As outlined in earlier blog posts, the digital transformation of industrial processes is currently driving the next generation of asset management solutions that are destined to be intelligent, automated and cost-efficient. Nevertheless, the transition from current paradigms to Industry 4.0 compliant, fully digital approaches to asset management is an extremely challenging task. It entails changes in the technological infrastructure, the asset management processes and the business management of industrial organizations. Therefore, understanding the challenges and identifying possible solutions across all these directions is the key to a successful transition to Intelligent Asset Management (IAM).

Setting the Scene: The ISO 55000 Asset Management Standard

The exploration of the ISO 55000 standard provides a good way for understanding the various dimensions of asset management, including the roles of people, organizations and technology. ISO 55000 is an international standard that was launched in 2014 and which addresses the management of assets of any kind. In the scope of ISO 55000 an asset is defined as a tangible or intangible item that has value for an organization. Likewise, the standard defines asset management as the set of coordinated activities that enable an organization to realize value from its assets, with the goal of maximizing their potential value. In this context, asset management activities include planning and implementation actions that consider the lifecycle of each asset and attempt to optimize its value throughout its lifetime. Asset management activities are also striving to meet performance standards in ways that maximize safety and sustainability. Prominent examples of asset management activities include smart planning, assets operations, maintenance, engineering, assets upgrading and more.  The latter activities leverage a wide range of methodologies and tools, such as tools for life cycle assessment.

The standard highlights the importance of asset management plans in maximizing the value of assets. Such plans provide a roadmap for optimizing cost, risk and performance across the asset lifecycle. Likewise, a strategic asset management plan is used to specify how asset management objectives are linked to the strategic goals of the organization. Moreover, such a plan outlines how the asset management activities can support the accomplishment of the strategic objectives of an enterprise, based on the active engagement of people and the exploitation of available technological infrastructures and business processes of the organization. In this way, an ISO 55000 compliant asset management plan starts from the strategic goals of the industrial organizations and drills down on fine-grained activities (e.g., maintenance, repairs, replacements) that should be carried out at the level of each individual asset of the company’s asset portfolio.

The advent of Industry 4.0 enables the collection and processing of large amounts of data for individual assets and their lifecycle processes. Therefore, it provides the means for digitizing and automating interactions between the various stakeholders, but also for eliminating errors and increasing the accuracy of asset management processes. For example, based on predictive analytics, Industry 4.0 can derive insights on how to optimize the maintenance of an asset. Likewise, using sensor data it’s possible to create accurate life cycle assessments for an asset, as a means of optimizing processes like refurbishment and remanufacturing. However, in order to exploit the potential of Industry 4.0 and Intelligent Asset Management (IAM), industrial organizations have to introduce changes in their technology infrastructures and their methodologies, but also in their managerial decision making.

The Technological Perspective: Industrial IT Infrastructure Modernization

Intelligent Asset Management (IAM) in the scope of a fully digital shop floor requires upgrading legacy IT infrastructures towards a modernization direction. Specifically, the main technological enablers of IAM include:

    • Cloud Computing: Industrial organizations are expected to transition to the cloud, in order to benefit from its scalability, capacity and quality of service propositions. The latter are important for gathering and consolidating large volumes of asset-related data from a variety of data sources including sensors, quality management systems, asset management databases and business information systems – e.g., ERP (Enterprise Resource Planning) systems. Beyond their capacity and scalability, cloud computing infrastructures alleviate the technology heterogeneity of the systems that contribute data and services for IAM, while facilitating integration tasks.
    • Internet of Things: Industry 4.0 models for asset management ask for real-time visibility on the status of the assets. This is nowadays possible based on Internet of Things (IoT) technologies like sensors, Wireless Sensor Networks (WSN) and Radio Frequency Identification (RFID). Furthermore, IoT systems with actuation capabilities and cyber-physical parts are essential towards increasing the intelligence and automation of asset management operations (e.g., changing the operational settings of a machine to prevent wear).
    • Big Data Management: IAM is a data-intensive discipline, which asks for a modernization of the data management infrastructures of industrial organizations. Legacy historian databases must be complemented with systems for managing Big Data, including data streams with high ingestion. The latter include data lakes, streaming middleware platforms, noSQL databases and more.
    • Cybersecurity: The digitalization of industrial processes does not come without security risks. Recent cybersecurity incidents in industrial plants underline the need for strong security measures, which must protect industrial organizations from attacks at both IT (Information Technology) and OT (Operational Technology) levels. For example, it’s important to deploy measures that ensure the trustworthiness of data collection devices, along with policies that ensure confidential digital communications across the supply chain.

The Methodological Viewpoint: Enabling Data-Driven Predictive and Prescriptive Approaches

The availability of large amounts of digital data about assets and asset management processes is also driving the future of asset management methodologies. As a prominent example, IAM is associated with a shift from conventional preventive maintenance to emerging condition-based maintenance paradigms. Predictive maintenance is considered the most widespread condition-based maintenance modality. It is empowered by the execution of predictive analytics on large volumes of digital data about the assets. As another example, predictive analytics can be also used to enable industrial organizations to anticipate the quality of their products and processes, based on fully digital approaches to lean manufacturing and quality management i.e. Digital Lean Manufacturing (DLM) and Digital Quality Management (DQM) approaches.

Data driven approaches to asset management are also driving a shift to more prescriptive approaches as well. The latter employ prescriptive analytics in order to provide actionable recommendations about maintenance, repair, operation and management of the assets’ lifecycle. Data driven recommendations are likely to provide insights beyond conventional knowledge of human experts. Moreover, they provide fully digital ways for closing the loop to the field, leveraging on data-driven insights about the condition of the assets and possible actions to be undertaken on them.

Industry 4.0 is also imposing methodological changes in the collection and processing of datasets.  For example, there is also a need for alleviating fragmentation in the storage and collection of industrial datasets. Hence, methods and techniques that enable the unification, correlation and interoperability of diverse data sources are required. This poses additional methodological challenges to vendors and integrators of digital automation and IAM solutions in industrial environments.

The Impact on Business Management: New Business Models and Human Resources Requirements

The digitalization of industry is also introducing new business considerations for the management of industrial enterprises. Specifically, it empowers novel data-driven models to managing assets and asset management processes such as Asset as a Service (AaaS) and Maintenance as a Service (MaaS). AaaS provides the means for representing assets (including intangible assets) in a digital form. The latter enables the execution of digitally enabled functions for their optimization (e.g., optimizing the cost of maintenance and repairs based on simulations and digital twins). Similarly, MaaS provides a novel maintenance model, where OEMs (Original Equipment Manufacturers) provide data-driven recommendations for the optimal maintenance of the assets when and where required. MaaS and AaaS models introduce changes to the value chain of asset management, as they enable more direct business interaction between asset owners and OEMs.

The introduction of AaaS and MaaS has also an impact on the managerial decision making of industrial organizations. Once upon a time, decisions were driven by CAPEX (Capital Expenditure) considerations only. The advent of the “on-demand” and “as-a-service” business models puts emphasis on OPEX (Operational Expenditure) instead. This implies a change in the culture and style of business management, which should strive to prioritize OPEX and to consider the importance of value-added services offered by the OEM.

One of the most important impacts of IAM on the business management of industrial organizations concerns human resources management. Currently, industrial enterprises are faced with a significant and proclaimed talent gap in digital technologies such as Big Data, IoT and cloud computing. The business management needs to fill in this gap through hiring new staff, but also through reskilling and upskilling existing employees. Likewise, measures and policies for lifelong learning and training should be established as a matter of priority. Workers with new Industry 4.0 profiles and digital skills must complement existing multi-disciplinary asset management teams, which already comprise a wide array of different roles like portfolio managers, financial advisors, asset analysts and more.

SAP’s Intelligent Asset Management (IAM) Solutions

As a leading vendor of industrial solutions, SAP offers an innovative and versatile Intelligent Asset Management infrastructure that can greatly help enterprises in confronting the above-listed challenges. At the technological forefront, SAP IAM facilitates the process of cloud migration, while providing a rich set of tools for IoT integration and Big Data analytics over asset-related datasets. Furthermore, it offers a very rich knowledge base that can boost learning, training, upskilling and reskilling processes.

Beyond vendors of industrial solutions like SAP, consulting companies play a significant role in the emerging Intelligent Asset Management landscape. For instance, SAP consultants will advise industrial organizations on the asset management business models that they will have to employ, with emphasis on models that prioritize OPEX over CAPEX. Likewise, consultants can help companies reengineer their ISO 55000 compliant processes in ways that yield maximum benefit from the deployment and use of digital technologies. Furthermore, SAP consultants will lead the design and implementation of novel training programs that will enable enterprises to successfully deploy and fully leverage SAP’s IAM solutions.  Hence, the choice of the right consulting firm is a key to the success of your IAM strategy. Gentackle experts have proven experience in the deployment of complex, integrated IAM projects in ways that maximize ROI (Return on Investment) on your SAP IAM solutions.

Overall, the on-going industrial digitalization provides a wealth of opportunities for designing and implementing effective asset management strategies. Nevertheless, the proper exploitation of these opportunities requires significant investments not only in technology modernization, but also on novel methodologies and business models like AaaS and MaaS. Choosing the right technology partner is a key perquisite for successful IAM implementations. Gentackle experts possess the knowhow and expertise that leads to successful implementations and creates competitive advantages that could position a company among the “best in class” in its industry.