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How to Use a Line Chart for Vessel Arrival Predictions

Dockflow's vessel prediction line chart visualizes how arrival estimates evolve over time from multiple data sources. This powerful tool helps you understand prediction accuracy, identify trends, and anticipate potential delays or early arrivals.

Overview​

The prediction chart provides:

  • Historical prediction tracking - See how ETAs have changed over time
  • Multi-source comparison - Compare predictions from AIS, terminals, and carriers
  • Trend analysis - Identify improving or degrading predictions
  • Early warning signals - Spot sudden changes indicating potential issues
  • Data confidence assessment - Evaluate which sources are most reliable
Why This Matters

Understanding prediction evolution helps you:

  • Make better decisions about pickup timing
  • Anticipate delays before official notifications
  • Optimize warehouse and transportation planning
  • Identify unreliable predictions early
  • Build confidence in your operational planning

Chart Components​

Axes Explained​

X-Axis: Prediction Date​

What it shows: When each prediction was made (recorded)

Format: Date and optionally time (e.g., "Jan 15" or "Jan 15 10:00")

Reading the axis:

  • Left side = Earlier predictions (further in the past)
  • Right side = More recent predictions (closer to now)
  • Progression shows how predictions have evolved chronologically

Example:

Jan 10 --- Jan 15 --- Jan 20 --- Jan 25 --- Today
(Old)                                      (Recent)

Y-Axis: Predicted Arrival Date​

What it shows: The predicted arrival date (what each prediction said)

Format: Date and optionally time (e.g., "Feb 5" or "Feb 5 08:00")

Reading the axis:

  • Lower values = Earlier predicted arrival
  • Higher values = Later predicted arrival
  • Vertical movement indicates prediction changes

Example:

Feb 8  ← Later arrival
Feb 6  ← Target arrival
Feb 4  ← Earlier arrival

Data Points and Lines​

Individual Data Points (Dots)​

Each dot on the chart represents a single prediction and shows two pieces of information:

Horizontal position (X-axis): When the prediction was made Vertical position (Y-axis): What arrival date was predicted

Example dot:

Position: X = Jan 20, Y = Feb 6
Meaning: On January 20, the system predicted arrival on February 6

Lines Connecting Points​

Lines connect predictions from the same data source over time, creating a visual trend:

Line direction:

  • Horizontal = Stable prediction (arrival date not changing)
  • Upward slope = Predictions getting later (delays emerging)
  • Downward slope = Predictions getting earlier (ahead of schedule)
  • Jagged = Volatile predictions (frequent changes)

Color-Coded Data Sources​

Different colors represent different data sources:

Blue Line: AIS (Automatic Identification System)​

Source: Vessel positioning and movement data

Characteristics:

  • Updates frequently (often hourly when vessel is moving)
  • Based on vessel speed, heading, and position
  • Generally accurate for vessels in transit
  • May be less reliable in port areas or during stops

Reliability: High for open ocean, moderate near ports

Green Line: Terminal API​

Source: Terminal operations systems

Characteristics:

  • Updates when terminal receives schedule information
  • Based on port operations planning
  • Often most accurate close to arrival
  • May not show data until vessel is nearby

Reliability: Very high within 24-48 hours of arrival, limited earlier

Yellow Line: Carrier Real-Time Updates​

Source: Shipping line operational data

Characteristics:

  • Updates based on carrier schedule changes
  • Reflects carrier's operational planning
  • May include planned slow-steaming or route changes
  • Sometimes conservative (builds in buffer time)

Reliability: Good for schedule changes, may lag AIS for actual position

Data Source Availability

Not all shipments have all three data sources. Coverage depends on:

  • Carrier's data-sharing agreements
  • Terminal's technical capabilities
  • Vessel's AIS transmission
  • Route and geography

Reading the Chart​

Basic Interpretation​

Converging Lines​

When all three colored lines come together (converge):

Meaning: All data sources agree on the predicted arrival date

Implication: High confidence in the prediction

Example:

     AIS ────→
   Terminal ──→  All lines meet at Feb 6
   Carrier ───→

Action: Plan confidently based on this date

Diverging Lines​

When colored lines spread apart (diverge):

Meaning: Data sources disagree on arrival date

Implication: Lower confidence, uncertainty exists

Example:

   AIS ────────→ Feb 5
   Terminal ───→ Feb 6
   Carrier ────→ Feb 7

Action: Monitor closely, prepare for range of arrival dates

Advanced Pattern Recognition​

Stable Horizontal Line​

Pattern: Line stays at same Y-axis value over time

Meaning: Predictions consistently point to same arrival date

Example:

Carrier: ──────────────── (flat at Feb 6)

Interpretation: Stable schedule, no changes expected

Confidence: High (unless contradicted by other sources)

Gradually Rising Line​

Pattern: Line slopes upward over time

Meaning: Predicted arrival date getting later (delays developing)

Example:

AIS: ↗ ↗ ↗ (Feb 4 → Feb 5 → Feb 6 → Feb 7)

Interpretation: Progressive delay, likely due to:

  • Slow-steaming or speed reduction
  • Weather delays
  • Port congestion ahead
  • Route deviations

Action: Communicate delay to stakeholders, adjust plans

Gradually Falling Line​

Pattern: Line slopes downward over time

Meaning: Predicted arrival getting earlier

Example:

Terminal: ↘ ↘ ↘ (Feb 8 → Feb 7 → Feb 6)

Interpretation: Ahead of schedule, possibly due to:

  • Increased vessel speed
  • Favorable weather
  • Earlier port availability
  • Route optimization

Action: Accelerate preparations, notify receiving team

Sudden Jump​

Pattern: Sharp vertical movement in single update

Meaning: Significant prediction change

Example:

Carrier: ──── | ──── (Feb 5 → Feb 10 in one jump)
              ↑
           Sudden change

Interpretation: Major schedule change:

  • Weather event announced
  • Port closure or delay
  • Vessel mechanical issue
  • Route change

Action: Investigate cause, update stakeholders immediately

Oscillating/Jagged Line​

Pattern: Frequent up-and-down movements

Meaning: Volatile, unstable predictions

Example:

AIS: ↗ ↘ ↗ ↘ ↗ (Feb 5 → 7 → 6 → 8 → 6)

Interpretation: Uncertainty or data quality issues:

  • Vessel speed frequently changing
  • Inconsistent data from source
  • Complex route with many variables
  • Data refresh timing issues

Action: Rely on most recent stable source, don't overreact to each change

Practical Applications​

Trend Monitoring​

Use case: Understanding if predictions are improving or degrading

How to read:

  1. Look at the overall direction of lines over time
  2. Assess whether recent predictions are converging or diverging
  3. Compare rate of change between sources

Example analysis:

Week 1: All sources predict Feb 6 (converged)
Week 2: Sources spread apart (Feb 5-7)
Week 3: Sources converge again at Feb 7

Interpretation: Initial prediction was optimistic,
delay became apparent, now stabilized at later date.

Operational impact:

  • Week 1: Plan for Feb 6 arrival
  • Week 2: Prepare for Feb 5-7 range, add buffer
  • Week 3: Finalize plans for Feb 7 arrival

Early Warning Signals​

Use case: Detecting potential issues before official delay announcements

What to watch for:

1. Source disagreement:

If AIS shows getting later while Carrier holds steady
→ Carrier may not have updated schedule yet
→ Real delay likely developing

2. Sudden AIS change:

AIS jumps from Feb 5 to Feb 8 in single update
→ Vessel slowed or stopped unexpectedly
→ Investigate: weather, mechanical, port issues?

3. Terminal pessimism:

Terminal predicts later than carrier/AIS
→ Terminal may know of port congestion
→ Discharge may be delayed even if vessel arrives on time

Action steps:

  1. Don't wait for official notification
  2. Proactively contact carrier or forwarder
  3. Adjust plans based on most realistic prediction
  4. Communicate early warnings to stakeholders

Comparative Source Analysis​

Use case: Determining which source is most reliable for your shipments

Analysis approach:

Step 1: Track prediction accuracy over multiple shipments

Source        Accuracy (within 24 hrs of actual)
AIS           85%
Terminal      92%
Carrier       78%

Step 2: Identify patterns

- Terminal most accurate close to arrival
- AIS best during transit
- Carrier often conservative (adds buffer)

Step 3: Adjust decision-making

>7 days from arrival: Rely on carrier schedule
2-7 days from arrival: Monitor AIS closely
<2 days from arrival: Trust terminal predictions

Planning Decision Points​

Use case: Deciding when to commit to operational plans

Decision framework:

Low confidence (diverging sources):

  • Don't commit to fixed pickup times
  • Build extra buffer into plans
  • Maintain flexible warehouse schedule
  • Keep stakeholders informed of uncertainty

Medium confidence (mostly converged):

  • Make tentative commitments
  • Maintain some flexibility
  • Confirm plans as arrival approaches
  • Set backup options

High confidence (fully converged, stable):

  • Commit to detailed operational plans
  • Schedule pickup appointments
  • Finalize warehouse and labor allocations
  • Confirm customer delivery dates

Chart Interpretation Examples​

Example 1: Stable, Reliable Prediction​

Chart pattern:

Feb 6 ──────────────────────── All three sources
      (flat, converged lines)

Interpretation:

  • All sources agree consistently
  • No changes over extended period
  • High confidence in Feb 6 arrival

Recommended actions:

  • Finalize all arrival preparations
  • Schedule pickup for Feb 6 or 7
  • Confirm customer delivery dates
  • Allocate warehouse resources

Example 2: Developing Delay​

Chart pattern:

Feb 8        Carrier ────────
Feb 7    Terminal ─────↗
Feb 6  AIS ─────↗↗↗
Feb 5
       Jan 15  Jan 20  Jan 25

Interpretation:

  • AIS showing progressive delay
  • Terminal adjusting predictions upward
  • Carrier hasn't updated yet (lag)
  • Likely arrival: Feb 7-8 (not Feb 6)

Recommended actions:

  • Prepare for Feb 7-8 instead of Feb 6
  • Notify stakeholders of potential delay
  • Contact carrier for official update
  • Adjust pickup and delivery schedules

Example 3: Source Disagreement​

Chart pattern:

Feb 7  Carrier ────────────────
Feb 6  Terminal ──────↘───────
Feb 5  AIS ─────────↘──────────
       Jan 10   Jan 15   Jan 20

Interpretation:

  • AIS and Terminal showing earlier arrival
  • Carrier maintaining conservative prediction
  • Likely arriving Feb 5-6 (ahead of carrier schedule)

Recommended actions:

  • Don't wait for carrier update
  • Prepare for earlier arrival (Feb 5-6)
  • Proactively coordinate pickup
  • Potential competitive advantage if ready early

Example 4: Volatile Predictions​

Chart pattern:

Feb 8     ↗
Feb 7  ↗  ↓  ↗  AIS (jagged line)
Feb 6  ↓  ↗  ↓
Feb 5
      Jan 10  Jan 15  Jan 20

Interpretation:

  • Frequent prediction changes
  • Uncertainty due to variable conditions
  • Hard to pin down exact arrival

Recommended actions:

  • Wait for predictions to stabilize
  • Build extra buffer into plans
  • Don't commit to tight schedules
  • Consider widening pickup window

Best Practices​

Regular Monitoring​

  1. Check frequently during transit - At least daily as arrival approaches
  2. Focus on trends, not single points - Don't overreact to one data point
  3. Compare all sources - Use full picture, not just one line
  4. Track closer to arrival - Predictions become more accurate over time

Decision-Making Guidelines​

  1. Early in transit (>2 weeks out):

    • Use predictions for rough planning only
    • Don't commit to fixed schedules yet
    • Monitor for major changes

  2. Mid-transit (1-2 weeks out):

    • Refine operational plans
    • Make tentative commitments
    • Increase monitoring frequency

  3. Near arrival (<1 week out):

    • Finalize all arrangements
    • Lock in pickup schedules
    • Trust most recent converged predictions

  4. Imminent arrival (<48 hours):

    • Terminal predictions most reliable
    • Confirm all logistics
    • Be ready to execute

Documentation and Communication​

  1. Screenshot key changes - Document significant prediction changes
  2. Share with stakeholders - Keep teams informed of evolving predictions
  3. Explain uncertainty - When sources diverge, communicate the range
  4. Provide context - Explain why predictions changed (weather, congestion, etc.)

Troubleshooting​

Chart Not Displaying​

If the prediction chart doesn't appear:

  1. Check data availability - Chart requires multiple prediction points
  2. Verify shipment status - May only show for active in-transit shipments
  3. Confirm data sources - Requires at least one prediction source
  4. Browser issues - Try refreshing or different browser
  5. Contact support - May be a permissions or configuration issue

Missing Data Sources​

If some colored lines don't appear:

  1. Check carrier integration - Not all carriers provide real-time data
  2. Verify vessel type - Small vessels may lack AIS
  3. Review terminal capabilities - Not all terminals have API integration
  4. Geographic coverage - Some regions have limited data
  5. Timing - Some sources only provide data when vessel is nearby

Contradictory Predictions​

If sources show very different predictions:

  1. Consider recency - Most recent update may be most accurate
  2. Evaluate source reliability - Some sources more accurate than others
  3. Check for announcements - Official carrier delays or port issues
  4. Investigate externalities - Weather, port congestion, strikes
  5. Use conservative estimate - Plan for later date if uncertain

Chart Doesn't Update​

If the chart seems stale:

  1. Check data sync timing - Updates every 4-8 hours typically
  2. Verify vessel is moving - Stationary vessels may not generate new predictions
  3. Review system status - Check for known data feed issues
  4. Refresh manually - Force page reload
  5. Contact support - May be a data integration issue

Frequently Asked Questions​

Q: Why do predictions sometimes get worse (less accurate) over time? A: External factors can emerge (weather, congestion) that weren't predictable earlier. Predictions reflect best available information at each point in time.

Q: Which data source should I trust most? A: It depends on timing. Generally: Terminal API is most accurate within 48 hours, AIS is reliable during transit, and carrier data is good for schedule changes.

Q: Can I export the prediction chart? A: Currently, charts are view-only. You can screenshot for documentation. Export functionality may be added in future.

Q: Do prediction changes trigger notifications? A: Not automatically, but you can set up automations to flag shipments with significant ETA changes. See Managing Notifications.

Q: Why does the carrier prediction sometimes never change? A: Carriers may maintain their original schedule even as actual arrival changes, especially if vessel is still expected to make up time. This is why comparing sources is valuable.

Q: How far back does the prediction history go? A: Typically from when the shipment was created in Dockflow or when tracking began, whichever is earlier.

Q: Can I see prediction charts for past shipments? A: Yes, historical prediction charts remain accessible in completed tradeflows for reference and analysis.

Q: What's considered a "significant" prediction change? A: Generally, changes >24 hours are significant and warrant investigation. Changes <6 hours are often just refinements.

Support​

Questions about prediction charts?