DM5 | DM6 | Average | Accept/Reject |
---|---|---|---|---|---|---|---|---|
Reduced paperwork & cost savings | 3 | 2 | 3 | 3 | 3 | 3 | 2.83 | R |
Reduction in transaction time | 3 | 3 | 4 | 4 | 4 | 3 | 3.50 | R |
Enhanced transaction security | 4 | 4 | 5 | 4 | 4 | 3 | 4.00 | A |
Optimization in port operations | 4 | 4 | 4 | 4 | 4 | 3 | 3.83 | R |
Sustainability promotion | 3 | 3 | 4 | 4 | 3 | 4 | 3.50 | R |
Technological implementation cost | 3 | 3 | 3 | 4 | 4 | 3 | 3.33 | R |
Regulatory compliance | 4 | 3 | 3 | 4 | 4 | 4 | 3.67 | R |
Resistance to change | 2 | 3 | 4 | 3 | 4 | 4 | 3.33 | R |
Technology challenge | 4 | 3 | 4 | 4 | 5 | 3 | 3.83 | R |
Circular management | 2 | 2 | 5 | 3 | 2 | 4 | 3.00 | R |
Good practices in the supply chain | 5 | 4 | 4 | 4 | 4 | 3 | 4.00 | A |
Cyber-technological implementation | 2 | 3 | 4 | 4 | 5 | 3 | 3.50 | R |
Collaborate and coordinate actors | 3 | 3 | 4 | 4 | 4 | 4 | 3.67 | R |
Implement leadership strategies | 3 | 3 | 3 | 4 | 4 | 4 | 3.50 | R |
Spend on technology assets | 3 | 3 | 4 | 3 | 5 | 4 | 3.67 | R |
Risk Identification | 5 | 4 | 3 | 4 | 4 | 4 | 4.00 | A |
Manage contingencies | 5 | 2 | 5 | 5 | 5 | 3 | 4.17 | A |
Port infrastructure investment | 4 | 4 | 3 | 3 | 4 | 4 | 3.67 | R |
Enhanced security | 5 | 4 | 4 | 4 | 4 | 3 | 4.00 | A |
Ensure transparency of information | 4 | 3 | 4 | 4 | 4 | 4 | 3.83 | R |
Staff technical competencies | 4 | 3 | 3 | 4 | 4 | 4 | 3.67 | R |
Governance affects decision making | 4 | 3 | 3 | 4 | 4 | 4 | 3.67 | R |
Linguistic Variables | Abbreviation | Prioritization |
---|---|---|
Absolutely Low | AL | 1 |
Very Low | VL | 1.5 |
Low | L | 2 |
Medium | M | 2.5 |
Equal | E | 3 |
Medium High | MH | 3.5 |
High | H | 4 |
Very High | VH | 4.5 |
Absolutely High | AH | 5 |
KIND | 1 | 1 | −1 | 1 | 1 |
---|---|---|---|---|---|
Enhanced Transaction Security | Good Practices in the Supply Chain | Risk Identification | Manage Contingencies | Enhanced Security | |
Expert 1 | H | AH | AH | AH | AH |
Expert 2 | H | H | H | L | H |
Expert 3 | AH | H | E | AH | H |
Expert 4 | H | H | H | AH | H |
Weight Coefficients Vector | Enhanced Transaction Security | Good Practices in the Supply Chain | Risk Identification | Manage Contingencies | Enhanced Security |
---|---|---|---|---|---|
W1j | 0.184 | 0.204 | 0.204 | 0.204 | 0.204 |
W2j | 0.214 | 0.214 | 0.214 | 0.143 | 0.214 |
W3j | 0.218 | 0.197 | 0.170 | 0.218 | 0.197 |
W4j | 0.196 | 0.196 | 0.196 | 0.217 | 0.196 |
1 | 1 | −1 | 1 | 1 | |
---|---|---|---|---|---|
Weight | 0.2029 | 0.2027 | 0.1957 | 0.1946 | 0.2027 |
C1 | C2 | C3 | C4 | C5 | |
Valparaiso | 4 | 5 | 5 | 5 | 5 |
San Antonio | 4 | 4 | 4 | 2 | 4 |
Coquimbo | 4 | 4 | 3 | 5 | 4 |
Lirquen | 3 | 3 | 4 | 3 | 3 |
MAX | 4 | 5 | 5 | 5 | 5 |
MIN | 3 | 3 | 3 | 2 | 3 |
Port | CCM | φ | UCM | φ | ICM | φ | Utility Values | Rank Order | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
u1(ai) | Rank | 0.5 | u2(ai) | Rank | 0.5 | u3(ai) | Rank | 0.5 | ||||
Valparaiso | 0.810 | 1 | 1.0000 | 0.189 | 2 | 0.631 | 0.951 | 2 | 0.879 | 0.927 | 0.927 | 1 |
San Antonio | 0.456 | 3 | 0.5324 | 0.256 | 4 | 1.000 | 0.848 | 4 | 0.650 | 0.614 | 0.614 | 3 |
Coquimbo | 0.807 | 2 | 0.8815 | 0.096 | 1 | 0.319 | 0.958 | 1 | 1.000 | 0.860 | 0.860 | 2 |
Lirquen | 0.360 | 4 | 0.3605 | 0.192 | 3 | 0.751 | 0.928 | 3 | 0.771 | 0.522 | 0.522 | 4 |
Paper | Legal/Political | Social | Ambiental | Economic | Technological |
---|---|---|---|---|---|
[31] | New regulations; data privacy | Expert responses; worker resistance; job security. | Blockchain integration | ||
[32] | Regulatory compliance. | Adaptation; stakeholder participation. | Data and information management rights distribution; operations and logistics services improvement. | Information systems adaptation; data distribution; Blockchain integration. | |
[21] | Regulatory compliance. | Stakeholder involvement. | Financial procedures; operational efficiency. | Blockchain, platform; cargo tracking; document workflow management; security in operations. | |
[22] | Legal changes; compliance with advanced industry standards. | New practices; workforce management. | Eco- friendly supply chain; reducing paper usage. | Supply chain efficiency; cost reduction in operations. | Blockchain integration of systems; smart supply chain networks; digitalization of documentation. |
[17] | Legal adaptation; standardization. | Resistance to change; collaboration. | High costs | Blockchain integration; document flow management; financial processes; device connectivity; integration of systems; big data management. | |
[25] | Governance models; regulatory aspects. | Stakeholder collaboration. | Operational efficiency; improve logistic; transactional operations. | Systems; automation of transactional operations; Platforms; frameworks. | |
[33] | Legal conditions in digital processes. | Stakeholder coordination; community engagement. | Business networks; operational efficiency. | Smart contracts; Blockchain implementation; digitizing documentation; connectivity and data exchange. | |
[24] | Global standards; regulatory compliance. | Decision-makers at the operational level. | Reducing paper usage. | Transaction-related business challenges. | Document flow management; real-time data sharing; data traceability; interoperability among different actors. |
[26] | Government regulations; customs authorities; privacy regulations. | Trust issues; limited understanding among stakeholders; adoption process. | Operational and logistic efficiency. | Blockchain; technological development in export and import logistics chains. | |
[30] | Governmental support. | Human capital; knowledge and experience; resistance to change. | Environmental impacts. | Efficiency; business models; global trade. | Decentralized platform; improve security; integration. |
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Durán, C.; Yazdi, A.K.; Derpich, I.; Tan, Y. Leveraging Blockchain for Maritime Port Supply Chain Management through Multicriteria Decision Making. Mathematics 2024, 12, 1511. https://doi.org/10.3390/math12101511
Durán C, Yazdi AK, Derpich I, Tan Y. Leveraging Blockchain for Maritime Port Supply Chain Management through Multicriteria Decision Making. Mathematics. 2024; 12(10):1511. https://doi.org/10.3390/math12101511
Chicago/Turabian StyleDurán, Claudia, Amir Karbassi Yazdi, Iván Derpich, and Yong Tan. 2024. "Leveraging Blockchain for Maritime Port Supply Chain Management through Multicriteria Decision Making" Mathematics 12, no. 10: 1511. https://doi.org/10.3390/math12101511