Better planning, dynamic service levels, and the ability to leverage consumer behavior data: Here’s a look at five application scenarios that are supported by SAP HANA.
SAP’s fast-close is now 30 percent quicker than before — the German national soccer team can keep a permanent eye on its players’ performance and the German Cancer Research Center in Heidelberg can scan the genetic makeup of countless patients in an instant. In short, SAP HANA is the ideal vehicle for analyzing financial data, developing match tactics, and identifying disease patterns. SAP in-memory technology is particularly valuable in five fields of application:
1. Flexible planning
Until now, solutions for sales and financial planning have lacked the ability to take account of unpredictable factors, such as international currency developments, into forecast calculations and to respond automatically when these basic conditions change. SAP HANA, on the other hand, incorporates the predictive analysis library, which lets enterprises perform statistical algorithms without having to carry out complicated ABAP programming themselves. The algorithms recognize patterns in planned/actual deviations and issue alerts if those deviations become extreme.
In the spring of 2011, for example, the Swiss franc increased so much in value that it was worth almost as much as the euro. In September, the Swiss National Bank decided to intervene and set a minimum exchange rate of 1.20 francs to the euro. For businesses that are based in Switzerland and trade with countries in the European Union, an intervention such as this has an enormous impact on both profits and returns – and therefore also on decisions relating to sales and financial planning. In the SAP HANA scenario, the system issues an alert seconds after any significant change in the value of a currency occurs, containing information about how the company’s targets and planned values need to be adjusted to mitigate the effects of the change.
2. Optimizing travel costs and times
Any company that fails to meet its financial targets will first respond by cutting costs in areas that won’t prove too “painful,” such as travel. However, closer inspection reveals that travel-related expenses and flat rates for items like tickets and taxi rides are only part of the story: There are “opportunity costs” and “soft costs” to consider as well.
Employees who are on the move can certainly work intermittently on their mobile devices between appointments, but they can by no means operate at full capacity. And cost-cutting directives can lead to employees getting up very early in the morning to get to their appointments on time rather than staying in a hotel the night before. But employees who haven’t had enough sleep are less productive than those who have.
Companies that do include these “soft travel costs” in their calculations often base their forecasts on observations made by employees in the travel department or on a rigid set of parameters defined by IT. This is referred to as the “deductive approach,” in which systems only analyze certain data from the outset. A holistic approach is very different, it involves collecting data about the punctuality and service quality of airlines, using a traffic-jam alert system, a geocoded empirical dataset, a multimodal transportation price overview, and, finally, employee feedback.
Flexible models in SAP HANA use this data to identify patterns and logic in order to make the best possible decision about a travel itinerary. That saves companies money, reduces frustration levels, and makes the most of valuable employee resources.
3. Managing customer behavior
SAP HANA gives companies the power to monitor their planning systems and optimize their travel costs. But systems that autonomously adapt their behavior to customer requirements in real time are also possible.
Take, for example, a gas station convenience store. As well as receiving information about how many bottles of beer and wine are sold on a Saturday, store operators could factor special events ― such as soccer world cup matches ― into their sales planning and pricing with the help of SAP HANA decision tables. For example, the system could read Twitter feeds about the current score during a match and set prices dynamically, based on the likely mood among fans. Integrated algorithms in SAP HANA incorporate additional criteria dynamically, independently, and neutrally.
In another example that is already a reality, the SAP HANA system at a large supermarket chain incorporates a weather forecast algorithm that triggers the price of barbecue charcoal to increase on the first warm day of the year. The corresponding algorithms are preconfigured in SAP HANA, thus saving the company the effort and expense of programming them separately. On top of all that, the combination of SAP Business Suite and SAP HANA (SAP Business Suite powered by SAP HANA) makes it possible to take action not just 24 hours in advance, but at very much shorter notice. Airlines, telecommunication companies, and energy suppliers are just some of the industries that are benefiting from this ability to “self-customize.”
4. Service level management
When a company completes its negotiations with an IT service provider, the details of the service or services concerned are defined in a service level agreement (SLA). The SLA might stipulate, for example, that the response time for a master data query may not exceed three seconds. However, this contractually agreed level of service depends on many factors, including the server configuration, storage capacity, and rate of data throughput. It is of immense practical interest to the service provider to know exactly where it needs to make adjustments to reduce response times at minimal expense. However, the speed at which technology is advancing means that the nature of the adjustments required ― and their impact on the system ― are changing all the time.
SAP Business Warehouse can certainly store information about contexts like these. But adjustments entail costly and time-consuming projects and leave IT personnel having to constantly tweak and fiddle with data flows. Thanks to SAP HANA and SAP’s new definition language, data models can be designed much more flexibly. Now in addition to faster processing speeds SAP HANA is offering a raft of completely new benefits. To profit from these, customers do need to migrate from SAP Business Warehouse to SAP BW powered by SAP HANA – but that’s no great obstacle.
Dynamic data modeling with SAP HANA allows the collaborative use of data across enterprises
5. Incorporating sensor data
Sensors are all around us. They register where a cell phone user is located, where there are hold-ups on the freeway, and what the current temperature is. Vehicle sensors measure how fast a car is traveling and the driver’s acceleration and braking patterns. Data like this can be recorded and passed on to third parties.
For example, it is very much in the interests of vehicle insurers to discover more about their customers’ willingness to take risks so that they can adapt their insurance premiums to match a client’s style of driving. Billing models like this, known as “pay how you drive,” are already being used in the US. The only drawback so far has been that insurers and automakers use very different terminology, which complicates the process of exchanging the necessary meta data. Technical terms that are self-explanatory for car manufacturers often mean nothing to an insurer. This leads to a great deal of alignment effort and manual work at the interface between these two “worlds.” If the two parties were both to use a dynamic data model based on SAP HANA, then the data could be used seamlessly by both. It could also be updated semi-automatically – for example using new available algorithms for text analysis. And whenever new sensors are added, the insurer could adjust its price models immediately.
Dynamic data models have not yet become fully established throughout the business world. They’re definitely on their way, though ― as are algorithms, which will become an increasingly prominent feature of enterprise software solutions.