BhinnekaAI/ESCL-Logistics-Audit

Overview

ESCL-Logistics-Audit evaluates methods that identify logistics discrepancies, inventory mismatches, and cost inflation in corporate supply chain records.

Dataset information

Property	Description
Dataset name	ESCL-Logistics-Audit
Full name	Enterprise Supply Chain Ledger Discrepancy
Publisher	BhinnekaAI
Dataset Creator	Christopher Alberto Hamonangan
Domain	Logistics, inventory, and supply chain auditing
Data modality	Structured numerical tabular data
Feature count	5 numerical features
Data type	Synthetic
Task	Supply chain audit anomaly detection
Expected anomaly proportion	Approximately 7.0%
Label convention	0 = normal ledger record; 1 = audit discrepancy

Dataset Structure

random-seed

• Directory: random-seed
• Files: 5 files, 5,000 observations each.

seed42

• Directory: seed42
• Files: 5 files with sizes 100, 1,000, 10,000, 50,000, and 100,000.

Feature Specification

Feature	Description	Data Type
delivery_days	Transit duration in days	Discrete (Poisson)
stock_discrepancy	Physical inventory minus recorded inventory	Continuous
logistic_cost	Total financial shipping cost	Continuous
package_weight_kg	Measured package weight in kilograms	Continuous (Exponential)
return_rate	Customer product return ratio	Continuous (Beta)

Mathematical Formulation

Normal logistics costs scale proportionally with delivery duration:

logistic_cost = 10 * delivery_days + 20 + N(0, 5)

Anomalies disrupt this cost-to-duration ratio or introduce severe inventory discrepancies.

Loading the Dataset

import pandas as pd

df = pd.read_csv("seed42/dataset_10000_rows_seed42.csv")
X = df.iloc[:, :-1].values
y = df['is_audit_discrepancy'].values