
STEP 7
Predictive Maintenance
Publishing Date: 03-Mar-2026
Predictive techniques shift obsolescence management from reacting to shortages to forecasting availability risks. This step shows how to use operational data, lifecycle intelligence, and AI‑based modelling to anticipate component decline, supply fragility, and compliance exposure. Learn how predictive insights support timely redesigns, sourcing decisions, and maintenance planning—reducing lifecycle costs and strengthening long‑term resilience.

Author: Andrew Heath (Director Digital Solutions, Sourceability)
Theme
Standard & Clause
Predictive Risk Identification
IEC 62402 §9.1–§9.3 • SD-22 §3.3
Data Acquisition & Monitoring Inputs
IEC 62402 §8.10 • SD-22 §3
Integration with PLM / ERP / Risk Systems
IEC 62402 §7.2 • SD-22 §4.4
Analytical & Predictive Techniques (Forecasting,
AI/ML)
IEC 62402 §8.6 • SD-22 §4.4
Cross-Functional Oversight & Governance
IEC 62402 §6.3 • SD-22 §2.2
KPIs & Continuous Improvement
IEC 62402 §11.2 • SD-22 §4.4
Andrew
Heath
Position
Director Digital Solutions
Company
Sourceability
Andrew Heath is currently serving as the Director and Global Lead of Digital Solutions at Sourceability. Previously, he has led top electronics distribution and information management companies such as Arrow, SiliconExpert, and IHS (now Accuris), having over 30 years of experience.

About the author

John Wombwell
John Wombwell, founder of CNX Technology and Innovation Group, has over 25 years of experience in Component Engineering and Obsolescence Management. A Royal Air Force veteran, his career spans work with radar, missile, and communication systems across defense, medical, and rail industries worldwide. Now focused on advancing proactive obsolescence management, John shares his expertise through CNX as initiator of the 12 Steps framework and the International Institute of Obsolescence Management.
