Haben Gebreweld

Heavy machinery industries face a critical adoption gap. While intelligent maintenance can reduce downtime by 30% and save $200,000+ annually per operation, fewer than 15% of organizations successfully implement it. Why? Four interconnected barriers block adoption: data barriers (insufficient failure data, severe imbalance, high collection costs), organizational barriers (skills gaps, change resistance), technology barriers (infrastructure needs, integration complexity), and economic barriers (unclear ROI, high upfront costs). Current solutions address these in isolation, standards focus only on data infrastructure, academic research only on algorithms. No framework guides organizations through the complete adoption journey.

This research develops and validates a comprehensive, lifecycle-based adoption framework through: (1) systematic analysis of 86 industry studies identifying adoption barriers, (2) industry-wide surveys mapping real-world challenges, (3) stakeholder interviews capturing organizational needs, and (4) pilot validation with equipment manufacturers and operators. The framework provides diagnostic tools for each barrier type, proven mitigation strategies (including the SD-PdM methodology for data challenges), and phased implementation roadmaps. It guides organizations from readiness assessment through deployment and scaling, with feedback loops ensuring continuous adaptation.

Organizations gain a validated pathway to intelligent maintenance adoption: diagnostic clarity (identify which barriers affect your operation and when), targeted solutions (evidence-based strategies matched to your organizational maturity and resources), reduced risk (phased approach prevents costly false starts), and measurable progress (validated metrics track advancement). Equipment manufacturers can embed the framework into connected service offerings. Technology vendors gain adoption best practices. Operators receive practical guidance eliminating guesswork from digital transformation.

Existing solutions fail because they're fragmented. Data-focused standards (OSA-CBM, ISO 13374) assume organizational readiness that doesn't exist. Academic research optimizes algorithms without addressing implementation realities. Consultancies offer generic advice without diagnostic depth. This framework uniquely integrates all adoption dimensions, data, organization, technology, economics, into one actionable system validated in real operations. It's the missing bridge between theoretical potential and industrial reality.