|By Bob Gourley||
|January 3, 2012 06:00 AM EST||
For the second post on the McKinsey report on Big Data, we take a look at the impact of Big Data on production, supply, and logistics. When we discuss Big Data, there’s often an implicit assumption that it has to do entirely with IT functions. However, Big Data actually will have a large impact on manufacturing value chains.
While the manufacturing sector has historically been a driver of GDP and employment for many developed economies, globalization has made it a global activity with extended supply chains made possible by advances in information and communications technology as well as reduced transportation and market entry costs. This has resulted in incredibly complex and fragmented webs of globe-spanning chains, and increasing country specialization in specific changes of the production process. Manufacturers, in turn, have assembled global production and supply chain networks for cost advantage.
Where does Big Data come in? McKinsey argues that manufacturers will need to leverage large datasets in order to continue achieving large levels of productivity. The “raw material” is also very much available:
Manufacturing stores more data than any other sector—close to 2 exabytes of new data stored in 2010. This sector generates data from a multitude of sources, from instrumented production machinery (process control), to supply chain management systems, to systems that monitor the performance of products that have already been sold (e.g., during a single cross-country flight, a Boeing 737 generates 240 terabytes of data). And the amount of data generated will continue to grow exponentially. The number of RFID tags sold globally is projected to rise from 12 million in 2011 to 209 billion in 2021. IT systems installed along the value chain to monitor the extended enterprise are creating additional stores of increasingly complex data, which currently tends to reside only in the IT system where it is generated. Manufacturers will also begin to combine data from different systems including, for example, computer-aided design, computer-aided engineering, computer-aided manufacturing, collaborative product development management, and digital manufacturing, and across organizational boundaries in, for instance, end-to-end supply chain data.
McKinsey forecasts the biggest applications of big data will be in Research and Development, Supply Chain, and Production functions:
- The collaborative use of data can enhance product lifecycle management, using customer data to create better design to value, and better enable open innovation through sorting of high-value ideas.
- Manufacturers can use data to improve demand forecasting and supply planning, but can also use big data to integrate reams of data from various sources in near-real time to adjust production.
- Big data can also revolutionize the production process by creating “digital factory” simulations to better uncover optimal production layousts, sprinkle sensors throughout the supply chain and production process to reduce waste and cut operations and maintenance costs.
- Marketing and sales/after sales support can also use sensor data and analytics to make customer service more reliable. As the report notes, “a repair technician can be dispatched before the customer even realizes that a component
is likely to fail.”
However, the report goes on to note that Big Data may pose some cultural challenges for typical manufacturing organizations. One telling anecdote from the report described oil refineries that rely on managers with spreadsheets instead of algorithms using data collected directly from machinery. In order to reap the benefits from Big Data, manufacturers will have to develop capabilities and human resources up to the task of getting the best use out of the vast amount of information potentially available to them.
We all know that data growth is exploding and storage budgets are shrinking. Instead of showing you charts on about how much data there is, in his General Session at 17th Cloud Expo, Scott Cleland, Senior Director of Product Marketing at HGST, showed how to capture all of your data in one place. After you have your data under control, you can then analyze it in one place, saving time and resources.
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