Supply chain analysis: Port component
24 March 2025 - Written by ML
Overview
This report pulls together key facts relating to the shipping industry. It is meant to be a living and changing document to assemble ideas and sources in the effort to establish a petrochemical decision support system.
Categories and content are added/removed on an ad-hoc basis as they are found to be useful or not. Or as their use becomes superceded.
Key sources
| source | purpose | url |
|---|---|---|
| AtoBviaC | Port to port distances | https://atobviac.com/Pages/HistoryOfTheTables |
| Lloyds Register | Classification services | https://www.lr.org/en/ |
| Worldscale | Bulk Oil rates | https://www.worldscale.co.uk/ |
## Dataframe saved to C:/Users/ml/project/DomEnergyMarch/apexsources.csvInternational bodies
| term | name | meaning | url |
|---|---|---|---|
| UNECE | United Nations Economic Commission for Europe | promote pan European economic integration | https://unece.org/mission |
| LOCODE | UN/LOCODE | United Nations Code for Trade and Transport Locations | https://service.unece.org/trade/locode/af.htm |
| INCOTERMS | International Commercial Terms | supplier/buyer shipment designations | https://en.wikipedia.org/wiki/Incoterms |
| PAYTERMS | PAYTERMS | terms relating to payments in international sales | https://unece.org/sites/default/files/2023-10/rec17_1982_ece-trd-142E.pdf |
| Portwatch | IMF|Portwatch | event tracker and shipping data | https://portwatch.imf.org/pages/about |
| IMO | International Maritime Organization | global regulator shipping | https://www.imo.org/ |
| IACS | International Association of Classification Societies | various shipping related codes design/build | https://iacs.org.uk/ |
| Lloyds List | Lloyds List | Shipping industry commentary | https://www.lloydslist.com/ |
## Dataframe saved to C:/Users/ml/project/DomEnergyMarch/term_definitions.csvDSS plan - Grok
Key Points
Plan Overview
Koukoutsis paper(Koukoutsis et al., 2020) suggests an outline approach to establish a decision support system that Grok reviewed. The summary and suggestions (that had been extracted by Mistral and summarized by Mistral) were then provided to Grok to reason over. The following was output.
Initial Analysis and Study: This phase involves creating a “thematic list” of general issues/problems the DSS must address, followed by thematic decomposition into subcategories and fundamental, non-separable issues. For example, the transport issue of “social/environmental impacts of transport” is divided into harmful impacts (e.g., noise, congestion, pollutant emissions) and beneficial impacts (e.g., improved connectivity, transport time reduction, growth). Methods to acquire or compute indicators are developed using mathematical models, with a comprehensive list of required data and necessary software programs identified. A critical aspect is compiling a robust set of metadata to describe indicators, data, methods, and justify their selection, emphasizing maintainability and upgradability.
Expert Involvement: The initial analysis involves experts from various scientific disciplines, supported by Information Technology and Communication (ITS) engineers, ensuring domain knowledge is integrated into the design.
Implementation by ITS Experts: Using the results from the initial study, ITS experts implement a user interface tier (via Internet or Intranet) for user communication, databases or data warehouses to handle large data sets (possibly with specialized hardware), systems for communication and mediation with external databases, and advanced software tools like web services and containers. Some servers may use in-memory technology for better performance, though this is noted as costly.
Importance of Metadata: The plan highlights that neglecting metadata can make the DSS a “black box,” hindering maintenance, improvements, and updates. Users, often experts themselves, need detailed knowledge of indicator acquisition and evaluation.
Key Insight: The primary challenge in DSS design is not hardware or software limitations but the effective organization and management of information, underscoring the need for a structured approach.
Detailed Comparison Table
To summarize the comparison, the following table outlines the user’s plan components, industry best practices, and gaps:
| Component | User’s Plan | Industry Best Practices | Gaps/Notes |
|---|---|---|---|
| Requirement Analysis | Initial analysis with thematic lists and decomposition | Thorough understanding of needs, user roles, and decisions supported | Aligns well, but end-user involvement needed |
| Data Management | Lists required data, sets up databases, emphasizes metadata | Data quality assurance, integration, storage, and management | Data quality processes missing |
| Model Development | Develops methods to compute indicators using mathematical models | Develop and validate analytical models for insights | Model validation not explicitly mentioned |
| System Architecture | User interface tier, data tier, communication with external databases | Modular, scalable design, integration with existing systems | Scalability and modularity could be emphasized |
| User Interface Design | Implements user interface tier for communication | User-friendly, intuitive design, meeting decision-makers’ needs | Aligns, but end-user feedback could enhance |
| Implementation and Testing | Implied in implementation by ITS experts | Build, test, and validate system functionality | Testing not detailed, iterative approach suggested |
| Deployment and Maintenance | Emphasizes metadata for maintenance, uses advanced tools | Deploy, maintain, update, and ensure sustainability | Strong on metadata, but feedback mechanisms needed |
| Risk Management | Not mentioned | Identify, assess, and mitigate risks during development and deployment | Significant gap, needs inclusion |
| Cost-Benefit Analysis | Not mentioned | Ensure benefits justify costs, especially given high investment | Gap, critical for justification |
| Change Management | Not mentioned | Prepare organization, manage resistance, ensure adoption | Gap, essential for successful deployment |
| Legal/Ethical Considerations | Not mentioned | Address privacy, security, compliance with regulations | Gap, especially if handling sensitive data |
| Technology Selection | Uses advanced tools like web services, containers | Select sustainable, supported technologies aligned with long-term goals | Ensure sustainability, potential gap |
This table highlights the alignment and areas for enhancement, providing a structured view of the comparison.
Areas for Improvement
Based on the comparison, several areas can be enhanced to align more closely with best practices: 1. End-User Involvement: While the plan involves experts, engaging end-users throughout the development process is crucial to ensure the system meets their needs and is intuitive. This can improve adoption and effectiveness, as end-users may have insights into practical challenges not captured by experts alone. 2. Iterative Development: Adopting an iterative or agile development methodology can allow for rapid adjustments based on feedback, especially given the complexity of DSS projects. This contrasts with the seemingly linear approach in the plan, which may benefit from flexibility to adapt to evolving requirements. 3. Data Quality Assurance: The plan mentions compiling a list of necessary data but does not explicitly address data quality issues, such as cleaning, validation, and integration from multiple sources. Ensuring high data quality is essential for reliable decision-making, and processes should be implemented to address this. 4. Model Validation: While methods to compute indicators are developed, validating these models using historical data or expert judgment is not mentioned. Validation is critical to ensure the models provide accurate and reliable recommendations, enhancing trust in the system. 5. Scalability and Modularity: The plan mentions using advanced tools and in-memory servers, which suggests consideration for performance, but designing for scalability and modularity can facilitate future expansions or modifications. A modular architecture can make maintenance easier and support growth as data or user needs increase. 6. Training and Documentation: The plan emphasizes metadata, which is part of documentation, but providing comprehensive training for users is essential to maximize the system’s effectiveness. This ensures users can leverage the DSS fully, especially given its complexity. 7. Feedback Mechanisms: Incorporating features for collecting user feedback post-deployment can enable continuous improvement. This is not mentioned in the plan but is a best practice for ensuring the DSS evolves with user needs and organizational changes.
An unexpected detail is the strong emphasis on metadata, which is often overlooked in DSS development but crucial for maintainability. This focus can set the system apart by ensuring long-term usability and upgradability, aligning with industry trends toward sustainable systems.
Miscellaneous content
1. Atobiac(AtoBviaC, 2025) content GBP 150-4000/year - 1667 ports
Source: AtoBviaC
2. IMO(IMO, 2025) Ships’ Routeing sample GBP167/copy - 800 pages
3. Map of Transhipment routes(Notteboom et al., 2022)
Source: Notteboom, Port Economics, 2022
4. Gas carrier - Risk criteria Matrix(IACS, 2016)
5. Lloyds Register(ShipRight, 2021) methodology for risk assessment
Source: Lloyds’s Register, Risk based certification, September 2021
6. Ship unit rate considerations - distance matters(AtoBviaC, 2025)
Source: AtoBviaC
References
(United Nations Economic Commission for Europe, 2025) (UN/LOCODE, 2025) (International Commercial Terms, 2025) (Global International Ports, 2020) (IMF Portwatch, 2023) (Vilhelmsen et al., 2015) (Blank & Deb, 2020) (Shih et al., 2023) (Lloyd’s Register, 2017) (IACS, 2003) (ShipRight, 2021) (Worldscale, 2025) (Stopford, 2008)