Building Up the Layers of Industrial IoT Economic Value & Differentiation: For OEMs and Their Customers

Building Industrial Internet of Things (IIoT) strategic value goes far beyond just connecting equipment. For Original Equipment Manufacturers (OEMs) and enterprises, focused investment unfolds in distinct levels of sophistication and economic value, directly enhancing the performance and lifespan of their assets deployed at their customers’ end and deepening relationships with their customers. Each Level not only deepens internal operational capabilities but critically, expands the value of data across diverse business functions, moving Industrial IoT from an important operational technology to a strategic enterprise asset that drives customer success and new revenue streams. 

Understanding these Levels and the "integration thresholds" to reach them allows OEMs to maximize their investment value, providing a clear roadmap for full Industrial IoT & AI business transformation. The Industrial IoT Maturity Model: Capabilities, Integration, and Expanding Value 

We can conceptualize Industrial IoT maturity in five progressive Levels (building from a disconnected baseline), each offering unique capabilities, demanding deeper integration, and unlocking value across new spheres of business interest – for the OEM and, crucially, for their customers utilizing their equipment.  

The Baseline: Manual/Disconnected Assets 

1. Data collection: Data collection for fielded equipment is largely manual, relying on customer-reported issues, scheduled service visits, or visual checks. OEM decision-making for product support is reactive, based on delayed information or intuition. 

1. Depth of Integration: OEM's internal systems (e.g., customer support, engineering) have no digital communication with their deployed machines or the customer's operational systems. 

Key Integration Thresholds:  As Level 0 is the baseline or starting point, no Industrial IoT or AI integrations have been implemented for fielded assets. 

Value Creation & Business Impact: Value is minimal, localized, and delayed. OEM's monitoring and analytics are limited to reactive customer support, troubleshooting based on limited information, and basic warranty management. Customer impact is high downtime and unpredictable performance of their purchased assets. 

Level 1: Connected Assets & Remote Monitoring 

The next level after baseline is the foundational level where OEMs focus on bringing individual fielded assets online. Basic sensor data (e.g., temperature, pressure, vibration, run status) is collected from OEM assets deployed at customer sites and transmitted via edge gateways to the OEM's central platform or cloud. This involves establishing OT-to-edge/cloud connectivity using protocols like MQTT, OPC UA, or Modbus TCP. This adds value to OEMs and their customers as they get real-time visibility into the health and operational status of deployed equipment. It further enables OEMs to remotely monitor their products, detect simple anomalies early, and provide initial remote diagnostics, reducing customer downtime and the need for frequent on-site checks.  

Key Integration Thresholds: 

• Device-level connectivity: Ability to integrate sensors (native or retrofitted) with the OEM's equipment, establish network access (wired/wireless), and configure data transmission from diverse industrial protocols at the customer site.  

• Basic data ingestion platform: A system capable of receiving, storing, and displaying time-series data from a fleet of fielded assets.  

• Foundational network security: Securing the connection from the deployed device to the OEM's central data platform, respecting customer network policies. 

Value Across Business Interests: 

• Customer Value: Customers gain basic remote monitoring services and potentially enhanced initial support from their OEM solution provider, leading to improved equipment uptime.  

• OEM Service & Support: Enhanced ability to remotely verify issues, improving first-time fix rates for field technicians and reducing truck rolls. 

• OEM Product Management/Engineering: Initial insights into real-world product usage patterns, environmental conditions, and basic performance parameters. 

• OEM Sales: Ability to offer "connected product" as a feature, enhancing product value proposition. 

• OEM IT/OT Leadership: Provides initial justification and visible early wins for digital transformation efforts focused on customer success.  

 Level 2: Real-time Monitoring & Descriptive Analytics 

This Level moves beyond individual assets to aggregating data from multiple fielded assets across various customer sites. Data is contextualized with OEM service records (e.g., maintenance history), customer data (e.g., asset ID, location), and potentially customer MES/ERP data (if shared). This enables real-time dashboards for the OEM's service teams, KPI tracking for fleet performance (e.g., fleet uptime, common failure modes), and descriptive analytics ("what happened?").  

OEMs gain a comprehensive understanding of their fielded fleet's operational performance, common issues, and service needs, allowing for better diagnostic capabilities and faster, more accurate remote troubleshooting for customers. Customers benefit from more efficient and effective OEM support.  

Key Integration Thresholds: 

• Data normalization and contextualization: Robust capabilities to combine data from diverse OEM assets with service histories, customer profiles, and potentially customer operational data. 

• Scalable data storage and processing: Implementing data lake/warehouse solutions capable of handling increasing volumes of data from a distributed fleet.  

• Advanced analytics and visualization tools: Dashboards, reporting, and business intelligence capabilities tailored for fleet management and customer support. 

• Integration with OEM enterprise systems: Establishing APIs or connectors to pull contextual data from CRM, ERP (for spare parts), and service management systems. 

Value Across Business Interests: 

• Finance: Clearer understanding of service costs, warranty claims, and efficiency gains from improved remote diagnostics.  

• Supply Chain & Procurement: More accurate insights into spare parts consumption patterns across the fielded fleet, improving inventory management and reducing carrying costs.  

• Engineering/R&D: Granular data on product performance in diverse real-world conditions informs critical design improvements, identifies component weaknesses, and provides detailed troubleshooting insights for future product iterations.  

• Executive Leadership: Data-driven insights into overall product performance in the field, enabling strategic resource allocation for product development and service expansion. 

• Sales/Customer Service: Ability to make more reliable commitments on product performance and service response times, enhancing customer trust and satisfaction.  

 Level 3: Predictive Analytics & Diagnostics 

Leveraging historical and real-time data with Machine Learning (ML) models, this Level predicts future events like equipment failure in fielded OEM products, quality deviations, or even optimal service windows. These models identify patterns that precede issues, enabling proactive interventions. Integration extends to the OEM's Field Service Management or Customer's CMMS for automated work order generation.OEMs shift from reactive/preventive service to condition-based, predictive maintenance for their customers, significantly minimizing unplanned customer downtime and optimizing service schedules. Customers gain increased uptime and predictable operations. 

Key Integration Thresholds:

• High-quality and sufficient data volume: Access to clean, labelled historical data from fielded assets for accurate ML model training (e.g., failure modes, operational conditions, service records). 

• Data science and ML capabilities: Expertise in building, deploying, and managing complex ML models tailored for predicting asset health and performance. 

• Integration with operational systems for action: Automated triggers to OEM's field service dispatch, spare parts inventory, or customer's CMMS based on predictive insights. 

• Edge AI (optional but increasingly beneficial): Deploying ML models closer to the customer's equipment for faster anomaly detection and reduced data transmission latency. 

 Value Across Business Interests: 

• OEM Supply Chain & Procurement: Predictive spare parts ordering and proactive logistics across the service network, optimizing inventory levels and significantly reducing urgent rush orders for field repairs. 

• OEM Finance: Significant cost avoidance due to reduced unplanned customer downtime (leading to fewer penalties or service credits), lower repair costs, and optimized service technician scheduling. New revenue streams from predictive maintenance contracts. 

• OEM Customer Service/Field Service: Ability to proactively schedule service, predict customer needs, and fundamentally transform customer relationships by offering "uptime as a service" agreements. 

• OEM Product Management/R&D: Real-world performance and predicted failure data provide critical feedback for next-generation product design, feature enhancements, and reliability improvements, leading to more robust products and services. 

• OEM Strategic Planning: More accurate long-term capacity planning for service resources, spare parts manufacturing, and new product development based on predicted asset health and lifespan across the installed base. 

 Level 4: Prescriptive Analytics & Closed-Loop Automation 

This advanced Level goes beyond prediction to recommend what actions to take or even autonomously take action to optimize fielded OEM equipment. AI/ML models suggest optimal operational adjustments, reconfigure parameters (if OEM allows remote control), or initiate maintenance processes based on predictions and desired outcomes. Digital twins are often crucial here for simulating and optimizing changes for specific deployed assets before remote deployment. Deep, bidirectional integration between the OEM's insight platform and the customer's equipment control systems allow insights to directly influence asset performance. 

OEMs move from just providing products to becoming performance partners, ensuring their equipment runs at peak efficiency for customers. Customers gain guaranteed operational performance, maximized asset utilization, and potentially reduced operational costs. This enables new outcome-based service contracts (e.g., "pay-per-use," "guaranteed uptime"). 

Key Integration Thresholds:  

• Reliable, low-latency communication: Essential for real-time control feedback loops to customer-site equipment. 

• Robust security for control systems: Paramount importance given the direct influence on customer-owned physical assets and processes. 

• Trust in AI/ML recommendations and automation: Both OEM service teams and customer operational teams need to trust the system's ability to act autonomously or guide critical decisions, requiring strong change management and clear governance. 

• Digital Twin infrastructure: Ability for the OEM to create, maintain, and leverage dynamic virtual models of their deployed assets for simulation and optimization. 

• Organizational change management: From selling products to managing and optimizing outcomes, and for customers from owning assets to consuming performance, it is a win for OEMs 

 Value Across Business Interests: 

• Finance & Cost Accounting: Granular, real-time cost optimization for service delivery and warranty management. Enables new revenue streams from outcome-based service contracts and performance guarantees. 

• Sales & Marketing: Guaranteed, optimized equipment performance enables more aggressive market positioning, differentiation through superior reliability and efficiency, and highly customized performance-based offerings. 

• Human Resources: Shifts the required OEM workforce skills towards AI specialists, remote automation engineers, and strategic account managers focused on customer outcomes. 

• Business Development: Creation of entirely new service lines or business models leveraging autonomous capabilities, such as remote fleet optimization as a service or shared-risk contracts based on performance. 

• Environmental, Social, and Governance (ESG): Automated optimization of fielded assets can lead to significant, measurable reductions in energy consumption, waste, and emissions for customers, allowing OEMs to highlight their contribution to sustainability. 

 Level 5: Autonomous Ecosystems / Cognitive Operations 

At the pinnacle of Industrial IoT maturity, the OEM, its customers, and potentially the customer's supply chain partners form a digitally connected and self-optimizing ecosystem. AI agents make decisions and initiate actions across distributed fielded assets with minimal human intervention, anticipating disruptions across an entire installed base and self-healing. This requires end-to-end horizontal and vertical integration, leveraging federated data platforms and advanced AI orchestration across all parties. 

The OEM transforms into an indispensable, long-term partner, providing autonomous optimization services that ensure the highest possible value from their products across an entire installed base. Customers gain ultimate efficiency, resilience, and adaptability from their OEM-supplied assets. 

Key Integration Thresholds: 

• Standardized data models and interoperability: Crucial for seamless data exchange across OEM systems, customer systems, and potentially other partners in the value chain. 

• Advanced, pervasive cybersecurity: Protecting highly interconnected and autonomous systems that span organizational boundaries. 

• Ethical AI governance: Establishing frameworks for responsible, explainable, and transparent AI decision-making where OEM insights influence customer operations. 

• Cross-organizational collaboration frameworks: Robust legal and technical frameworks for secure data sharing and joint optimization with customers and their partners. 

• Culture of radical innovation: An organizational culture for both the OEM and its customers that embraces radical automation, continuous data-driven evolution, and pushes the boundaries of traditional product-service relationships. 

 Value Across Business Interests: 

• OEM Strategic & Corporate Development: Ability to rapidly pivot business models, enter new markets, or forge complex strategic alliances based on real-time global insights into asset performance and market needs. 

• OEM Competitive Advantage: Establishes unparalleled agility, efficiency, and resilience that fundamentally differentiates the OEM in the global market, creating defensible long-term partnerships. 

• Global Supply Chain: True end-to-end transparency, dynamic routing, and automated response to disruptions across the entire product lifecycle and customer value chain. 

• Risk Management: Proactive identification and mitigation of systemic risks across deployed assets, supply chain, and market fluctuations through predictive and prescriptive foresight. 

• Talent Management: Becomes a magnet for top-tier talent in AI, robotics, and complex systems integration, enhancing the OEM's intellectual capital and fostering innovation. 

The Data Multiplier Effect & Non-Linear Value Creation for OEMs & Their Customers 

As an OEM progresses through these Industrial IoT levels, the underlying operational data from their fielded assets experiences a "multiplier effect." A simple sensor reading from a customer's machine evolves from a standalone piece of information to a rich, contextualized insight that can predict future events, prescribe optimal actions, and ultimately, drive autonomous, enterprise-wide optimization not just for the OEM, but for their customers' operations. This transformation from raw data to a strategic asset is the ultimate promise of mature Industrial IoT adoption, demanding a holistic, cross-functional approach to technology, people, and processes that benefits both the OEM and their most valued customers. 

Overcoming Excessive Costs, Delays, and Risks with the Right Strategic Partner 

The journey through these Industrial IoT sophistication levels clearly illustrates a path to profound economic value and differentiation for OEMs and the enterprises they serve. From enabling basic remote monitoring to orchestrating autonomous ecosystems, the transformative power of connected data to enhance operational efficiency, revolutionize service models, and forge deeper customer relationships is undeniable. The "Data Multiplier Effect" promises not just incremental gains, but a fundamental shift in how products are delivered, optimized, and monetized. 

However, realizing this ambitious vision is not without challenges. The complexity of integrating disparate systems, the significant upfront investment in specialized talent and infrastructure, the inherent risks of cybersecurity in interconnected environments, and the extended time-to-market for bespoke solutions can be daunting. These hurdles often slow adoption, increase costs, and delay the very competitive advantages they seek to unlock. 

This is precisely where the Flex Platform offers a critical advantage. By leveraging the Flex Platform, OEMs and enterprises can greatly reduce their cost, mitigate risk, and accelerate their time-to-market for achieving these transformative business advantages. Flex provides a robust, scalable, and secure foundation that abstracts much of the underlying complexity, as well as multiple core services for building high-scale, secure, and reliable systems. And the Flex Platform provides extensive application layer code that OEMs and Enterprises can tailor to meet their specific needs. All of this enables organizations to rapidly deploy, manage, and scale their Industrial IoT initiatives across all maturity Levels.  

The future of industrial value lies in intelligent, connected assets, and with the right AIoT platform, that future is within reach.