Capacity, tank volume, and cargo volume are difficult to coordinate
Complete English presentation of the source project analysis, preserving the full narrative, tables, figures and evidence boundaries.
Arctic Phoenix Group | Oil, Gas, and Petrochemical Business Cases
Pain Point 02
Capacity, tank volume, and cargo volume are difficult to coordinate
Simultaneously schedule ships, berths, pipelines, tank capacity, and orders using the space-time capacity model and rolling optimization
Section Three: The Top Ten Pain Points of the Oil, Gas, and Petrochemical Business in Logistics and Finance | Complete Case Report
1. Executive Summary
| Core Judgment: Use a time-space capacity model and rolling optimization to simultaneously schedule vessels, berths, pipelines, tank capacity, and orders. This plan is not about launching a single-point system; rather, it integrates contracts, physical goods, ownership, risk, and cash flow into the same main transaction line, and drives business actions through exception management. |
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Case Business Profile
Case Enterprise: Naphtha and aromatics trading platform, averaging 12 sea shipments per month and multiple batch pipeline transports.
Case cargo: 70,000 tons of naphtha, requiring matching MR/LR vessel types, dedicated tanks, berth windows, and downstream unit maintenance periods.
Typical mismatch: the ship has arrived while the tank is not yet emptied; the pipeline batch is delayed, causing high-cost temporary ship chartering and tank switching.
Four questions that management needs to answer
When, where, and with what amount does the real risk enter the transaction?
Which entity owns the trusted data, disposal rights, and final responsibility?
When deviations occur, which set of plans is feasible in terms of business, operations, compliance, and funding at the same time?
Is the final performance evaluated based on book gross profit, cash profit, or risk-adjusted return?
| Target Indicator: End-to-end logistics cost per ton = total of freight, demurrage, tank reversal, storage, empty trips, expedited shipping, and losses / delivered tons. |
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2. Representative Cases and Quantitative Impact
Figure 1 (Data Chart): Profit or Liquidity Impact Bridge of Anonymized Representative Cases
The source-document visual is presented here as an English-native analytical frame. The adjacent English narrative and tables preserve the full evidence and quantitative context.
| Project | Influence | Unit/Caliber |
|---|---|---|
| Planned Transaction Contribution | +110 | ten thousand US dollars |
| Empty run/low load | -18 | ten thousand US dollars |
| Waiting berths conflicting with berths | -14 | ten thousand US dollars |
| Tank dumping and temporary storage | -12 | ten thousand US dollars |
| Time charter premium | -21 | ten thousand US dollars |
| Final transaction contribution | +45 | ten thousand US dollars |
Case Analysis: Individual losses are often not fatal; the real problem is the accumulation of information delays, non-transferable contracts, resource constraints, and capital costs on the same shipment. If performance is still assessed based on contract gross profit, the risks will be concentratedly exposed after settlement.
3. Root Cause Diagnosis
| Root Cause 1: Sales, chartering, terminals, and warehousing optimize local objectives separately, lacking a unified constraint model. |
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| Root cause 2: Tank capacity is managed according to nominal capacity, without deducting unavailable tanks, bottom oil, maintenance, and compatibility restrictions. |
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| Root Cause 3: Small batch orders were not evaluated for consolidation, splitting, shared shipments, or return shipment opportunities. |
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| Root cause 4: The plan is statically frozen on a weekly basis, making it impossible to quickly find a new solution when facing changes in shipping schedules or production volumes. |
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Risk transmission chain
Business commitments → Resources/documents/funding constraints are not synchronized → Delayed detection of anomalies → Increased cost of temporary handling → Expansion of ownership, credit, or compliance risks → Final cash profit deviates from the contracted judgment.
Control Design Principles
A fact: The same transaction, batch, physical goods, ownership, and cash flow use a unified ID and timeline.
One owner: Key exceptions must have a clearly responsible person, authorized boundaries, and deadlines.
An economic perspective: each action shows incremental cost, risk release, and customer impact.
A set of evidence: all approvals, changes, documents, measurements, and communications are traceable.
4. Solution Architecture
| 1. Control Layer: Constructs spatiotemporal capacity digital models of ships, berths, tanks, pipelines, batch processes, product grades, and orders. |
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| 2. Control Layer: Define the available tank volume as the nominal capacity minus bottom oil, unusable space, committed volume, and safety margin. |
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| 3. Control Layer: Optimize loading for scrolling, consolidation, port arrival sequence, inter-tank transfers, and return cargo, outputting cost and risk boundaries. |
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| 4. Control Layer Set a 48/72-hour lock window; automatic optimization outside the window, and changes within the window must show incremental costs and be approved. |
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| 5. Control Layer: Assess based on end-to-end logistics costs rather than individual freight rates, to avoid low freight rates resulting in high demurrage and tank reversal. |
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End-to-end closed loop
Identify exposure → Quantify scenarios → Formulate alternative plans → Approve according to authorization → Execute and leave traces → Transaction-level settlement → Review and update rules and models.
5. Process, Organization, and Internal Control Implementation
| link; segment; part | primary responsibility | Key Controls/Evidence |
|---|---|---|
| Transaction access | Business Manager | Business objectives, competitors, products, routes, quotas, and profit bottom line |
| Planned Commitment | Operations/Logistics | Resource feasibility, time window, contingency plan, and incremental cost |
| Execution Monitoring | Control Tower / Treasury | Event timeline, anomaly classification, permissions, and escalation |
| Cargo Ownership / Funds | Finance/Legal | Documents, Guarantees, Release of Goods, Payment, and Reconciliation |
| Final settlement | Financial control | Accruals, claims, financing, foreign exchange, ECL, and final profit |
| Review and improve | Risk Committee | Root Cause, Control Failure, Model Bias, and Accountability Loop |
Critical Authorization Boundary
When the price or profit is below the bottom line, exceeds the risk limit, or changes the ownership of goods or payment path, approval must be escalated.
AI recommendations should not automatically execute trades, release goods, make withdrawals, grant credit, or lift compliance restrictions.
In emergencies, pre-approved scripts can be used, but evidence and review must be completed within the specified time limit.
6. Implementation Roadmap and Data Foundation
| Phase 1 0–6 weeks: Sort out the resource ledger, compatibility matrix, and capacity caliber. |
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| Phase 2, Weeks 7–18: Launch the rolling schedule and conflict heatmap, covering core ports. |
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| Phase 3 19–36 weeks: Connect charter inquiry and sales commitments, forming end-to-end optimization and revenue sharing. |
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Minimum viable dataset
Transaction ID, contracts and terms, goods batches, quantity and quality, resource/location events, title documents, counterparties and banks, currency cash flow, expense accruals, approvals and exception records. Missing data should explicitly indicate confidence levels and must not be disguised as facts using model outputs.
Change and Governance
Data and rules are jointly owned by business, logistics/operations, treasury, risk, legal compliance, and finance.
Pilot with two to three high-value links, and expand based on verifiable cash savings and risk reduction.
Complete model validation, permission testing, disaster recovery, audit logs, and manual takeover drills before going live.
7. Value Indicators, AI Applications, and Management Boundaries
| Indicator | Typical baseline | Recommended Goals |
|---|---|---|
| Vessel Effective Loading Rate | 82% | ≥93% |
| Peak tank capacity utilization | 96% | 80%—90% |
| Proportion of chartered ships | 18% | ≤5% |
| Average Equal Berth | 42 hours | ≤16 hours |
| Empty backhaul rate | 55% | ≤35% |
AI Applicable Scenarios
Predict the probability distribution of available container capacity, arrival time at the port, and downstream pickup speed.
Solve for the minimum total cost scheme under the constraints of product compatibility, draft, berth, and contract window.
Candidate matching is conducted for small single shipments and return shipments, but confirmation is done by chartering and operations personnel.
| Polestar Metric: End-to-end logistics cost per ton = total all-inclusive sum of freight, demurrage, tank reversal, warehousing, empty mileage, rush fees, and losses / delivered tons. |
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Case Scope and Limitations
The volumes, prices, rates, losses, baselines, and targets in this report are anonymized professional scenario data used to illustrate decision-making logic and do not constitute factual statements, valuations, legal opinions, or investment advice for any specific company. Implementation should be recalibrated based on actual contracts, applicable laws, bank credit, port/warehouse regulations, hazardous materials classification, and audited financial data.
Reference caliber
Asian Development Bank (ADB), Trade and Supply Chain Finance Program: The global trade finance gap is about 2.5 trillion USD (2025/2026 estimate), https://www.adb.org/subjects/trade-and-supply-chain-finance
International Chamber of Commerce Digital Standards Initiative (ICC DSI): Trade digitalization, electronic transferable records and document interoperability, https://dsi.iccwbo.org/
International Maritime Organization (IMO), IMDG Code 2024 Edition (including Amendment 42-24, mandatory from 2026-01-01), https://www.imo.org/en/publications/pages/imdg code.aspx
UNECE, Dangerous Goods: ADR/RID and other dangerous goods transport frameworks, https://unece.org/transport/dangerous-goods