How SRF technical textiles are transforming into a digital organization of the future, IT News, ET CIO

By – Vanshika Sharma

India’s textile industry is one of the most distinctive, teeming with cultural traditions and techniques. With the support of constantly evolving technology, the industry has undergone a rapid transition over the years. The advanced adoption of technologies has improved machinery, quality assessment, workforce efficiency, and data management for organizations.

SRF Technical Textile, a multi-business chemical organization that manufactures industrial and specialty intermediates, is a leader in textile technological innovation. The organization is well known for its adoption of modern technologies and is the first to welcome new technology initiatives.

Pursuing the outlook, SRF Textile Technique initiated the digital path by focusing on production, process and people. All the elements will allow the company to accelerate its expansion in the market.

“At SRF, the technical textile industry, we have always been among the first to adopt the latest technologies. We focus on increasing predictability, sustainable operations and a faster decision-making process. This is the goal, and we will continue to dive into more digital, ”said Bimal Puri, Chief Information Officer of SRF Technical Textile.

In the textile sector, product quality is considered the king of the market, higher product quality translates into better reputation and sales to consumers. Therefore, to maintain product quality, the company focuses on AI, video, and predictive analytics.

By leveraging AI, Puri says, the company wants to step up standardization of product quality and utilization rates.

“The organization actively uses fiber video analytics. It is a technique for detecting visible damage to the tissue with which the operating team can take quick action before it is done on the entire tissue. Thus, this protects the product from any future risk and any loss of costs, ”he added.

In addition, predictive analytics is adopted to estimate product quality in disaster situations to improve fiber rating. Puri expands and says predictive analytics can help detect a product’s temperature and alert them of a disaster if it gets out of hand. Hence, it protects them from product damage and increases machine uptime while reducing maintenance costs.

Improve employee efficiency and automation

To prevent damage to machines, an early warning system is activated in real time to detect machine damage and protect equipment from downtime. He clarified: “For example, if the engine of the machine is overheated or if there is a problem, the sensors report the cause and notify the team of the same thing that did not lead to further damage to the machines. “.

In addition to making product quality a priority, increasing team productivity within the organization is just as vital. By focusing on this, the organization ensures that all decisions are based on late-breaking data and real-time analysis. Puri cited that real-time scans can help an employee with a manufacturing delay, alerting them in advance, saving them time to complete other activities.

In addition, the implementation of workshop automation or Automatic Guided Vehicles (AGVs) will speed up the process and allow the staff on duty to work more efficiently. The AGV is computer controlled and can load carriers that traverse the floor of a facility without anyone on board, as the combination of software and sensor-based navigation systems direct their movement.

“All of these initiatives help mitigate risk because they help us be on the stand and protect ourselves when the machine requires immediate action. This makes the process simpler, faster, accessible and controllable, which will help the organization in the long run, ”he said.

Supply chain and data plans to follow

The organization increases its visibility in the supply chain, which will lead to automated planning and monitoring of the supply chain. It provides data on logistics and supply chain operations in near real time. This data helps organizations manage inventory shortages, avoid bottlenecks, meet regulatory requirements, and monitor products until delivery.

Since industrial machines are capable of recording data, the organization uses normal analysis, a data mining technique that detects or observes the regular behavior of a set of data. Anomalous data reveals significant situations, such as a technology failure, or prospective possibilities.

Going forward, they plan to elevate their data science and analytics team to mop up relevant information, enabling the team to make concrete decisions. He adds, “We are working on dedicated efforts to bring the data science and analytics team on board and make us a cultivated data-driven organization.”

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