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How data analytics improve productivity & profitability in the manufacturing sector?

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How data analytics improve productivity & profitability in the manufacturing sector?


In this high-tech era, analytics is vitally essential to increase efficiency and productivity without compromising quality. Besides, new metrics can lead to better business practices and foster innovation. If one has not adjusted to the latest advancements in technology, his company may be left behind.

What's Big Data Analytics?

Big data refers to incredibly large or complex data sets that can be analyzed using advanced statistical tools and methods to identify patterns, trends, and associations, particularly about processes, behavior, and interactions. Advanced data analytics uses high-level tools and techniques to discover dependencies, identifying cause and effect, and future project trends, events, and behaviors.

One of these statistical techniques in multivariate data analysis (MVDA) provides a way to analyze data with more than one variable at a time. This gives manufacturers the ability to function advanced statistical methods such as 'what-if' calculations, to identify where the processes veer away, and to provide future proof of different aspects of their operations.

There are five critical areas in which big data is making progress in manufacturing :

  • Product quality : Product quality is a "prime concern" for most manufacturing companies. Manufacturing companies often already have the data to improve the quality of assembly lines but have not connected to data sources to provide concrete action. For example, the use of predictive analytics in testing may lead to significant cost savings, but a single product could require hundreds or even thousands of tests. This number can be reduced significantly through pattern recognition and analysis of extensive data. Analytics will specify the amount and types of tests that are indispensable. Also, sensor data analytics can identify and detect defects earlier, reducing the time and money spent on process and process adjustments.
  • Maintenance : Operational data from virtually any machine type can now be collected and analyzed, all in real-time. Thus, the need for maintenance and preventive measures can be forecasted well before action is genuinely needed. This reduces downtime and eliminates warranty costs. Big data also identifies machines that need to be replaced and extends the equipment's life in proper working order. Manufacturers can avoid sudden blunders that could destabilize an industry without warning.
  • Real-time tracking : To ensure maximum quality and quantity, manufacturers must track and evaluate data from their production lines regularly, preferably in real-time. Constant input from assembly lines, including important sensor data, may be factored into decision making and combined with financial information. Big data in day-to-day activities lead to growth opportunities, resource maximization, and potential cost savings. However, as is usually the case, one must have the right tools to do the right job.
  • Supply Chain Management : Suppliers share big data with manufacturing companies to increase transparency and communicate effectively between parties. For example, the manufacturer can identify delays and react accordingly, thus reducing processing time. In the same nerve, manufacturers may submit production metrics to suppliers to meet their needs. With better exposure to supplier quality levels and other performance data, manufacturers can better assess, manage and negotiate risk management aspects. Because supplier needs are quantifiable, companies can make informed risk management decisions and develop appropriate strategies.
  • Warranties : Daily hassles with manufacturers after sales occur – namely warranty service and product recall – may spiral out of control if left unchecked. Big data can help identify potentially problematic areas in the manufacturing process and prevent problems before they occur. Not only does this save industries money, but it also results in better and more marketable products.

The efficient use of data is an alchemist of the many challenges facing manufacturers today. Manufacturers need to find a way to improve efficiency and generate insight, and Big Data Analytics provides competitive edge companies need to succeed in an increasingly complex environment. With the accurate data integration and management framework, manufacturers can eventually maximize their data's strategic value, boost operations, increase profitability, and enhance relationships with consumers and suppliers. Exerting Big Data to work has never been more critical, and now is the time to get data integration and management software to unlock data value.

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