Apache Hadoop, Big Data Analytics for Manufacturing

How big data can improve manufacturing

Manufacturing industry needs revolutionary methods to optimize operations, improve product quality at a reduced distribution cost – Apache Hadoop and Big Data Analytics is the solution!

A traditional method of data analysis and improvement doesn’t work anymore. We need advanced and more specific methods of data analysis that can help manufacturers find new opportunities to increase volume, reduce cost and improve product quality. Apache Hadoop and Big data analytics has emerged as two super solid technologies that help manufacturers in increasing their yield. Manufacturers generate data from various sources including shop-floor production log, real-time equipment performance data, maintenance registers, and sometimes from vendor performance-guarantee sheets. Now, once data is gathered the real challenge comes in “ How to manage such a complex pool of data?” Well, technological advancement has given us two very precious and effective methods namely, Apache Hadoop and Big Data. Using Apache Hadoop and Big Data Analytics, the manufacturers are able to access hidden data and integrate all of this data across several sources in order to get valuable insights. These insights greatly help in improving design and production, quality of product, forecasting, and more specific product distribution. Moreover, the hidden bottlenecks that hamper the production process can be identified and rectified.

Both Apache Hadoop and Big Data Analytics have greatly helped manufacturing units in optimizing their business outcome.

  • Here are some of the use cases:

    Quality Control with real-time and assembly line historic data
    High tech manufacturers use Apache Hadoop that stores long histories of sensor data that can help find subtle anomalies that indicates product flaws. It enables high speed, real-time, early warning analytics that compares real-time measurements with other contrasting data and then compare to quality models. This way, using Apache Hadoop and Big data, we can easily identify the defects at an early stage and other potential design or process flaws and improve our product quality.

    Assurance of just-in time delivery of raw material
    Two major issues that every manufacturer faces- how to minimize raw material inventory and how to avoid stock out stage. To minimise the inventory and keep delivery just-in time and reduce stock-out stage, technologies like Apache Hadoop and Big Data is required. Hadoop stores unstructured data at a very low rate. It offers them ease of digging into supply chain history that gives manufacturers a greater lead-time to adjust to supply chain interruptions. This way the manufactures considerably reduce their supply chain cost and improve on margins of final product.

    Organised and Cost effective Supply Chain and Logistics
    Every manufacturer wants an organised and low cost transportation and process options. Using Apache Hadoop and Big Data it is possible. With these large volume of historic data can be analysed using which businesses can calculate optimal delivery routes and reroute dynamically to minimize the impact of arbitrary obstacles like traffic, weather etc. This way these businesses can save a lot and can offer premium delivery services to its customers at competitive prices.

    Better Product Configuration Planning
    With appropriate product configuration planning production can be accelerated by offering fast delivery times for the manufacturing of various product configurations. Hadoop analysis greatly helps in this process as it offers advance pattern analysis system through which the most popular configurations can easily be predicted.

    Spot on Market Pricing and Planning
    Doing proper market pricing and planning of a product yields more profits. Any business would welcome such a market pricing and planning model that assures maximized profits. Hadoop and Big data analysis offers such an environment wherein a manufacturer can easily analyse product quality, seasonality, demand and other supply related factors. Hence, a better market pricing and planning model can be generated. For more information contact us here.

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