Predictive Logistics — Port Call Duration Analysis | June 2026
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1. Historical Dataset & Outlier Cleaning
342 port calls in 3 cargo volume classes (Fort / Moyen / Faible), duration in days. Outliers removed via IQR×k (default k=2.5). Dataset: MEDFRET Mediterranean containerised lines.
📊 A separate Excel audit workbook (medfret_OUT.xlsx, live formulas — see link top-right) exists alongside this dashboard. The two are fully independent: this page never reads the workbook, and the workbook is never regenerated from this page. Updating one does not update the other — regenerate the workbook manually (python3 build_medfret_excel.py) if you want it to reflect a change made here.
Total Raw
342
Port calls
After Cleaning
327
15 outliers removed
Fort avg
2.66d
High volume
Faible avg
0.81d
Low volume

Cleaning Method: IQR × k

Calls outside Q1−k×IQR / Q3+k×IQR excluded. Negative and missing values also removed.

1.02.5 (default)5.0

Scatter: All Port Calls by Volume Class

Fort — Reference Table

Moyen — Reference Table

Faible — Reference Table

All Port Calls (cleaned)

#IDVolume ClassDuration (days)Status
2. Statistical Model Comparison — 6 Models
CDF MSE (lower=better). Best: Log-Normal for Fort & Moyen, Gamma for Faible. All classes show right-skew — Gaussian is never optimal.

Histogram (cleaned data)

Cumulative Distribution — CDF

Model Fit Comparison — CDF MSE — 6 models

Optimal Model Parameters

Validation Statistics

3. KPI Success Probabilities — Port Inclusion Decision
P = Φ((target−μ)/σ). Operational targets: Fort ≤ 3.0d, Moyen ≤ 2.0d, Faible ≤ 1.0d. Duration is the binding constraint.

Fort — KPI Status

Target3.0 days
DB Average2.66 days
Std Dev0.46 days
CV17.3%
Margin of Error±0.19d
P(success)77%

Moyen — KPI Status

Target2.0 days
DB Average1.58 days
Std Dev0.53 days
CV33.7%
Margin of Error±0.12d
P(success)78%

Faible — KPI Status

Target1.0 days
DB Average0.81 days
Std Dev0.33 days
CV40.0%
Margin of Error±0.04d
P(success)72%

KPI Summary Table

Volume ClassTarget (days)DB AvgDB StdCVMarginP(success)
Fort3.02.660.4617.3%±0.19d77%
Moyen2.01.580.5333.7%±0.12d78%
Faible1.00.810.3340.0%±0.04d72%
Overall—————72%

Overall = min of all three. Faible is binding at 72%. Need target ≥ 0.94d for 80% on Faible.

Target Sensitivity — Faible Class

4. Monte Carlo Simulation (n = 1,000 trials)
Box-Muller on N(μ,σ). P20/P50/P80 = conservative/median/optimistic. Seed=42.

Fort

DB Mean2.66 d
2.27d
P20
2.67d
P50
3.08d
P80

Moyen

DB Mean1.58 d
1.11d
P20
1.58d
P50
2.01d
P80

Faible

DB Mean0.81 d
0.53d
P20
0.81d
P50
1.09d
P80

Fort — MC Distribution

Moyen — MC Distribution

Faible — MC Distribution

Monte Carlo Summary Table

Volume ClassDB MeanP20 (conservative)P50 (median)P80 (optimistic)Spread P80−P20
Fort2.66 d~2.27 d~2.67 d~3.08 d0.81 d
Moyen1.58 d~1.11 d~1.58 d~2.01 d0.90 d
Faible0.81 d~0.53 d~0.81 d~1.09 d0.56 d
5. Key Findings & Operational Recommendations
▮ Port inclusion feasible for all volume classes. Success probability is 72–78% against operational targets — above the 70% threshold for route planning decisions.
▮ Faible is the binding constraint. CV=40% is the highest variability class; it drives the overall P(success) down to 72%. Need to extend the Faible target to ≥0.94d for 80% confidence.
▮ Right-skew confirmed in all classes. Log-Normal dominates Fort (MSE=0.0036) and Moyen (MSE=0.0007); Gamma best for Faible (MSE=0.0005). Gaussian never optimal — avoid symmetric assumptions in port planning.
▮ 15 data quality issues detected. 12 in Faible + 2 in Moyen + 1 in Fort. Some records contained negative timestamps (apparent data entry errors). Cleaned via IQR×2.5.
▮ Sample size caution for Fort. n=23 after cleaning → margin ±0.19d (7% of mean) vs ±0.04d for Faible (n=232). Fort predictions carry more uncertainty; target larger sample before committing to high-volume rotation.

Decision Summary — Include in Rotation?

VolumeMeanTargetP(success)Decision
Fort2.66d3.0d77%Include
Moyen1.58d2.0d78%Include
Faible0.81d1.0d72%Include / Review target

Best Statistical Model per Class

FortLog-Normal (σ=0.173, scale=2.616) MSE 0.00361
MoyenLog-Normal (σ=0.316, scale=1.504) MSE 0.00068
FaibleGamma (α=6.28, β=0.130) MSE 0.00047

MC P20/P50/P80 Summary

Fort~2.3d / ~2.7d / ~3.1d
Moyen~1.1d / ~1.6d / ~2.0d
Faible~0.5d / ~0.8d / ~1.1d

Comparison with Prévision projet Methodology (R&D Projects)

MetricPrévision projet (R&D Projects)MEDFRET (Port Calls)Note
VariableDuration (months)Duration (days)Same concept, different scale
Classes1st in Fam / Innovative / FollowerFort / Moyen / Faible3 complexity/volume levels
CleaningIQR×2.5IQR×2.5Identical method
Outliers removed2 / 151 = 1.3%15 / 342 = 4.4%Higher data quality issues in logistics
Best model (high)Log-NormalLog-NormalConsistent right-skew behaviour
Best model (low)Log-Normal / WeibullGammaFaible more symmetric
Monte CarloBox-Muller N(μ,σ) 1,000 trialsBox-Muller N(μ,σ) 1,000 trialsIdentical method
KPI probability29% (duration)72–78%Port targets more conservative relative to mean
Pearson r (QQ)0.988–0.9980.955–0.978Good but slightly lower — logistics more noisy
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