Zebra Technologies' Subramaniam Thiruppathi on how India's manufacturing sector embraces technology

The tech landscape is rapidly evolving in India with automation, robotics, and artificial intelligence (AI) helping improve efficiency and quality. Despite this boom, digital transformation remains challenging. 

In an interview with TechCircle, US-headquartered tech solutions provider Zebra Technologies’ Director ISC Subramaniam Thiruppathi discussed how technologies like Gen AI, machine learning (ML), and automation are being adopted, the challenges in integrating these technologies, and the focus on digital transformation. Edited Excerpts: 

How does the adoption of new technologies like Gen AI, AI, and ML in India's manufacturing sector compare to other markets?

In India, the manufacturing sector has always been strong, contributing about 14.5% to 15% of the Gross Domestic Product (GDP), with nearly half of this from the automobile sector. Recently, in late 2023 and early 2024, we've seen a surge in activity from Electronic Manufacturing Services (EMS) and pharmaceutical companies. Major tyre manufacturers are also considering India as a key manufacturing hub. This trend has led to significant technology adoption, which is exciting for Zebra. Manufacturing is a major revenue driver for us, especially since it involves our high-end and mid-range skills, contributing significantly to our bottom line. 

Which manufacturing sector benefits most from these deployments? 

In manufacturing, we effectively collect and utilise data in about 10 to 15 key areas. Automation used to be limited to the edges of factories, such as finished goods and raw material warehouses. However, the focus has shifted to comprehensive supply chain and warehouse management, which Zebra India has been implementing for the past 20 years. 

Manufacturers now emphasize predictive maintenance, performance optimisation, and reducing paperwork. For example, a pharmaceutical client went paperless, resulting in faster and more efficient operations. Their audit process now managed through tablets, allows real-time digital data collection and immediate use, enabling predictive maintenance.

Since mid-last year, we've been promoting paperless operations. Chief Executive Officers (CEOs) and Chief Technology Officers (CTOs) recognised the potential for automating quality assurance. With digitised data, they can predict which employee batches produce higher-quality outputs. This approach, applied in companies making products like shoes and bags, helps identify employees needing additional training and improves demand prediction. 

Previously, demand prediction was mostly done at the store level. In the late 1990s and early 2000s, when mobile phones became common, we used to send SMS messages for campaigns. As more Gen X customers entered markets in India and Europe, companies started registering customer numbers and implementing membership programs at retail stores. This allowed for more precise demand prediction, beyond just point of sale (PoS) data from scanners.

In India, EMS companies are adopting new technologies to compete with international competitors. Their peak period starts in October and extends into November, coinciding with new smartphone releases after September announcements. It's estimated that around $10 billion worth of mobile phones are shipped from Chennai alone, a figure expected to rise significantly with the news that the iPhone 16 Pro will be manufactured in India. This growth is driven by India's transparency and technology that collects and shares data with original equipment manufacturers (OEMs), building trust. 

An example from our European customers in the environmental sector involves raw materials like rubber, cocoa, wood, or coffee. It is crucial to demonstrate that no forest land is destroyed and only currently cultivated lands are used; otherwise, shipments to Europe are not permitted. This regulation has created numerous opportunities for our track-and-trace solutions. Customers want to know the origins of their products, including which vendors and batches were used. 

I'm particularly excited about India's manufacturing sector. As India aims for a $5 trillion economy, the manufacturing sector, currently 16-17% of the GDP, is projected to contribute up to 25% by 2025. This growth is promising. While the automobile sector currently accounts for nearly half of the manufacturing share, I expect EMS to match this level, with pharmaceuticals and heavy engineering also becoming significant contributors. 
 
Why are Indian manufacturers slow to innovate despite prioritising digital transformation? How prepared are they to integrate AI, ML, and automation into their workgroups? 

I wouldn't say innovation was lacking. Indian manufacturers used to focus on mass-producing for our internal market, leading to low export levels. Now, these factories aim to boost production by three to four times. To do this, they need technology for high-quality, error-free, and predictively maintained goods. 

Recently, I visited a tyre company implementing machine data collection at every stage of its process, including touchpoints with workers and supervisors. They gather this data to assess efficiency and figure out how to use their existing infrastructure to double production. As India aims to become the largest automotive exporter in the next five years, auto parts industries, including tyre manufacturers, must scale up. Since building ten new factories isn't feasible, technology is essential to improve efficiency and productivity.

In regions like Pune or Chakan, technology adoption is on the rise. Companies are keen to install sensors and collect data to track batches and enhance production. In critical sectors like pharmaceuticals, technology use is even more advanced. For example, Zebra is collaborating with major pharmaceutical firms to implement technologies that support predictive maintenance and prevent costly batch rejections. 

How does the growing need for AI, ML, and automation in manufacturing benefit your company?

Yes, AI can improve product quality by reducing errors. We can integrate technology in several ways, such as using Radio Frequency Identification (RFID) for scanning vendor materials, implementing fixed industrial scanners on assembly lines, and deploying machine vision to inspect finished products. Zebra is quickly growing in this field, expanding resources, and hiring in industrial hubs like Ahmedabad and Bengaluru. We're also strengthening our presence in key industrial corridors like Chennai-Bengaluru. Manufacturers are eager to enhance business and reduce errors to meet global standards.

How does your company secure data in AI-integrated manufacturing systems? 

Recently, we were with a major automotive customer in Delhi when the CrowdStrike incident caused a global IT outage for all Windows PCs and tablets. Fortunately, this customer had switched to Android and primarily uses Zebra products. This experience highlighted the growing importance of cybersecurity in our interconnected world.

Zebra provides customers with LifeGuard for Android, the industry's first security lifecycle offering for enterprise-class mobile computers. LifeGuard offers security updates for up to 10 years on select Zebra devices, providing predictable periodic updates and legacy operating system (OS) support. This helps businesses significantly reduce their total cost of ownership.

How do you ensure that your AI and automation solutions remain at the forefront of technological advancements?

We excel at collecting extensive edge data. The pandemic has accelerated this trend, pushing us to adapt quickly. Initially, data collection was focused on supply chain logistics, but now it includes everything from tracking products to verifying their authenticity. For example, auto companies are increasingly implementing track-and-trace projects to ensure the genuineness of spare parts. Consumers and governments demand transparency about product origins and authenticity. Zebra stays relevant by investing in AI, ML, and emerging technologies like fixed industrial scanning, machine vision, RFID, and ultra-wideband (UWB) chipsets.