AI Decision Intelligence Platform
AI Decision Intelligence Platform
AI Decision Intelligence Platform
Role: Advanced Product Designer
Duration: 4-Week Sprint
Role: Advanced Product Designer
Duration: 4-Week Sprint
Team: Product Owner, Lead Developer
Stakeholders: Warehouse managers, potential clients
Team: Product Owner, Lead Developer
Stakeholders: Warehouse managers, potential clients
OVERVIEW
OVERVIEW
OVERVIEW
How might we help warehouse managers quickly spot inefficiencies in AS/RS systems using AI-powered insights they can act on?
Honeywell introduced Decision Intelligence to automate warehouse operations and optimize efficiency through AI-driven insights. Designed as a key upgrade to their Momentum platform, the feature needed to deliver measurable impact while signaling the future of smart warehousing. Alongside leading the UX design, I also created a visual identity for Decision Intelligence to support its positioning within Honeywell’s broader product ecosystem.
Honeywell launched Decision Intelligence to automate warehouse operations with AI-driven insights—paving the way for smarter logistics.
I led UX design and crafted its visual identity to align with Honeywell’s broader ecosystem.
IMPACT
IMPACT
IMPACT
Reduced manual updates by 40% through AI-driven automation within the first quarter—improving shift transition efficiency by an average of 26%.
Reduced manual updates by 40% through AI-driven automation within the first quarter—improving shift transition efficiency by an average of 26%.
40% fewer manual updates. 26% faster shift transitions—with AI automation.
CHALLENGE
Make complex logistics data actionable for non-technical users
Warehouse managers lacked real-time insight into system efficiency, leading to operational drag across: Manual adjustments taking 20–30 minutes per shift Increased dwell and travel times due to poor forecasting Inefficient inventory allocation, causing 15% of orders to require last-minute intervention
Warehouse managers lacked real-time insight into system efficiency, leading to operational drag across: Manual adjustments taking 20–30 minutes per shift Increased dwell and travel times due to poor forecasting Inefficient inventory allocation, causing 15% of orders to require last-minute intervention
Warehouse managers lacked real-time system insights, causing delays in inventory, labor, and order fulfillment.
Manual setting changes took 30–45 mins per shift
Poor forecasting increased dwell/travel times
15% of orders needed last-minute fixes or manual searches
Without automation, performance suffered from guesswork and lag.
Without automation, performance suffered from guesswork and lag.
Without automation, performance suffered from guesswork and lag.
DESIGN STRATEGY
Making AI actionable at a glance
To bring clarity to a complex system, we designed around how warehouse teams actually work—what they need to adjust, monitor, and trust in real time. We structured the interface into three core layers:
1. Smart Configuration
Simplified Operators needed more than control—they needed clarity. We used progressive disclosure and intuitive grouping to let users adjust AI parameters without getting lost in technical jargon.
2. Monitoring That Builds Trust
We added safety rails and fallback protocols. Alerts were designed to keep teams proactive—not reactive—with human-readable, high-signal messaging.
3. Planning with Visual Intelligence
AS/RS Activity Profile became the heartbeat of the tool, showing SKU popularity tiers at a glance. This helped teams optimize storage and container planning based on real-time and forecasted movement.
We designed around what warehouse teams need: clarity, control, and trust.
Smart Configuration
Progressive disclosure made it easy to adjust AI parameters without overwhelm.
Trustworthy Monitoring
We layered in safety rails and proactive alerts that teams could rely on.
Data-Driven Planning
Visualized SKU popularity helped managers optimize container placement in real time.
We focused on what teams need to see, adjust, and trust—without adding more noise.
Smart Configuration
Grouped AI settings into simple, editable blocks. No jargon. Just control where it matters.
Monitoring & Alerts
Added fallback protocols and clear alerts to support safety and proactive action.
Visual Planning
Created an AS/RS Activity Profile that shows SKU popularity at a glance—helping managers optimize storage in real time.
Each layer worked together to turn complexity into clarity—making decision-making faster and more confident.
There’s more behind the visuals—process, rationale, and key moments that shaped the final experience.
Each layer worked together to support smarter decisions—without second-guessing the tech behind it.
Together, these layers created a single source of truth—one that helps teams move faster and make smarter decisions with confidence.
We designed around what warehouse teams need: clarity, control, and trust.
Smart Configuration
Progressive disclosure made it easy to adjust AI parameters without overwhelm.
Trustworthy Monitoring
We layered in safety rails and proactive alerts that teams could rely on.
Data-Driven Planning
Visualized SKU popularity helped managers optimize container placement in real time.
Under the hood: AI, warehouse data, and frontline decision-making.
The deep dive shares how we balanced complexity and clarity—designing for insight, not overload.
Under the hood: AI, warehouse data, and frontline decision-making.
The deep dive shares how we balanced complexity and clarity—designing for insight, not overload.
SOLUTION
A smart, responsive dashboard that drives faster decisions and smoother ops
We launched a streamlined automation interface called Decision Intelligence—designed to adapt dynamically to the evolving needs of each warehouse shift.
Key features included:
A centralized configuration UI for AS/RS systems, offering intuitive control over every subsystem
Real-time pacing metrics to monitor throughput and identify slowdowns before they escalated
AI-backed auto-allocation of storage zones, bins, and dwell times based on shifting inventory patterns
Clear protocols for overrides and exception handling, giving managers control in edge cases
We also introduced the AS/RS Activity Profile—a data visualization and planning tool that used SKU ranking curves to optimize container profiles. This helped classify inventory into popularity tiers, informing smarter storage decisions and reducing retrieval time.
Together, these features created a system that didn’t just automate—it learned and improved.
The platform saved time, reduced manual configuration, and evolved in step with real operational trends.
We delivered Decision Intelligence—a streamlined interface with centralized AS/RS configuration, real-time pacing metrics, and AI-powered zone and bin allocation.
The AS/RS Activity Profile enabled smarter storage decisions based on SKU ranking, while override protocols ensured control. The system saved time and continuously adapted to shifting operational needs.
