Big data has applications in just about every industry – retail, healthcare, financial services, government. Any organization that can assimilate data to answer nagging questions about their operations can benefit from big data. All big data projects start with a viable use case. Here are four sample big data use cases for the manufacturing industry.

A big data use case provides a focus for analytics, providing parameters for the types of data that can be of value and determining how to model that data using Hadoop analytics. For example, answering a question such as “where is the next big market for my product” is harder to answer than “who is likely to buy more product in the United States.” Here are four sample big data use cases in the manufacturing industry:

1. SUPPLY CHAIN MANAGEMENT

Big data provides manufacturers the ability to track the exact location of their products. This ability to track is one of the primary features that have been available for the manufacturers. Traceability is essential as often several products that have been released by manufacturers are lost and are difficult to trace. The coordinates of a parcel can be set up by using barcode scanners and radio frequency transmission devices. These devices transmit the location of the product, pinpointing where it is at a given point in time.

Determining what the customers need is one of the pain points when organizations want to forecast the current market trends. Big data helps here as it predicts the demands of the customers and also assists in providing the timings of when they want the product delivered. This forecasting can be done when companies refer to their supply chain and can help an organization in`improving the profitability and workforce.

2. Custom Product Design

Tata Consultancy Services cites the case of a $2 billion company that generates most of its revenue by manufacturing products to order.

Using big data analytics this company was able to analyze the behavior of repeat customers. The outcome is critical to understanding how to deliver goods in a timely and profitable manner.

Much of the analyses centered on how to make sure strong contracts were in place. The company also was able to shift to lean manufacturing to determine which products were viable and which ones needed to be scrapped.

3. MACHINE MAINTENANCE

When an organization is manufacturing anything, it plans to maintain its machinery; starting from how the machine is currently operating to what upgrades are essential for them to keep running efficiently. Using sensors, information can continuously be collected from these machines. The data gathered from devices helps organizations in determining when and how intense maintenance is required by a specific machine. Big data analytics can help manufacturers in keeping track of their machines by continually analyzing and focusing on how to improve the efficiency of devices.

4. DAILY PRODUCTION

For an organization to operate efficiently, they need to monitor their everyday activities and monitor how useful are their production capabilities and how their day-to-day business is performing financially. Big data analytics can assist manufacturing companies in doing so by providing them intuitive insights from the information that is being collected from various sources. These sources can be their operational machines, databases keeping a log of the number of units produced, and employee records. This information can help companies in making decisions related to making changes that can be profitable for the organizations.

5. Better Quality Assurance

Intel has been harnessing big data for its processor manufacturing for some time. The chipmaker has to test every chip that comes off its production line. That normally means running each chip through 19,000 tests.

Using big data for predictive analytics Intel was able to significantly reduce the number of tests required for quality assurance. Starting at the wafer level, Intel analyzed data from the manufacturing process to cut down test time and focus on specific tests.

The result was a savings of $3 million in manufacturing costs for a single line of Intel Core processors. By expanding big data use in its chip manufacturing, the company expects to save an additional $30 million.