A ZeroKey time and motion study uses Quantum RTLS to automatically measure how work moves through a process. 

Instead of relying on stopwatches, manual observations, or self-reported time, the system captures real-time movement, location, and task context so teams can understand where time is spent, where delays occur, and how work compares to the standard. 

Traditional time studies capture a small sample of observed work. ZeroKey can capture continuous time and movement data across real production conditions. 

That means lean teams can move from “what did we see during the study?” to “what happens every shift, every cycle, and every day?” It also reduces observer bias and gives teams a more reliable baseline for kaizen, labor planning, and standard work improvement. 

ZeroKey can support simple workstation studies, complex human-driven workflows, mobile work, and non-linear processes. 

Examples include: 

• Time at station 

• Step and cycle time 

• Walk time and search time 

• Waiting and idle time 

• Material handling time 

• Tool or equipment interaction time 

• Time spent near a product, cast, cart, pallet, fixture, or work zone 

• Handoffs between people, areas, or process stages

   

This flexibility matters when the work does not happen neatly at one bench or follow a fixed sequence. 

No. ZeroKey can be useful whether the process is paper-based, Excel-based, manually observed, partially digital, or already connected to MES / ERP systems. 

Teams can start by measuring movement and proximity, then add zones, workflows, operator prompts, integrations, or OmniVisor AI as the project matures. 

Tasks can be identified in several ways depending on the process. 

For structured work, tasks can be tied to workstation zones, digital work instructions, step triggers, tool use, or system events. For less structured work, tasks can be inferred from location, movement, dwell time, proximity to a product or asset, and other context. 

For example, in a cement manufacturing process, worker proximity to a specific cast can be used to automatically bin time against that cast, without requiring the worker to manually log each activity. 

Yes. This is one of the strongest use cases for Quantum RTLS. 

Many high-value time and motion problems involve workers moving between stations, materials, vehicles, parts, tools, and equipment. Because Quantum RTLS tracks position in 3D, studies can be built around movement paths, zones, proximity, dwell time, and interactions. 

OmniVisor AI™ turns the movement data from Quantum RTLS into operational insights that are easier to use. 

Teams can automate reporting and ask questions such as where time is being lost, which steps are varying by shift, why a process is missing takt, or which areas have the most waiting or rework. OmniVisor AI can help surface bottlenecks, explain variance, and recommend actions without forcing teams to manually dig through raw data. 

Personnel tracking can be configured to support process improvement without becoming individual surveillance. 

Data can be aggregated, anonymized, grouped by role, shift, workstation, process step, or area, and used for kaizen, staffing models, standard work, and process design. Tracking also only occurs inside the Quantum RTLS system coverage area, and sensitive areas such as bathrooms, locker rooms, or break areas are completely excluded from coverage and reporting. 

Most teams start with one process, station, cell, route, or work area where there is a clear operational question. 

Examples include: Why is cycle time varying? Where is labor being lost? How much time is spent searching or walking? Where do handoffs stall? How much time is spent at each cast, order, cart, or work zone? 

A Starter Kit can be used to validate the first study area, collect baseline data, and prove whether the method captures the process accurately enough to scale.