Research & Operational Intelligence
Foysonis develops the measurement frameworks that define how warehouses understand, benchmark, and improve their decision-making capability.
We don't just build software. We study how warehouses actually work — and publish what we learn.
Where We're Focused
Decision Latency in Warehouse Execution
Most warehouses measure throughput, accuracy, and labor cost. Almost none measure how long it takes to turn awareness into action. We believe decision latency is the single most important unmeasured KPI in warehouse operations — and we've built the frameworks to quantify it.
AI Explainability for Operational Environments
AI in warehouses can't be a black box. Operators need to trust the recommendation, supervisors need to audit the logic, and executives need to prove ROI. Our research focuses on making every AI-generated decision transparent, auditable, and understandable by the person receiving it.
Human-AI Teaming in High-Tempo Logistics
Warehouses operate at a pace that leaves no room for ambiguity. Our research explores how AI-generated decisions integrate into the flow of human work — not as interruptions, but as natural extensions of operational judgment.
Proprietary Measurement Frameworks
These frameworks are developed from real operational data across warehouse environments. They're designed to give operations leaders quantifiable baselines for capabilities that have historically been unmeasurable.
Decision Latency Index™ (DLI)
Measures the elapsed time from risk formation to operator action across your operation. DLI captures the gap between when a condition deviates from plan and when someone acts on it — not when they see it, when they act.
How fast your warehouse converts awareness into action. High DLI means your team sees problems but doesn't act fast enough.
Timestamped events from WMS (condition detected) correlated with timestamped actions (task assigned, labor moved, wave adjusted).
Execution Friction Score (EFS)
Quantifies the cognitive load your operators carry per shift. EFS measures how many context switches, manual lookups, system hops, and judgment calls an operator must make to complete their tasks.
How hard your operators work to do simple things. High EFS means your systems create friction instead of removing it.
Screen transitions, manual overrides, exception handling frequency, and time-between-actions analysis.
Zone Intelligence Rating (ZIR)
Ranks each warehouse zone on a spectrum from predictable to reactive. Predictable zones have consistent throughput, low exception rates, and stable labor requirements.
Which zones need attention and why. Low ZIR zones are candidates for layout changes, slotting optimization, or labor rebalancing.
Throughput variance, exception frequency, labor utilization stability, and cross-shift performance consistency.
Labor Deployment Efficiency (LDE)
Compares planned vs. actual labor allocation against throughput outcomes. LDE reveals whether your labor is where it should be — not just whether people showed up.
Whether your labor planning matches reality. Low LDE means your labor is deployed based on schedule, not demand.
Planned headcount by zone vs. actual headcount vs. throughput achieved, measured in 30-minute intervals.
Wave Completion Confidence Score (WCCS)
A real-time probability that the current wave will complete on time, updated every 5 minutes. WCCS combines current pick rate, remaining volume, labor capacity, and historical completion patterns.
Whether to intervene now or let the wave run. A dropping WCCS is an early warning system for missed SLAs.
Real-time pick completion rate, remaining order volume, available labor, zone throughput trends, and historical wave completion patterns.
How We Build These Frameworks
Every benchmark starts with real operational data. We don't build models from theory — we build them from 28 years of implementation experience across companies like RedPrairie/JDA, Lowes, FedEx, Sephora, PepsiCo, and Aritzia.
As FAST deploys across more warehouses, these benchmarks become cross-customer intelligence — allowing every warehouse on the platform to compare their performance against anonymized industry baselines.
Start with the problem operators actually describe ("I didn't know until it was too late")
Identify the measurable signal that corresponds to the problem
Build the measurement framework from real WMS transaction data
Validate against operational outcomes (did faster decisions lead to better results?)
Publish the framework so the entire industry can benefit
Want Access to Our Research?
We publish findings on decision latency, AI explainability, and warehouse intelligence regularly. Sign up for research updates or talk to our team about benchmarking your operation.