RESEARCH PAPER
AI-enabled dependability diagnostics for industrial product lifecycle management
1
Instytut Zarządzania, Uniwersytet Ekonomiczny w Poznaniu, Poland
These authors had equal contribution to this work
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2026-05-14
Final revision date: 2026-06-29
Acceptance date: 2026-08-28
Online publication date: 2026-09-10
Corresponding author
Ireneusz Rutkowski
Instytut Zarządzania, Uniwersytet Ekonomiczny w Poznaniu, Al. Niepodległości 10, 61-875, Poznań, Poland
Instytut Zarządzania, Uniwersytet Ekonomiczny w Poznaniu, Al. Niepodległości 10, 61-875, Poznań, Poland
KEYWORDS
reliabilitydigital twinpredictive maintenanceartificial intelligenceIndustrial Internet of Thingslife-cycle cost
TOPICS
- Reliability, Availability, Maintainability and Safety Analysis
- Risk, Hazard, Uncertainty and Technical Safety Management
- Maintenance Engineering, Asset Management, Life Cycle Costs and Logistics
- Diagnostics, Condition Monitoring and Fault Detection
- Industrial Applications, Field Studies and Case Reports
- Digital Twins, IoT and Cyber Physical Systems
- Artificial Intelligence and Data Science in Reliability and Maintenance
ABSTRACT
Industrial products increasingly operate as connected cyber-physical technical items whose reliability, maintainability, safety and life-cycle cost depend on IoT monitoring, artificial intelligence and digital twins. This paper develops a software-ready diagnostic method and instrument for assessing organisational and technical readiness for AI-enabled industrial product lifecycle management. The proposed AI-IPLM-RMLCC Diagnostic Engine is derived from a structured synthesis of scientific publications and applied in a diagnostic case study of QUAY BHU Sp. z o.o., a real medium-sized manufacturing enterprise located in Wielkopolska, Poland. It combines operational indicators, maturity levels, implementation-risk scoring, evidence-confidence ratings and composite indices. The instrument helps identify readiness gaps, prioritise risk reduction and link digital deployment to dependability and life-cycle cost outcomes.
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