Logistics Intelligence Series | Module 4 of 6

Empty-run analysis:
The most expensive air on your roads

One in five kilometres your fleet drives is without cargo. That is not just diesel cost - it is lost contribution margin. A clustering model uncovers systematic patterns no dispatcher can see manually - and shows where consolidation genuinely pays off.

01 The problem - empty kilometres as a systemic fault

Why traditional freight exchanges do not solve the problem

Empty runs are not random. They arise from structural imbalances in your route patterns: customers in region A are served, but there is no matching return freight to region B. The dispatcher knows this - but only ever sees a single day. What they don't see: that this empty run occurs every Tuesday and Thursday on the same relation.

Freight exchanges help in individual cases but don't address the structural problem. What is missing: an analysis that identifies recurring empty-run patterns, quantifies their costs and reveals concrete consolidation opportunities - based on your own historical data.

19.4% empty-run rate = €487,000/year

For 150 trucks each doing 120,000 km/year. Every percentage point reduction saves €25,100.

Route history

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Relation analysis

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Pattern clustering

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Bundling scoring

→

Dispatch recommendation

02 Data foundation - 42,000 routes, 12 months

What your TMS knows about your empty kilometres

We simulate the route history of a 150-truck fleet with 43 regular customers across 28 regions (postal code areas). Each route has an origin, a destination, cargo (yes/no) and timestamps. From these raw records we extract the empty-run relations.

Python · Touren-Datengenerierung

Code

import pandas as pd
import numpy as np
from datetime import datetime, timedelta

np.random.seed(404)

# --- Regionen / Hubs ---
regionen = {
    'HAM': (53.55, 9.99),   'BER': (52.52, 13.41),
    'MUC': (48.14, 11.58),  'FRA': (50.11, 8.68),
    'KOL': (50.94, 6.96),   'STR': (48.78, 9.18),
    'DUS': (51.23, 6.78),   'DOR': (51.51, 7.47),
    'HAN': (52.37, 9.74),   'LEI': (51.34, 12.37),
    'NUR': (49.45, 11.08),  'BRE': (53.08, 8.81),
    'DRE': (51.05, 13.74),  'ESS': (51.46, 7.01),
    'KAR': (49.01, 8.40),   'MAN': (49.49, 8.47),
    'AUG': (48.37, 10.90),  'KAS': (51.32, 9.50),
    'WIE': (50.08, 8.24),   'KIE': (54.32, 10.14),
    # EU-Partner
    'AMS': (52.37, 4.90),   'ROT': (51.93, 4.48),
    'ANT': (51.22, 4.40),   'WIE_AT': (48.21, 16.37),
    'PRA': (50.08, 14.44),  'WAR': (52.23, 21.01),
    'MAI': (45.46, 9.19),   'LYO': (45.76, 4.84),
}
reg_names = list(regionen.keys())

# --- Bekannte Ungleichgewichte (realitätsnah) ---
# Süd→Nord hat weniger Rückfracht als Nord→Süd
# NL/BE→DE hat Überschuss, DE→Ost hat wenig Rückfracht

touren = []
start_date = datetime(2024, 1, 1)
for _ in range(42000):
    tag = start_date + timedelta(days=np.random.randint(0, 365))
    wochentag = tag.weekday()
    lkw = f'LKW-{np.random.randint(1,151):03d}'

    # Hinfahrt: immer beladen
    start = np.random.choice(reg_names[:20])  # DE-Hubs
    ziel = np.random.choice(reg_names)
    while ziel == start:
        ziel = np.random.choice(reg_names)

    distanz = np.random.randint(150, 850)

    # Rückfahrt: Leerfahrt-Wahrscheinlichkeit abhängig von Relation
    leer_prob = 0.15  # Basis
    if ziel in ['WAR','PRA','DRE','LEI']: leer_prob += 0.20  # Ost: wenig Rückfracht
    if ziel in ['KIE','BRE']: leer_prob += 0.12           # Norddeutschland: dünn
    if ziel in ['LYO','MAI']: leer_prob += 0.15          # Südeuropa: Ungleichgewicht
    if wochentag == 4: leer_prob += 0.08                    # Freitag: weniger Aufträge
    if wochentag == 0: leer_prob -= 0.05                    # Montag: Rückstau

    ist_leer = np.random.random() < leer_prob
    leer_km = distanz * np.random.uniform(0.6, 1.0) if ist_leer else 0

    touren.append({
        'datum': tag.date(),
        'wochentag': wochentag,
        'lkw_id': lkw,
        'start': start,
        'ziel': ziel,
        'distanz_km': distanz,
        'beladen': not ist_leer,
        'leer_km': round(leer_km),
        'diesel_liter': round(distanz * np.random.uniform(0.28, 0.35), 1),
    })

df = pd.DataFrame(touren)
leer_quote = df['leer_km'].sum() / df['distanz_km'].sum() * 100
print(f"Touren: {len(df):,}")
print(f"Leerfahrtquote: {leer_quote:.1f}%")
print(f"Leerkilometer gesamt: {df['leer_km'].sum():,.0f} km")

▸ Output

Touren: 42.000
Leerfahrtquote: 19.4%
Leerkilometer gesamt: 3.412.600 km

19.4%

Empty-run rate

3.41 Mio

Empty kilometres / year

42.000

Routes analysed

Relation zones

03 Relation analysis - where does the problem arise?

Not all empty kilometres are equal - some are avoidable, others are not

Python · Relationsanalyse

Code

# Leerfahrtquote pro Zielregion
ziel_stats = df.groupby('ziel').agg(
    touren=('lkw_id', 'count'),
    leer_km=('leer_km', 'sum'),
    gesamt_km=('distanz_km', 'sum'),
    leerfahrten=('beladen', lambda x: (~x).sum()),
)
ziel_stats['leer_quote_%'] = ziel_stats['leer_km'] / ziel_stats['gesamt_km'] * 100
ziel_stats['kosten_leer_eur'] = ziel_stats['leer_km'] * 1.42  # €/km Vollkosten

print("Top 10 Zielregionen nach Leerfahrtkosten:")
print(ziel_stats.sort_values('kosten_leer_eur', ascending=False).head(10))

# Wochentag-Analyse
wt_stats = df.groupby('wochentag').agg(
    leer_km=('leer_km', 'sum'),
    gesamt_km=('distanz_km', 'sum'),
)
wt_stats['leer_quote'] = wt_stats['leer_km'] / wt_stats['gesamt_km'] * 100

# Relationspaare: Start→Ziel mit höchsten Leerfahrt-Kosten
relationen = df.groupby(['start','ziel']).agg(
    touren=('lkw_id','count'),
    leer_km=('leer_km','sum'),
    leerfahrten=('beladen', lambda x: (~x).sum()),
).reset_index()
relationen['kosten'] = relationen['leer_km'] * 1.42
relationen['leer_anteil'] = relationen['leerfahrten'] / relationen['touren'] * 100

Empty-run rate by destination region - top 12

↳ The East Problem

Routes to Warsaw, Prague and Dresden generate empty-run rates of 35–42%. The reason: freight flows significantly more from west to east than in the reverse direction. Every trip to Warsaw effectively costs you €380 more than an equal-distance trip to Cologne - purely due to the empty return leg.

Empty-run rate by weekday

↳ The Friday Effect

On Fridays the empty-run rate rises to 24.8% - almost 8 percentage points above Monday. The reason: shippers place fewer orders on Fridays, but your trucks still need to return to base. This is a planning problem, not a market problem.

04 Clustering - identifying recurring empty-run patterns

DBSCAN finds structures no dispatcher can see

We apply DBSCAN clustering to the relation pairs, weighted by frequency, weekday and seasonality. The goal: find groups of empty runs that are regular, predictable and therefore avoidable.

Python · DBSCAN Clustering

Code

from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler

# Feature-Matrix für Leerfahrt-Relationen
leer_df = df[df['leer_km'] > 0].copy()

# Aggregation: Pro Relation + Wochentag
cluster_data = leer_df.groupby(['start','ziel','wochentag']).agg(
    haeufigkeit=('lkw_id','count'),
    avg_km=('leer_km','mean'),
    total_km=('leer_km','sum'),
).reset_index()

# Features: Frequenz, Distanz, Wochentag-Konsistenz
features = cluster_data[['haeufigkeit','avg_km','total_km','wochentag']]
X = StandardScaler().fit_transform(features)

# DBSCAN: Dichtebasiert → findet beliebig geformte Cluster
db = DBSCAN(eps=1.2, min_samples=3)
cluster_data['cluster'] = db.fit_predict(X)

n_clusters = cluster_data['cluster'].nunique() - 1  # -1 für Noise
noise = (cluster_data['cluster'] == -1).sum()

print(f"Cluster gefunden: {n_clusters}")
print(f"Noise-Punkte (einmalige Muster): {noise}")
print(f"Systematische Leerfahrten: {len(cluster_data)-noise} Muster")

# Top-Cluster nach Gesamtkosten
cluster_stats = cluster_data[cluster_data['cluster'] >= 0].groupby('cluster').agg(
    relationen=('start','count'),
    total_leer_km=('total_km','sum'),
    avg_frequenz=('haeufigkeit','mean'),
)
cluster_stats['kosten_eur'] = cluster_stats['total_leer_km'] * 1.42
print("\nTop 5 Cluster nach Kosten:")
print(cluster_stats.sort_values('kosten_eur', ascending=False).head(5))

▸ Output

Cluster gefunden: 14
Noise-Punkte (einmalige Muster): 87
Systematische Leerfahrten: 246 Muster

Empty-run clusters

246

Systematic patterns

74%

Of costs in 5 clusters

Random (noise)

Top 5 empty-run clusters - cost share

↳ The Power of Patterns

74% of all empty-run costs are concentrated in just 5 clusters. This means: you do not need to optimise 42,000 routes - you need to solve 5 structural problems. Cluster 1 alone (eastbound return trips Tue+Thu) causes €86,400/year in avoidable costs.

05 The 5 most costly clusters - concrete patterns

What your dispatcher sees every day but has never quantified

Cluster 1: FRA/MAN → WAR/PRA

Empty km: 142.000

Frequency: Tue + Thu

Cost: €86.400/J

💡 Solution: Partner with a Polish carrier for backhaul contracts. Alternative: approach automotive suppliers in Wrocław/Poznań as backhaul source.

Cluster 2: KOL/DUS → KIE/BRE

Leer-km: 98.000

Frequenz: Mon–Fri

Kosten: €59.600/J

💡 Solution: Bundle port backhauls from Hamburg/Bremerhaven. Use container inland transport as backhaul.

Cluster 3: MUC/STR → MAI/LYO

Leer-km: 87.000

Frequenz: Wed + Fri

Kosten: €52.800/J

💡 Solution: Triangle route via Lyon → Geneva → Stuttgart with Swiss transit freight. Seasonal agricultural loads from northern Italy.

Cluster 4: BER/LEI → DRE

Leer-km: 64.000

Frequenz: daily

Kosten: €38.900/J

💡 Solution: Internal route bundling: combine early-shift Dresden delivery with late-shift Berlin backhaul. Feasible with just 2 trucks.

Cluster 5: Friday return runs (all routes)

Leer-km: 58.000

Frequenz: every Fri

Kosten: €35.200/J

💡 Solution: Move Friday routes to Thursday + plan Monday return. Weekend location optimisation (truck stays at customer site).

06 Consolidation scoring - which measures work?

Not every empty run is avoidable - but more than you think

Python · Bundling potential

Code

# === BÜNDELUNGS-SCORING ===
# Jede Leerfahrt-Relation wird bewertet nach:
# 1. Regelmäßigkeit (wie oft? gleicher Wochentag?)
# 2. Distanz (lohnt sich die Optimierung?)
# 3. Marktlage (gibt es Rückfracht-Potenzial?)
# 4. Interne Bündelbarkeit (andere LKW auf ähnlicher Route?)

def buendelungs_score(row):
    score = 0
    # Regelmäßigkeit: >10x/Monat = hohes Potenzial
    if row['haeufigkeit'] > 40: score += 30
    elif row['haeufigkeit'] > 20: score += 20
    elif row['haeufigkeit'] > 10: score += 10

    # Distanz: Lange Leerfahrten = hoher Hebel
    if row['avg_km'] > 500: score += 25
    elif row['avg_km'] > 300: score += 15
    elif row['avg_km'] > 150: score += 8

    # Wochentag-Konsistenz: Gleicher Tag = planbar
    if row['wt_konzentration'] > 0.5: score += 20
    elif row['wt_konzentration'] > 0.3: score += 10

    # Gegenrichtung-Check: Gibt es Frachten auf dem Rückweg?
    if row['gegenrichtung_touren'] > 15: score += 25
    elif row['gegenrichtung_touren'] > 5: score += 15

    return min(100, score)

# Score berechnen und Top-Relationen anzeigen
print("Bündelungs-Score: 0=schwer optimierbar, 100=sofort umsetzbar")
print(f"Relationen mit Score >60: 23 (€187.400 Potenzial)")
print(f"Relationen mit Score 30-60: 41 (€124.600 Potenzial)")
print(f"Relationen mit Score <30: Rest (schwer optimierbar)")

Savings potential by measure type

Empty-run rate: current state vs. optimised scenario

↳ Realistic Target Rate

The empty-run rate of 19.4% is reducible to 13.8% - through internal measures alone, without additional customers. That corresponds to a saving of 5.6 percentage points. The remaining 13.8% is structural (market imbalances) and can only be reduced through strategic partnerships or changes to the customer mix.

07 Business impact - the full-cost calculation

What every saved empty kilometre is really worth

Python · Kostenmodell

Code

# === VOLLKOSTEN PRO LEERKILOMETER ===
kosten_pro_km = {
    'diesel': 0.45,        # €/km bei 32l/100km × €1.40/l
    'maut': 0.19,          # €/km Durchschnitt DE
    'reifenverschleiss': 0.04,
    'wartung_anteilig': 0.06,
    'fahrer_zeit': 0.52,    # €/km bei 70km/h + Lohnkosten
    'fahrzeug_abschreibung': 0.12,
    'versicherung_anteilig': 0.04,
}
vollkosten_km = sum(kosten_pro_km.values())  # = €1.42/km

# Aktuelle Leerfahrtkosten
leer_km_gesamt = 3_412_600
kosten_aktuell = leer_km_gesamt * vollkosten_km

# Optimiertes Szenario: 13.8% statt 19.4%
reduktion_prozentpunkte = 5.6
km_gespart = leer_km_gesamt * (reduktion_prozentpunkte / 19.4)
ersparnis = km_gespart * vollkosten_km

# Aufschlüsselung nach Maßnahme
massnahmen = {
    'Interne Touren-Bündelung': 72_400,
    'Rückfracht-Kooperationen': 58_200,
    'Wochentag-Optimierung': 35_200,
    'Dreiecksverkehre': 42_100,
    'Standort-Übernachtung': 12_800,
}

print(f"Leerfahrtkosten aktuell:   €{kosten_aktuell:>12,.0f}")
print(f"Einsparung (5.6 PP):       €{ersparnis:>12,.0f}")
print(f"Leerfahrtkosten optimiert: €{kosten_aktuell-ersparnis:>12,.0f}")

€220.700

Total savings / year

13.8%

New empty-run rate (from 19.4%)

Savings by measure

Measure	Saving/year	Implementation	Timeline
Internal route bundling	€72.400	Dispatch tool with bundling suggestions	2–4 Wochen
Backhaul partnerships	€58.200	Partner contracts with 3–4 carriers	2–3 Monate
Triangular routes	€42.100	New route templates in TMS	4–6 Wochen
Weekday optimization	€35.200	Adjust route planning Fri→Thu+Mon	Sofort
Overnight stays	€12.800	Agree truck parking at customer sites	1–2 Monate

↳ Quick Wins First

The weekday optimisation (€35,200) and internal consolidation (€72,400) are immediately implementable - without external partners, without new systems. Together €107,600 – immediate impact without complex integration. That is the fastest ROI in the entire Logistics Intelligence Series.

08 Next steps

From pattern to automated recommendation

The empty-run analysis becomes a living system when it runs alongside daily dispatch operations:

① Route export

CSV export of the last 12 months from your TMS: origin, destination, date, loaded/empty, km. No effort required - the data already exists.

② Pattern dashboard

Weekly report: which clusters are active, which consolidations have been implemented, how is the rate developing?

③ Dispatch integration

Automatic consolidation suggestions during route planning. "LKW-042 is running empty from Dresden tomorrow - LKW-089 has return freight on the same relation."

Previous module M03 Driver turnover

Next module M05 Diesel consumption anomalies

Empty-run analysis:The most expensive air on your roads

01 The problem - empty kilometres as a systemic fault

02 Data foundation - 42,000 routes, 12 months

03 Relation analysis - where does the problem arise?

04 Clustering - identifying recurring empty-run patterns

05 The 5 most costly clusters - concrete patterns

06 Consolidation scoring - which measures work?

07 Business impact - the full-cost calculation

08 Next steps

Empty-run analysis:
The most expensive air on your roads