AI in Predictive Maintenance

How a leading manufacturer reduced unplanned downtime by 20-40% on targeted assets and cut maintenance costs 15-25% through shift from reactive to predictive.

01 PROBLEM STATEMENT

Reactive Maintenance is Killing your OEE

A leading manufacturer with critical production assets across multiple plants operated entirely on reactive and calendar-driven maintenance schedules. Equipment was serviced either after failure causing unplanned downtime and production losses or at fixed intervals regardless of actual condition, leading to over-maintenance and wasted spare parts. Maintenance planners had no visibility into which assets were at risk. Sensor and historian data from OT systems sat unused. Production schedules were repeatedly disrupted by unexpected breakdowns. OEE targets were missed quarter after quarter, and maintenance costs were rising while asset reliability was not improving.

02 CURRENT CHALLENGES

What the manufacturer was struggling with

Reactive

Maintenance approach

Assets serviced only after failure, causing unplanned downtime and production losses, or at fixed intervals regardless of condition.

No Visibility

Failure prediction

Maintenance planners had no data-driven way to prioritise which assets were at risk of imminent failure.

Rising

Maintenance costs

Over-maintenance at fixed intervals wasted spare parts. Emergency procurement after failures added premium costs.

Missed

OEE targets

Unplanned downtime from unexpected failures repeatedly disrupted production schedules and pulled OEE below targets.

03 SOLUTION OVERVIEW

STAR’s Approach – AINE Predictive Maintenance AI

STAR Systems deployed AINE Predictive Maintenance AI, ingesting sensor and historian data from OSIsoft PI, Honeywell Uniformance, or SCADA exports. The platform trains failure prediction models per asset class and generates SAP PM work orders automatically when an anomaly is detected. Real-time sensor data is streamed via MQTT broker or OPC-UA edge connector. Asset hierarchy is mapped from CMDB for context. Maintenance planners review AI work orders before dispatch. False positives are tracked monthly, and STAR retrains asset-specific models every 6 months or after a major overhaul.

AI PATTERN

Failure Prediction + Anomaly Detection + Work Order Automation

04 WORKFLOW PROCESS

Step-By-Step: How Failures are Predicted and Prevented

Step 1 – Data Ingestion: Sensor and historian data ingested from OSIsoft PI, Honeywell Uniformance, or SCADA. Asset hierarchy mapped from CMDB.

Step 2 – Model Training: Failure prediction models trained per asset class using historical sensor data and past failure logs.

Step 3 – Real-Time Monitoring: Live sensor data streamed via MQTT broker or OPC-UA edge connector. Anomaly detection runs continuously.

Step 4 -Anomaly Detected: When sensor patterns indicate imminent failure, AI flags the asset and calculates time-to-failure estimate.

Step 5 – Work Order: Generated SAP PM work order auto-generated via RFC or SAP BTP. Maintenance planner reviews before dispatch.

Step 6 – False Positive Tracked: Actual outcomes logged. Models retrained every 6 months or after major overhaul. False positive rate tracked monthly.

05 KEY FEATURES

What the Platform Does

Asset-Class Failure Prediction:

Models trained per asset class – pumps, motors, conveyors – using sensor data and historical failure logs. Predictions specific to each equipment type.

SAP PM Work Order Automation:

When anomaly detected, SAP PM work order generated automatically via RFC or SAP BTP. Planner reviews before dispatch – AI accelerates, human decides.

Real-Time Streaming from OT:

Live sensor data streamed via MQTT broker or OPC-UA edge connector. Continuous anomaly detection – not batch-based, not delayed.

CMDB Asset Hierarchy Mapping:

Asset hierarchy and context imported from CMDB. Failure predictions linked to specific production lines and maintenance teams automatically.

Model Retraining Schedule:

Models retrained every 6 months or after major overhaul. False positive rate tracked monthly. STAR manages retraining no burden on plant maintenance team.

Planner in the Loop Controls:

Maintenance planner reviews every AI-generated work order before dispatch. Human judgment retained for criticality and scheduling decisions.

06 BUSINESS OUTCOMES

What Changes After Go Live

20-40%

Unplanned downtime reduction

15-25%

Maintenance cost reduction

Measurable

OEE improvement per asset

Condition Driven

Maintenance planning shift

CFO

Unplanned downtime reduction of 20-40% on targeted assets.
Maintenance cost reduction 15-25% through shift from reactive to predictive. Spare parts inventory optimised.

COO

OEE improvement measurable per asset.
Maintenance planning moves from calendar-driven to condition-driven. Fewer schedule disruptions.

CEO

Production reliability improvement: fewer schedule disruptions from unexpected failures.
Safety incident risk reduced through early failure detection.

Maintenance Head

Planners focus on high-risk assets flagged by AI instead of running fixed-interval schedules on all equipment.
False positive rate tracked monthly.

07 REAL-WORLD SCENARIO

A Day in the Life – Before and After

Before	After
Critical conveyor motor fails during production shift. Line down for 6 hours. Emergency spare parts procurement adds 30% premium cost.	AI flags the motor 48 hours before failure based on vibration and temperature anomalies. Spare parts ready. Maintenance scheduled during planned downtime window.
Pump serviced every 3 months per calendar schedule regardless of actual condition. Over-maintenance wastes spare parts and technician hours.	Pump serviced only when sensor data indicates wear approaching threshold. Service interval extends to 5 months, saving parts and labor without increasing failure risk.
Maintenance planner has no data to prioritise work orders. All assets treated equally. High-risk equipment not identified until failure occurs.	AI work orders ranked by time-to-failure estimate and asset criticality. Planner focuses team on the 20% of assets driving 80% of downtime risk.
Production manager asked to meet OEE targets but has no insight into which equipment is at risk. Schedule disruptions are reactive surprises.	Production manager sees flagged assets in advance. Can plan buffer inventory or alternate routing before failure occurs. OEE targets met consistently

08 ROI AND VALUE JUSTIFICATION

Why this Numbers Work

Value Driver	Indicative Impact	How It Is Realised
Unplanned downtime	20-40% reduction on targeted assets	Early failure detection allows scheduled maintenance during planned windows instead of emergency stops during production shifts.
Maintenance cost	15-25% reduction	Shift from fixed-interval over-maintenance to condition-driven service optimises spare parts usage and eliminates emergency procurement premiums.
OEE Per Asset	Measurable improvement	Fewer unplanned stops and better maintenance planning mean assets run closer to design capacity more consistently.
Positive ROI timeline	Within 6-9 months of go-live	Downtime reduction and maintenance cost savings on even a small asset subset exceed platform and managed service costs within three quarters.

09 NEXT STEPS

Discovery Call

30-min call to map your asset base, OT data sources, and current maintenance approach.

Pilot Scoping

We identify 5-10 critical assets for a 12-week pilot with live sensor data ingestion.

Pilot Delivery

AI failure predictions run on your assets. False positive rate and lead time tracked weekly.

Business Case

Downtime avoided, cost savings, and OEE improvement measured from your pilot and presented to leadership.

Schedule a Free Consultation

AI in Predictive Maintenance