Honeywell
Industrial AICritical infrastructureAutomation

Scaling industrial software and predictive AI.

Honeywell builds automation and software for industrial operations. In Brazil, I progressed from Product Intern to Product Manager and Senior Product Manager between 2016 and 2021. After supporting order research, I set OptiVision priorities for orders and invoicing, then led an asset reliability programme across three sites with an associate PM and twelve specialists, linking investment and release decisions to client commitments.

Product Intern → Product Manager → Senior Product Manager2016–2021
Products and platforms in my scope

Honeywell Forge APMPlatform · Asset performance

Combines equipment condition and operating data so leaders can assess emerging issues and prioritise assets for maintenance.

Predictive AnalyticsCapability · Industrial AI

Uses equipment history and machine learning to identify deterioration, giving specialists time to plan an inspection or repair.

Asset Reliability · 2018–2021
01What was the friction?

Maintenance priorities needed a common basis for investment.

Plant directors had to decide which equipment deserved limited inspection capacity and funding. Across three Brazilian sites, separate condition reports gave uneven weight to production impact, available backup and the evidence behind an alert. In 2018, I brought operations, reliability and finance leads into a joint review of 72 candidate assets, defining the information needed to compare interventions and establish a product scope each site could operate.

How discovery established the product requirements

The same alert could carry a different business consequence.

A compressor with no available backup had a different investment priority from a pump with spare capacity. I made production exposure and recoverability part of the review, giving plant leaders a shared basis for choosing work.

How operating context changes the priority
AssetProduction exposureBackupPriority
CompressorMain production lineUnavailableFirst
Feed pumpSupporting serviceAvailableScheduled
Product requirement: show condition, production exposure and available backup in the same decision.
02What approach and priorities did I choose?

I concentrated investment on the assets carrying the highest risk.

I chose critical rotating equipment as the first scope and ranked release requests by production exposure, evidence quality and the effort required to act. As Honeywell Forge became available in 2019, I aligned the programme with its asset monitoring and predictive capabilities. The roadmap connected early warnings to inspection decisions, with explicit conditions for supported use and an investment review before adding further equipment classes.

What I prioritised and how the work was sequenced
01 / FirstCapital discipline

Prioritise production exposure

Critical equipment concentrated the cost of disruption. I put production impact, available backup and intervention effort into the release sequence agreed with client leaders.

ExposureBackupEffort
02 / RequiredModel governance

Define the permitted use

Model performance varied across operating modes. I set the conditions for specialist review and suspension, giving each release a defined scope of supported use.

ScopeOwnerReview
03 / ExpandProgramme growth

Fund the next supported scope

Expansion required a practical maintenance response. I tied new equipment classes to evidence, inspection capacity and a commercial plan the delivery group could fulfil.

EvidenceActionFunding
03How did I lead the team?

I led the PM and aligned investment with site delivery.

I led an associate PM and coordinated twelve specialists within a R$2.4M delivery allocation. The PM owned the maintenance journey and user evidence; discipline leads owned model performance and site delivery. I coached their recommendations, settled competing priorities and chaired monthly reviews with plant, finance and account leaders to agree release scope, spending and commercial commitments.

How responsibility and decision authority progressed
Felipe Löwe
Felipe LöweSenior Product Manager

I owned programme priorities, release criteria and the R$2.4M delivery allocation, aligning product decisions with the operating and commercial commitments of three clients.

Investment sequencePM developmentModel release policy
Delegated

Associate PM

Owned the maintenance journey and user evidence, with authority to prioritise improvements within the agreed release scope.

Retained

Senior Product Manager

Resolved competing site priorities, approved supported model scope and agreed changes to spending and client commitments.

Client plant leaders approved maintenance interventions; I governed the product scope supporting their decisions.
04Which bottleneck shaped the delivery plan?

The operating history determined where predictive models could be used.

Twelve of the 72 assets lacked enough operating history for a reliable assessment. I validated release readiness with the team and focused capacity on the sixty supported assets, each with a defined operating range, reliability owner and recovery rule for using predictive advice in maintenance plans.

How evidence coverage determined the scope of AI use

Constraints · Honeywell

Asset Reliability · 2018–2021
How evidence coverage determined the scope of AI use
The bottleneck

Twelve candidate assets had incomplete histories or too few examples of their normal operating modes. A model fitted to that evidence could turn ordinary production changes into warnings that consumed scarce inspection capacity.

My decision

I agreed a release of sixty qualified assets with the team, confirming operating limits for every model. The analytics lead owned the missing evidence, while the associate PM kept the qualification plan visible in client reviews.

How we changed the plan

The revised release policy connected model scope to a named specialist, a maintenance response and a clear condition for suspending predictive advice.

  1. Define model scopeSupported operating modes
  2. Assign reviewReliability specialist
  3. Control re-entryEvidence before resuming
60 / 72Assets approved for supported use2021 review · Twelve in qualification

What I carried into the next release

I used coverage gaps to sequence investment in data and model improvement. Expansion followed evidence from the actual operating modes and the site’s ability to act on a warning.

05How did we deliver and validate the product?

Each warning led to a reviewable maintenance decision.

We used Forge asset monitoring and predictive analytics to connect equipment behaviour with production exposure and an inspection plan. In the example below, the reader reviews a compressor, checks its supported operating mode and schedules an inspection. The release work combined dated equipment events with maintenance walkthroughs, so model coverage, warning time and the practical ability to intervene shaped the scope I approved for daily use.

Workflow reconstruction / Honeywell Forge APM
Compressor C-14 · Site B

Assess the maintenance priority.

Supported mode

The equipment signal rises beyond its expected range; main-line exposure and unavailable backup place this compressor first for inspection, giving the specialist a clear reason to review it during the shift.

Earlier observationsLatest
Expected rangeObserved signal
Production exposureMain lineAvailable backupNone availableNext actionInspect this shift
Check the operating mode, then schedule an inspection of the priority asset.
06What did the OKRs and KPIs show?

The programme improved reliability and secured committed client scope.

By the 2021 review, sixty assets were in supported use and unplanned downtime had fallen from 25 to 17 hours per 1,000 scheduled asset-hours. Eighteen of 24 historical events received at least seven days of warning, two below target, which kept specific failure modes under specialist review. Three clients signed R$7.2M in two-year scope, and I aligned the next delivery allocation with those commitments and the remaining coverage gaps.

2021 programme review · Objective 2: Expand supported coverage and secure commercial value.

Assets in supported daily use

72 selected candidates · Three client sites

60 of 7283% of the candidate cohort
60 supported12 in qualification
Target ≥60 qualified assetsTarget met
What the result changed

The coverage target was met across three sites. I mapped evidence needs with the PM for these twelve assets still awaiting a supported model scope.

See the measurement details

Supported use required current data, a validated operating range, a reliability owner and a defined maintenance response. Sixty of the 72 selected assets met all four conditions at the review. The remaining twelve stayed in qualification, with predictive outputs excluded from maintenance prioritisation.

Owner
Associate PM · Deployment lead
Review
2021 asset qualification review

Signed programme expansion

Three agreements · Two-year contract value

R$7.2MR$1.2M above target
Target ≥R$6.0M in signed scopeTarget met
What the result changed

Three clients committed further scope. I aligned the contracted features, support requirements and staffing with the next programme delivery budget.

See the measurement details

Signed two-year agreement values of R$1.8M, R$2.4M and R$3.0M total R$7.2M, covering software, implementation and support. The measure records committed contract value; recognition follows delivery terms. The R$2.4M allocation in the leadership section is the separate internal budget for the current programme.

Owner
Senior Product Manager · Account leads
Review
2021 signed client agreements
Ongoing operational reviewKPI → owner → next action
9 / 40alerts without action

Manage the review burden

Nine of forty distinct alerts in a four-week live review led to no useful maintenance action. The analytics lead grouped their causes for the next model revision.

Analytics lead · Monthly
12 / 72assets in qualification

Close the evidence coverage gaps

Each remaining asset retained an evidence owner and a supported-mode requirement, which I reviewed before allocating further release capacity.

Associate PM · Fortnightly

The 60-asset downtime study, 24-event historical evaluation, 72-asset qualification review and 40-alert live review have separate populations and measurement windows.

Product context & measurement notes

The move into industrial predictive analytics

The programme began with scope definition in 2018 and adopted Forge after its 2019 launch. Honeywell’s 2020 APM documentation combines equipment performance, predictive models and maintenance planning. I used those capabilities within the agreed asset classes and supported operating conditions.

Reading the programme measures

The operating programme spans three Brazilian sites, with sixty assets in supported use. Historical event coverage and live alert burden are separate evaluations. Downtime measures equipment stop-hours, while commercial value covers full two-year agreements. The delivery allocation and signed client scope retain separate financial definitions.

Continue the conversation.

OptiVision

OptiVision is Honeywell’s order-to-invoice software for pulp, paper and flat-sheet manufacturers. It connects customer orders with production planning, quality, inventory and shipment information, helping teams assess available stock and capacity before confirming a delivery commitment.

Customer orders

The requested quantity and date establish the demand that sales and planning need to confirm.

Production and stock

Available stock and scheduled production show when the complete order can be prepared.

Shipment planning

Loading and transit requirements connect the production plan to a feasible customer delivery date.

My product scope

I owned regional requirements, release priorities and client acceptance for the domestic order journey. Engineering and application leads delivered the agreed scope, while client sales and planning leaders approved customer commitments.

Invoicing & Credit

Invoicing and credit are capabilities within OptiVision. The product maintains commercial information such as prices and additional charges, supports invoicing, and exchanges order and shipment information with enterprise systems. In this scope, the client’s ERP retained fiscal processing.

Agreed prices

The order retains agreed prices, freight and additional charges as the reference for billing.

Credit information

Credit information informs order readiness and directs commercial exceptions to client finance.

Invoice preparation

Shipment quantities and approved terms guide billing and the handoff to the client’s ERP.

My product scope

I prioritised consistent commercial terms from order acceptance through billing and agreed exception rules with client finance leaders. Those leaders retained authority over prices and credit decisions, while I owned the product requirements and release criteria.

Honeywell Forge APM

Honeywell Forge Asset Performance Management combines machinery surveillance, performance monitoring and predictive analytics. Its asset models use operating data to identify deterioration and opportunities for corrective action, giving reliability teams a basis for planning maintenance.

Equipment condition

Live readings and operating history reveal changes in the equipment for specialist review.

Business exposure

Production impact and available backup show why an asset takes priority in the site’s plan.

Maintenance planning

Specialists link the warning to an inspection plan and propose further work for plant approval.

My product scope

I directed the programme scope, investment sequence and release criteria across three sites. The associate PM owned the user journey, while reliability and analytics leads provided the evidence required for supported use.

Predictive Analytics

Within Forge APM, predictive analytics uses equipment history and machine learning to identify departures from expected behaviour. Honeywell described these capabilities in 2019, and its 2020 APM material connects them to asset reliability and maintenance planning.

Dated operating history

Representative operating history defines the conditions each asset model can support.

Early warning

Changes in equipment behaviour give specialists time to assess and plan an inspection.

Specialist assessment

The specialist checks the signal, mode and response before the plant agrees further work.

My product scope

I set conditions for the asset classes and operating modes that could enter the release. Warning coverage, alert burden and the site’s capacity to inspect each issue informed further investment and model expansion.

Expanded evidence