Making facility operations autonomous through technology
GWES is a Logistics OS (operating system) that centrally handles the capture, analysis, decision-making, and instruction required to run an entire logistics facility. Where a WMS is an execution-layer system that controls individual task instructions, GWES functions as the management and decision-making layer that takes a facility-wide view and drives optimization and autonomy.
Related guides: What Is a WES? | What Is a Logistics OS? | WMS vs WES vs WCS
The logistics industry stands at a historic turning point. The growth of e-commerce is driving greater product variety, smaller lot sizes, and shorter lead times; labor shortages and an aging workforce are becoming acute; global supply chains are growing more complex; and shippers are demanding higher standards of quality and visibility — all at once.
These challenges cannot be met by operations that rely on the experience and intuition of the front line. A systematic approach to improvement, grounded in data and technology, is essential.
Just as an operating system on a PC manages hardware and applications as a unified whole, GWES manages every resource in a logistics facility — people, equipment, inventory, and space — in an integrated way and sustains the optimal operating state. It integrates with existing systems such as WMS, ERP, and material-handling equipment while taking responsibility for the decision-making and management of facility-wide operations.
With these technologies, the way a logistics facility is run changes as follows.
A smartphone OS such as iOS or Android sits above the SoC and sensor array and provides AI frameworks as an integrated whole. In-vehicle operating systems for connected cars (QNX / Automotive Grade Linux) likewise manage the sensor fusion of LiDAR, cameras, and other inputs together with autonomous-driving algorithms.
GWES follows the same design philosophy. As an OS that sits above a facility’s WMS, material-handling equipment, and IoT sensors, it brings intelligence to facility operations through four algorithms.
GWES develops and integrates the following four algorithms to automate the decision-making, forecasting, and optimization involved in running a logistics facility. Each algorithm works on its own, but they also reinforce one another to bring intelligence to facility operations as a whole.
For combinatorial optimization problems such as staffing, task assignment, and travel-path design, GWES applies mixed-integer programming (MIP) and constraint programming. It rapidly derives feasible solutions that maximize KPIs while satisfying large numbers of constraints simultaneously.
Large language models generate operational reports automatically, infer root causes and propose countermeasures when anomalies are detected, and extract knowledge from the knowledge base — turning unstructured data into practical insight.
Supervised learning with gradient boosting (XGBoost / LightGBM) and neural networks delivers high-accuracy prediction of task times, quality assessment, and early detection of equipment degradation.
Seasonal decomposition (STL) and state-space models are applied to time series such as outbound volume, inbound volume, and workload. Short- to medium-term demand forecasting and anomaly detection enable forward-looking resource allocation.
GWES comprises twelve modules across three layers — infrastructure, visualization (L2), and optimization (L4). Each module performs its primary function at a particular level while being designed to work across levels. In particular, WF (Workload Forecasting) plays a different role at each stage from L2 through L5, and RA (Resource Allocation) becomes the agent that autonomously executes at L5 the decision logic defined at L4.
The order in which modules should be considered depends on a facility’s challenges and operating maturity. Checking your current state with a quick assessment — before or after reading the module descriptions — makes it easier to identify the implementation path closest to your own situation.
The data collection, integration, transformation, and processing foundation used by every GWES module. It operates in common across all levels.
Modules that deliver their primary functions at Level 2 (visualization and analysis). WF in particular plays a central role spanning L2, L4, and L5.
At L4, the ideal decision logic is defined; at L5, the system executes that logic autonomously. The move from L4 to L5 is the shift from “people choosing the optimal solution” to “the system executing the optimal solution automatically.”
GWES is implemented in four phases. Because each phase confirms results before moving to the next, investment risk is minimized. ROI is typically recovered within 12 to 18 months.
Where does your logistics facility stand today, and what should come next?
Start with a current-state assessment.