Big Data Analytics Advancing Industries in 2018

By Jyoti Nigania |Email | May 10, 2018 | 8055 Views

Manufacturing is important part of the world's economic development, but the roles it plays in advanced and developing economies has shaped dramatically. In developing countries, manufacturing operations bring new employment opportunities that are changing the societies. Manufacturing always remains important as a job initiator in the whole world. 
Big Data is important in mapping productivity and efficiency gains and uncovering new insights to drive innovation. All knows that Big Data analytics are mandatory to compete successfully in a data-driven economy, and they are also investing in data integration and management assets to attain digital transformation and gain  advantage.

Procurement the Asset Performance and Efficiency:
Since manufacturing profits rely heavily on maximizing the value of assets, asset performance gains can lead to big productivity improvements even if asset performance is only improved on the margins. By the same token, a reduction in asset breakdowns can reduce inefficiencies and prevent losses. For these reasons, manufacturers focus on maintenance and continuously optimize asset performance.
Machine logs contain data on asset performance. The Internet of Things adds a new dimension with connected assets and sensors capable of measuring, recording and transmitting performance in real time. This data is potentially of great value to manufacturers, but many are overwhelmed by the sheer volume of incoming information. Data analytics can help them capture, cleanse and analyze machine data to reveal insights that can help them improve performance.
In addition to enabling historical data analysis, Big Data can drive predictive analytics, which manufacturers can use to schedule predictive maintenance. This allows manufacturers to prevent costly asset breakdowns and avoid unexpected downtime. Big Data analytics can have a significant impact.

Improves Production and Supply Chains:
In an increasingly global and interconnected environment, manufacturing processes and supply chains are long and complex. With the right analytics, manufacturers can zero in on every segment of the production process and examine supply chains in minute detail, accounting for individual activities and tasks. This ability to narrow the focus allows manufacturers to identify bottlenecks and reveal underperforming processes and components. Big Data analytics also reveal dependencies, enabling manufacturers to enhance production processes and create alternative plans to address potential pitfalls.
Product Customization:
Traditionally, manufacturing focused on production at scale and left product customization to enterprises serving the market. Big Data analytics is changing that by making it possible to accurately predict the demand for customized products. By detecting changes in customer behavior, Big Data analytics can give manufacturers more lead time, providing the opportunity to produce customized products almost as efficiently as goods produced at greater scale. Innovative capabilities include tools that allow product engineers to gather, analyze and visualize customer feedback in near-real time.

Big Data Toolkit:
So, what are the tools manufacturers are successfully using today to optimize asset performance, improve production processes and facilitate product customization? Here are essential Big Data analytics tools:

  • Data storage: The first step in putting Big Data to work is to have the ability to gather and store information. Manufacturers use data storage tools to maintain vital information on equipment, production processes and supply chain operations data they can analyze to drive improvements.

  • Data tools: Big Data arrives in a variety of formats from a range of sources. It comes in structured and unstructured forms. To render it usable, manufacturers need a way to ensure data quality and integrity. They need data cleanup tools that allow them to transform unrefined information into a readable, unified dataset that multiple stakeholders can use. They also need data cleanup tools to standardize data and put it into formats that can be used by multiple applications and systems.

  • Profiling tools: Manufacturers need transparency into production and supply chain operations, and profiling tools can capture information up to the metadata level. This allows manufacturers to keep an end-to-end inventory of critical data, enabling them to maximize the value of their information.

  • Data mining tools: Success in manufacturing depends on being able to quickly access information to make the right production and supply chain decisions. Data discovery tools allow manufacturers to get the information they need when they need it.

  • Data mapping tools: Manufacturers must be able to understand how information flows through systems, processes and supply chains. Data mapping tools are ideal for helping manufacturers identify dependencies and pinpoint potential bottlenecks. They can also enhance security by enabling manufacturers to identify risk and leakage.

  • Data analysis: Analytics give manufacturers insight by identifying patterns, measuring impact and predicting outcomes. The ability to analyze equipment failures, production bottlenecks, supply chain deficiencies, etc., enables better decision-making.

  • Data visualization: Believing and data visualization tools provide a picture of analytics results with unmatched clarity. User-friendly graphs and charts enable manufacturers to more readily comprehend the story the data tells and make needed changes. 

  • Data monitoring: Data quality is of paramount importance, so manufacturers need a way to ensure compliance with data quality standards, oversee equipment performance and review production process efficiency. Monitoring tools enable manufacturers to automate quality assurance processes.
These are the primary tools manufacturers are using to put Big Data to work for them. By deploying these tools, manufacturers can maximize machine performance, optimize production, streamline supply chains and generate productivity and efficiency gains.

Conclusion:
Companies should find a way to increase efficiency and generate visions, and Big Data Analytics provide the competitive advantage to companies in an increasingly complex environment. Those who are looking for a competitive advantage through Big Data analytics should look for complete solutions that integrate and manage critical data. With the right data mixing and management platform, manufacturers can finally leverage their data's strategic value, improving operations, increasing profits and make strong relationships with customers, partners and suppliers. 

Source: HOB