Leverage technical innovations to master current business challenges and shape future business models: That is the core task of SAP Leonardo. The SAP HANA-based innovation system brings a host of benefits to companies of all shapes and sizes.
At SAPPHIRE NOW, Mike Flannagan, senior vice president of Analytics at SAP, described the role of SAP Leonardo like this: “SAP Leonardo is about accelerating time to value by finding generalized, common business problems across an industry.”
Built on SAP Cloud Platform, SAP Leonardo packages a world of innovative technologies such as Internet of Things (IoT), machine learning, and blockchain. The 10 following industry scenarios are already proving themselves as use cases at customers today.
1. Predictive Maintenance: Health Check for the Rail Industry
Signal, switch, and train data is not available in a common database, leading more often than not to unforeseen delays in rail traffic. The outcome: Loss of revenue and high costs for maintenance of technical assets.
SAP Cloud Platform makes it possible for users to display and process such data in a closed system. With the help of SAP Predictive Maintenance and Service and machine learning, they can predict the need for maintenance early on, reducing train down times and cancellations, as well as detect and eliminate potential bottlenecks in the rail network in real time.
- Holistic view of all technical assets and their condition
- Fewer interruptions in rail traffic
- Higher productivity by way of increased automation
- Cost savings through preemptive maintenance
2. Telemetrics: Increasing Capacity Utilization of Tools
At major construction sites, required tools are often either not where they need to be, get “borrowed” by colleagues, or don’t work. This slows down progress because the agreements on who uses what tools when only exist ‘on paper.’
Machines, devices, and tools constantly send telemetrics data to SAP Cloud Platform, which enables them to be precisely detected and localized. Additionally, the user can check to see how long the equipment has been in operation and, if it hasn’t been used much, decide whether it can be dispensed with. Replacements or repairs for defective equipment can then be ordered directly through the integrated SAP ERP system. This precise insight into operating times and equipment optimization make pay-per-use models possible.
- Equipment manufacturers learn how customers utilize their tools
- New business models become possible
- Optimized capacity utilization of tools
- Theft of tools can be prevented
3. IoT: Connecting Logistics Ecosystems
Many IT systems, particularly in harbors, are mission critical and only updated when absolutely necessary. Such legacy or silo-based systems, however, only stand in the way of collaboration. Digitalized processes enabling a link to information from external partners in the ecosystem are not possible.
Sensor data regarding water levels, traffic density, and air quality, as well as digital maps and time planning now converge in one portal: the “Harbour Control Center,” available on SAP Cloud Platform, is the weather and sensor data hub for trucking companies, shipping companies, authorities, and terminal operators.
- Terminal operators can dispatch more goods
- Shorter wait times and less fuel consumption for shipping and trucking companies
- Higher container turnover at the harbor
4. Intralogistics: Enabling and Simplifying Just-in-Time Processes
Manufacturers use either their own trucks or hire transport companies to ship goods between suppliers and customers. And although the just-in-time production process depends on these deliveries reaching the recipient as scheduled, a lot of supply chain decision-makers often have no clue where their own or commissioned shipments are at a given time, because the systems they use are either poorly or not at all integrated.
All vehicles are tracked on a map and routes and fuel consumption are transparent. Vehicles are outfitted with on-board units that transmit relevant data for analysis and reporting to SAP Cloud Platform. Thanks to sensor data and machine learning, transportation management systems are able to re-plan routes and offer alternative delivery options as needed.
- Tracking of vehicles on maps
- Analysis of vehicle usage
- Geofencing functionality alerts user in case of delay
5. EDGE: Fueling Airplanes
At many airports today, delivery tickets for kerosene are still created manually on paper, which leads to delays in control and inspection processes, for example, and to errors when transferring the data into the system. What’s more, there is no transparency regarding the amount of fuel available.
EDGE technology makes it possible to use offline-capable mobile devices, even in areas with poor Internet connection. The data collected is transmitted directly to SAP Cloud Platform with direct connection to the ERP system.
- Information about fuel availability at the push of a button
- Faster and simpler delivery of invoices
- Improved data quality
6. Digital Twin: Collaboration of Assets and Optimization of Maintenance
Machine and device data is not recorded in the same system, resulting in considerable effort to consolidate and make the data available for analysis. Maintenance server providers, however, can only do their work reliably and in accordance with the contract if they have access to the data for the assets in question. Power plants, for example, must also comply with the reference designation system for power plants (RDS-PP) and its reliability-centered maintenance” standard for asset management.
Since the data is transferred according to industry standard, internal employees and external partners such as service providers can now call up and process machine data in real time via SAP Asset Intelligence Network. The contracting entity — for example, the operator of a wind-energy farm — is notified accordingly via the maintenance planner once the maintenance and service work has been completed.
- Simple and easy access to asset data, even for external partners
- Lower maintenance costs
- Time savings of one hour per day for the service engineer
7. Health Check for Refineries
There is often little transparency in refineries when it comes to the material used and the production costs of the refinery process, in which mineral oil products such as kerosene, gasoline, and diesel are produced from crude oil. Planning and production are difficult to synchronize in real time; instead, this is usually done on a monthly basis.
SAP Manufacturing Integration and Intelligence (SAP MII) enables users to bundle and migrate diverse data from the refinery process into IT processes. Examples of such data include vibration alarms on machines, flow measurements, historical data, and tank levels. This enhances transparency with regard to the supply chain, the condition of the equipment and machines used, and ultimately the profitability of end products.
- Real-time transparency of the production process
- Real-time calculation of product profitability
8. Getting a Good Grip on Your Fleet
A lot of companies have movable assets — such as a fleet of company cars, buses, forklifts, and elevators — but know little about the current state of those assets and their deployment. Integrating data from third-party systems is a particular obstacle where this is concerned.
Users can take advantage of the SAP Vehicle Insights application to track their assets and record and analyze telemetric data. Fleet managers can see precisely how heavily their vehicles are used, and plan asset resources more efficiently. In addition, they can calculate toll expenses and fuel taxes in advance and decide when to use state roads versus toll highways.
- Lower fleet costs
- Lower maintenance costs
- Real-time fleet data
9. Machine Learning: Making Power Networks More Reliable
Operators in the electricity and gas supply business oftentimes do not have a holistic overview of the related technical infrastructure. The result: high costs for maintaining transformers and circuit breakers in substations, for example. They also lack the transparency and information needed to make informed investment decisions.
Sensor data from substations, electrical, and gas grids, as well as measurement data recorded manually, flow non-stop into a central portal. SAP Predictive Maintenance and Service analyzes these metrics and compares current data with historical data. Thanks to pattern recognition and machine learning, the system can identify those assets to be included in the next inspection round. As such, users benefit from a maintenance management system that allows for precise maintenance and repair planning.
- Lower operating costs
- More accurate maintenance and repair planning
- Higher technical availability of systems
10. Machine Learning: Pattern Recognition for Optimal Service Planning
One challenge in delivering the service level agreed with customers is having adequate staff on hand to deal satisfactorily with inquiries. However, such resource planning is often vague because too many systems and people are involved and the forecast is not accurate enough.
To accurately determine how much service staff will be required in the coming weeks or months, you need facts and figures on current inquiries and past development. This required data is brought together on a common platform and accessible on the SAP Fiori interface. Users can take advantage of comparable patterns identified through machine learning to make precise forecasts for service personnel and to schedule their respective tasks.
- Required information is centralized
- Machine learning algorithms calculate the headcount required
- Improved planning