Isomorph2026/isomorph-supply-chain-sample

ISOMORPH Sample

A single-rollout subset of the ISOMORPH release, intended for users who want to inspect file formats and run loaders before pulling the full dataset. We release multivariate logistics time-series data generated by a digital-twin (DT) simulator of a multi-echelon supply chain with an explicit routing network, with the underlying dynamics formulated as a Markov chain.

Overview

The simulator advances a directed routing graph of factories, intermediate warehouses, and a customer-facing destination forward in discrete time. At each step, random Poisson customer demand arrives at the destination, is served from on-hand stock, and triggers replenishment along the network. The network is a directed graph $G=(\mathcal{N},\mathcal{E})$ with three node roles (factories, warehouses, destination), integer per-edge transit times $\tau_e$, and per-step volume capacities $K_e V_e$; Dijkstra's algorithm routes every shipment. The dynamics form a Markov chain $\xi_{t+1} = \Psi(\xi_t, y_t, L_t)$, where $\Psi$ is a deterministic transition map and $(y_t, L_t)$ are the only random inputs (Poisson customer demand and Gaussian source lead times). The demand intensity is a five-component sum: yearly seasonality, weekly seasonality, AR(1) drift, per-item bursts, and a shared macro shock that lifts every item's intensity simultaneously. The state vector $\xi_t$ records on-hand stock, backlog, outstanding orders, in-transit shipments, and a smoothed demand estimate at every location for every item. The released rollout runs on a 13-node US topology (3 sources, 9 warehouses across 5 tiers, NYC destination).

Contents

Item	Notes
manifest.csv	One row, the baseline output_item50 rollout
output_item50/	Rollout directory; see File schema below

Rollout summary

• Family: baseline
• Catalogue size C = 50 items
• Horizon T = 52,560 days
• Seed: 2025
• All knobs at simulator defaults; see manifest.csv for exact values
• Approximate on-disk size: ~1.9 GB

Manifest schema

manifest.csv has one row per rollout (just one in this sample) and the following columns:

Column	Meaning
rollout	Rollout name (matches the directory under this variant)
family	One of baseline, mixture, uq
scenario	Scenario label within the family
n_items	Catalogue size C (50 or 200)
seed	RNG seed (2025 here)
horizon	Length T in days (52,560 here)
path	Relative path to the rollout directory inside this variant
phi_lo, phi_hi	AR(1) coefficient bounds for the demand process
shock_count_scale	Multiplier on per-day shock count
shock_height_scale	Multiplier on shock magnitude
burst_rate_scale	Multiplier on burst arrival rate
burst_height_scale	Multiplier on burst magnitude
containers_scale	Multiplier on edge container counts (capacity)
ss_scale	Multiplier on safety-stock thresholds
leadtime_scale	Multiplier on edge lead times

Three additional simulator knobs are held fixed and therefore omitted from the manifest: seasonal_scale = 1.0, base_lambda_lo = 80.0, base_lambda_hi = 250.0. The exact values are still recorded in output_item50/scenario.json.

File schema

Directory layout:

isomorph_sample/
├── manifest.csv
├── README.md
├── LICENSE
└── output_item50/
    ├── daily_records.parquet
    ├── shipments.parquet
    ├── service_summary.parquet
    ├── inventory_history.parquet
    ├── backlog_history.parquet
    ├── intransit_history.parquet
    ├── demand_signals.npy
    ├── demand_signals_cols.txt
    ├── scenario.json
    ├── edge_list.parquet
    ├── edge_utilisation.npy
    └── edge_saturation.npy

T is the run horizon (52,560 for the released rollout), C is the catalogue size (50), and E is the number of edges in the network.

File	Shape / format	Contents
daily_records.parquet	rows: T x C	Per-day, per-item demand and service: day, item, demand, served_from_stock, new_backlog_today, dest_on_hand_end_before_ship, dest_backlog_end_before_ship
shipments.parquet	rows: variable	One row per shipment: day, arrival_day, from, to, item, units, path_nodes, edge_times
service_summary.parquet	rows: C	Per-item totals: total_demand, served_from_stock, new_backlog_added, fill_rate_stock_only
inventory_history.parquet	rows: T	On-hand inventory over time
backlog_history.parquet	rows: T	Backlog over time
intransit_history.parquet	rows: T	In-transit units over time
demand_signals.npy	array: T x C, float	Item-level demand series (same content as the demand column of daily_records.csv, dense form)
demand_signals_cols.txt	text	Item IDs in column order of demand_signals.npy
scenario.json	JSON	Exact CLI knobs used to produce the rollout
edge_list.parquet	rows: E	Edge metadata: edge_id, from, to, travel_time_days, container_volume, num_containers, cap_per_day
edge_utilisation.npy	array: T x E, float	Per-edge fractional utilisation
edge_saturation.npy	array: T x E, float	Per-edge fractional saturation (cap-relative throughput)

Quick load

import pandas as pd
import numpy as np

manifest = pd.read_csv("manifest.csv")
row = manifest.iloc[0]

X = np.load(f"{row.path}/demand_signals.npy")          # T x C
cols = open(f"{row.path}/demand_signals_cols.txt").read().strip().split(",")
records = pd.read_parquet(f"{row.path}/daily_records.parquet")

Next step

For the full benchmark (49 rollouts including mixture sweeps and 20 LHS UQ perturbations), see ../isomorph_full/.

Licence

CC-BY-4.0 (see LICENSE).