Unleashing AI and IoT for Enhanced Energy Efficiency in Smart Factories

In today’s rapidly changing business landscape, smart factories are pushing the boundaries of sustainability, efficiency, and innovation. By leveraging the power of Artificial Intelligence (AI) and the Internet of Things (IoT), smart factories are transforming industrial landscapes and achieving significant cost and energy savings. In this article, we’ll explore how AI and IoT are redefining energy efficiency in smart factories, looking at real-life case studies like the Rexel and Siemens collaboration and Mitsubishi Heavy Industries’ (MHI) Energy Cloud Factory service package. Additionally, we’ll examine the role of advanced analytics, energy harvesting, and AI-driven energy storage solutions in enhancing energy efficiency.

The Dynamic Duo: AI and IoT Energising Smart Factories

AI and IoT technologies are ushering in a new era of energy efficiency for smart factories. Let’s take a look at some key aspects:

  • Unleashing Smart Energy Management: AI-powered energy management systems enable smart factories to monitor and control energy consumption in real-time, adjusting processes to maximise efficiency. By using IoT sensors and AI algorithms, these systems identify energy-intensive operations, spot inefficiencies, and provide actionable insights to fine-tune energy use.
  • Embracing Predictive Maintenance: IoT devices integrated into factory equipment constantly monitor performance, detecting potential issues before they result in costly downtime or energy waste. By implementing AI-driven predictive maintenance, businesses can proactively address problems, extending equipment life and reducing overall energy consumption.
  • Crafting Optimised Production Processes: AI and IoT technologies facilitate the creation of digital twins, simulating production processes and identifying opportunities for improvement. Real-time data analysis from IoT sensors, combined with AI algorithms, can suggest process adjustments that decrease energy consumption and boost operational efficiency.

The Next Frontier: Advanced Analytics, Energy Harvesting, and AI-Driven Energy Storage Solutions

Advanced Analytics: Delving Deeper into Energy Consumption Patterns: Smart factories can take advantage of advanced analytics tools to gain more profound insights into their energy consumption patterns. By using machine learning techniques, businesses can predict future energy needs and optimise their production schedules accordingly. Moreover, advanced analytics can help factories identify potential energy savings opportunities by evaluating and comparing the energy efficiency of various equipment models and configurations.

Energy Harvesting – Converting Waste Energy into Valuable Resources: By incorporating energy harvesting technologies, smart factories can capture and reuse waste energy generated during industrial processes. IoT sensors and AI algorithms can monitor waste heat, vibration, and other forms of energy loss, guiding factories to implement energy recovery solutions that convert waste energy into usable electricity. This not only enhances overall energy efficiency but also reduces the factory’s dependence on external energy sources.

AI-Driven Energy Storage Solutions – Maximising Energy Utilisation: Smart factories can benefit from AI-driven energy storage solutions that optimise energy utilisation by intelligently managing energy storage and distribution. These solutions use AI algorithms to analyse real-time energy consumption data, allowing factories to store excess energy during periods of low demand and release it when demand peaks. This approach ensures optimal energy utilisation, lowers energy costs, and minimises the factory’s environmental impact.

Pioneering Success Stories: Rexel, Siemens, and MHI’s Energy Cloud Factory

Rexel and Siemens: A Potent Collaboration

Global electrical wholesaler Rexel and industrial titan Siemens have teamed up to develop a comprehensive energy efficiency solution for smart factories. This partnership unites Rexel’s expertise in energy distribution and control with Siemens’ advanced automation technology.

Their groundbreaking solution, built on Siemens’ Totally Integrated Automation (TIA) platform, integrates AI and IoT capabilities to monitor and control energy consumption across factory operations. The TIA platform also features advanced data analytics and energy management software, enabling businesses to assess their energy performance, identify opportunities for improvement, and make informed decisions to enhance efficiency. This powerful collaboration demonstrates the potential of AI and IoT to drive energy efficiency and cost savings in the manufacturing sector.

MHI’s Energy Cloud Factory: A Service Package Revolutionising Energy Efficiency

Mitsubishi Heavy Industries (MHI) has introduced its Energy Cloud Factory service package, a game-changing solution designed to optimise energy consumption in smart factories. The package combines AI, IoT, and advanced analytics to provide a comprehensive suite of energy management tools. With real-time monitoring, predictive maintenance, and energy storage solutions, MHI’s Energy Cloud Factory is reshaping the way factories approach energy efficiency.

The service package enables businesses to collect and analyse data from IoT devices embedded throughout their production processes, generating actionable insights to improve efficiency. By implementing the Energy Cloud Factory, smart factories can reduce energy costs, minimise waste, and decrease their environmental footprint.

In conclusion…

As the pursuit of sustainability and cost savings continues to shape the future of manufacturing, AI and IoT technologies are proving essential in revolutionising energy efficiency in smart factories. From AI-powered energy management systems to IoT-enabled predictive maintenance, these technologies unlock new levels of efficiency and innovation. With real-life success stories like the Rexel and Siemens collaboration, as well as Mitsubishi Heavy Industries’ Energy Cloud Factory service package, it’s evident that the integration of AI and IoT is the key to unlocking the full potential of smart factories. By embracing these cutting-edge solutions, businesses can achieve significant cost savings while contributing to a more sustainable future.

EdgeMethods, a leading provider of data solutions, is dedicated to helping businesses harness the power of AI and IoT to drive sustainable growth and innovation. With our extensive expertise and state-of-the-art tools, EdgeMethods can support your organisation in adopting AI and IoT technologies to optimise energy efficiency and elevate your smart factory initiatives.

Author: admin

An engineer using a tablet, standing in front of solar panels and an illuminated industrial facility at sunset, showcasing smart factory energy efficiency.

In today’s rapidly changing business landscape, smart factories are pushing the boundaries of sustainability, efficiency, and innovation. By leveraging the power of Artificial Intelligence (AI) and the Internet of Things (IoT), smart factories are transforming industrial landscapes and achieving significant cost and energy savings. In this article, we’ll explore how AI and IoT are redefining energy efficiency in smart factories, looking at real-life case studies like the Rexel and Siemens collaboration and Mitsubishi Heavy Industries’ (MHI) Energy Cloud Factory service package. Additionally, we’ll examine the role of advanced analytics, energy harvesting, and AI-driven energy storage solutions in enhancing energy efficiency.

The Dynamic Duo: AI and IoT Energising Smart Factories

AI and IoT technologies are ushering in a new era of energy efficiency for smart factories. Let’s take a look at some key aspects:

  • Unleashing Smart Energy Management: AI-powered energy management systems enable smart factories to monitor and control energy consumption in real-time, adjusting processes to maximise efficiency. By using IoT sensors and AI algorithms, these systems identify energy-intensive operations, spot inefficiencies, and provide actionable insights to fine-tune energy use.
  • Embracing Predictive Maintenance: IoT devices integrated into factory equipment constantly monitor performance, detecting potential issues before they result in costly downtime or energy waste. By implementing AI-driven predictive maintenance, businesses can proactively address problems, extending equipment life and reducing overall energy consumption.
  • Crafting Optimised Production Processes: AI and IoT technologies facilitate the creation of digital twins, simulating production processes and identifying opportunities for improvement. Real-time data analysis from IoT sensors, combined with AI algorithms, can suggest process adjustments that decrease energy consumption and boost operational efficiency.

The Next Frontier: Advanced Analytics, Energy Harvesting, and AI-Driven Energy Storage Solutions

Advanced Analytics: Delving Deeper into Energy Consumption Patterns: Smart factories can take advantage of advanced analytics tools to gain more profound insights into their energy consumption patterns. By using machine learning techniques, businesses can predict future energy needs and optimise their production schedules accordingly. Moreover, advanced analytics can help factories identify potential energy savings opportunities by evaluating and comparing the energy efficiency of various equipment models and configurations.

Energy Harvesting – Converting Waste Energy into Valuable Resources: By incorporating energy harvesting technologies, smart factories can capture and reuse waste energy generated during industrial processes. IoT sensors and AI algorithms can monitor waste heat, vibration, and other forms of energy loss, guiding factories to implement energy recovery solutions that convert waste energy into usable electricity. This not only enhances overall energy efficiency but also reduces the factory’s dependence on external energy sources.

AI-Driven Energy Storage Solutions – Maximising Energy Utilisation: Smart factories can benefit from AI-driven energy storage solutions that optimise energy utilisation by intelligently managing energy storage and distribution. These solutions use AI algorithms to analyse real-time energy consumption data, allowing factories to store excess energy during periods of low demand and release it when demand peaks. This approach ensures optimal energy utilisation, lowers energy costs, and minimises the factory’s environmental impact.

Pioneering Success Stories: Rexel, Siemens, and MHI’s Energy Cloud Factory

Rexel and Siemens: A Potent Collaboration

Global electrical wholesaler Rexel and industrial titan Siemens have teamed up to develop a comprehensive energy efficiency solution for smart factories. This partnership unites Rexel’s expertise in energy distribution and control with Siemens’ advanced automation technology.

Their groundbreaking solution, built on Siemens’ Totally Integrated Automation (TIA) platform, integrates AI and IoT capabilities to monitor and control energy consumption across factory operations. The TIA platform also features advanced data analytics and energy management software, enabling businesses to assess their energy performance, identify opportunities for improvement, and make informed decisions to enhance efficiency. This powerful collaboration demonstrates the potential of AI and IoT to drive energy efficiency and cost savings in the manufacturing sector.

MHI’s Energy Cloud Factory: A Service Package Revolutionising Energy Efficiency

Mitsubishi Heavy Industries (MHI) has introduced its Energy Cloud Factory service package, a game-changing solution designed to optimise energy consumption in smart factories. The package combines AI, IoT, and advanced analytics to provide a comprehensive suite of energy management tools. With real-time monitoring, predictive maintenance, and energy storage solutions, MHI’s Energy Cloud Factory is reshaping the way factories approach energy efficiency.

The service package enables businesses to collect and analyse data from IoT devices embedded throughout their production processes, generating actionable insights to improve efficiency. By implementing the Energy Cloud Factory, smart factories can reduce energy costs, minimise waste, and decrease their environmental footprint.

In conclusion…

As the pursuit of sustainability and cost savings continues to shape the future of manufacturing, AI and IoT technologies are proving essential in revolutionising energy efficiency in smart factories. From AI-powered energy management systems to IoT-enabled predictive maintenance, these technologies unlock new levels of efficiency and innovation. With real-life success stories like the Rexel and Siemens collaboration, as well as Mitsubishi Heavy Industries’ Energy Cloud Factory service package, it’s evident that the integration of AI and IoT is the key to unlocking the full potential of smart factories. By embracing these cutting-edge solutions, businesses can achieve significant cost savings while contributing to a more sustainable future.

EdgeMethods, a leading provider of data solutions, is dedicated to helping businesses harness the power of AI and IoT to drive sustainable growth and innovation. With our extensive expertise and state-of-the-art tools, EdgeMethods can support your organisation in adopting AI and IoT technologies to optimise energy efficiency and elevate your smart factory initiatives.

A humanoid robot analysing data on multiple computer screens in a high-tech industrial control room.

Overall Equipment Effectiveness Artificial Intelligence (OEEAI) solutions are emerging as increasingly effective tools for enhancing energy management. These systems use advanced analytics and machine learning to provide a comprehensive view of production efficiency, focusing particularly on energy utilisation. OEEAI solutions are engineered to measure three critical components of manufacturing performance: Availability, Performance, and Quality. By integrating these components with energy consumption data, OEEAI tools offer a robust analysis of energy efficiency which allows manufacturers to pinpoint inefficiencies in energy use across the entire production cycle.

A key capability of OEEAI is the management and optimisation of cycle times. OEEAI systems analyse operational data in real time to identify deviations from optimal cycle times that consume excessive energy. By adjusting the cycle times to their ideal parameters, factories can significantly reduce wasted energy, thereby enhancing overall energy efficiency. Additionally, OEEAI’s can automatically capture and analyse ‘reason codes’, which correspond to specific causes of downtime and energy waste. These codes categorise various interruptions and inefficiencies, providing manufacturers with clear insights into energy losses during production halts. Such insights highlight specific areas whereby energy conservation can be achieved.

Another key feature of OEEAI solutions is the comparison of ‘ideal’ versus actual energy consumption metrics. By setting benchmarks for energy usage based on optimal machine performance and comparing these to actual consumption, OEEAI tools help identify areas where energy use can be minimised. This comparison not only highlights inefficiencies but also promotes a deeper understanding of energy expenditure patterns within the production process.

It is important to note that OEEAI solutions are not merely diagnostic tools, they enable continuous improvement in energy management by providing actionable insights that can drive significant reductions in energy consumption. For example, with real-time monitoring and historical data analysis capabilities, OEEAI solutions identify trends and anomalies in energy usage. This capability allows for the ongoing refinement of manufacturing processes and the implementation of energy-efficient practices. Whether through adjusting machine settings, altering production schedules, or retrofitting equipment for better energy conservation, OEEAI facilitates a proactive approach to energy management.

As for future advancements, the utility of OEEAI solutions will certainly increase as the associated technology advances. The integration of OEEAI solutions with IoT devices and smart sensors, alongside advancements in predictive and prescriptive analytics, will drastically enhance the granularity and accuracy of data collection. This will allow for even more precise energy monitoring and management, leading to finer adjustments in energy consumption and greater savings. These technologies will not only predict when and where energy waste might occur but also recommend interventions to prevent it. Furthermore, as automation technology evolves, the ability of OEEAI systems to make real-time adjustments based on AI-driven decisions will become more prevalent. This will ensure that energy management strategies are dynamically optimised without human intervention, promoting efficiency and reducing reliance on manual processes.

OEEAI solutions represent a transformative approach to energy management in industrial manufacturing. By providing detailed insights into energy consumption and enabling continuous improvement, these tools help companies achieve significant energy savings and enhance sustainability. As the technology develops, the integration of AI with real-time data analytics promises to further revolutionise energy management practices, setting a new standard for industrial efficiency. Edge Methods is at the forefront of deploying OEEAI solutions tailored to meet specific manufacturing needs, ensuring that our clients not only keep pace with technological advancements but also lead in operational sustainability. 

A vibrant green plant growing from a glowing electronic circuit board, symbolising sustainability and technology.

Sustainability at EdgeMethods
At EdgeMethods, sustainability is fundamental to how we operate, the partnerships we build, and the solutions we offer. We believe businesses play a critical role in addressing global challenges such as climate change, resource depletion, and environmental degradation. For us, sustainability is more than a responsibility—it’s an opportunity to create real-world impact.
Our approach centres on helping businesses align environmental responsibility with profitability. By integrating sustainability into our operations and offerings, we empower companies to thrive while contributing to a greener planet. It’s a win-win: tackling urgent challenges while achieving long-term growth, cost savings, and operational efficiencies.

Our Commitment
Sustainability is central to our operations, and we’ve made it a priority to integrate it into every decision we make. Supported by Microsoft Azure, our technology solutions enable businesses to:

  • Quantify the Business Value of Energy & Sustainability Use Case.
  • Optimise energy & operational efficiency.
  • Minimise their environmental impact.
  • Boost profitability.

We also recognise the transformative potential of Industry 4.0. By leveraging IoT applications, machine learning, and advanced data insights, we help businesses revolutionise their approach to sustainability.
At EdgeMethods, we are inspired by the passion of our young and dynamic team. We believe their dedication, combined with our commitment to responsible business practices, positions us to be part of the solution to our most pressing global challenges.

Sustainability for Our Customers
The majority of CO2 emissions stem from business activities, which is why companies must take the lead in driving sustainability. At EdgeMethods, we’re focused on creating tools and software that empower industries to reduce their environmental footprint while improving operational outcomes.
Our solutions offer tangible benefits:

Our Progress
EcoVadis Silver Accreditation (2024): With a score of 72, we’re in the 93rd percentile globally, a leap from 62 and the 83rd percentile in 2023. This progress highlights our dedication to continuous improvement.
Future Goals: We are aiming for Gold Accreditation next year and striving for Platinum in the near future, setting a benchmark for sustainable business practices.

Our Pillars
1) Environment: Enabling businesses to meet net-zero targets by reducing emissions, conserving resources, and driving energy efficiency.
2) Social Responsibility: Investing in the younger generation and giving them the opportunity to make an impact. We focus on empowering young professionals to solve global challenges while providing the tools and platforms needed to drive meaningful change.
3) Governance: Ensuring ethical decision-making, transparency, and accountability across all levels of our operations.
Through our ESG efforts, we empower our clients to align with global reporting standards while driving meaningful, long-term value.

Engaging with the Broader Sustainability Community
Sustainability is a shared journey, and at EdgeMethods, we actively participate in the global conversation. We collaborate with thought leaders, educate businesses, and lead initiatives that drive measurable action.

  • Our Initiatives Webinars and Thought Leadership: We’ve hosted a three-part series covering net-zero strategies, avoiding greenwashing, and improving sustainability reporting. These sessions provide actionable insights for businesses navigating evolving regulations.
  • Knowledge Sharing: Through blogs, case studies, and industry publications, we share real-world applications of sustainability-driven technologies.
  • Collaborative Partnerships: We work closely with industry leaders and governmental organisations to address challenges like carbon emissions and the energy transition. One example is our collaboration with Innovate UK (Smart Factory Division), part of the UK government’s initiative to advance manufacturing sustainability. This partnership has helped us drive innovation in data-driven solutions for smart factories. By engaging with the sustainability community, we’re helping businesses adapt to changing expectations, meet regulatory demands, and create lasting impact.

Bring Sustainability to the Heart of Your Business
Whether you’re looking to streamline operations, reduce costs, or meet sustainability goals, EdgeMethods is here to help. Contact us today to learn how our solutions can drive measurable change for your business and the planet.

A businesswoman and her colleague analysing data visualisations on large digital screens in a modern office, representing energy strategy and data-driven decision-making.

In the current climate, with energy bills skyrocketing, alongside pressure for reduced environmental impact, the push for companies to employ sustainable energy management is ever-growing. This need is especially pronounced in the manufacturing and industrial sectors, where many key players are aiming to reduce their environmental impact and manage their energy output strategically and cost-effectively. It’s no wonder that emerging technology has a key part to play, with a proven strategy emerging from the integration of data analytics, machine learning (ML), and artificial intelligence (AI). A combined and strategic use of these technologies can have the desirable result of reducing environmental impact, whilst enhancing operational efficiency to secure a competitive advantage.

Sustainable Energy Management – What is it?

Sustainable energy management represents a two-fold strategy to leverage renewable energy sources and cutting-edge technologies to optimise energy consumption, mitigate environmental impact, and boost business efficiency.

The world is in the middle of a global energy crisis of unprecedented depth and complexity. This crisis has led to volatility and steep spikes in energy prices, particularly for natural gas in the European markets. Despite this turmoil, the growth in renewables has remained robust. The deployment of renewable energy, energy storage, and innovative financing solutions has not only become more accessible but also cost-effective. Importantly, amidst these challenges, the strategic use of data analytics, ML, and AI is playing a crucial role. These technologies are helping to optimise energy management, making it more sustainable and efficient, and are thus pivotal in navigating the complexities of the current energy landscape. Let’s explore how…

The Role of Data Analytics

Data analytics plays a crucial role in sustainable energy management, enhancing operational efficiency and promoting environmental sustainability. Through in-depth analysis of energy use, sourced from smart meters and IoT devices, businesses can identify inefficiencies and improve operations. These platforms use descriptive, predictive, and prescriptive analytics to understand energy patterns, forecast demands, and recommend efficiency improvements.

Predictive analytics has transformed energy management, enabling precise demand forecasting and consumption management. This proactive approach reduces waste, encourages green technology use, and helps businesses navigate energy price fluctuations. By applying data analytics strategically, companies actively contribute to a more sustainable energy ecosystem and drive global efforts towards energy efficiency. For example, Toshiba performs advanced analytics on building-related data to enhance asset performance and reduce the environmental footprint. This resulted in energy consumption being cut by 16% with carbon emissions reducing by 54%.

The Role of Machine Learning

Predictive analytics are driven by predicted modelling. Meaning that predictive analytics and machine learning go hand-in-hand, as predictive models typically include a machine learning algorithm. ML algorithms therefore enable predictive insights into peak load times and enable optimised energy distribution. However, as per Arthur Samuel’s definition from 1959, this technology also gives ‘computers the ability to learn without being explicitly programmed’. As the ML algorithms become more intelligent, they can overcome program instructions to make highly accurate, data-driven decisions.

A prime example of ML’s impact is Google’s deployment of DeepMind’s algorithms, achieving a 40% reduction in energy used for cooling data centres. This achievement not only showcases ML’s effectiveness in enhancing energy efficiency but also its potential in creating smart grids and energy systems that dynamically adjust to energy supply and demand, leading to a more sustainable and resilient infrastructure.

The Role of Artificial Intelligence

AI is another key piece of the puzzle. The role of AI in energy management represents a significant leap towards automating and refining the processes that ensure energy efficiency and sustainability. By utilising AI, organisations can analyse vast amounts of historical data, real-time sensor feedback, and environmental conditions to predict future energy requirements and optimise machinery and system operations. This capability not only reduces waste but also enhances the integration of renewable energy sources, like solar and wind, into the power grid, promoting a stable and sustainable energy infrastructure.

AI’s impact extends to building management systems (BMS) as well, where it dynamically adjusts heating, cooling, and lighting according to the actual needs of the building, factoring in occupancy, weather, and other environmental conditions. This intelligent control helps in significantly lowering energy consumption and reducing the carbon footprint of buildings. Moreover, AI streamlines supply chain logistics, leading to more energy-efficient transportation methods and reducing the overall energy demand of logistics and supply chains. AI also plays a pivotal role in predictive maintenance, identifying potential equipment failures before they happen, thus preventing downtime and ensuring that operations run smoothly and efficiently.

Furthermore, AI aids in carbon footprint monitoring, allowing companies to set realistic emission reduction goals and track their progress accurately. In the realm of manufacturing, AI optimises operational parameters, leading to substantial energy savings and contributing to more eco-friendly manufacturing processes. AI’s contributions to eco-friendly product design further underscore its role in promoting sustainability, ensuring products are designed to use minimal energy throughout their lifecycle.

Incorporating AI for energy management has been exemplified by the UK-based company Grid Edge with its “Flex2X” system. This system utilises AI algorithms to analyse data from a building’s energy management system alongside weather conditions and other factors. It optimises energy use in real-time, making buildings flexible participants in energy markets. This approach has led to significant cost savings, carbon reduction, and has enabled buildings to adjust their energy load dynamically, contributing to grid stability and the integration of renewable energy sources.

Operational Efficiency Through Technology

The integration of data analytics, ML, and AI with energy management streamlines business processes, reduces waste, and lowers energy use, achieving significant operational efficiency and sustainability gains. But without this expertise in-house, it is difficult for companies to implement these technologies synergistically and for the optimum effect. Edge Methods offers expertise in deploying these technologies, helping businesses cut energy costs, reduce their carbon footprint, and enhance operational excellence. Our approach not only aims at meeting sustainability objectives but also at bolstering business resilience and competitiveness in today’s fast-paced market.