CPLOM.ai
Cross-Layer Predictive Logistics Optimization Model
Technical Publication

Cross-Layer Predictive Logistics Optimization Model (CPLOM)

A technical white paper describing a cross-layer, real-time predictive control architecture for multi-warehouse logistics operations, combining multi-horizon forecasting, quantitative time-series signals, and hybrid neural–deterministic verification.

Author: Dmitry Chistyakov
Role: Chief Technology Officer · Enterprise IT Architect
Version: 1.0
Publication date: 2025-12-13
Real-time logistics Cross-layer orchestration Multi-horizon forecasting Quantitative signals Hybrid verification

Executive Abstract

CPLOM is a predictive management architecture for multi-warehouse logistics operations under volatile demand and tightly coupled constraints across routing, workforce, facilities, and compute infrastructure. CPLOM treats the logistics network as a nonlinear dynamic system and implements a closed-loop control cycle: data → indices → forecast → quantitative signals → hybrid verification → action → updated system state.

The architecture integrates multi-horizon forecasting with quantitative time-series filters (e.g., MA/EMA, Bollinger Bands, momentum, volatility analysis) applied to normalized operational indices, and introduces a hybrid verification meta-layer based on a Confidence Index (CI) to mitigate stochastic variability of neural outputs in high-stakes decisions.

The paper presents a high-level technical overview intended for professional audiences and avoids proprietary implementation details.

Suggested citation
Chistyakov, D. (2025). Cross-Layer Predictive Logistics Optimization Model (CPLOM). Technical White Paper (v1.0). CPLOM.ai.
Archive Version: SSRN Working Paper No. 6279198
Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6279198

Architecture (High-Level)

  • Data Acquisition Layer: event-driven ingestion (orders, telematics, WMS/TMS, external signals, infra telemetry)
  • Real-Time Processing Layer: normalization and computation of operational indices (time-series ready)
  • Predictive Modeling Layer: continuity constraints and pattern-based multi-horizon forecasts
  • Quantitative Control Layer: MA/EMA, bands, momentum/volatility signals as control triggers
  • Hybrid Verification Meta-Layer: multi-pass inference, CI, deterministic checks and fallbacks
  • Orchestration: capacity profiles and controlled scaling back to baseline

Implementation Footprint

Production deployments include multiple operating models and environments.

  • Rx2Go.ai — integrated logistics platform (flagship implementation)
  • Rx4Route.com — platform-as-a-service adaptation (external fleets)
  • Sattera.com — lightweight deployment for smaller courier operators
  • International rollout — market expansion deployment

Details are summarized at an architectural level; commercial terms and sensitive operational data are omitted.

Publications

  • 2026-__-__ — Article title (Publisher) — link
  • 2026-__-__ — Article title (Publisher) — link

Author

Dmitry Chistyakov

Chief Technology Officer · Enterprise IT Architect
Focus: large-scale distributed systems, real-time optimization, predictive control, cloud-native orchestration.

Homepage: cplom.ai  ·  LinkedIn: Dmitry Chistyakov

Disclaimer

© 2026 Dmitry Chistyakov. All rights reserved.
CPLOM is an architectural methodology authored by the above-named author. This publication describes high-level concepts and does not disclose proprietary implementation details, confidential customer data, or sensitive operational information.