Making the right calls in a sea of noise – how AI brings predictive outcomes to asset based industries

As our world becomes increasingly digital, the potential applications of AI are limitless and well documented. While opinions vary on how best to apply AI, there is no question that the digital age is generating a vast amount of data that, if properly harnessed, can help businesses navigate uncertainty and achieve more predictable outcomes. This can lead to greater efficiency, productivity, and profitability across industries.

Today, businesses are confronted with numerous challenges such as inflation, supply chain delays, natural disasters, and global pandemics. In a world characterized by instability, AI has the potential to make businesses more efficient. A key benefit of AI is its ability to process large amounts of data and generate recommendations based on various parameters, without requiring human intervention. In addition, when combined with an end-to-end ERP system, AI can play a vital role in streamlining business operations.

Optimizing predictive asset maintenance systems

Across asset-intensive industries, from heavy manufacturing to oil and gas and various utilities, we are seeing growing use of predictive asset maintenance.  Predictive asset maintenance powered by AI can offer a multitude of advantages to businesses. For instance, in industries such as field service, scheduling plays a crucial role in ensuring optimal outcomes. It is essential to know who should be where, when, and equipped with the right tools.

Cubic Transformation Systems (CTS) is an example of a company that recognizes the traditional break/fix service model is no longer meeting customer demands. Instead, customers now require guaranteed outcomes such as uptime, peace of mind, and results. By utilizing IFS’s Field Service Management and its AI-powered Planning and Scheduling Optimization (PSO), CTS was able to increase uptime by 20%.

By considering a set of objectives and conditions, AI can predict the optimal deployment of personnel and necessary spare parts, ensuring that all components are in place for effective scheduling. This is achieved by combining historical data and information regarding the typical lifespan of equipment and its last replacement with IoT data on the present state and usage of the equipment. Through this, the precise condition of each component in the system can be calculated for better planning and scheduling.

In the renewable energy sector, field service organizations can use AI to perform maintenance on wind turbine parts before they fail, ensuring uninterrupted power generation. By utilizing historical trends, weather patterns, and information from sensors on the equipment, along with forecasted supply chain delivery times, the maintenance team can pre-emptively place orders for spare parts and perform required maintenance work in advance.

US-based energy provider Xcel Energy recently underwent an end-to-end scheduling transformation for gas and electric distribution. This is an example of how, by incorporating workforce management and AI-powered PSO, businesses can improve their models by digitizing and automating manual processes to ensure customer satisfaction.

AI has the ability to analyze the maintenance history of the equipment, along with the specific fault code, providing valuable insights into the potential causes of the issue. Based on this analysis, AI can determine the required repair parts and immediately submit a work order. This helps businesses avoid outages and reduce downtime by eliminating the need for a diagnostic examination before repairing the equipment after an outage. As a result, businesses can significantly reduce their costs and downtime.

Combining AI and IoT to enable predictive business outcomes

With the help of AI-driven digital twin technologies, it's becoming easier to gain a comprehensive understanding of a business by collecting data from IoT sensors. This data can then be used to create end-to-end solutions that streamline operations throughout the organization. Using AI as the foundation, businesses can integrate all of their services, resulting in a more efficient and effective operation.

Although most manufacturing AI is still in its early stages, advanced technologies such as Industry 4.0, data transformation, and smart factories are helping to reshape the industry. Previously, IoT sensors on intelligent machines collected data without being connected to the business outcome. However, with the help of complementary ERP tools, that use a combination of AI-powered anomaly detection and machine learning algorithms to analyze trending data, businesses can now make accurate predictions and automate workflows without the need for human intervention.

The manufacturing industry will benefit greatly from AI, as it is capable of monitoring the performance of each machine and detecting any deviations from expected output. IFS's self-learning AI solution is an  example, as it can effectively monitor assets, machines, systems, and industrial processes, analyzing and detecting unusual behaviour and the root causes of failures in real-time. Outside of this, AI can also accurately predict the demand for each product, optimizing inventory levels and production schedules accordingly. With the ability to coordinate with suppliers and customers, AI ensures timely delivery and customer satisfaction.

Furthermore, by integrating AI with ERP systems, businesses can harness the power of data and automation to optimize their processes, reduce costs, improve quality, and increase profitability. Indeed, to get the most value from the data collected by IoT sensors, it's crucial to use AI to drive outcomes. Otherwise, the data cannot deliver its full potential to the overall business.

Streamlining the factory floor with AI and automation

The introduction of digitalization has brought about a new era of smart factories that utilize connected devices, machinery production systems, and a combination of IoT and AI to enhance and optimize processes.

Recent IFS research has shown that 50% of manufacturing organizations view improving operational excellence as a top priority in the next two years. Followed closely by 43% wanting to improve the agility and flexibility of their operations. By utilizing an AI-enabled ERP system manufacturers can have the agility to transform their shop floor processes.

For example, manufacturing execution systems (MES) play a pivotal role in modern manufacturing plants. They offer real-time visibility into production processes by tracking inventory, work orders, and equipment status. Integrated MES capabilities can automate shop floor reporting and quality inspections, which can help organizations reduce production costs, enhance equipment efficiency and throughput, and improve quality.

As the world generates more and more data from IoT sensors, shipping data, and meteorological stations, AI is maturing at the right time to help humans make sense of all this information. With proper AI configurations, businesses with an AI-enabled, ERP-integrated operation can finally have full visibility of their business and streamline their operations. Ultimately, it can lead to improved efficiency and enhanced visibility of operations in automated manufacturing, allowing businesses to make actionable decisions based on the signal in a sea of noise.