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A Logistics OS for Integrated Facility Operations

Autonomizing Facility Operations with Technology

GWES is a Logistics OS that unifies the information collection, analysis, decision-making, and instructions needed to operate an entire logistics facility. While a WMS controls individual work instructions as an execution system, GWES oversees the whole facility and functions as a management and decision-making system that drives optimization and autonomy.

GWES Login Screen
Logistics OS

Why a Logistics OS?

The logistics industry is at a historic inflection point. We face simultaneous pressures: explosive e-commerce growth driving greater product variety with shorter lead times, severe labor shortages and aging workforce, increasing complexity of global supply chains, and escalating shipper demands for quality and visibility.

Traditional operations relying on experience and intuition cannot address these challenges. Systematic improvement approaches rooted in data and technology are essential.

GWES functions as a unified management platform for all warehouse resources—people, equipment, inventory, and space—much as an operating system integrates hardware and applications in a computer. It manages and coordinates entire facility operations while integrating with existing systems like WMS, ERP, and material handling equipment.


Core Values

Four Core Values of GWES

Workload Forecasting
AI-powered predictions of inbound/outbound volumes and task demands based on historical data. Dramatically improves staffing accuracy and capacity planning.
Real-Time Visibility
Unified visualization of task progress, inventory status, and workload across all areas. Enables cross-facility comparison and monitoring.
Optimization
Data-driven, holistic optimization of inventory placement, material flow, consolidation decisions, dispatch timing, and staffing allocation.
Scalability
Unified platform enables rapid, cost-effective expansion to new facilities and business models.
Transformation

The GWES Transformation

GWES technology transforms logistics facility operations in the following ways:

Before ─ Traditional Operations
Decision Basis Relies on experience, intuition, and individual judgment
Management Scope Single-facility, process-specific optimization
Improvement Cycle Monthly or quarterly batch analysis
Scalability Custom builds per facility; difficult to scale
→
After ─ With GWES
Decision Basis Data-driven, logic-based, quantitatively justified decisions
Management Scope Multi-facility, holistic facility-wide optimization
Improvement Cycle Real-time visibility and continuous improvement
Scalability Common platform enables rapid deployment
Technology Platform

Technology & Platform Architecture

Smartphone OSs (iOS/Android) position themselves above hardware components and sensors, providing integrated AI frameworks. Similarly, automotive operating systems (QNX/Automotive Grade Linux) coordinate sensor fusion and autonomous driving algorithms.

GWES adopts this same architectural approach—positioning itself above warehouse WMS, material handling equipment, and IoT sensors to enable facility intelligence through four core algorithms.

Smartphone OS
Application
Social media, Camera, Maps, Payments
AI / Framework
Core ML, Neural Engine, Face recognition, Voice recognition
OS Core
iOS / Android (Linux Kernel)
Hardware
SoC, Sensors, Communication modules
Connected Car OS
Application
Autonomous driving, Predictive maintenance, Digital cockpit
AI / Framework
Sensor fusion, Path planning, Object detection
OS Core
QNX / Automotive Grade Linux
Hardware
ECU, LiDAR, Camera, V2X communication
GWES — Logistics OS
Application
Staff allocation optimization, Shipment forecasting, Quality management, Facility autonomy
AI / Algorithm
Mathematical Optimization LLM (Generative AI) Discriminative Regression Time Series Analysis
OS Core
GWES
Infrastructure
WMS, Material handling, IoT sensors, ERP
Four Core Algorithms Powering GWES

GWES integrates four proprietary algorithms to automate decision-making, forecasting, and optimization in warehouse operations. These algorithms work independently yet synergistically to enable facility-wide intelligence.

01
Mathematical Optimization
Combinatorial Optimization

Applies mixed-integer programming (MIP) and constraint programming to complex combinatorial problems like staffing allocation, task assignment, and workflow design. Rapidly identifies optimal solutions satisfying multiple constraints while maximizing KPIs.

02
LLM (Generative AI)
Large Language Models

Leverages LLMs to auto-generate operational reports, estimate root causes of anomalies, propose remediation, and extract insights from knowledge bases. Transforms unstructured data into actionable intelligence.

03
Discriminative Regression
Supervised Learning Models

Uses gradient boosting (XGBoost/LightGBM) and neural networks for supervised learning to accurately predict task durations, assess quality, and detect equipment degradation signals.

04
Time Series Analysis
Temporal Pattern Recognition

Applies seasonal decomposition (STL) and state-space models to shipment volumes, inbound flows, and workload patterns. Enables short-to-medium term forecasting and anomaly detection for forward-looking resource planning.

Architecture

3 Layers
12 Modules

GWES comprises 12 modules across three layers: Infrastructure, Visualization (L2), and Optimization (L4). Each module delivers primary functionality at a specific level while collaborating across layers. Notably, Workload Forecasting spans L2 to L5, while Resource Allocation at L4 defines decision logic that L5 autonomously executes.

INFRASTRUCTURE Infrastructure Layer

Data collection, integration, transformation, and processing foundation used by all modules across all levels.

DC
ALL LEVELS
Data Connector
Collects and normalizes data from equipment, WMS, ERP, and other external systems—the core data pipeline for GWES.
Learn more
BM
ALL LEVELS
Base Module
Transforms collected data into common master data and unified formats, enabling consistent analysis and cross-facility comparison.
Learn more
ME
ALL LEVELS
Map Editor
Manages processing pipelines for all modules, serving as the compute, aggregation, and analytics engine with orchestration of scheduling, retry, and load balancing.
Learn more
LEVEL 2 VISUALIZATION Visualization Layer

Modules delivering primary functions at the Visualization level. Workload Forecasting particularly spans L2, L4, and L5 as a core capability.

WF
L2 L4 L5
Workload Forecasting
Integrates inbound/outbound data, historical performance, and external factors to forecast future workload. Core module spanning L2-L5 across all operational levels.
Learn more
PA
L2
Progress Analyzer
Visualizes and analyzes actual progress against plan in real-time. At L4, supports dynamic plan adjustments based on progress data.
Learn more
WA
L2
Workload Analyzer
Structurally analyzes workload across processes and facilities. Visualizes load imbalances and bottlenecks to support resource reallocation and process improvement decisions.
Learn more
IA
L2
Inventory Analyzer
Multi-faceted analysis of warehouse inventory status. Visualizes aging, turnover, and placement efficiency, providing input to Storage Optimizer for placement decisions.
Learn more
LEVEL 4 OPTIMIZATION Optimization Layer

At L4, "ideal decision logic" is defined; at L5, the system autonomously executes it. The L4→L5 progression represents a shift from "humans selecting optimal solutions" to "system automatically executing optimal solutions."

SO
L4 L5
Storage Optimizer
Optimizes warehouse inventory placement considering shipment frequency, material flow efficiency, and picking speed. Calculates optimal storage locations and zoning based on Inventory Analyzer data.
Learn more
RO
L4 L5
Routing Optimizer
Optimizes picking routes, transport paths, and task sequences. Accounts for warehouse layout and order characteristics to calculate shortest, most efficient material flows.
Learn more
LO
L4 L5
Loading Optimizer
Optimizes consolidation and shipment planning. Considers package shapes, loading constraints, and delivery sequence to calculate optimal consolidation patterns.
Learn more
DO
L4 L5
Dispatch Optimizer
Optimizes shipment timing, carrier selection, and priority sequencing. Provides decision logic accounting for delivery windows, costs, and customer priority.
Learn more
RA
L4 L5
Resource Allocator
Optimizes allocation of people, equipment, and space. Uses Workload Forecasting and Progress Analyzer data to dynamically allocate resources according to demand.
Learn more
Implementation

Phased Implementation Approach

GWES deployment proceeds in four phases with measurable results at each stage, minimizing investment risk. ROI is typically achieved within 12–18 months.

PHASE 1
Diagnostic Assessment
Systematization
Systematic evaluation of current operations and maturity assessment of each functional area, followed by roadmap development.
PHASE 2
Visibility Foundation
Visibility
Deploy Workload Forecasting, Progress Analyzer, and Workload Analyzer to establish data foundation with workload prediction, progress visibility, and load analysis.
PHASE 3
Optimization Rollout
Optimization
Deploy Storage Optimizer, Routing Optimizer, and Resource Allocator to achieve optimization of inventory placement, material flow, and staffing. Quantitatively measure results.
PHASE 4
Full Deployment & Autonomy
Autonomy
Expand proven modules across all facilities. Progressively enable autonomous execution of optimization logic.
Movie

GWES Overview Video

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