Role of Big data in manufacturing industry

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The manufacturing industry is in the midst of a revolution due to the exclusive technological advances in this sector. The application of Big Data in manufacturing allows informed strategies to create the roadmap to the future. This industry is one of those that has undergone monumental changes in recent decades. In addition to simply automating the processes involved, manufacturing industries also take advantage of technologies for other purposes. Big data in manufacturing is mostly affected by trends and big data possibilities due to the nature and amount of data produced by it

The main requirement for almost all manufacturing companies is to be able to forecast the number of units that will be produced. Previously, this forecast was made based on estimates. These estimates led to errors and losses, as they were often incorrect, which led organizations to produce units in quantities greater than or less than required. 

The use of Big Data in manufacturing has allowed companies to overcome these obstacles and be able to develop products according to market needs. By using Big Data analysis, organizations can determine the demand for products. This information can be collected through customer feedback and can be further analyzed to determine which product is in greatest demand. However, most manufacturers are still trying to figure out the potential of big data tools, but there are already some pioneers in the manufacturing industry who have provided some of the following use cases

Use Cases of big data in the manufacturing industry

  • Big Data in manufacturing ensures traceability

Since manufacturing involves a great deal of supply chain management, the traceability that accompanies Big Data helps track products shipped from factories. Traceability is necessary to search and monitor the progress of the supply and maintain efficient relationships with customers. Another reason why monitoring of manufactured products, during the delivery phase, becomes important is that when monitoring is not available to people, the products are impossible to track if they are lost. With Big Data analysis, it can be overcome with the use of sensors. These can transmit the coordinates of the packages and help an organization to effectively track a shipment.

  • Cost reduction

Big Data help in changing the way production processes are carried out. The information produced by the data can help reduce production and packaging costs. In addition, companies that implement data analysis can also reduce transportation, packaging and even warehouse costs, which – in turn – can cut inventory costs and contribute to huge savings.

  • Improved product development

Logically, big data analysis can also be used for the continuous development of products. With the continuous evaluation of customer opinions, new trends and wishes in the product market can be sensed, taken up, the effects of possible changes to the range and its characteristics iteratively “tried out” and finally implemented effectively where there is the greatest chance of success, as, in the intelligent, networked companies should be: fast, agile and effective.

  • Predictive and preventive Maintenance

Thanks to the sophisticated sensor technology that is readily available today, operational data can be collected and analyzed in real-time virtually from any type of machinery or consumer product.

When operating data is analyzed using the pattern recognition method, future failures and the need for maintenance can be predicted well in advance! This allows you to avoid downtime and maintenance-related costs. At the same time, preventive maintenance will dramatically extend the life of the machines, avoiding irreversible failures.

Predictive maintenance is a phenomenon that is not only used for industrial products, but also for consumer products, and often the need for maintenance will depend on the use of the product. In consumer electronics, producers generally track consumer activity on the device and then notify you in advance of the ideal maintenance time. This creates an ideal user experience and, at the same time, dramatically reduces maintenance and warranty costs for the manufacturer.

  • Improved customer service

Manufacturers are increasingly using big data analysis solutions in marketing to increase customer loyalty in “after-sales support” and service. Data from social media, customer care (CRM), sales and marketing are compared in order to better assess customer sentiment towards the company and to react to it in real-time. With such information, a manufacturer can move from mere mass production to mass-adapted production, right through to individualization, in which the products can be increasingly tailored to the individual customer or the market segment – or at least communicatively explained and “packaged” individually.

  • Optimized operational processes

Big data analytics software can help companies optimize their way of working and work more closely and consistently with customers and suppliers. The technology offers opportunities to identify new ways of increasing sales or to play through their potential in the form of “what if” scenarios before you actually invest. Some manufacturers have built entirely new data-driven businesses using big data analytics.

A good example of this is the automotive industry, in which, with the advent of connected mobility solutions such as connected cars, the manufacturer is no longer just an automobile manufacturer, but a software and platform company.

Conclusion

Finally, considering some of the examples of big data use cases in the manufacturing industry, it is possible to realize that it is a predominant trend in large industries. Its use is not simple, but if well executed it has many positive results to reap in the short, medium and long term. There are dozens of others. If you can narrowly define the problem and gather the right data, you can take advantage of big data to solve almost all manufacturing problems.

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