Unlocking AI-driven Growth at Honeywell Forge From 1 Pilot to 3 Plants

Unlocking AI-driven Growth at Honeywell Forge From 1 Pilot to 3 Plants

Unlocking AI-driven Growth at Honeywell Forge From 1 Pilot to 3 Plants

From basic task management to advanced collaboration tools,
our pricing plans adapt to your productivity needs as you grow.

Streamline your day for you and your team with the most efficient task management and to-do list app available.

See Solution

See Solution

See Solution

The challenge that started it all

The challenge that started it all

The challenge that started it all

Honeywell Forge Performance+ is an enterprise SaaS platform for industrial operations. When we started, plants in chemicals and mining were still tracking costly downtime in spreadsheets, with no standardization across sites and no way for executives to see the $$ impact.

My role

Lead UX Designer

Collaborators

PM, Engineers, Multiple SMEs

Timeline

4 months

Taxonomy Trust · Acquisition

100% Standardized

Built shared language → faster onboarding & cleaner analytics.

Taxonomy Trust · Acquisition

100% Standardized

Built shared language → faster onboarding & cleaner analytics.

Daily Active Users · Activation

5K+ DAU

Feature adoption proves day-to-day operational value.

Plant Validation · Retention

1 to 3 plants

Stickiness across shifts led to 3-plant adoption.

Corrective Maintenance · Growth

~45%

Predictive work orders, early-fault detection, and RCA focus teams on highest-impact fixes.

And our vision was set to

And our vision was set to

Show ROI in dollars, and build a system built to scale.

Show ROI in dollars, and build a system built to scale.

Design a standardized loss categorization system that works across industries,

Design a standardized loss categorization system that works across industries,

Target bad actors
Translate downtime hours into real money (Variable Contribution Margin),

Target bad actors
Translate downtime hours into real money (Variable Contribution Margin),

Roll out an AI assistant to help operators fix problems faster - cutting resolution time by 60%

Roll out an AI assistant to help operators fix problems faster - cutting resolution time by 60%

Then we’d do more than fix one plant’s problem - we’d unlock adoption across accounts, challenge incumbents like GE and AVEVA, and open up a $4.8M pipeline in three years.

Then we’d do more than fix one plant’s problem - we’d unlock adoption across accounts, challenge incumbents like GE and AVEVA, and open up a $4.8M pipeline in three years.

-GTM strategy

🚨 BUT WAIT!!

🚨 BUT WAIT!!

Isn’t OEE just another operations metric? Well… sort of. But that was the trap.

Plants already had OEE dashboards - problem was, no one trusted them. Each site categorized downtime differently, operators hated logging it, and executives couldn’t tie “hours lost” to actual dollars.

Why it made sense for Honeywell
Forge

Why it made sense for Honeywell
Forge

Why it made sense for Honeywell Forge

Downtime didn’t live with just one person — it traveled across the plant floor. Here’s how it moved

Downtime didn’t live with just one person — it traveled across the plant floor. Here’s how it moved

3+

Industries

65%

of Chemicals

We did a quick landscape scan

We did a quick landscape scan

We did a quick landscape
scan

  • of chemical businesses said digitalization would impact them in 3 years → the market was already warming up.

  • of chemical businesses said digitalization would impact them in 3 years → the market was already warming up.

  • of chemical businesses said digitalization would impact them in 3 years → the market was already warming up.

  • Existing OEE seen as “niche for Chemicals” → we had an opening to scale into Mining & Energy.

  • Existing OEE seen as “niche for Chemicals” → we had an opening to scale into Mining & Energy.

  • Existing OEE seen as “niche for Chemicals” → we had an opening to scale into Mining & Energy.

Background
Background
Background

And how did we
amplify the function
of the design

How did we amplify the function of the design?

How did we amplify the function of the design?

Stakeholder workshops

SME interviews

We ran a UX research kick-off workshop with the team to understand the key personas for the MVO release and we started putting together a couple of research scripts to get detailed insights into their world.


We wanted to make sure that the challenges and solutions that are developed are applicable industry-wide and not just for one specific use case.

01

Plant Manager

02

Process Supervisor

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

03

Area/Unit Manager

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

04

Maintenance Manager

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

01

Plant Manager

02

Process Supervisor

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

03

Area/Unit Manager

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

04

Maintenance Manager

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

01

Plant Manager

02

Process Supervisor

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

03

Area/Unit Manager

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

04

Maintenance Manager

We conducted user interviews, surveys, and analyzed in-app analytics to understand the pain points and user needs. We also studied competitor apps and industry trends to gather insights

badge icon

the aha moment

badge icon
badge icon

the aha moment

badge icon
badge icon

the aha moment

badge icon

From Noise to Signal

From Noise to Signal

From our research, we prioritized these three core challenges for the MVO.

From our research, we prioritized these three core challenges for the MVO.

From our research, we prioritized these three core challenges for the MVO.

1

Lack of Loss Monetization

Lack of Loss Monetization

To understand how much the unit is making for them in terms of $, loss in Downtime

hours.

2

Loss Categorization

Loss Categorization

Lack of standard Loss Categorization system and easily accessible Equipment Parent Child Hierarchy (from CMMS system) so that loss events can be properly aggregated over different time periods and across different sites. 

Lack of standard Loss Categorization system and easily accessible Equipment Parent Child Hierarchy (from CMMS system) so that loss events can be properly aggregated over different time periods and across different sites. 

Lack of standard Loss Categorization system and easily accessible Equipment Parent Child Hierarchy (from CMMS system) so that loss events can be properly aggregated over different time periods and across different sites. 

3

Delayed Actionability

Delayed Actionability

Embedding an AI assistant closed the gap between alert and resolution - cutting time-to-fix dramatically.

Embedding an AI assistant closed the gap between alert and resolution - cutting time-to-fix dramatically.

Embedding an AI assistant closed the gap between alert and resolution - cutting time-to-fix dramatically.

From many ideas to one "Best" solution

From many ideas to one "Best" solution

From many ideas to one "Best" solution

We conducted a design workshop with the cross-functional team to map out the features for the user stories.

Tackling the Chaos

So many spreadsheets. So many definitions of the same loss event. We realized the first problem wasn’t dashboards or alerts - it was taxonomy.


We sat down with TPOs, PMs, and SMEs across chemicals and mining. Together, we restructured the entire loss classification model - what became Object Model 2.0.


Pros

-Single codebase → simpler maintenance.

-One “source of truth” → reduces confusion.

-Simpler onboarding for new users.



Cons

-Hard to scale with large KPI sets.

-“One size fits all” → Noise overload → users revert to Excel.

Consolidated Dashboard Approach

Pros

-High relevance → dashboards feel tailored.

-Easier to test role-specific features.

-Easier to test role-specific features.



Cons

-Hard to scale with large KPI sets.

- “One size fits all” → Noise overload → users revert to Excel.

Role-Based Dashboard Approach


Pros

-Single codebase → simpler maintenance.

-One “source of truth” → reduces confusion.

-Simpler onboarding for new users.



Cons

-Hard to scale with large KPI sets.

-“One size fits all” → Noise overload → users revert to Excel.

Consolidated Dashboard Approach

Pros

-High relevance → dashboards feel tailored.

-Easier to test role-specific features.

-Easier to test role-specific features.



Cons

-Hard to scale with large KPI sets.

- “One size fits all” → Noise overload → users revert to Excel.

Role-Based Dashboard Approach


Pros

-Single codebase → simpler maintenance.

-One “source of truth” → reduces confusion.

-Simpler onboarding for new users.



Cons

-Hard to scale with large KPI sets.

-“One size fits all” → Noise overload → users revert to Excel.

Consolidated Dashboard Approach

Pros

-High relevance → dashboards feel tailored.

-Easier to test role-specific features.

-Easier to test role-specific features.



Cons

-Hard to scale with large KPI sets.

- “One size fits all” → Noise overload → users revert to Excel.

Role-Based Dashboard Approach

Messy sketches, sticky notes, and debates became the

backbone

backbone

backbone

of RCA workflows

Experimenting with AI assisted workflows to

shorten

shorten

shorten

the resolution time

Streamline tracking: Move from spreadsheets to standardized loss buckets.

Streamline tracking: Move from spreadsheets to standardized loss buckets.

Streamline tracking: Move from spreadsheets to standardized loss buckets.

Overview Dashboard → unit performance → equipment → loss events → financial impact.

Overview Dashboard → unit performance → equipment → loss events → financial impact.

Overview of Production and OEE metrics


PROBLEM #1
Dashboards Without Direction

Monitor progress against the production plan and maximum capacity in real-time to drive decisions about which inefficiencies to address and which to accept as operational constraints.


  • Drill-downs into “bad actors” → equipment, product, or unit driving the biggest losses.

  • Prioritization at a glance → which fixes would move the needle first.

  • Dollar impact (VCM) → so they could make the case to execs in their language.

Overview of Production and OEE metrics


PROBLEM #1
Dashboards Without Direction

Monitor progress against the production plan and maximum capacity in real-time to drive decisions about which inefficiencies to address and which to accept as operational constraints.


  • Drill-downs into “bad actors” → equipment, product, or unit driving the biggest losses.

  • Prioritization at a glance → which fixes would move the needle first.

  • Dollar impact (VCM) → so they could make the case to execs in their language.

Boost visibility: Give managers real-time clarity into bottlenecks.

Boost visibility: Give managers real-time clarity into bottlenecks.

Boost visibility: Give managers real-time clarity into bottlenecks.

One level deeper

Plant managers will make faster decisions when they can directly see which units and issues rather than having traditional OEE visualizations that emphasize technical metrics.

One level deeper

Plant managers will make faster decisions when they can directly see which units and issues rather than having traditional OEE visualizations that emphasize technical metrics.

One level deeper

Plant managers will make faster decisions when they can directly see which units and issues rather than having traditional OEE visualizations that emphasize technical metrics.

Spot opportunities: Highlight bad actors so fixes become projects, not guesswork.

Spot opportunities: Highlight bad actors so fixes become projects, not guesswork.

Spot opportunities: Highlight bad actors so fixes become projects, not guesswork.

Drill-downs into “bad actors” → equipment, product, or unit driving the biggest losses.

Prioritization at a glance → which fixes would move the needle first.

30k-ft View vs Drill-Down Flow

30k-ft View vs Drill-Down Flow

30k-ft View vs Drill-Down Flow

Test A: Compact Layout

 → All KPIs crammed on a single screen. Managers said it was “fast but overwhelming.”


 Result: Quick scanning, but 60% missed deeper drill-downs.

Test B: Drill-Down Layout

 → Simplified overview with click-through detail.


 Result: 80% task success. Plant managers reported “I finally see the bad actors clearly.”

How do we go from knowing what’s broken to fixing it fast, without leaving the system?

How do we go from knowing what’s broken to fixing it fast, without leaving the system?

How do we go from knowing what’s broken to fixing it fast, without leaving the system?

Operators were still switching between Forge, manuals, and tribal knowledge just to troubleshoot one alert. That created delays and eroded trust in digital tools. So we built something better.

Operators were still switching between Forge, manuals, and tribal knowledge just to troubleshoot one alert. That created delays and eroded trust in digital tools. So we built something better.

Closing the loop with next
actions

Closing the loop with next
actions

Closing the loop with next
actions

Dynamic next
actions

Dynamic next
actions

Dynamic next
actions

Smart Context Detection

Intelligent Action Suggestions

Clean, Uncluttered Responses

Background
Hand Image
Smiley Image
Background
Hand Image
Smiley Image
Background
Hand Image
Smiley Image

Rethinking feedback frequency

Rethinking feedback frequency

Rethinking feedback frequency

Before this project, I knew how to design

Before this project, I knew how to design

Before this project, I knew how to design

dashboards and

dashboards and

dashboards and

workflows for usability

workflows for usability

workflows for usability

but after this project,

but after this project,

but after this project,

  • Systems thinking • Data flow • Executive reporting

  • Systems thinking • Data flow • Executive reporting

  • Systems thinking • Data flow • Executive reporting

Balancing UX, Business,
and Team Leadership

Balancing UX, Business,
and Team Leadership

Balancing UX, Business,
and Team Leadership

 I now design with a holistic product strategy lens: not just features, but scalability, monetization, and adoption.

I now design with a holistic product strategy lens: not just features, but scalability, monetization, and adoption.

 I also discovered my ability to lead a small design team, balancing UX with product management expectations.

Your browser does not support HTML5 video.

 I now design with a holistic product strategy lens: not just features, but scalability, monetization, and adoption.

 I also discovered my ability to lead a small design team, balancing UX with product management expectations.