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Big Data Use Cases in Manufacturing

Big Data Uses in many fields such as healthcare, manufacturing, etc. as big data is used to refer to data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. Analyzing big data uses in the manufacturing industry can reduce processing flaws, improve product quality, increase efficiency, and save time and money. Product quality and defects tracking 

Big Data Use Cases in Manufacturing

A big data uses 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. 

Location Tracking it is now possible to track the condition of the good in transit and estimate the losses. It is now possible to gather real-time data about traffic and weather conditions and define routes for transportation. This will help logistic companies to mitigate risks in transport, improve speed and reliability in delivery. 

Extracting process improvement  A vertically integrated precious-metal manufacturer’s ore grade declined. The only logical way to avoid a loss was to improve metal extracting and refining processes. Using sensor data, the manufacturer’s big data solution identified what factors influenced output the most.

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.

Early-stage vehicle quality assurance BMW used big data to detect vulnerabilities in their new car prototypes. Data was collected from sensors on the tested prototypes and cars already in use. Due to big data analysis, BMW’s solution spotted weaknesses and error patterns in the prototypes and in cars already in use. It enabled engineers to remove uncovered vulnerabilities before the prototypes actually went into production and helped reduce recalls of cars already in use.

Machine Maintainance 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.

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 with intuitive insights from the information that is being collected from various sources. 

Accessible raw materials To avoid costs connected with supply chain failures, one enterprise needed a better way to manage raw materials delivery. They decided to use their suppliers’ route details and weather data provided by a trustworthy external source to identify the probability of delivery delays.

Better Quality Assurance  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.

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