How Hadoop Helps Manufacturing Industry for Real Time Analysis ?

How Hadoop Helps Manufacturing Industry for Real-Time Analysis

Did you know manufacturing is also one of those industries that use Big Data Hadoop? If you are planning to make your career in manufacturing industry and going to enroll for big data Hadoop training courses in Delhi, then this blog is really going to help you a lot. Keep reading.

 

Manufacturers since the very beginning have been using data to boost up their efficiency and quality; however they are also finding that lean production and cost cutting aren’t enough to remain competitive. In today’s perspective, the goal is to integrate and gain insights from data across their complex universal and often split supply chains.

 

Manufacturers produce and store data from various resources across the supply chain, involving process control instruments, supply chain management methodologies and monitoring systems that keep a check on the performance of products after they have been supplied or sold. Being access to avail this hidden data and integrate them across multiple sources offers valuable insights as well as competitive edge. These insights can be used to improvements in design and production, forecasting the product quality more targeted

 

Manufacturing Use Cases

There are some example use cases that show how big data and Hadoop are used in this sector to help to optimse tasks, improve quality and cut the costs.

 

How it’s used in Manufacturing Industry?

Assembly Line Quality Assurance: You can take measurements of work-in-progress products to identify manufacturing defects soon, while also knowing any other potential process or design flaws exist there. Since defects are generally the resultant of many factors, analyzing long accounts of assembly line sensor data can identify even the tiniest glitches that stimulate product flaws. Apache Hadoop stores the long accounts of sensor data while also enables high-speed, early warning and real time analytics that compare real-time measurements with other dissimilar data, and then correlate to quality models.

 

Preventive Maintenance: By monitoring equipment or product utilization in a live setting you can minimize non-productive time (NPT) to find the patterns that signify imminent failure. For revenue-producing operations equipment, downtime results in considerable lost revenues and costly repairs. The MapR Converged data platform offer incessant analysis of an entire system and enables you predict the occurrence of failure, so preventive maintenance can thwart failure. For consumer products, breakdowns or replacement requirement will rely highly on usage patterns & tracking those patterns help manufacturers to aware consumers when their products need certain maintenance.

 

Monitoring Product Quality through Telemetry Data: Once a product is made and shipped, businesses may have very little details on its functionality. In order to become capable of predicting the potential product component failures, they leverage the MapR converged Data Platform to merge reading from sophisticated sensors, data feeds from customer devices, and use Apache Mahout along with many other analytics methods as well as libraries to forecast about the time and reason of future failures.

 

Supply Chain and Logistics: Take an account of the movement of vehicles & products to find out the costs of various transportation as well as process options. By implementing Hadoop to analyze large volumes or historical, time-stamped location data, industries can calculate optimal routes of delivery and enable dynamic rerouting to cut back the impact of arbitrary obstacles like fuel prices, traffic and weather conditions. Companies can also take advantage of the optimal delivery system as a revenue-boosting basis for premium/expedited delivery facilities to the consumers.

 

Product Configuration Planning: It helps you speed up the production rate by offering fast delivery times for the makers of millions of different product configurations. With the help of advanced pattern analysis in Hadoop, you can predict the most popular configurations.

 

Real-time Parts Flow Monitoring: It’s the next step after just-in-time supply chain optimization. By implementing sensors to all parts of the production process as well as tracking them in real time, manufacturing companies can get a real time view to their production process. Apache Hadoop offers an affordable enterprise data hub for gathering the readings from the sensors and enabling both real-time & batch analysis to optimize production quality as well as production.

 

Market Pricing and Planning: It can help businesses maximize their profits. For example, a company in agriculture sector can use Hadoop to analyse the seasonality, quality, demand and other supply factors. This can help them advise farmers when to bring food to the market & how to plan for the upcoming season.

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