Sensors or AI? Another Chicken and Egg

Unlocking AI in Industry 4.0: Is SCADA Enough or Do You Need New Sensors?

It’s Chicken and the Egg as usual.

The promise of Industry 4.0—where AI and data-driven technologies transform industrial operations—has sparked a vital debate within the sector: Do companies need to invest in a new generation of IoT sensors, or is the data already collected by SCADA (Supervisory Control and Data Acquisition) systems sufficient? This "chicken and egg" dilemma is common in discussions about AI-driven industrial solutions. On one hand, sensor manufacturers argue that sophisticated sensors are essential for unlocking the full potential of AI. On the other, software experts often say that SCADA systems provide enough valuable data for many AI applications, making additional hardware investments unnecessary—at least initially.

The reality, as in many technological debates, is that both sides have valid points. For some use cases, SCADA data is entirely sufficient to extract actionable insights through AI, allowing companies to avoid the costs of deploying new sensors. However, for more advanced applications—particularly those requiring real-time, high-granularity data—modern sensors can fill gaps that SCADA systems cannot address. The best approach often lies in a hybrid model: leveraging existing infrastructure to get started with AI and deploying additional sensors only when the business case justifies it.

The Argument for SCADA: Leveraging Existing Data

SCADA systems have been a cornerstone of industrial automation for decades, collecting data from machines, pumps, valves, and other equipment to monitor and control critical processes. This wealth of operational data is often underutilized, making it a prime target for AI applications without the need for costly new sensors.

Why SCADA Data Might Be Enough for Some AI Use Cases:

• Rich data already exists: SCADA systems track key metrics like temperature, pressure, flow rates, RPMs, and more. With the right AI algorithms, this data can be analyzed to optimize processes, detect anomalies, and improve efficiency.

• Cost efficiency: Avoiding upfront investments in new sensor hardware minimizes initial costs and accelerates the return on investment (ROI) from AI.

• Faster deployment: Because SCADA systems are already integrated into most industrial environments, applying AI to this data allows companies to achieve results without the complexity of installing new infrastructure.

Use Case 1: Commercial Refrigeration – Energy Management with Common Controllers

In commercial refrigeration systems, controllers commonly used already track significant amounts of data, which can be highly beneficial for AI-based energy management solutions. These controllers gather detailed information on system performance, which AI can analyze to reduce energy usage and optimize operations.

Data Tracked by existing Controllers	Description
Compressor operation times	Tracks when and how long compressors are running, providing insights into efficiency.
Refrigerant pressure levels	Monitors refrigerant pressures, which can be analyzed for system load and leak detection.
Temperature set points and fluctuations	Detailed tracking of temperature settings and any deviations from the desired ranges.
Evaporator and condenser performance	Data on heat exchange efficiency, including fan speeds and performance under different load conditions.
Defrost cycles	Information on the timing and frequency of defrost cycles, which AI can optimize to reduce energy consumption.
Power consumption	Monitors energy usage at the component level, offering insights into the overall efficiency of the refrigeration system.
Alarms and event logs	Records alarms for temperature excursions or equipment failures, allowing AI to correlate events with system performance.

With AI analyzing this SCADA data, companies can optimize refrigeration performance, avoid unnecessary compressor cycling, and fine-tune defrost cycles—all without adding new sensors. AI can also predict when refrigeration units are working harder than necessary, allowing for adjustments that save energy while maintaining product integrity.

Use Case 2: Oil Refining – Process Optimization

In oil refining, SCADA systems are used to monitor various key metrics like pressure, temperature, and flow rates across different refining stages, such as distillation and cracking. AI can leverage this SCADA data to optimize refinery operations, improving throughput while reducing waste and energy consumption.

Data Tracked in Refining SCADA Systems	Description
Flow rates of crude and refined products	Tracks the movement of raw materials and finished products.
Temperature and pressure in cracking units	Critical for maintaining optimal refining conditions.
Operational data from machinery	RPMs, valve positions, and pump operations are recorded for detailed analysis.

By applying AI to SCADA data, refiners can detect inefficiencies or deviations from optimal operational parameters and adjust processes accordingly. For instance, AI can flag small temperature or pressure changes that might affect the quality or output of refined products, allowing for real-time optimizations. SCADA data alone can provide sufficient insights for these optimizations in many routine cases.

When SCADA Isn’t Enough: Where IoT Sensors Come Into Play

While SCADA systems provide valuable data, there are instances where they fall short in terms of granularity, frequency, or the specific types of data needed for more sophisticated AI applications. This is where IoT sensors make their case. Modern sensors can collect high-resolution, real-time data that SCADA systems may miss, especially for applications requiring precision measurements or predictive analytics.

Use Case 3: Oil Refining – Environmental Compliance and Emission Monitoring

Environmental regulations in the oil refining industry are becoming increasingly stringent, particularly regarding emissions monitoring. While SCADA systems track the operational data of refining processes, they often don’t monitor environmental conditions, such as air quality, emissions, or specific pollutants.

Environmental Data from IoT Sensors	Description
CO2 and NOx levels	Critical for regulatory compliance.
Particulate matter in emissions	Monitors pollutants in real-time to avoid fines.
Air quality monitoring	Ensures safety and compliance with regulations.

IoT sensors that monitor emissions in real time allow refineries to stay compliant with strict regulations, reducing both legal risks and environmental impacts.

Why Starting with AI Makes Sense: A Business Model Perspective

From a business strategy standpoint, starting with AI before investing in new sensors makes financial sense for several reasons. For software and services companies developing AI-driven solutions, the margins on hardware sensors are often substantially lower than those of AI and analytics platforms. By focusing on AI that utilizes existing SCADA data, businesses can deliver value quickly while avoiding the high capital costs associated with deploying new sensors.

AI Margins vs. Sensor Margins:

• AI solutions are software-based: This means they have relatively low deployment costs compared to hardware, which often requires manufacturing, installation, and maintenance.

• Higher scalability: Once developed, AI solutions can be deployed across multiple locations and clients with minimal incremental costs, leading to higher margins.

• Longer-term value: AI and analytics software tends to generate ongoing revenue through subscriptions or service agreements, while sensors are often a one-time sale with lower margins.

The Hidden Costs of Sensor Deployment: Downtime and Integration

Another key factor that affects both the cost and complexity of sensor deployment is the need for downtime during installation and integration. Installing new sensors across a facility often requires shutting down critical operations or diverting resources, which can lead to loss of production time and increased operational costs.

• Downtime for sensor installation: In many industrial environments, installing new sensors requires halting processes or machines, which can disrupt workflows and reduce productivity. In sectors like oil refining, where downtime can cost millions of dollars per day, minimizing these interruptions is crucial.

• Complex integration: Once installed, new sensors need to be integrated with existing systems, which can involve significant engineering and IT resources. This process can introduce delays, require specialized skills, and add costs related to system updates and configuration.

In contrast, deploying AI on top of SCADA data does not require any downtime. The data is already being collected, and AI software can connect to SCADA controllers such as those from RDM or Danfoss without interrupting operations. This allows for faster deployment and quicker ROI, while also reducing the risks and costs associated with downtime.

The Path Forward for AI and Industry 4.0: Balance and Strategy

For companies embarking on their Industry 4.0 journey, the key to success lies in striking the right balance between leveraging existing infrastructure and strategically deploying new technologies. Starting with AI powered by SCADA provides a low-risk, high-reward entry point that helps companies quickly realize the benefits of digital transformation without the upfront costs of a sensor deployment. As they grow more familiar with AI and identify specific use cases where additional granularity or real-time data is needed, they can then invest in targeted sensor deployments, ensuring that their capital expenditure delivers tangible value.

This hybrid approach—starting with AI and adding sensors as necessary—allows businesses to stay agile, competitive, and scalable in the evolving landscape of Industry 4.0. By taking a pragmatic and phased approach, companies can maximize their operational efficiency while minimizing unnecessary costs and disruptions.

In the end, the path forward isn’t about choosing between SCADA or sensors—it’s about knowing when to use each to its fullest advantage. The goal is to harness the power of AI to optimize operations, boost productivity, and future-proof the business for the challenges and opportunities that Industry 4.0 brings.