Smart maintenance protects and improves what matters most in operations: machine availability, process stability, and consistent product quality. It shifts maintenance from reactive repairs to proactive by using digital technologies in an integrated way. This creates immediate impact, especially by reducing unplanned downtime and emergency interventions. 

At the same time, smart maintenance reduces overall maintenance costs by identifying risks early, extending asset lifecycles, and delaying capital-intensive replacements. 

Beyond cost and availability, proactive maintenance also supports: 

• higher and more consistent product quality, 

• improved safety and compliance, 

• increased energy efficiency, 

• more reliable and resilient supply chains. 

Together, these effects lead to more stable production and higher overall equipment effectiveness, with resources used more efficiently.

The development of maintenance practices shows how organizations manage reliability and risk more effectively over time: 

1. Corrective and reactive maintenance represent the starting point. Equipment is repaired only after failures occur, which leads to high downtime, limited predictability, and significant operational losses. While reactive maintenance restores functionality, it remains event‑driven and resource‑intensive. 

2. To improve stability, many companies introduced preventive maintenance, planning interventions at fixed intervals. This reduces breakdowns but can lead to unnecessary work because interventions do not reflect actual asset conditions. 

3. The next step is condition‑based maintenance, where real‑time data and defined thresholds trigger targeted actions. Maintenance becomes more focused, but it still largely reacts to observed deviations. 

4. Predictive maintenance builds on this foundation. By applying advanced analytics and machine learning, patterns of wear and degradation can be detected early. Failures are anticipated rather than observed, allowing maintenance activities to be planned proactively and aligned with production priorities. 

5. At the highest maturity level, prescriptive maintenance closes the loop. Systems not only predict failures but also recommend optimal actions and timing while considering operational impact, cost, and resource availability. Maintenance then evolves into a strategic capability that continuously optimizes performance across the value chain. 

Smart maintenance does not appear overnight. Successful organizations evolve step by step and build on the following three blocks: 

1. Start with high-impact assets to create value early 
Focus on assets with the highest operational and financial criticality to capture quick wins. A stepwise approach helps overcome key technical barriers such as retrofitting legacy equipment, integrating heterogeneous machine landscapes, and ensuring reliable data connectivity. 

2. Build data transparency to enable proactive intervention 
Use digital tools to increase data transparency and address fragmented data and quality issues. Improved data availability enables the early detection of wear and degradation, allowing timely interventions before failures occur. This reduces downtime, supports smoother operations, and lowers disruption costs. 

3. Combine technology with a capability shift 
Keep human expertise at the core while maintenance organizations evolve. Specialists must translate data into decisions, balance operational trade-offs, and embed new routines. This requires upskilling in IT, analytics, and cybersecurity, combined with strong cross-functional collaboration. 

Smart maintenance therefore combines technology, processes, and people into a practical performance lever. Close collaboration between maintenance experts and data specialists is essential for turning insights into effective actions. 

Successful organizations define a clear target picture, supported by a well-defined ambition, scope, and roadmap. This guides the transition from traditional to smart maintenance. 

At the organizational level, companies need to clarify roles and responsibilities, adjust KPIs, and redesign planning routines to encourage preventive behavior instead of reactive firefighting. This requires strong change management, as it challenges established habits and performance metrics. 

Execution follows a structured, value-driven rollout. It begins with a strategic classification of assets based on criticality, downtime impact, and maintenance cost defining clear priorities for sensor deployment, retrofitting, and system integration. 

As organizations implement solutions, they embed real-time monitoring and predictive workflows into daily operations, supported by digital tools that enhance transparency and track value creation through meaningful KPIs. Continuous capability building and change management ensure that new behaviors are sustainably anchored. 

This phased and scalable approach avoids isolated pilots and enables sustainable value realization at scale. 

Smart maintenance is expected to evolve from isolated initiatives into a standard capability across many industries. 

• Trend 1: AI-driven shift to proactive operations 
  Advances in artificial intelligence will further increase the reliability of predictive and prescriptive maintenance. Machines will detect wear earlier and trigger the right actions at the right time, shifting operations from reactive firefighting to planned, proactive interventions. 

• Trend 2: Human-AI collaboration as the new standard 
  AI will increasingly translate data into actionable insights and recommendations, while automation takes over routine detection, prioritization, and workflow orchestration. At the same time, human expertise remains critical for decisions that require operational judgment, safety considerations, and trade-offs across production, quality, and cost. 

• Trend 3: End-to-end integration across operations 
  Maintenance will become fully integrated with production planning, logistics, and supply chain processes. Prescriptive analytics will optimize decisions based on operational impact, cost efficiency, and energy consumption. Intuitive tools and natural-language interfaces will make advanced analytics accessible to a broader workforce. 

Organizations that want to unlock measurable value from predictive maintenance – or scale existing initiatives into a holistic capability – should act now. Success starts with a clearly defined target picture and a concrete execution roadmap.

1. Reposition maintenance as a strategic value driver 
Move beyond viewing maintenance as a cost center and actively manage it as a core lever for availability, quality, and operational resilience. 

2. Accelerate the shift to predictive maintenance 
Drive the transition from reactive and preventive approaches to data-driven predictive – and ultimately prescriptive – maintenance to anticipate failures and reduce downtime and costs. 

3. Capture early value through critical assets and data transparency 
Prioritize high-impact assets and establish end-to-end data transparency to detect issues early, intervene proactively, and use resources more efficiently. 

4. Transform capabilities, organization, and ways of working 
Build integrated capabilities across maintenance, IT, and analytics, while redesigning roles, KPIs, and incentives to reward prevention over firefighting. 

5. Execute with a clear target picture and scalable rollout 
Define a clear target state and execution roadmap, then implement smart maintenance through a phased, value-driven rollout to scale from pilots to a sustainable, end-to-end capability. 

Drüppel, M. / Lee, Y. / Pfeil, C.