Using in-memory computing T-Systems assisted a large South African freight rail corporation to make near real-time decision-making. Just one benefit is improved cargo allocation by the equivalent of an extra train per day. This extra revenue, running into millions of rands per year, has within months covered the entire cost of the solution.
Managing the operational aspects of a hospital is a complex task. The correct and rapid response is vital, especially when critical failures occur. With the volume and variety of data to consider, it can take hours to determine the appropriate course of action. To overcome this challenge, T-Systems is assisting a large South African Private Healthcare Provider to achieve the goal of real-time decision making.
Using Big Data technology and Analytics, the Digital Boardroom gives the hospital manager a real-time view of all events. The manager can perform a variety of what-if analyses to determine the optimum response and direct and lead the teams. For the day-to-day operational activities, the digital dashboards give a live display of all the KPI-related metrics.
Getting to know your customers better, finding out what they want and having an overview of the entire customer journey from the time they enter the store until they make their purchase – these scenarios are now also possible at physical retail stores and can be realized through T-Systems Indoor Analytics solutions. These solutions anonymize and record customer flows in the store and then use big data applications to provide informative analyses that can be used in management decisions.
Using Big Data and Predictive Analytics, T-Systems can predict with almost 80% accuracy when failures will occur on locomotives for a large European Rail company. The Rail company can recall the locomotive before the failure, thereby preventing costly breakdowns and disruption of service. The benefits are substantial cost savings, improved service, and possible safety improvement.
Deutsche Bahn is about to begin using a new data analytics service provided by T-Systems to improve their existing real-time system for projecting arrival and departure times on their rail passenger services. The timetable data for more than two million stops per day will be compared minute-by-minute against the current real-world transport situation. This comparison is then used as the basis for forecasting probable arrival times and, at the same time, for predicting the impact of the real-time information on subsequent passenger transfer connections.
This will improve passenger information on delays in both long- and short-distance rail traffic, and will be continually enhanced based on additional data. Using smartphones and apps, but also via signage at rail stations, Deutsche Bahn customers will be provided with real-time information on expected departure times up to 90 minutes in advance.