Industrial AI Focus Shifts toward Sustainable Use Cases, so Why Are Adoption Rates Slow?

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By Alexander McQueen | 2Q 2024 | IN-7348

For the past few years, Artificial Intelligence (AI) and analytics tools have been used by industrial leaders to improve operational productivity. Now, greater attention is being placed on AI as a solution to reduce energy consumption and waste throughout operations. Despite this, industry-wide adoption of the technology has been slow.

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Untapped Opportunity in Industrial AI Data Analytics


Industrial and manufacturing organizations have access to huge quantities of operations data generated by meters, sensors, machinery, and equipment. This available data are increasing exponentially through the introduction of Industrial Internet of Things (IIoT), edge devices, Artificial Intelligence (AI), and Machine Learning (ML) technologies. Data hold significant value for analyzing operations and identifying inefficiencies, yet only a small portion of these data are used. According to Schneider Electric, around 70% of available industrial data are not used; however, over the last few years, companies have placed greater focus on better utilizing these data.

AI has emerged as a vital tool for maximizing the value of under-utilized, disconnected industrial data. AI and ML facilitate the processes of filtering and structuring data from Internet of Things (IoT) sensors and edge equipment, providing an efficient way to collect, manage, analyze, and interpret data. This is key to maximizing visibility and control of industrial and manufacturing operations and processes. Once the data have been collected and stored, AI analytics help create useful insights and identify areas of opportunity for improving operational efficiencies. The introduction of generative AI is further revolutionizing the way companies interpret, utilize, and convert data into actionable insights. Generative AI will help industrial players adopt AI though its more seamless integration in existing processes and workforce thanks to its natural language recommendations and support aspects.

AI Enchances Sustainable Manufacturing


Manufacturing is one of the leading industries in terms of data generation and companies are beginning to use AI to tools to analyze vast data lakes and identify patterns and trends. So far, this has predominantly been used to enhance processes such as demand prediction, inventory management, predictive maintenance, and quality management. Over the last few years, however, amid increasing Environmental, Social, and Governance (ESG) disclosure requirements and stakeholder pressure for sustainable operations, the importance of sustainability in industry and manufacturing has reached the forefront of attention. Companies are now realizing new uses of AI based on real-time operational data on emissions, energy, and waste. AI is enabling this transition to more sustainable operations by supporting environmentally-conscious decision-making and practices.

For example, AI can play a crucial role in reducing energy consumption and waste in manufacturing by analyzing data patterns to identify inefficiencies that are contributing to excess energy use and waste. For energy consumption, AI-powered data analytics can enable companies to predict and optimize consumption in real time, providing insight into areas throughout the manufacturing process that could be more energy-efficient. Similarly, AI can support the identification of wasteful manufacturing practices, offering solutions to make the process more sustainable and cut unnecessary waste. Predictive maintenance capabilities also enhance asset management, boosting circularity by extending the lifetime of machinery and equipment, and delaying the need for costly upgrades.

Companies like AVEVA have developed AI solutions to enable customers to respond to changing conditions in real time to maximize sustainability and profitability. The company’s solutions are helping improve industrial operations by enabling design simulations, optimizing production efficiency, improving energy efficiency, and maximizing yield through reduced waste. This new era of AI has also seen new AI-centric and innovative solution providers emerge, such as Fero Labs. Fero Labs is an industrial process optimization software provider, offering an AI Software-as-a-Service (SaaS) platform for prediction and optimization of industrial processes. The solution gives users the tools for root cause analysis, anomaly detection, multivariate forecasts, scenario testing, and live predictions to target and eliminate process inefficiencies to reduce overall emissions without compromising product quality.

Industrials Face AI Implementation Challenges


The modern factory sits on a goldmine of unused production data, generated at incredible speeds and volumes by automated industrial equipment and IoT sensors. Deploying AI is becoming essential to unlocking the value of these data and driving ESG strategies for cleaner and safer production. Despite the clear benefits of AI-powered analytics tools for improving efficiency, profitability, and sustainability of operations, companies are presented with several challenges when integrating AI into operations, preventing widespread adoption across the industry. These challenges are often most significant for legacy companies averse to change.

  • The common first challenge is that companies lack the infrastructure to effectively store and manage the data. Legacy industrial organizations traditionally have had little expertise in data management, so they face the issue of siloed data across Manufacturing Execution Systems (MESs), data historian, supply chain management, and other internal software platforms. This disconnect means companies cannot effectively combine and leverage the data to drive operational improvements.
  • Another challenge is that legacy infrastructure may not be designed to work with AI, meaning companies need to make expensive upgrades to existing infrastructure to facilitate AI adoption.
  • Scalability is also an issue. As businesses grow, data capture and the demand for AI increases, creating extra requirements for data storage and management infrastructure. This is typically in the form of on-premises data centers or cloud storage, both of which generate extra costs and data security concerns.
  • Finally, there is a technical skills gap preventing companies from adopting effective AI solutions. Implementing AI technology requires comprehensive understanding to leverage complex algorithms and data models, which few companies have in-house.

The industrial and manufacturing sectors need digital transformation to enable the seamless integration of AI analytics tools. As companies become more digitized, building robust data foundations is the key first step to create actionable, data-driven improvements to operational efficiencies. Industrial AI is a valuable tool to eliminate data silos by aggregating data into a single database, which can then be standardized, analyzed, and contextualized to form strategies to reduce emission, energy, and waste throughout industrial processes. Industrial leaders will embrace and invest in AI solutions internally or through partnerships to optimize performance and gain a competitive edge.


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