The manufacture and assembly of massive fuselage assemblies and other large structures generates a river of data. In fact, the bill of materials for a single fuselage alone can be millions of rows of data. In-house production processes and testing, as well as other manufacturers and customers created data flows that overwhelmed previous processes and information systems. Spirit’s customer base had grown substantially since their 2005 divestiture from Boeing, resulting in a $41 billion backlog of orders to fill. To address this backlog, meet increased customer demands and minimize additional capital investment, the company needed a way to improve throughput in the existing operational footprint. Spirit had a requirement from customers to increase fuselage production by 30%. To accomplish this goal, Spirit needed real-time information on its value chain and workflow. However, the two terabytes of data being pulled from their SAP ECC was unmanageable and overloaded their business warehouse. It had become time-consuming and difficult to pull aggregate data, disaggregate it for the needed information and then reassemble to create a report. During the 6-8 hours it took to build a report, another work shift (they run three per day) would have already taken place, thus the report content was out-of-date before it was ever delivered. As a result, supervisors often had to rely on manual efforts to provide charts, reports and analysis.

At times, ULA has as many as 15 different operating systems dedicated to overlapping processes, such as rocket design, testing, and launch. Multiple systems created unnecessary costs and unwanted confusion among workers at offices, factories, and launch sites in different location. In order to improve collaboration and transparency during vital activities that directly influence mission success, ULA wanted to improve data sharing and streamline manufacturing processes.

For ARI, customer service is key. Seven global customer contact centers handle about 4 million calls annually, and this sea of daily information needs to be analyzed, which took up to two days manually, so negatively affecting service levels. With the amount of data ARI collects doubling every 14 months, their in-house ETL data warehouse limited their ability to fully help customers reduce fleet costs and gain operational efficiencies.