Cengiz Γzemli
Academic
- Thread Author
- #1
With the widespread adoption of Industry 4.0 technologies, particularly artificial intelligence (AI), manufacturers are prioritizing predictive maintenance applications. However, despite strong interest and awareness of the benefits in this area, companies are struggling to translate impressive proof of concepts into scalable value.
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π Market Size and Benefits: Why Is It So Important?
According to Grandview Research, the global predictive maintenance market is expected to grow from $14.29 billion in 2025 to $98.16 billion by 2033, representing a compound annual growth rate (CAGR) of 27.9%. A 2022 report by Deloitte shows that early adopters are already realizing tangible benefits:
[]Up to 15% reduction in downtime
[]20% increase in labor efficiency
[]30% reduction in inventory levels due to decreased need for "just in case" stocking
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π§ What Are the Key Factors Hindering Predictive Maintenance?
Automation World consulted two experts to explore the bottlenecks in this area and ways to accelerate adoption: Michael Cooper from Rockwell Automation and Matt Bernhard from TwinThread.
π‘ Data Preparation and Lack of Context
Bernhard states that the biggest obstacle is often data preparation. While companies focus on AI solutions, they may neglect to establish the necessary data systems and infrastructure for these solutions to create value. Furthermore, the context of the data is critical. For example, when performing predictive monitoring for rotating equipment, a complete picture cannot be obtained without information such as products on the line, the operational status of the equipment, or quality metrics. This deficiency can lead maintenance teams to distrust the data.
π οΈ Deployment Complexity and Ownership Gap
Cooper points out three main issues:
[
- ]Deployment complexity: Most applications still feel like custom projects and cannot be scaled across different facilities or assets.
[]Perception of transformation: Many teams assume a complete transformation is required, not knowing where to start or how to progress incrementally.
[]Ownership gap: Even after the application is deployed, there is uncertainty about who will be responsible for it in the long term and how the system will be maintained.
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π§ Lack of Technical Expertise: Which Skills Are Needed?
βοΈ The Gap Between Data Science and Process Knowledge
According to Bernhard, most manufacturers do not have a large data science team. Those who do, while often highly technically proficient, may be unfamiliar with factory floor maintenance or processes. This creates a disconnect. Subject-matter experts are needed to make sense of sensor data. These individuals could be process engineers or reliability leaders who understand the data and grasp both the technical and commercial aspects of the business.
π Hardware Integration and Change Management
Cooper states that the biggest gaps are not in data science or machine learning (ML). The real challenges are:
[]Hardware (OT) integration and making machine signals usable.
[]Translating signals into actual maintenance actions.
- Continuous ownership of thresholds, settings, and system behavior.
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π° Hidden Costs: Beyond Software Licensing
πΈ Scalability and Unexpected Expenses
Bernhard notes that while his company's licensing approach is transparent, some software solutions, when designed without scalability in mind, can surprise customers with expansion costs. Unexpectedly high bills can arise, especially with token-based systems.
π Integration and Internal Resources
According to Cooper, the software itself is rarely the largest cost item. Integration effort, internal resources, and change management are the real factors determining the total cost of ownership (TCO).
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β±οΈ Measurable ROI and Budget Cycles
π― Quick Wins Focused on Business Value
Bernhard emphasizes that manufacturers should first focus on business value. By identifying high-value use cases, it is possible to prove value in 90 days or less. Then, by planning expansion within a six-month period, value can continue to be seen across multiple lines in the same facility or in various use cases across different facilities.
β‘ Rapid Value in Targeted Assets
Cooper states that value can be seen in targeted assets within months. For example, with Fiix CMMS + FactoryTalk Optix, maintenance teams can quickly gain value by responding to issues faster, reducing inspections, and tracking downtime more accurately. The true long-term return on investment (ROI) comes from scaling these solutions across assets and facilities after change management is complete and new best practices optimized for CBM are implemented. However, budget cycles expecting immediate and predictable returns may not align with this process.
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πΊοΈ An Incremental Journey: Start Small, Move Fast
π± Think Big, Start Small, Move Fast
Bernhard's philosophy is based on "think big, start small, move fast." Companies can start by using solution templates for use cases such as asset reliability or anomaly detection. They begin with small steps by solving a specific problem, then help expand with other solution templates like machine centering or optimizing line efficiency.
π Change Management and Customer Ownership
Cooper states that positioning predictive maintenance as a journey is the most important change. Start small with high-value assets, deliver quick wins, standardize how deployments are replicated, and gradually transfer ownership to the customer. If predictive maintenance is positioned as a major upfront transformation, most customers will be hesitant to even start.
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With the right strategy and incremental approach, predictive maintenance can transform from just a cost item into a powerful tool that increases operational efficiency and profitability for manufacturers. The key is to focus on business value, not technology, and to proceed with the right steps.
