The Reliability Illusion: Yang Ming Through the Cargo Receiving Window

Why this article exists (before we look at any charts)

In the System Baseline edition of The Reliability Illusion, we established a simple but critical shift:

Vessel schedule reliability doesn’t break at arrival.
It breaks inside the cargo receiving window.

That window - defined by the Earliest Return Date (ERD) and the CY Cutoff - is where export execution actually happens.

This edition applies the same framework to Yang Ming (YMLU) to understand how cargo receiving windows behave in practice - where they stay predictable, and where they don’t.

All terms used here are defined in the Reliability Series - Methodology Appendix:
https://www.tradelanes.co/blog/reliability-series-methodology-appendix

Data scope (Yang Ming sample)

This analysis is based on an observational system sample of executable export port-calls and is not a statistically randomized sample.

• Port-calls: 118
• Vessels: 45
• Ports: 7
• Carrier: YMLU (Yang Ming)

Filters applied:

• ERD and CY Cutoff both required
• Drift >40 days treated as data error and excluded

Section 1 - How often do Yang Ming receiving windows actually move?

A receiving window is considered moved if either ERD or CY Cutoff shifts by one calendar day or more from its originally published value.

Figure 1 - Receiving Window Stability (Yang Ming)

• Stable receiving windows: 35.59%
• Moved receiving windows: 64.41%

Plain English meaning:
For Yang Ming in this sample, receiving window movement is the majority state. That doesn’t automatically mean something is “wrong” - but it does mean exporters should expect plans to require re-validation more often than not.

Section 2 - Drift isn’t chaos; it has a shape

Drift measures how far ERDs or CY Cutoffs move between original and final values, expressed in calendar days.

Figure 2 - ERD Drift Distribution (Yang Ming)

• 0-1 day: 51.69%
• 1-2 days: 8.47%
• 2-3 days: 7.63%
• 3+ days: 32.20%

Plain English meaning:
Yang Ming shows a meaningful tail: nearly one in three port-calls experienced 3+ days of ERD drift. That tail is where execution pain concentrates.

Static buffers are built for the middle of the curve.
Operational pain lives in the tail.

Section 3 - ERD vs CY: where Yang Ming risk concentrates

Across this sample, ERD drift is higher on average than CY drift, even though CY still exceeds ERD at the 1-day threshold.

Figure 3 - ERD vs CY Drift (Yang Ming)

Average drift:

• Mean ERD drift: 2.11 days
• Mean CY drift: 1.76 days

Threshold comparison:

• ≥1 day drift: ERD 48.31% vs CY 49.15%
• ≥2 days drift: ERD 39.83% vs CY 28.81%
• ≥3 days drift: ERD 32.20% vs CY 22.03%

Plain English meaning:
This is a useful reminder that “the constraint” can differ by carrier. In this sample, ERD movement becomes the dominant driver as drift grows (2+ and 3+ days), while CY remains the more common issue at the 1-day threshold.

Operationally, that often feels like:

• early acceptance dates shifting enough to force replans, and
• cutoffs still moving often enough to create late friction.

Section 4 - Timing matters more than averages

A late-stage change is defined as a change to ERD or CY Cutoff that occurs within the final 72 hours before the receiving window opens.

Figure 4 - Late-Stage Receiving Window Changes (Yang Ming)

• ERD changed in last 72 hours: 33.05%
• CY Cutoff changed in last 72 hours: 22.03%

Plain English meaning:
Roughly one in three ERDs and about one in five CY Cutoffs changed inside the final 72 hours. That is the execution lock-in problem: the plan can look workable until close-in changes reduce options.

So far, we’ve looked at how windows move.
Next, we look at where.

Section 5 - Volatility is not evenly distributed across ports (Yang Ming)

The Port Volatility Index (PVI) reflects how quickly static planning assumptions break at a port.

Figure 5 - Port-Level Drift (Yang Ming)

Below are Yang Ming’s port environments in this sample, ordered by PVI. Ports with very small sample sizes should be interpreted cautiously.

USSAV (PVI 10.0, n=14)

• Mean ERD drift: 4.71 days
• Mean CY drift: 4.57 days
• Stable window rate: 7.14%
• ERD late-stage change: 35.71%
• CY late-stage change: 28.57%

What this feels like:
This is where volatility concentrates for Yang Ming in this dataset. Both ERD and CY move far enough that static buffers stop being reliable. Execution becomes a re-validation workflow.

USNYC (PVI 5.3, n=9)

• Mean ERD drift: 1.56 days
• Mean CY drift: 3.60 days
• Stable window rate: 33.33%
• ERD late-stage change: 33.33%
• CY late-stage change: 22.22%

What this feels like:
A “CY-dominant” port environment. ERDs move, but CY moves more and can break plans late.

USTIW (PVI 3.2, n=62)

• Mean ERD drift: 2.38 days
• Mean CY drift: 1.32 days
• Stable window rate: 30.65%
• ERD late-stage change: 41.94%
• CY late-stage change: 14.52%

What this feels like:
ERD volatility dominates here, and late-stage ERD change is high. Exporters can be forced into replans earlier than expected.

USLAX (PVI 0.4, n=25)

• Mean ERD drift: 0.79 days
• Mean CY drift: 0.82 days
• Stable window rate: 56.00%
• ERD late-stage change: 12.00%
• CY late-stage change: 28.00%

What this feels like:
More predictable overall, with CY timing still worth watching closer to execution.

USOAK (PVI 0.0, n=6)

• Mean ERD drift: 0.23 days
• Mean CY drift: 0.33 days
• Stable window rate: 66.67%
• ERD late-stage change: 33.33%
• CY late-stage change: 33.33%

What this feels like:
Small sample, but a common pattern: low average drift does not guarantee low late-stage risk.

USORF (PVI 9.4, n=1) and USCHS (PVI 0.3, n=1)

These ports have one port-call each in this sample. They should not be over-interpreted.

Section 6 - Severity still exists, even when averages look manageable

Figure 6 - Top 10 Highest-Severity Yang Ming Events

Top examples:

• YM TRILLION (USSAV): ERD 18d, CY 18d, late-stage 1, drift score 37
• YM TRAVEL (USSAV): ERD 9d, CY 8d, late-stage 2, drift score 19
• YM WARRANTY (USNYC): ERD 6d, CY 10d, late-stage 2, drift score 18

Plain English meaning:
These are stress tests, not typical shipments. They show how quickly drift can stack when multiple changes coincide, especially in the highest-volatility port environments.

Static buffers fail in these scenarios by design.

Section 7 - The KPI that matters for Yang Ming

Figure 7 - Receiving Window Movement Rate (Yang Ming)

• Moved receiving windows: 64.41%
• Stable receiving windows: 35.59%
• Scope: 118 port-calls • 45 vessels • 7 ports

Plain English meaning:
For Yang Ming in this sample, movement is the norm. The operational risk is driven by both magnitude (the 3+ day tail) and timing (late-stage changes).

Section 8 - Why static buffers fail (and why this repeats)

Figure 8 - Static Buffer vs Dynamic Time Buffer (DTB)

Plain English meaning:
When drift has a long tail and late-stage changes are common, fixed buffers are routinely exceeded. Planning must adapt to observed behavior, not assumptions.

Before we move to the next carrier

A vessel can be “on time” and still break export execution if the receiving window shifts underneath it.

This Yang Ming edition shows:

• receiving window movement is the majority state in this sample,
• ERD drift becomes the dominant driver at higher thresholds (2+ and 3+ days), and
• volatility is highly port-dependent, with USSAV carrying the strongest signal.

Methodology and definitions:
Reliability Series - Methodology Appendix
https://www.tradelanes.co/blog/reliability-series-methodology-appendix

Next in the Carrier Reliability Series

Maersk - publishing soon.