AI Boosted Asset Maintenance for Process Industries

[sergey_nozhenko]

Introduction & challenges in traditional asset management

Process industry & natural resources, or the Materials sector, are industries that manufacture chemicals, construction materials, forest products, glass, paper & packaging products, metals, minerals and mining commodities. In this sector products are produced by mostly continuous processing and process manufacturing but also a mix with discrete in the downstream value chain. These are highly asset intensive with complex production assets, plants, machinery, and equipment are running twenty-four hours a day, three hundred sixty-five days a year. Business success is strongly tied to productivity, asset availability and reliability of these assets because every shutdown or breakdown causes significant losses in production and margin.

These industries today face different types of pressures, both external and internal. Some of the driving external challenges include regulatory and compliance, sustainability requirements, talent and workforce shortage, as well as the acceleration in adoption of new technologies like AI. The internal struggles are linked to aging infrastructure, asset reliability, and necessity of saving costs.

Artificial Intelligence (AI) can play a big role in the transformation of these industries by optimising processes, predicting equipment failures, improving people efficiency with agentic approaches, as well as improving reliability of the assets with end-to-end integration.

Industry perspectives & some key aspects

Asset reliability and uptime are the most critical KPIs in process industries, where continuous operations depend on minimizing downtime and ensuring optimal performance of highly capital-intensive and complex assets throughout their lifecycle.

Uptime, or continuous operations, are essential for these industries. The assets usually run continuously (excluding annual or bi-annual planned shutdowns), and the overall business performance is highly dependent on Assets Reliability.

What are some of the key characteristics of process industries that make it unique?

1. Companies in process industries are all highly asset & capital-intensive and normally have a wide variety of complex assets. Examples include fixed & continuous plants, like furnaces, crackers, reactors, distillation columns, mills, power generators, pumps and compressors as well as heavy equipment & mining machinery 
2. Process Industry manufacturing runs as one production line, as one live organism, where breakdown or shutdown of even one equipment or unit stops the entire line and halts production. Bringing the whole production line to normal process conditions usually takes hours and even days, and during this time the business often spends many additional resources, raw materials and energy just to “start-up the line”
3. The Mechanical Integrity of the Assets is key to ensuring that equipment and machinery are designed, installed, operated, and maintained in a way that supports their ongoing safety, reliability, and functionality throughout their lifecycle. These assets have high operational risk and work with dangerous substances (like acids, fuels) in high volumes. A top priority for all asset intensive companies is working to prevent    malfunctions & failures may cause danger to human life, significant damage to environment, and financial losses and penalties.
4. In general, the life cycle of the production in process industries could last for years and demands very high investments and maintenance costs. To be sure the assets provide good return on investment, these assets are managed under the end-to-end business process “Acquire to Decommission”, which includes:

• Acquisition: including design, procurement, engineering & construction or modernization during Overhauls, with subsequent handing over to maintenance & operations. There are high CAPEX costs involved in the entire lifecycle,
• Maintenance Execution: starting from definition of maintenance strategy, asset condition monitoring, planning and execution of Maintenance. This stage also involves high OPEX costs and efforts for daily usage and maintenance,
• Decommission: Including deconstruction of the assets at the end of life.

SAP portfolio and differentiators in Asset Maintenance:

The SAP portfolio for Asset Management delivers a comprehensive, integrated suite of solutions that optimize every aspect of asset lifecycle management: End to End Business Process “Acquire to Decommission”

This end-to-end process includes steps:

• Manage asset master data
• Define asset maintenance strategy
• Define maintenance plans and activities
• Monitor asset health and maintenance demand
• Plan asset maintenance tasks and resources
• Perform Asset Maintenance
• Analyse asset and maintenance performance

• SAP S/4HANA Plant Maintenance (PM) streamlines asset management, enabling efficient planning, execution of maintenance orders, and management of task lists to reduce downtime and extend asset life.
• SAP Asset Performance Management helps organizations define asset strategies, analyse risks, connect to asset data and monitor asset conditions with predictive analytics, supporting proactive decision-making and reducing unexpected failures.
• SAP for Resource Scheduling ensures optimal utilization of maintenance teams and resources, improving productivity and minimizing operational bottlenecks.
• SAP Service & Asset Manager is a mobile app designed to support the work of service technicians and asset maintenance teams. It provides offline access to key information such as work orders, asset history, and spare parts inventory, enabling users to efficiently manage and execute service tasks even without network connectivity.
• The SAP Partner Ecosystem Solutions further enrich this portfolio with specialized capabilities such as Asset Master Data governance, Shutdown Turnaround & Outage, Risk based inspections enabling tailored solutions for complex asset-intensive environments.
• SAP Business AI to give more power. Business AI enhances asset maintenance processes across portfolio by automating tasks enhanced decision making, detecting anomalies, intelligent recommendations and agentic workflows. This also helps drives a shift by enhancing operational efficiency, enhancing decisions making also leading to reliability centric and prescriptive maintenance processes, leveraging data to anticipate equipment failures, automate workflows, and empower technicians.

Together, this extensive SAP solution portfolio provides customers in process industries with end-to-end visibility, improved reliability, and cost-effective asset management, driving operational excellence and sustainable growth.

The AI opportunity to transform Asset Maintenance practices for Process Industries

The Asset Management process supported by SAP have been quite mature and well-established for many years. While the traditional approach to manage assets has matured, new innovative technologies like AI are driving a huge transformation and are beginning to unlock new increases in key areas such as asset uptime and reliability.

Adding AI to asset management is a total game-changer for Process Industries. SAP Business AI really stands out by offering smart, predictive, and proactive ways to manage assets, making operations smoother and more efficient.

SAP Business AI uses machine learning and predictive analytics to shake up traditional asset management. In the SAP Portfolio we are delivering AI embedded scenarios in IAM Solution Portfolio: SAP Asset Performance Management (APM), 

Here’s AI-assisted capabilities by SAP which are already available for each area of asset management process flow:

Define asset maintenance strategy

• Joule AI assistant helps to SAP Asset Performance Management (SAP APM) users with application help (link), application navigation and even in performing transactions using natural language (link)
• ML for Failure Curve Analytics FCA which is part of SAP APM is capable to predict probabilities of failures for technical objects via calculation of Remaining Useful Life (link)
• ML & Joule for Failure Mode and Effects Analysis FMEA assessments (link) is the scenario where ML helps to analyse and interpret system data such as past maintenance history and manufacturer recommendations to identify and propose unique failure patterns

Monitor asset health and maintenance demand

• Machine Learning for Anomaly Detection – this ML capability provided by SAP APM allows the maintenance engineers and planner to effectively Identify and monitor unusual patterns or behaviour in technical objects (link) with lesser efforts
• Next release of ML for Anomaly Detection capability also leverages ML to periodically re-evaluate and re-define the optimal threshold values (red, yellow, green zones) for alerting (link)
• ML to monitor asset conditions from visual inspections is another SAP APM capability which collects images from cameras monitoring assets together with AI-derived indicator values, displays them in context of the asset history including other sensor data, and defines condition monitoring rules to create automated alerts and/or maintenance notifications (link)Plan asset maintenance tasks and resources
• Maintenance Planner Agent which is capability of SAP S/4HANA helps maintenance planner to manage and prioritize a growing list of maintenance tasks with natural language conversation (link)
• ML to Maintenance Order Recommendation helps user to create a new maintenance order in SAP S/4HANA via recommendation of historical one for copy – based on similarity of incidents (link)

Perform Asset Maintenance

• Voice-to-text feature enables maintenance technicians to convert voice input into structured text, which is locally stored in Service and Asset Manager and later synchronized with the SAP Business Suite. By streamlining data capture, it helps technicians save significant time when recording job completion details while ensuring higher data quality, as no critical information is lost. It also enhances the mobile user experience by guiding users seamlessly through the job completion process (link)

Manage asset master data

• SAP Partner Solution. Another example of AI for the Assets Maintenance is the improvement of EAM Master Data Management (Assets, Spare Parts, Measuring Point, Failure Modes, Task Lists, other objects) – ensuring the data quality, conversion of legacy master data from diverse data sources, checking for data white spaces and redundancies, and data enrichment with GenAI. This scenario and solution are supported by SAP Partner Ecosystem (link).  

Analyse asset and maintenance performance

• SAP Business Data Cloud enables real-time, unified asset data for manufacturing, allowing asset managers to make faster, data-driven decisions and optimize maintenance strategies. By integrating SAP and third-party data (e.g. vendor provided asset health monitoring application), the platform breaks data silos and enhances predictive analytics with comprehensive business data semantic layer. This allows asset management experts both utilizing SAP provided intelligent dashboards and to build their own AI assisted applications to get business insights and forecasts.

Examples of SAP Business AI in IAM

Several top process manufacturing companies have started to successfully use SAP Business AI in their asset management. Here are a few examples:

Petrochemical Industry

A strong regional player in the petrochemical industry, has achieved a significant breakthrough in asset management by implementing SAP Data Platform with Data Storage and Machine Learning capabilities enabled. By leveraging these platforms, the company has established a robust system for asset health monitoring and predictive maintenance, specifically targeting pumps and compressors.

This approach involves continuously monitoring critical parameters such as vibration and temperature of key ball bearings and other vital components using IoT sensors and advanced analytics. Through the application of machine learning models, company can now identify potential failure patterns and predict equipment failures before they occur, triggering proactive maintenance actions in SAP ERP Plant Maintenance. This digital transformation minimizes unplanned downtime, optimizes maintenance schedules, and extends asset lifespans, ultimately leading enhanced operational reliability.

Renewables company 

A renewables company delivering 100% green power through multiple technologies (Biomass, Solar, Wind) across several geographies, resulting as a spin off from a Pulp and Paper company. After a round of investments, company felt the need to better manage their Power Generating Plants.

More than a Predictive Center, customer needed to replicate the operational data available in the plants and build a technical centre to support the operations and maintenance of all the plants. To achieve this, they have built solution based on SAP APM and SAP Datasphere infused with SAP AI capabilities not just to gather and store all the data to monitor complex assets health, but to predict and prevent malfunctions of the energy generation plant with all the electricity and steam generators.

Power Generation Industry

A large power generation company adopted SAP Predictive Insights to model digital twins to monitor and autogenerate alerts in case of abnormal behaviours. The company achieved exceptional results: 11% reduction of production losses, 35% productivity gains of maintenance, 100% reliability of generated alerts out of digital twin.

Construction Materials company

Company successfully implemented advanced condition monitoring and predictive maintenance using SAP Intelligent Asset Management and Asset Performance Management solutions, resulting in a significant reduction in unplanned downtime and millions in annual cost savings per plant.

The integration of IoT data with business systems, including the use of wireless sensors and AI-driven analytics, enables proactive identification of equipment failures, optimization of maintenance schedules, and minimization of unnecessary preventive tasks, improving overall operational efficiency. By implementing AI-powered predictive maintenance across multiple plants, the company has achieved $2 million in annual savings per plant by preventing unnecessary repairs.

Please follow this link to know more.

Benefits leveraging AI in asset maintenance for Process Manufacturing

SAP Business AI for Asset Maintenance provides additional value & benefits where traditional approach reached the limits, some of these are:

• Helps increase reliability and prevents production losses

In process manufacturing, precision and efficiency are crucial. SAP Business AI provides insights into asset performance like predicting failures or recognizing abnormal patterns in health conditions data, which allows companies reduce downtime and to prevent excessive losses of production and revenue

• Reduces Costs & Unplanned Downtime

Maintenance and operational costs can be a major headache for process manufacturers – especially when downtime and related maintenance were not planned. With a predictive maintenance approach, SAP Business AI helps cut down on how often assets fail and how severe those failures are, saving money on repairs and lost productivity

• Optimizes both Maintenance Resources and Duration

Integrating AI into both the optimized planning and assistance in execution steps of the maintenance process enables the automation and optimization of tasks (as it was shown above), reducing workload and allowing users to focus on their primary responsibilities instead of spending time in the information system. Another effect of optimized planning and execution with AI is shortening the maintenance time thanks to assisted scheduling and execution.

What is next

Some of the capabilities SAP is planning for the next releases include the following: Improvements of AI-assisted Visual Inspections and Asset Condition Monitoring, Improved Anomaly Detection with Anomaly Scoring, Improvements of Joule Assistant to assist in user navigation and executing of system transactions like Risk and Criticality assessments, Management of Recommendations and Alerts, and others.

For more details please visit SAP Roadmap.

Getting SAP Business AI is more than just a tech upgrade; it’s a strategic investment for the future of process manufacturing. Embracing this will help companies handle the complexities of asset management with confidence and agility, setting them up for long-term success and growth.

Now for specific AI scenarios fitting the industry, and how to make them real - SAP supports & provides AI discovery services for customers with outcome being a list of customer specific scenarios, realization roadmaps and business outcomes.

Today business AI in asset maintenance for process industries is no longer a trend but a strategic imperative for achieving operational excellence & elevating the maintenance strategy management to the next level.