5 ways to increase efficiency, optimize processes, and reduce costs with data and Industrial AI
Companies in the manufacturing and process industries, as well as the energy and utilities sectors, face growing challenges in optimizing their processes, operations, and decision-making. Luckily, they are also sitting on a goldmine of data, which could help with solving the challenges. This article presents five ways Industrial AI and custom AI agents can help companies achieve key business goals.
The complexity of industrial environments and processes makes it impossible for any expert to grasp fully. Neither do the best systems manage all information involved in OT, IT, and any business operations. Companies are looking for viable solutions to reduce inefficiencies, quality issues, and costs caused by unplanned downtime or non-optimal asset operations. They also need to ease the lack of a qualified workforce.
The most effective way to implement AI is not by trying to apply it everywhere, but by selecting specific use cases in the form of defined AI agents. This approach also enables identifying and measuring the impact accurately.
Self-learning, straightforward, and secure
Most importantly, an AI agent can digest information, have self-learning capabilities, and adapt to changes. It is tailor-made for the company, designed to solve and help with a specified and clearly identified task, such as creating actionable information from raw data. An agent is assigned for a particular role, and it uses specific data assets for its reasoning.
AI agents are a relatively straightforward way to implement artificial intelligence. They can be installed in limited environments and with limited access to data. This means that companies can securely control and limit the data accessible to each AI agent, ensuring compliance with internal policies and external regulations.
Practical applications of AI agents are numerous:
- Process control and resource optimization: AI agents can help fine-tune process parameters, improve throughput and quality, reduce material or energy consumption, and minimize waste.
- Predictive maintenance: AI agents can analyze sensor and historical data to forecast equipment failures before they occur, enabling proactive interventions, reducing unplanned downtime, and boosting the efficiency of maintenance operations.
- Supply chain intelligence: Agents can help minimize supply chain risks, optimize inventories, enhance procurement, and trace the root causes of quality issues across the supply chain.
- AI-enhanced reporting and decision-making: AI supports and accelerates human decision-making for the business leadership, plant floor workers, developers, and field technicians.
- Enhanced R&D and business development: By analyzing production data, quality inspection, maintenance, customer feedback, and external research, AI can help accelerate innovation and shorten the time-to-market.
Continuous improvement with a multi-agent architecture
Organizations can achieve the most powerful results by a multi-agent architecture, a combination of three types of AI agents.
If the aim is to, for example, continuously improve a process, the first assistant agent can figure out what seems to be the issue. The second AI assistant analyzes why the issue happened in the first place and which events could have led to the observed outcome. Then, an advisor agent “thinks” forward, informing humans how the issue could best be solved. Lastly, an automation agent could take corrective actions: For instance, in an energy company, it can limit overproduction when energy prices are low.
However, before any of these improvements are possible and Industrial AI can create value for companies, their data foundation must be in order. Companies must first master the data battle to unlock their potential with Industrial AI.
Are you curious to find out how to develop AI agents in an industrial context? Learn the tips and tricks in detail by downloading our guidebook Create value with Industrial AI!