# VynFi Supply Chain OCEL (vynfi-supply-chain-ocel) — HF dataset
# regeneration recipe.
#
# Native OCEL 2.0 event log from a manufacturing supply chain.
# Process types covered: P2P, O2C, manufacturing operations.  Event
# log is the primary artefact, with object-level data
# (orders / invoices / shipments / receipts) and anomaly labels
# alongside.
#
# Volumes (post-v5.9.0 refresh): targeting ~100k events for
# usability — the v0.x dataset shipped 20 010 events which was
# quick to prototype against but tight for any serious process
# mining.  Scaling: 5 companies × 12 months × ~120 chains for
# each of P2P and O2C (≈14 k chains × ~7 events per chain) puts
# the event log in the 90 k–110 k range, with proportional
# growth in objects and anomaly labels.  Per-company JE volume
# kept at `ten_k` to bound the in-memory orchestrator footprint
# during OCPM event-log construction (`hundred_k` × 5 × 12
# producing ~700 k JEs alongside ~14 k chains pushed peak heap
# past 8 GB during native-OCEL graph assembly).
#
# Reproducibility:
# - global.seed pinned;
# - 3 % document-level fraud injection (matches the published
#   3 % rate);
# - ChaCha8 PRNG output is platform-stable.

global:
  industry: manufacturing
  start_date: "2024-01-01"
  period_months: 12
  seed: 20260509
  group_currency: USD
  parallel: true
  worker_threads: 4
  memory_limit_mb: 8192

# Five subsidiaries: one HQ, three plants, one international
# entity.  Big enough to exercise multi-company joins on objects;
# small enough to keep event counts focused on supply-chain
# behaviour rather than entity proliferation.
companies:
  - { code: "1000", name: "VynFi Mfg HQ",       currency: USD, country: US, annual_transaction_volume: ten_k, volume_weight: 0.30, fiscal_year_variant: K4 }
  - { code: "1100", name: "VynFi Mfg Plant 1",  currency: USD, country: US, annual_transaction_volume: ten_k, volume_weight: 0.22, fiscal_year_variant: K4 }
  - { code: "1200", name: "VynFi Mfg Plant 2",  currency: USD, country: US, annual_transaction_volume: ten_k, volume_weight: 0.18, fiscal_year_variant: K4 }
  - { code: "2000", name: "VynFi Mfg EU GmbH",  currency: EUR, country: DE, annual_transaction_volume: ten_k, volume_weight: 0.18, fiscal_year_variant: K4 }
  - { code: "3000", name: "VynFi Mfg APAC SG",  currency: SGD, country: SG, annual_transaction_volume: ten_k, volume_weight: 0.12, fiscal_year_variant: K4 }

chart_of_accounts:
  complexity: medium
  industry_specific: true

master_data:
  vendors:    { count: 100, intercompany_percent: 0.10 }
  customers:  { count: 120, intercompany_percent: 0.05 }
  materials:  { count: 400, bom_enabled: true, average_bom_depth: 3 }
  fixed_assets: { count: 50 }
  employees:  { count: 100 }

# Chain volume is the primary driver of event count — each
# P2P / O2C chain produces ~6-8 OCEL events (PO created →
# released → GR posted → invoice received → invoice posted →
# payment posted → payment cleared).  14 k chains × ~7 events
# ≈ 100 k events.
document_flows:
  p2p:
    enabled: true
    chains_per_month: 360
    three_way_match_rate: 0.92
    partial_delivery_rate: 0.12
    gr_ir_clearing_enabled: true
  o2c:
    enabled: true
    chains_per_month: 360
    credit_check_failure_rate: 0.04
    partial_shipment_rate: 0.10
    return_rate: 0.025

balance:
  generate_opening_balances: true
  generate_trial_balances: true
  reconcile_subledgers: true
  validate_balance_equation: true

period_close:
  enabled: true

# 3 % document-level fraud (matches the published rate of the
# previous dataset version).  Anomaly labels are produced for
# every fraudulent + selected non-fraudulent events in the
# OCEL log, joinable to events via `event_id`.
fraud:
  enabled: true
  fraud_rate: 0.02
  document_fraud_rate: 0.03
  propagate_to_lines: true

internal_controls:
  enabled: true
  coso_enabled: true
  include_entity_level_controls: true
  target_maturity_level: managed
  exception_rate: 0.02
  sod_violation_rate: 0.01

distributions:
  enabled: true
  industry_profile: manufacturing
  amounts:
    enabled: true
    distribution_type: log_normal
    components:
      - { weight: 0.65, mu: 5.5, sigma: 1.3, label: "routine" }
      - { weight: 0.28, mu: 7.8, sigma: 0.9, label: "significant" }
      - { weight: 0.07, mu: 9.5, sigma: 0.6, label: "major" }
    benford_compliance: true

temporal_patterns:
  enabled: true
  business_days:
    enabled: true
    half_day_policy: half_day
    month_end_convention: modified_following
  calendars:
    regions: [US, DE, SG]
  period_end:
    model: exponential
    month_end:   { start_day: -10, base_multiplier: 1.0, peak_multiplier: 3.5, decay_rate: 0.3 }
    quarter_end: { inherit_from: month_end, additional_multiplier: 1.5 }

# OCPM event log: native OCEL 2.0 generation with all relevant
# output formats.  `compute_variants: true` plus
# `include_object_relationships: true` mirror the v0.x dataset's
# `events`, `objects`, `anomaly_labels`, `document_events` tables.
ocpm:
  enabled: true
  generate_lifecycle_events: true
  include_object_relationships: true
  compute_variants: true
  max_variants: 0  # unlimited
  output:
    ocel_json: true
    flattened_csv: true
    event_object_csv: true
    object_relationship_csv: true
    variants_csv: true

audit:
  enabled: false  # Audit data has its own dataset

# Method-A flat edge-list provided for completeness — joinable to
# the OCEL events via shared document_id + entry_date.
graph_export:
  je_network:
    method: a

output:
  output_directory: "./output"
  formats: [csv, json]