This article is the fourth in an editorial series with a goal to provide strategic direction for enterprise thought leaders in the manufacturing sector for ways of leveraging the big data technology stack in support of analytics proficiencies designed to work more independently and effectively in today’s climate of striving to increase the value of corporate data assets.
In the last article, we provided an overview of the big data technology stack for the manufacturing sector. The complete insideBIGDATA Guide to Manufacturing is available for download from the insideBIGDATA White Paper Library.
The big data revolution is dramatically changing the manufacturing industry. The following driving factors have created opportunities for growth and have motivated the need for manufacturers to collect, store and analyze massive volumes of data—leading to the adoption of big data technology:
- Product quality/defect tracking
- Supply planning
- Manufacturing process defect tracking
- Supplier/supplier components/parts defect tracking
- Collecting supplier performance data to inform contract negotiations
- Forecasting of manufacturing output
- Increasing energy efficiency
- Simulation and testing of new manufacturing processes
- Enabling mass-customization in manufacturing
In realizing the above benefits, many manufacturers have implemented practices using big data including: log aggregation, monitoring, analysis and reporting; integrating sensors or embedding a log inventory system with machines on the manufacturing floor; this has enabled them to gain an understanding for what the machines are doing—allowing the data-driven organizations to grow, protect and bring added value to their business.
The “Maturity Pyramid” diagram below is a good way to visualize the big data adoption process. We can see that every new big data adopter starts at a different spot, i.e. every business has different tools, every business has a unique set of skills. With the maturity pyramid you need to take an executive approach to this realization, e.g. a consultative approach to the process as it is important to have clarity for where the company is positioned. The maturity pyramid is a good ground point for manufacturers to understand how to get to analytics.
Recent findings from a survey completed by LNS Research and MESA International—Attitudes on How Big Data will Affect Manufacturing Performance—include where big data is delivering the greatest manufacturing performance improvements today. The highlights from this survey include the following top three areas that big data can improve manufacturing performance: better forecasts of product demand and production (46%), understanding plant performance across multiple metrics (45%) and providing service and support to customers faster (39%).
As a clear indication for how solidly Dell is behind the big data adoption of big data solutions, Dell boasts several significant results! Dell, as a large manufacturing company, is taking this big data journey as well, and the company can speak to how it has achieved its goals, how the company built its big data solutions, where it failed, and what it will implement next time.
Dell is adopting big data solutions together with internal IT teams in order to enact the following enterprise initiatives:
- ETL offload
- SAP HANA deployment
- SAS to Statistica migration
- Dell works with customers in its Solution Centers to help them leverage Dell’s own experience with building big data solutions
- Dell’s own metrics demonstrates success when adopting big data (see chart below)
Dell has a great deal of expertise in building big data reference architectures, in that the company built the first custom designs for big data platforms as early as 2009. Dell has been in the big data space for that length of time, since 2009 with the platform and the first reference architecture in 2011.
If you prefer, the complete insideBIGDATA Guide to Manufacturing is available for download in PDF from the insideBIGDATA White Paper Library, courtesy of Dell and Intel.