The Evolution of Manufacturing & The Role of AI in Energy Management

For those who have spent decades in manufacturing, the transformation of the shop floor has been nothing short of revolutionary.

From the days of hard-wired logic, where machines relied on kilometers of cabling and hundreds of relays—leading to countless hours of troubleshooting—to today’s highly automated environments, the industry has come a long way. But with increased automation comes new challenges.

We’ve seen the transition from skilled electricians manually maintaining machines to engineers using digital terminals to monitor system statuses. Companies like Siemens, Allen-Bradley, and Honeywell have developed an extensive range of hardware and software, powering millions of machines worldwide.

Technological Milestones in Industrial Automation

• The evolution from CPUs processing 1MB of data at 100ms to 7ms was once considered groundbreaking.
• The introduction of two-wire field devices revolutionized sensor connectivity, while remote I/Os redefined cabling for electrical, electronic, and instrumentation systems.
• The arrival of DCS (Distributed Control Systems) and SCADA (Supervisory Control and Data Acquisition) marked a new era of real-time monitoring and supervisory control.

Challenges for Startups & Legacy Systems

Many startups today lack exposure to the vast array of industrial tools and technologies that have been developed over decades. In the realm of energy management, AI may seem like a promising solution, but legacy power systems and generators present significant limitations.

The industry has already addressed key challenges such as demand planning, power stabilization, load shedding, and energy conservation using technologies developed decades ago. Even IoT solutions, despite their potential, are often met with skepticism by manufacturing clients.

What Can AI Bring to the Table?

To explore AI’s potential, we need to categorize manufacturing entities:

1. Small-Scale Manufacturers

• Investment in IoT and AI remains a challenge due to the lack of a clear business case.
• Selling IoT-based energy solutions to small manufacturers is often met with resistance. If you are a sales person trying to sell IoT solutions to a small manufacturer, you will know what we are talking about!

2. Medium-Scale Manufacturers

• Energy bill savings could justify IoT adoption, but upfront investments in smart devices, data storage, and cloud computing are required.

3. Large & Very Large-Scale Manufacturers (e.g., Giga Factories)

• AI-driven energy management becomes a viable and valuable solution.

AI Applications in Energy Management

AI can enhance energy management in several ways, particularly in large-scale manufacturing:

1. Predictive Generator Failure – AI can layer on top of IoT to predict potential failures before they occur.
2. Grid Monitoring & Smart Switching – AI can optimize the transition between grid power and captive generators, improving stability and cost-efficiency.
3. Energy Consumption Comparisons – AI can compare energy usage across production lines, locations, and even global facilities, offering insights into cost optimization.
4. Carbon Footprint Tracking – For global manufacturers, AI can facilitate sustainability reporting and compliance with international carbon regulations.

The Discussion Continues

Are there other untapped areas where AI could enhance energy management? Let’s explore the possibilities together!