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Usage of Big Data Analytics for Plastics and Steel Manufacturing

Usage of Big Data Analytics for Plastics and Steel Manufacturing

Big data analytics refers to a mechanism whereby advanced computing and statistical tools are used to process and make use of all complex data. This results in providing valuable insights to businesses by detecting patterns and accurately defining the evolving market trends, correlations, and other vital information, which helps them to make decisions that will positively impact their corporation. This is the rationale behind infusing big data analytical capabilities into a company’s operations in any industry. Especially in manufacturing, whether it is plastics or steel moulding, big data plays a central role in understanding many things. This includes monitoring the progress of the factory, understanding consumer patterns, deriving hidden insights that are profitable for the businesses and finding ways to minimise the number of finances incurred, the energy wasted, and the overall waste produced. 

The wide adoption of big data analytics has resulted from significant growth in artificial intelligence (AI) and machine learning (ML) capabilities.  Hence, as noted in an article by the World Economic Forum, by 2030, the global GDP is predicted to be 14 per cent higher due to AI-powered capabilities such as big data analytics. This will account for more than USD 15.7 trillion in the global economy.

Role of Big Data Analytics in Plastic Manufacturing

One of the primary purposes of integrating big data analytics into plastic manufacturing is how it helps improve the quality control mechanisms of the plastic injection moulding process. This is the most popular cyclic manufacturing technology used in plastics, accounting for more than 30 percent of plastics produced. As indicated by its name, raw plastic materials are injected into a mould to create plastic products. The injection moulding process is concerned with specific levels of pressure, injection velocity, barrel temperature and other variables that impact the manufacturing process. Quality control can, however, be determined through three features. This includes assessing the stability of dimensions and weight of the produced parts, surface properties, and physical properties. Ensuring it meets the specified requirements is essential for creating a standard end product. Big data analytics helps manufacturers to monitor quality metrics accurately and predict what measurements lead to such a product being made. For instance, where quality metrics are not met, it could lead to plastic shrinkage, which occurs due to the density of the polymer, which varies from the processing temperature to the ambient temperature. Where the mould increasingly encounters residual stresses, the specific plastic part produced will be ejected from the mould or cracked with an external service load. As this either results in a defective product or leads to sinking marks in the moulding interior, plastic manufacturers must incur expenses and waste resources remaking them consistently.  However, manufacturers using big data analytics can detect shrinkage causes, notifying the factory manager in advance, thereby preventing such instances. Big data does not rely on domain expertise and is a tool that self-learns and can predict the final quality of a manufactured product. Hence, where a product fails to meet the quality threshold, the manufacturer can instantly stop the identical product from being manufactured in batches until the measurements are made again. All of this contributes to reducing waste and energy produced and streamlining the moulding process parameters.

The injection moulding process accumulates enormous amounts of data generated and collected through the sensors inserted into the injection moulding machine and cavity. Assessing what data is most relevant and essential is a common problem. Data acquisition and cleansing play an important role in big data analytics integrating all data to predict futuristic insights. For instance, accurate predictions can only be made where the manufacturer can depend on the data collected by its sensors. To avoid downtime, manufacturers that integrate big data analytics into their operations can monitor the lifecycle of assets used in plastic manufacturing. For instance, big data analytics can predict when equipment such as sensors require maintenance and schedule it based on their criticality.  

Big data analytics also helps plastic manufacturers track their material resources. The detailed automated reports that most digital solutions offer in big data analytical solutions provide hidden insights that help make critical decisions highlighting the need to eliminate inefficient processes.

Role of Big Data Analytics in Steel Manufacturing

Steel manufacturers have various processes in the different types of supply chains for Steel-based products. According to an article by McKinsey and Company, companies that have incorporated big data analytics have experienced an unprecedented impact in the steel industry that traditional approaches could never achieve.  Similar to the plastic industry, one area in which Steel manufacturers significantly need help is implementing a quality control mechanism for Steel moulding. The steel industry is no stranger to data storage issues, lack of cross-production data links, and erroneous datasets. However, integrating big data analytics can introduce quality metrics to monitor each phase of the manufacturing process. 

In Steel-based industries, big data analytics integrates different modes to detect anomalies at their initial stages. For instance, a grain-size monitor infuses acoustic analytics with big data. Here, the computer mimics the human ear whilst providing insights that no human could have detected by simply listening. It helps manufacturers monitor raw input materials in iron and steelmaking, such as sinter and pellets. This, in turn, helps to improve the quality of end products and optimises the production process. Data analytics, moreover, help in assessing quality by monitoring the temperature. By predicting how temperature can affect the overall quality of a specific Steel, hot-Steel temperature forecasting ensures that the right amount of heat remains consistent. Data related to charging, pressure, temperature, top gas, hot Steel, slag, and results from metallurgical models are all centralised in one central location for big analytics to be more accurate in their predictions. Hence, manufacturers can increase productivity by eliminating waste from excessive heat, thereby manufacturing standardised hot steel.

Big data analytics also play a central role in controlling factory emissions, helping manufacturers implement sustainable practices. It also monitors the lifecycle of all assets in the factory, thereby cutting unnecessary costs and fixing all issues during routine maintenance. Steel manufacturers can also comprehensively understand consumer trends, find which industry is most profitable in terms of providing its steel end-products and prioritise the type of steel items being manufactured. All this allows steel manufacturers to optimise their operations and stay in line with the industrial plays in the competitive steel industry.

Cerexio Wields Solutions With Big Data Analytical Capabilities and Other Industry 4.0 Technologies

Cerexio is one of the leading technology vendors in Asia and the world,  recognised for its digital solutions for modern manufacturing. Each of its suites comes packed with various modern technologies, including AI, ML, big data analytics, digital twin, predictive and prescriptive maintenance and more. With a Hadoop architecture that optimises big data capabilities, Cerexio can manage an enormous amount of data without affecting process speeds. We filter out complicated data structures and provide manufacturers with standardised data-tackling tools to help data players harness modern technologies and innovative solutions to capture actionable data insights. Centralise data from multiple sources into one place easily, take into account the detailed reports generated when making important business decisions, and stay ahead of the competition by having hidden information on consumer and marketing trends. Big data analytics will be a constant source of detecting the actual state of affairs in the production stage and the designing, storing and packaging phases in the supply chain. 

Connect with us to learn how our solutions are improved with data tackling, cleansing and securing features.

Big Data Analytics At The Core of Industry 4.0

At the very core of industry 4.0 technology is automation and data analytics. In other words, every emerging advanced technology, without big data analytics, cannot unlock its true power. While every other technology collects multiple data, big data analytics uses its historical capabilities from ML that provides meaning to the pool of information in store. Are you willing to let go of the futuristic capabilities big data analytical capabilities offer? 

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