The Future of Asset Management: Intelligence through Digitalization

Despite increased investments in production processes and industrial equipment, most industrial enterprises are far from optimizing the performance of their assets. Specifically, industrial organizations are still striving to cope with unplanned downtimes and outages, but also to ensure the highest possible asset availability and reliability. Moreover, they must deal with non-compliant asset management processes, as well as with inefficient maintenance procedures. Likewise, there are many cases where inaccurate master data cause inefficiencies. Furthermore, asset related data tend to be fragmented across multiple information systems, which makes it challenging for workers and decision makers to access the right data for their tasks.  In this landscape, industrial enterprises seek intelligent asset management solutions that will enable them to manage cost, risks and the performance of their assets in the most effective way.

Asset Management Trends in the Fourth Industrial Revolution (Industry 4.0)

In order to cope with the above-listed asset management challenges, enterprises can greatly benefit from the digital transformation of their production processes. This transformation is currently taking place at an accelerated pace as a result of enterprises’ efforts to ride the wave of the fourth industrial revolution (Industry 4.0). Industry 4.0 entails the complete digitization of production processes, based on the deployment of Cyber Physical Systems (CPS) and Internet of Things (IoT) devices in the manufacturing shop floor.  CPS and IoT systems enable the collection of large amounts of digital data from the physical systems and processes of an enterprise, which can be analysed in conjunction with datasets from business information systems (e.g., ERP systems) in order to optimize production processes and to improve managerial decision making. Moreover, CPS and smart objects (e.g., industrial robots, intelligent actuators, drones and automated guided vehicles) enable Industry 4.0 applications to influence the physical world towards automating and accelerating production tasks.  

Industry 4.0 is not empowered by a single technology, but rather by a pool of complementary digital enablers, including cloud/edge computing, the Industrial internet of things (IIoT), high speed connectivity as part of the fifth generation of mobile communications (5G), Virtual and Augmented Reality (VR/AR), Big Data, Artificial Intelligence,  as well as cyber security technologies. In terms of market momentum, Industry 4.0 represents a thriving market. According to the “IoT Analytics” research firm the market for Industry 4.0 products and services is expected to grow to $310B by 2023.  This growth accounts for investments on both manufacturing applications and the digital enablers that will support their development, deployment and operation in the shopfloor.

Industry 4.0 is revolutionizing production processes, including the ways industrial organizations manage their assets. In the area of asset management, it propels some of the main trends of our era such as:

• Process Harmonization Across Virtualized Manufacturing Chains and Globalized Operations: For nearly two decades, enterprises have become increasingly connected to one another as a result of globalization trends. Hence, the corporate boundaries in the manufacturing value chain are blurring, as companies are continually and seamlessly exchanging information about their production processes. The latter are likely to span multiple countries, as materials can be sourced in one country and production can take place in another, while distribution is likely to take place in multiple countries. The virtualization of the manufacturing chain asks for compatibility in the distributed production processes based on compliance with standards like ISO 55001 for asset management systems and ISO 14001 for environmental management.
• Holistic End-to-End Approaches to Asset Management: In a digitally interconnected world, enterprises are offered with opportunities to manage their assets end-to-end i.e. across their entire lifecycle. Industries can now access and manage information about the cost and performance of their assets throughout the assets’ lifecycle, while at the same time identifying the risks that are associated with each asset. In this way, they are becoming able to optimize assets’ performance and costs.
• Enterprise Collaboration on Asset Management and Service Delivery: Industry 4.0 enables a tighter and more effective collaboration across different stakeholders, such as Original Equipment Manufacturers (OEMs), Engineering, Procurement, and Construction (EPCs) enterprises, and service providers. These stakeholders tend to engage in asset-related operations including production, service, repair, management, deployment and use. In this context, their collaboration across the asset lifecycle can help them optimize asset management and service delivery. For example, OEMs can provide instant access to equipment information towards easing service and maintenance processes. Likewise, manufacturers can provide real-time insights on the performance of their assets to OEMs, which can take advantage of this information in order to improve their engineering and design processes.
• Instant Access to Reliable Information for Asset-Related Decision Making: Industry 4.0 applications make it easier than ever before for practitioners to access detailed, accurate and timely information about an asset. This facilitates informed and optimized decision making, along with instant sharing of relevant information with various stakeholders.
• Compliance as a Cost Optimization Driver: Standards compliance in asset management (e.g., ISO 55001) is currently becoming important not only for responding to globalization and enterprise collaboration challenges, but also in order to provide cost-effective products and solutions. Indeed, standards-based asset management helps companies to operate safely, reliably and in a cost-efficient way.

Collecting and Analyzing Digital Data for Intelligent Asset Management

In practice, intelligent asset management is all about collecting, consolidating and processing accurate, timely and high-quality data about the various assets, as a means of deriving insights about their current and future state. Accordingly, it’s also about routing these insights to appropriate stakeholders, including workers and the business management. Asset insights may relate to either Information Technology (IT) or Operational Technology (OT) systems of an industrial organization. Once provided to the right people, they can serve as a basis for data-driven cost, risk and performance management.

As a first step to intelligent asset management, organizations need to establish a digital infrastructure that enables seamless flow of asset management information both inside the organization (e.g., from the shop floor to enterprise applications) and across business actors of the manufacturing chain (e.g., OEMs, EPCs, suppliers, manufacturers). Typically, such a digital infrastructure consists of the following elements:

• A high-performance data collection infrastructure: It enables access to real world data about assets based on sensors and other connected devices. It typically comprises a high-speed networking infrastructure, along with a data streaming middleware platform, which facilitate real-time data collection from various actors (e.g., OEMs, EPCs) and across the entire lifecycle of the asset.
• A data routing infrastructure: This ensures the timely dissemination and delivery of asset data to different actors, notably actors interested in consuming and processing asset related datasets. It solves the problem of mapping a wide variety of heterogeneous data sources to the consumers that are interested in processing their data.
• A data analytics infrastructure: It facilitates the processing and analysis of the data, towards converting raw data to meaningful information and asset-related insights. This infrastructure enables the execution of data processing rules, but also of advanced data mining algorithms including Machine Learning (ML) and Deep Learning (DL).
• A data visualization infrastructure: This provides user-friendly visualization (e.g., dashboard) of asset information and insights, such as the outcomes of data analytics processes. It facilitates workers and the business management in understanding the status of the assets as a means of driving cost, performance and risk related decisions.

This digital infrastructure for Intelligent Asset Management falls in the realm of Big Data systems, given that it deals with large volumes of asset related data, which stems from a variety of heterogeneous sources, including sources that feature very high ingestion rates (i.e. high velocity). With such Big Data systems at hand, enterprises can implement Intelligent Asset Management workflows. The latter involve the following steps:

• Connecting to the Assets: This is a key prerequisite for collecting data about the assets and gathering information from both IT and OT systems. Data will be collected directly through the assets, but also based on IoT devices attached to the assets, such as vibration sensors, thermal cameras, ultrasonic sensors, acoustic sensors and more.
• Predicting the Asset Behavior: Leveraging on the IT/OT data about the assets, it’s possible to design and implement predictive analytics algorithms that forecast the future status of the assets. For example, predictive analytics algorithms can be used to calculate the Remaining Useful Life (RUL) of an asset, which can be later used to avoid unplanned downtime and to create strategies for maximizing the value that is derived from the asset.
• Simulating the Asset Behavior: The asset’s data can be also used to simulate its behavior under different assumptions about its operation. To this end, one of the most powerful Industry 4.0 applications is employed, namely the digital twin i.e. a faithful representation of the physical asset in the digital world. A digital twin is built based on data collected about the asset, including master data provided by the asset vendor (e.g., the OEM). Using a digital twin, it’s possible to simulate what-if scenarios about the operation of the asset, as a means of planning service and risk management processes.
• Sharing Asset Information: Insights derived through predictive analytics and simulation can be shared across the entire ecosystems of OEMs, EPCs, manufacturers, suppliers and service providers, in order to ensure that they all have the same view about the asset status and its operation.
• Implementing Collaborative Workflows: Information sharing provides an excellent foundation for implementing collaborative workflows across stakeholders. For example, OEMs can provide manufacturers with additional insights on the health of the asset and its maintenance needs. Manufacturers can then schedule service processes accordingly.

Maximizing Benefits with Artificial Intelligence, AR/VR and Cloud-Based Asset Networks

Data collection and data analytics provide a foundation for intelligent asset management in the Industry 4.0 era. On top of this foundation, more advanced asset management functionalities can be deployed, based on leading edge technologies such as:

• Augmented and Virtual Reality (AR/VR): AR/VR technologies can provide realistic cyber representations of assets that facilitate plant workers and decision makers to better understand the status of the assets. AR/VR representations tend to be much more interactive and realistic than conventional dashboards, as they provide a live view of the asset based on three-dimensional models with very rich information.
• Artificial Intelligence (AI) and Machine Learning (ML): AI systems, including ML algorithms can be used to extract knowledge about the future status and behavior of the asset. For example, ML algorithms can be trained on historical data in order to discover unknown patterns of degradation or even failures of the asset. The discovery of such patterns can prevent failures and unplanned downtimes, much better than when using human experts’ knowledge alone. As another example, AI and ML systems can provide prescriptive recommendations for maintaining and repairing the assets. Likewise, with the advent of “explainable AI systems”[1] it will be possible to provide workers and the business management with entirely new knowledge about why an asset could fail.
• Cloud-Based Business Networks for Asset Management: Cloud computing infrastructures and extranet networks facilitates the seamless flow of asset management information across the entire ecosystem of stakeholders like manufacturers and OEMs. This enables the implementation of advanced collaborative real-time asset management workflows with the participation of all stakeholders. Such networks can also enable the implementation of entirely new business models, such as models where the asset operator does not purchase the asset, but rather pays a monthly fee for using it. Likewise, it can enable models where OEMs provide diagnostic and service information about an asset, only when needed, as part of a Maintenance as a Service (MaaS) paradigm.

SAP’s Intelligent Asset Management Solutions

Partnering with the proper vendor is a key to digitally transforming asset management processes through offering a proper infrastructure for collecting, analyzing and sharing asset data, as well as for visualizing asset information and implementing advanced AI/ML analytics. SAP provides a pool of Intelligent Asset Management solutions, which enable the collection and analysis of asset information, as well as the sharing of assets’ insights across all stakeholders of the manufacturing value chain. In particular, SAP offers five principal cloud-based Asset Management solutions, including:

• SAP Asset Intelligence Network: It enables collection and tracking of information about equipment in a central cloud-based repository. Moreover, it allows equipment operators to access up-to-date maintenance strategies and manuals from manufacturers, while at the same time enabling manufacturers to automatically receive information about assets’ usage and failures.
• SAP Asset Strategy and Performance Management: This solution provides functionalities for measuring and improving the performance of assets and for enhancing maintenance strategies. It enables asset owners, managers, plant managers, and reliability engineers to improve control, while increasing the accuracy of maintenance planning.
• SAP Predictive Maintenance and Service: It provides predictive insights on assets’ conditions based on the collection, consolidation and processing of information from various sensors and business information systems, including enterprise resource planning (ERP), customer relationship management (CRM), enterprise asset management (EAM), and augmented reality systems.
• SAP Predictive Engineering Insights enabled by ANSYS: This solution supports asset management functionalities leveraging on real-time, predictive engineering analytics. The latter provide insights for reducing equipment downtime, increasing reliability, improving performance, and lowering operations and maintenance costs. In this direction, the solutions support also digital twins and pervasive simulation capabilities.
• SAP Asset Manager: It offers a mobile application that provides online and offline access to context-rich visualizations and actionable insights about assets. This mobile app facilitates the timely execution of end-to-end enterprise asset management (EAM) processes, through enabling business actors to access asset information regardless of time and their location.

Conclusion and Outlook

The advent of the digitization of industry and of the fourth industrial revolution, provides industrial organizations with unprecedented opportunities for managing assets’ costs, risks and performance in a holistic way. To this end, they can leverage advanced technologies like predictive analytics, artificial intelligence and digital twins, as a means of monitoring and predicting the performance of assets, people and production processes. Likewise, enterprises can share data across stakeholders like OEMs and EPC suppliers, in order to engage them in collaborative asset management processes. Furthermore, industrial enterprises can establish new business and operational models for managing assets, notably models that optimize the balance between operating expenditures (OPEX) and capital expenditures (CAPEX).

SAP provides a complete suite of cloud-based applications that enable the digital transformation of asset management processes and provide a host of intelligent asset management functionalities. Nevertheless, the successful deployment of the SAP Intelligent Asset Management applications requires an accompanying consulting effort towards customizing the operation of the suite to an enterprise’s needs. Gentackle is an experienced consulting firm with over 10 years of SAP Management Experience and over 15 SAP Rollouts. It specializes in Intelligent Asset Management and has a team of expert consultants with deep knowhow in next generation Asset Management solutions. As such it is the right partner for designing, developing and deploying your Intelligent Asset Management project, in a way that maximizes the value you get from your assets and sets you apart from your competitors.

[1]  AI systems that operate in a way that is understood by humans