Technology Adoption and Its Impact on Maintenance Productivity

Executive Overview

As the industry experiences increasing pressure to improve efficiency, reduce downtime, and address limited availability of skilled labor, manufacturers are seeking methods to improve productivity. By increasing productivity, manufacturers can optimize their workforce to achieve higher output, manage rising labor and energy costs, and minimize production delays. Additionally, greater productivity through improved tools and technology allows workers to focus more on specialized activities rather than repetitive tasks. Ultimately, improving employee productivity strengthens a plant’s overall operational resilience and long-term competitiveness.

Forward-looking manufacturers are adopting a wide variety of advanced asset performance management (APM) solutions to help improve maintenance efficiency. To better understand customer expectations and adoption trends around some of these advanced APM technologies, such as predictive maintenance (PdM) solutions, robotics and automatic lubrication systems, and their role in bringing productivity improvements, ARC Advisory Group launched a survey on Production Coverage Impact on Maintenance Productivity, and surveyed over 500 industry practitioners with knowledge or influence/decision-making power of technologies used in industrial maintenance and asset management.

Key findings from this research include:

• Manufacturers are increasingly leveraging advanced APM technologies—such as PdM, robotics, and automatic lubrication systems—to enhance overall plant productivity in the face of labor shortages, rising costs, and a growing demand for operational resilience.
• The adoption of PdM is accelerating, surpassing traditional condition-based maintenance approaches, as users recognize that AI/ML-powered analytics are more accessible and provide earlier, more accurate insights into asset health.
• Automation tools, including robots and automatic lubrication systems, can reduce routine inspection and lubrication tasks by 26–50 hours per week, enabling maintenance teams to concentrate on higher-value initiatives. However, significant upfront investment remains the primary obstacle to widespread implementation.
• AI-driven guided maintenance workflows offer substantial benefits by providing detailed step-by-step instructions, safeguarding institutional knowledge, and addressing skills gaps, thereby supporting safer and more efficient maintenance practices.

Climbing the APM Ladder

Significant advancements have been achieved in maintenance strategies within the industrial sector. A reactive maintenance strategy entails repairing equipment solely when failures occur; it remains a suitable strategy for various non-critical assets in a facility.

Preventive maintenance (PM) strategy typically involves organizations adhering to the original equipment manufacturers (OEM) recommended service intervals. This still represents the most widely adopted approach. According to the recent ARC survey, respondents indicated around 30 percent of their time is spent on preventative maintenance activities. In a previous ARC survey, “Rethinking Asset Performance Management” done in 2019, around 45 percent of survey respondents indicated heavy reliance on preventative maintenance. The comparison clearly highlights an increasing shift to more advanced maintenance strategies, such as predictive maintenance and AI/ML based anomaly detection. With the advancement of AI capabilities, sophisticated maintenance practices are becoming standard in the industry.

Condition-based maintenance (CBM) is another prevalent strategy, focusing on real-time monitoring of asset conditions to determine the appropriate timing for maintenance activities.

Table of Contents

• Executive Overview
• Climbing the APM Ladder
• Improvements with Automation
• Challenges with Automation
• Adding AI to the APM Mix
• Recommendations
• Appendix

View the complete Technology Adoption and Its Impact on Maintenance Productivity report.