Drive Business Insight with Shared Asset Intelligence

A significant decrease in the cost of sensor technology, combined with sensors generating an increasing volume of data, is resulting in a material change in how companies are leveraging smart products. In the early days of sensor and Internet of Things (IoT) technology, a smart product generated value simply by being smart. Now, a smart product’s value is measured more by its contributions to the organization as part of a larger network of connected assets.

Business networks are also changing how organizations manage and leverage information about their assets. When companies see the impact that connected assets can have on business processes, business models, and manufacturing, they recognize that true asset intelligence is derived not from sharing information across an internal network, but rather from sharing information across the entire ecosystem of suppliers, original equipment manufacturers (OEMs), service providers, and asset operators that have a stake in the asset life cycle.

This kind of shared asset intelligence requires full transparency into connected assets across this ecosystem of stakeholders, which is easier said than done when one-to-one relationships with stakeholders are no longer sufficient. Consider a major plant refurbishment. An asset management solution will provide the asset operator with visibility into the impact this will have on connected assets in the facility, but what about the affected OEMs, part suppliers, service providers, and outsourced maintenance providers? And what about the downstream implications for any assets that are delivered as a service?

SAP helps customers achieve this transparency by using SAP HANA to gain real-time insight into the condition of assets available to all stakeholders, and to provide a central repository for storing and sharing up-to-date asset intelligence.

True asset intelligence is derived from sharing information across the entire ecosystem of suppliers, OEMs, service providers, and asset operators.  

Real-Time Insight into Connected Assets

Fundamentally, SAP eases transparency into connected assets by connecting the shop floor to the top floor — in other words, by integrating business and real-world data, from SAP and non-SAP sources, into one system to provide CFOs and COOs with the information they need to drive their asset strategy and make insightful business decisions. 

Optimizing asset operations, however, requires a fuller business context so that all stakeholders in the asset life cycle have the specific real-time information they need to take action. SAP Predictive Maintenance and Service leverages the integrated sensor data, along with the high-performance processing of SAP HANA, to provide a real-time snapshot of an asset’s condition. The solution can then push this snapshot out to operations, service providers, and OEMs — anyone who has a stake in the asset life cycle.

SAP Predictive Maintenance and Service reflects how maintenance strategies are changing because of IoT technology. For a long time, breakdown, preventive calendar, or usage-based maintenance, where providers either waited for a machine to fail or scheduled regular maintenance intervals, were considered state-of-the-art strategies. Neither approach was very efficient, of course — the former could devastate a production line if a machine failed during peak production, and the latter was rife with inefficiency (for example, a machine could be taken offline for scheduled maintenance with no insight into the machine’s actual condition).

With SAP Predictive Maintenance and Service, proactive decisions can be made based on the actual, current condition of the asset, reducing overall maintenance costs while increasing asset availability or runtime. In a network of connected assets, such as a fleet of vehicles, deeper insight means getting an idea about the likelihood of failure for a specific vehicle so that resources can be allocated at the asset level instead of the unit level.

Storing and Sharing Asset Intelligence

Staying with the fleet example, think of how much master data information is required to become more predictive about maintaining each of those vehicles — how to operate the vehicle, its basic characteristics, and its recommended maintenance strategies, for example — and how this information can all change in an instant with new data coming in continuously from sensors.

In the past, there were gaps in how master data information for assets was processed. There were two reasons for this. First, information about a new asset, such as a binder or PDF of a maintenance manual, was usually loaded either manually or automatically in a one-to-one relationship between a supplier and operator. This was costly and inefficient because the same process would be repeated for each asset, but likely in a different format based on the particular OEM or provider. The second reason is simply that timely information wasn’t all that important — when each asset is maintained in the same way regardless of its actual condition, there is little urgency to make timely updates.

With sensor data providing more information more quickly, the need to analyze this information in real time to drive business decisions has become critical. This business challenge was the basis for the recent release of SAP Asset Intelligence Network.1 Running on SAP HANA Cloud Platform, SAP Asset Intelligence Network provides organizations with a central repository for storing and retrieving all information related to its connected assets, and sharing this information with stakeholders in the operation of these assets. For example, model information from an OEM can be transformed into information about a piece of equipment in a back-end system, ensuring a continuous flow of always-up-to-date information.

With sensor data providing more information more quickly, the need to analyze this information in real time to drive business decisions has become critical.

But this can also be a bilateral flow of information that enables increased efficiency and proactive problem solving. Consider a fleet operator being notified that a vehicle is likely to suffer a breakdown because of a faulty part. With real-time data shared between an asset operator and an OEM while the asset is in operation, the OEM can draw conclusions for a design change or advise specific maintenance activities.2 For a family of assets, there could even be additional recommendations based on the gathered data — a new asset system strategy, for instance. Operational-specific information about assets can be enormously beneficial for making improvements to further iterations of the asset.

In the recent past, connecting assets meant bringing data from the asset via proprietary interfaces into siloed systems for condition-based management. SAP Asset Intelligence Network, together with SAP Predictive Maintenance and Service, now extends this concept by coherently analyzing this data in a business context and providing transparency to all involved stakeholders. In summary, creating a smarter network of connected assets requires linking business data and real-world data in order to achieve optimal business decisions.

Building a Real-Time Enterprise

Analyzing all necessary information together on a single platform is the key to building a real-time enterprise that can face modern business challenges.

As an example, the Hamburg Port Authority was faced with the challenge of moving an increasing amount of product through the port and its terminals without the possibility of expanding its landscape. For this reason, driving greater efficiency became paramount. By leveraging IoT technology and SAP HANA Cloud Platform to create a network of connected assets, the Hamburg Port Authority can know exactly how to optimize traffic patterns, for example, for every arriving truck or departing ocean liner.

Creating a smarter network of connected assets requires linking business data and real-world data to achieve optimal business decisions.

SAP HANA enables a high level of efficiency in connected networks of assets by enabling the rapid assessment of collected data to determine which information needs to be stored for constant availability. Not all of the data collected from assets needs to be available for analysis all the time — for example, an airliner collects terabytes of data during a transatlantic voyage, but only some of that data, such as data that could indicate failure patterns, must always be available. Using SAP Predictive Maintenance and Service, operators can analyze an asset’s behavior and historical data from a single, central repository to make more informed decisions about which data is important for predicting failure. In the case of an airline, this could be temperature readings or vibration analysis, whereas another asset could have entirely different parameters.

The larger point is that IoT capability is meaningless without the ability to derive real-time value from the information that is being collected. By leveraging SAP HANA, SAP is providing manufacturers and asset operators with the tools they need for end-to-end management of a network of connected assets.

Connecting assets not just to internal asset management systems, but to an extended business network, is becoming a hallmark of asset-intensive organizations that fully embrace digital transformation. They take proactive decisions based on actual, live conditions being shared throughout the ecosystem.

1 For more on SAP Asset Intelligence Network, see Mike Lackey’s article “Responsive Manufacturing: Redefining the Digital Enterprise”. [back]

2 For more information about how real-time data can influence design, see Thomas Ohnemus’s article “Support for Connected Products Begins with Design”. [back]