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How manufacturers better predict and respond to business demands with in-memory computing

David_Dichmann

Discover how manufacturers can predict and respond to business demands with HPE Superdome Flex large-scale in-memory computing solutions for SAP HANA for manufacturing.

HPE Superdome Flex_manufacturing_blog.jpgAs you well know, for manufacturing to stay competitive globally and execute within budgets, it is all about efficiency. It is also about asset management and cost control, together with optimized processes—so you can provide on-time delivery.

Efficiency includes minimized returns, complaints and repairs. Modern supply chain reality means real-time supply-and-demand matching of thousands of manufacturing parts and components to meet completion schedules, as well as more frequent financial reports to make better business decisions about pricing, inventory and resource adjustment.

You need be flexible to predict future trends and act faster than competitors or to respond to unexpected demand. Efficiency also comes from a non-stop manufacturing processes and continuous business operations—because any downtime means less revenue, or even possible contractual penalties.

To help your manufacturing operations be more efficient, you need to be able to analyze supply and demand data, financial data and the manufacturing process with greater speed. You need to do this while more and more information is being made available with smart technologies and the Internet of Things. And you need to do this quickly as well as continuously to meet demand and avoid any planned downtime that could impact delivery schedules.

The new era of in-memory databases like SAP® HANA® has allowed manufacturers worldwide to dramatically increase the speed at which data can be identified, gathered and analyzed. Innovative systems like the new HPE Superdome Flex feature large-scale in-memory computing by being able to have up to 32 processors and 48 TB memory—enough enough for even the most demanding SAP workloads, while also offering the highest levels of mission-critical availability to ensure the continuity of your manufacturing business operations.

Use cases: customers leverage large-scale in-memory computing to their business benefit

We spoke with Kaeser Kompressoren about its need to meet workload performance needs with real-time analytics on equipment sensor data. With more than 5000 employees worldwide, Kaeser Kompressoren, headquartered in Coburg, Germany, is one of the largest providers of compressed-air systems and compressed-air consulting services. Kaeser has sensors in its equipment that capture environmental data (temperature and humidity, for example) and transmit it to a system running predictive analytics to determine if a part is going to fail and cause an outage.

Every time a compressor fails in an industrial process, it impacts customer productivity, and Kaeser must send out a troubleshooting team. As a result, the manufacturer wanted to avoid the service costs and negative impact to customer satisfaction resulting from any unscheduled outages. Kaeser now has an analytic system based using large-scale in-memory HPE servers running SAP HANA. Kaeser estimates that it saves more than $10M annually in break-fix cost avoidance and has reduced downtime by 60%. The company is also using this capability as a competitive advantage to improve customer satisfaction as well as to assist in improving the product design.

However, sensor data is not the only important thing we can better analyze using large-scale in-memory computing. We also spoke to Motech, a Taiwanese solar giant and the world’s largest manufacturer of solar cells. Motech reduced financial closing times from seven days to just one day so that it could deliver near-real-time cost/profit analysis, which led to speedier decision-making capabilities of company executives. Motech sees this flexibility as an important competitive differentiator. 

And we talked to Scott Sports, a Switzerland-based global sporting goods company. Scott Sports implemented a scalable solution with real-time reporting that improved business planning, operations and analytics capabilities in its finance department. Scott Sports realized an enhanced supply/demand matching, ensuring that they will continue to meet or exceed completion schedules.

For manufacturing organizations, when you add large-scale in-memory computing such as the HPE Superdome Flex for SAP HANA to your infrastructure, you can create new analysis methods that can increase proactive support, improve manufacturing processes, provide financial insight to decision makers—and make manufacturing more efficient.  

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About the Author

David_Dichmann

David focuses on SAP HANA lifecycle and ecosystem for HPE solutions for SAP HANA. He was previously employed by SAP focusing on SAP HANA Enterprise Architecture tools. David has more than 30 years of industry experience in both technical and business roles, working with small, start-up, and established businesses.

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