Digital Twins for Manufacturing: Moving Beyond Predictive Maintenance to Prescriptive Analytics

Manufacturers frequently encounter a common pattern: initial Industrial IoT projects focus on predictive maintenance, achieve moderate success identifying potential equipment failures, then struggle to demonstrate broader business value. This pattern reflects a fundamental limitation in how many organizations approach digital transformation.

Dr. PG Madhavan, who worked on early industrial IoT implementations and has held technical leadership roles at GE Aviation, Microsoft, and Rockwell Automation, argues that the industry has reached an inflection point. The technologies and methodologies now exist to move beyond symptom treatment—predicting when equipment will fail—to cause analysis that improves overall plant performance.

This article examines why predictive maintenance alone provides insufficient returns on digital twin investments and outlines the path toward prescriptive analytics that addresses root causes rather than symptoms.

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Evolution of Maintenance Strategies in Manufacturing

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Understanding the limitations of current approaches requires examining how maintenance strategies have evolved. Madhavan illustrated this progression using paper manufacturing, where thousands of rollers process material through various stages.

Breakdown maintenance: The traditional approach where operations continue until equipment fails, then repairs occur. For a paper mill, this might mean a few hours of downtime. However, in other industries, consequences prove more severe. Madhavan described a steel mill incident where a binding failure on a massive rolled steel sheet created forces that destroyed structural supports, resulting in three months of downtime and complete facility reconstruction.

Preventive maintenance: Organizations schedule maintenance activities based on time intervals or production cycles—applying bearing lubricant weekly, replacing components after specified hours of operation. This approach reduces catastrophic failures but often performs unnecessary maintenance and fails to account for actual equipment condition.

Predictive maintenance: Using sensor data and analytics to predict when specific equipment will fail, organizations perform maintenance only when needed. This optimization reduces maintenance costs compared to time-based schedules and prevents unexpected failures more effectively than preventive approaches.

Each evolution provided genuine improvements. However, Madhavan identified what he describes as a "kink" in the IoT arc—a point where the industry has stalled rather than continuing to progress toward more valuable applications.

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Limitations of Predictive Maintenance as Primary Use Case

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Predictive maintenance has become synonymous with Industrial IoT, widely regarded as the primary justification for IoT investments. However, this focus creates several problems for organizations seeking broader digital transformation.

Limited scope of impact: Predictive maintenance addresses equipment reliability but does nothing to improve process efficiency, product quality, energy consumption, or throughput. Organizations that view predictive maintenance as the primary IoT objective miss opportunities for substantially larger business impact.

Correlation without causation: Most predictive maintenance models identify patterns in sensor data that correlate with impending failures. These correlations enable predictions but provide no understanding of why failures occur or how operating conditions contribute to equipment degradation.

Maintenance-centric thinking: Framing IoT value primarily through maintenance optimization reinforces operational silos rather than enabling the cross-functional insights that characterize true digital transformation. Manufacturing excellence requires optimizing the entire production system, not just maintaining individual assets.

Diminishing returns: While initial predictive maintenance implementations often provide clear ROI, subsequent projects targeting less critical equipment yield progressively smaller benefits. Organizations eventually exhaust high-value predictive maintenance opportunities without establishing capabilities for other applications.

Madhavan argues that predictive maintenance represents an intermediate step rather than the destination. The real value lies in moving to prescriptive analytics that optimize plant operations holistically.

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Prescriptive Analytics: Optimizing Plant Performance

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Prescriptive analytics represents the next evolution beyond prediction—using data and models not just to forecast what will happen, but to recommend specific actions that achieve desired outcomes. In manufacturing contexts, this means identifying process changes that simultaneously improve quality, increase throughput, reduce energy consumption, and extend equipment life.

This capability requires fundamentally different approaches than predictive maintenance. Rather than monitoring individual assets for failure indicators, prescriptive systems must understand relationships between process parameters, equipment behavior, product characteristics, and business outcomes.

For a paper mill, prescriptive analytics might determine that adjusting roller speed profiles and pulp consistency in specific combinations increases production rate by three percent while reducing waste and improving sheet quality. These insights emerge from understanding causal relationships in the manufacturing process rather than correlating sensor patterns with equipment failures.

Implementing prescriptive analytics requires several capabilities:

Comprehensive data integration: Understanding process relationships requires data from multiple sources—process sensors, quality measurements, equipment monitoring, material properties, and operational parameters. Organizations need data infrastructure that integrates these diverse sources with consistent timestamps and context.

Causal modeling approaches: Correlation-based models that work for predictive maintenance prove insufficient for prescriptive applications. Organizations need modeling methodologies that represent actual cause-and-effect relationships in manufacturing processes.

Optimization frameworks: Prescriptive systems must balance multiple objectives—quality, throughput, cost, equipment life, energy consumption. This requires optimization algorithms that can consider trade-offs and constraints while recommending specific parameter adjustments.

Operational integration: Recommendations have no value unless operations teams can act on them. Prescriptive analytics requires integration with process control systems and workflows that enable rapid implementation of recommended changes.

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Causal Digital Twins and Systems Thinking

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Madhavan emphasizes the importance of causal digital twins—models that represent actual cause-and-effect relationships rather than purely statistical correlations. This distinction proves critical for prescriptive analytics.

Statistical models trained on historical data can identify patterns: when certain sensor combinations occur, equipment fails or quality degrades. However, these models cannot explain why relationships exist or predict behavior under conditions not present in training data. They work as long as process conditions remain within historical ranges but fail when organizations attempt process improvements or encounter novel situations.

Causal models incorporate understanding of physical principles, chemical reactions, mechanical behaviors, or other domain knowledge that explains system behavior. These models can predict outcomes for process changes never attempted previously because they represent actual mechanisms rather than observed correlations.

Developing causal digital twins requires systems thinking—viewing manufacturing processes as interconnected systems rather than collections of independent equipment. Systems theory, fundamental to engineering education, provides frameworks for representing these relationships.

Madhavan advocates for state space data models, which represent systems through state variables that capture internal system conditions and how they evolve based on inputs and interactions. This approach provides a more powerful framework for manufacturing digital twins than simple regression models that directly map inputs to outputs without representing internal process states.

State space models can incorporate known physical relationships while learning parameters from data, combining domain knowledge with machine learning in ways that pure data-driven approaches cannot. For manufacturing applications, this hybrid approach often proves more effective than either physics-based models requiring complete system knowledge or black-box machine learning models ignoring physical principles.

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Implementation Approach for Data Leaders

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Organizations moving from predictive maintenance toward prescriptive analytics should consider several practical steps:

Identify high-impact process optimization opportunities: Look beyond maintenance to process areas where small parameter changes could significantly improve quality, throughput, or efficiency. Target applications where current operations rely on operator experience rather than systematic optimization.

Assess data infrastructure requirements: Prescriptive analytics requires richer data than predictive maintenance—process parameters, quality metrics, material properties, and contextual information about operating conditions. Evaluate whether existing data collection provides sufficient coverage and resolution.

Build cross-functional teams: Prescriptive analytics requires combining domain expertise, data science capabilities, and operational knowledge. Process engineers who understand manufacturing mechanisms must work closely with data scientists developing models and operations teams implementing changes.

Start with hybrid modeling approaches: Rather than attempting pure physics-based models or pure machine learning, combine domain knowledge about process relationships with data-driven parameter estimation. This approach often provides better results with less data than purely statistical models.

Establish experimentation frameworks: Prescriptive analytics depends on understanding cause and effect, which requires controlled experimentation. Develop processes for safely testing process changes, measuring outcomes, and updating models based on results.

Create feedback loops: Implement systems that track whether recommended changes achieve predicted outcomes, identify when models require updates, and incorporate operational learnings back into analytics frameworks.

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Summary: Strategic Value of Digital Twins

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Digital twins provide value beyond predictive maintenance when organizations use them to understand and optimize manufacturing processes holistically. This requires moving from correlation-based predictive models to causal models that represent actual process relationships and enable prescriptive recommendations.

The distinction matters because it determines whether digital twin investments yield incremental improvements in maintenance efficiency or transformational improvements in manufacturing performance. Organizations that view predictive maintenance as the destination miss opportunities to apply IoT data and analytics to fundamental process optimization.

For data and analytics leaders, this perspective suggests different investment priorities. Rather than implementing predictive maintenance broadly across all equipment, focus on developing capabilities for causal modeling and prescriptive analytics in processes where optimization provides substantial business value. Use predictive maintenance as one application of comprehensive data infrastructure rather than the primary justification for IoT investment.

The systems thinking approach that Madhavan advocates—viewing manufacturing as interconnected processes rather than independent equipment—aligns with modern data architecture principles. Organizations building data infrastructure should ensure it supports cross-functional analysis and optimization rather than siloed applications focused on individual asset types.

Manufacturing excellence requires more than preventing equipment failures. It requires understanding and continuously improving the complex interactions that determine quality, efficiency, and cost. Digital twins provide the foundation for this understanding when implemented with appropriate modeling approaches and organizational capabilities.

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