Downtime is now costing a shocking $1.5 trillion a year for Fortune Global 500 industrial companies, roughly 11% of their annual revenues. Since 2020, this figure has surged by over 50%, driven by inflationary pressures and production lines running at higher capacity. As a result, unplanned downtime costs manufacturers significantly more today than in 2019–2020.
However, this is not simply an inevitable consequence asset-intensive industries are subjected to. There are ways to significantly reduce costs and boost productivity to navigate adverse conditions.
With digital transformation being a necessity in these industries, maintenance strategies will have to closely follow suit in digitization and predictive maintenance efforts to offset staggering costs.
Maintenance strategies in asset-intensive industries must fundamentally evolve with digital transformation to move from reactive or preventive approaches toward proactive, data-driven, and highly efficient models. This evolution is critical for mitigating the substantial financial burden of unplanned downtime, enhancing operational efficiency, and capitalizing on advanced technological capabilities.
Here’s how maintenance strategies need to transform:
Shift to Predictive Maintenance to Mitigate High Costs
- Despite a 23% reduction in production line failures, recovery times have increased, leading to only a marginal gain in production capacity.
- Digitization and predictive maintenance efforts are highlighted as having a significant impact on reducing incidents. Predictive maintenance, especially when powered by AI-driven machine health management platforms, is considered crucial for reducing costs and boosting productivity by anticipating failures before they occur.
Leveraging Data and Digital Connections for Improved Serviceability
- Manufacturing is increasingly moving towards a software-driven industry, extending beyond the factory floor to connecting with products in the field, similar to the automotive industry.
- Industrial manufacturers are enhancing digital connections to their products to gather usage and operational performance data, which is vital for improving product performance and serviceability.
- This enables customers to access portals to monitor equipment performance, schedule maintenance, and communicate with company representatives to resolve issues.
Integrating Smart Operations Technologies
- To manage rising material and labor costs, skill shortages, and geopolitical risks, manufacturers are continuing to invest in digital technologies that enable smart operations. These efforts focus on building a strong digital core and data infrastructure.
Key technologies include:
- MOM and MES for real-time shop floor visibility and enterprise integration
- IoT to transforms ordinary objects into intelligent assets
- Unified Namespace for centralized, standardized data access
- ERP Systems for a Centralized management of finance, procurement, inventory, and production
- Model-based enterprise to enable the digital thread
- Machine Learning (ML) for autonomous or assisted decision-making using Intelligent data processing (AI)
- These tools collectively support advanced maintenance and more resilient, efficient manufacturing.
These tools collectively support advanced maintenance and more resilient, efficient manufacturing.
Utilizing Simulation for Enhanced Decision-Making
Simulation technologies are gaining traction as manufacturers seek to manage disruptions and control costs.
Emerging applications include:
- Causal AI for simulating cause-and-effect dynamics and improving decision-making
- Production line simulation to identify bottlenecks and optimize workflows before implementation
- Metaverse-enabled factory simulation to boost throughput and lower operational costs
- Enterprise-level business scenario simulation to prepare for issues like workforce shortages or supply chain disruptions
Together, these tools enable proactive planning and smarter, data-driven responses to operational challenges.
Focus on Data Quality and AI Strategy
High-quality data is essential for enabling advanced, AI-driven maintenance strategies.
To support generative AI initiatives, many manufacturers are ramping up investments in data lifecycle management. However, persistent issues with data quality, contextualization, and validation continue to hinder AI adoption.
To overcome these barriers, organizations must develop a comprehensive AI and data strategy, including a clear operating model, governance structure, and risk assessment framework, to support sustainable, long-term AI investment.
Timenow aims to help clients pinpoint exactly where they stand today, identifying the small, strategic shifts that can unlock major operational gains. Whether it’s strengthening your data foundation, refining your maintenance approach, or preparing for tomorrow’s disruptions, even a modest change can drive measurable impact.
Where does your current strategy land? And what could one small shift unlock?
Let’s explore the answer together.