India’s manufacturing capabilities have gained increasing attention in discussions and news regarding US President Donald Trump’s high import tariffs. 

While the spotlight has intensified on the country, the notion of India as a sudden, ‘dark horse’ entrant in the smartphone manufacturing arena is far from accurate. India’s journey in electronics manufacturing through contract manufacturing, joint ventures, and self-established plants goes back nearly a decade. Besides, if one takes the name of any leading smartphone maker, they will likely find an assembly plant in India. 

Last month, it was reported that smartphones became India’s largest individual export commodity by value for the first time, reaching $18.31 billion. 

Besides global demand, much of India’s growth can be attributed to the government’s production-linked incentive schemes. These schemes offer 4%-6% cash incentives based on specific targets for companies across 14 manufacturing sectors. 

Numbers from both tech giants that make these smartphones and contract manufacturers who help assemble them indicate the same. The combination of the success of the PLI scheme and the impending tariffs from the US government contributes to strengthening India’s smartphone manufacturing industry. 

‘Apple Produces One in Five iPhones in India’

Notably, Apple, the world’s largest smartphone manufacturer and the biggest company by market cap, has actively sought to move its iPhone assembly from China to India to counter the anticipated tariffs.

To begin with, Apple itself has transformed India into a key assembly hub for the iPhone through contract manufacturing with Foxconn and Tata Electronics (which acquired Wistron and a majority share in Pegatron) to set up production facilities nationwide. 

Recently, Tata Electronics started production in its factory in Hosur, Tamil Nadu, which produces older iPhone models, while Foxconn’s facility in Bengaluru has commenced manufacturing the iPhone 16 and the 16e Series. Their facility in Sriperumbudur also assembles the iPhone 16 Pro and the 16 Pro Max models. 

Apple assembled iPhones worth $22 billion in the 12 months that ended in March, as reported by Bloomberg. This represents a 60% increase from the previous year, and now, Apple produces one in five iPhones in India, according to the sources cited by Bloomberg. 

In the company’s Q2 2025 earnings call, CEO Tim Cook said that for the June quarter, the majority of the iPhones sold in the United States will have India as the country of their origin. 

Besides, Apple exported iPhones worth ₹1.5 trillion from India in 2024-25, doubling the company’s committed target under the PLI scheme. 

Samsung and Google to Move Away from Vietnam?

Along with Apple, Samsung is another prominent name reaping the benefits of India’s manufacturing ecosystem. Research from Counterpoint also stated that Apple and Samsung alone accounted for 94% of India’s smartphone exports. 

Samsung started manufacturing mobile phones in India in 2007, and the company claims it is the “only brand that is truly made in India”. In 2018, the company unveiled its plant in Noida, which is touted as the “world’s largest mobile phone factory”. 

The Noida plant is also said to manufacture the company’s latest, flagship S25 series of smartphones. This plant has the capacity to produce 120 million units a year. The company has also outsourced the manufacturing of specific smartphone variants to contract manufacturers such as Dixon Technologies. 

Recently, MoneyControl reported that Samsung is planning to shift a portion of its smartphone and electronics manufacturing from Vietnam, its primary destination. The company reportedly discusses production adjustments with more contract manufacturers, favouring India. 

This is likely in lieu of higher import tariffs by the US on Vietnam (49%) compared to India (26%). 

Last year, Google announced that it had started manufacturing the Pixel 8 smartphone in Tamil Nadu, India, through Wowtek Technology, a subsidiary of Bharat FIH, also a part of Foxconn. 

However, in a development similar to Samsung and Apple, The Economic Times recently reported that Google’s parent company, Alphabet, has begun discussions with contract manufacturers Dixon Technologies and Foxconn to move a portion of Pixel smartphone production from Vietnam to India. 

Besides Apple, Samsung, and Google, Chinese giants like Xiaomi, subsidiaries of BBK Electronics (Oppo, Realme, OnePlus, Vivo), and Motorola have substantial manufacturing capabilities in India. Last December, Dixon Technologies and Vivo entered a joint venture, with the former having a majority stake. The development occurred after the company stated its ambitions to double production in the country. 

Similarly, Pete Lau, founder and CEO of OnePlus, told Moneycontrol that the company is also doubling its local manufacturing in India with new partnerships. Currently, OnePlus’ smartphones are being manufactured by Oppo India, in their Noida plant, which is said to manufacture three smartphones per second. 

Contract Manufacturers are Making Big Money

A significant beneficiary of India’s smartphone manufacturing arm is the contract manufacturers who earn groundbreaking revenue and cash-based incentives from the PLI schemes. 

Research from Counterpoint stated that Tata Electronics was the fastest-growing manufacturer in 2024, registered 107% year-over-year growth, thanks to the large volumes of the iPhone 15 and 16 series. 

Earlier this year, Dixon Technologies nearly doubled its revenue for the year ending March 2025, and Foxconn also doubled its revenue to $20 billion in FY 2024- 25. 

These figures will certainly continue to rise further, provided that assembly lines are shifted to India.

Regarding the PLI scheme, Foxconn received ₹2,807 crore in incentive payouts over three financial years, from 2022-23 to 2024-25. The company accounts for over 32% of the funds allocated for the incentives. 

Conversely, Tata Electronics obtained ₹2,067.51 crore, Pegatron received ₹1,724,36 crore, and Padget Electronics acquired ₹596 crore. Foxconn, Tata Electronics, and Pegatron together received more than 75% of the total disbursement. 

Besides, Foxconn and Dixon seek to receive more subsidies under the PLI schemes from unallocated funds, as reported by Bloomberg. If the request goes through, Foxconn could receive as much as ₹6 billion, and Dixon could receive ₹1 billion. 

With the emergence of numerous generative AI tools and software applications, the demand for smartphones as competent handheld devices is bound to rise. It will be intriguing to observe the trajectory of India’s growth in this context. 

The post India’s Smartphone Manufacturing Muscle is Now Stronger Than Ever  appeared first on Analytics India Magazine.

US chipmaker NVIDIA on Monday announced plans to produce its first-ever AI supercomputers domestically.  The initiative is part of its pledge to manufacture $500 billion AI infrastructure in the country over the next four years, in partnership with TSMC, Foxconn and Wistron, among others. 

The company commissioned over one million square feet of manufacturing space to build and test Nvidia Blackwell chips in Arizona and AI supercomputers in Texas. This follows the government’s push to incentivise domestic chip manufacturing to create local jobs and reduce dependency on countries like Taiwan and China. 

NVIDIA is building supercomputer production units in Texas with contract manufacturers Foxconn in Houston and a Taiwanese electronics manufacturer, Wistron, in Dallas. In both plants, mass production is expected to ramp up in the next 12-15 months. The Taiwan Semiconductor Manufacturing Company’s (TSMC) chip plants in Phoenix, Arizona, have already started producing Blackwell Chips. 

“Adding American manufacturing helps us better meet the incredible and growing demand for AI chips and supercomputers, strengthens our supply chain and boosts our resiliency,” said Jensen Huang, founder and CEO of NVIDIA, in the blog post. 

The company plans to use its advanced AI, robotics, and digital twin technologies to operate these facilities, including Omniverse, to create digital twin factories, and Isaac GR00T to build robots for automated manufacturing. 

Caught between the trade wars over the semiconductor supply chain, the US has seen significant changes in recent years. As reported by AIM earlier, increasing geopolitical tensions and disruptions in the supply chain have pushed the US to look at its overreliance on other countries like China, moving towards being a key player in the global semiconductor industry. 

The government also expects to obtain direct funding and loans from private investors, like Taiwan, to build “cutting-edge fabrication facilities,” aiming to increase the US workforce. In March 2025, the White House said that TSMC announced a $100 billion investment in Arizona’s semiconductor chip manufacturing unit, marking one of the most significant foreign direct investments in US history. 

President Donald J. Trump announces a new $100 billion investment by TSMC in U.S. chips manufacturing pic.twitter.com/2vLWX8h7TV

— Rapid Response 47 (@RapidResponse47) March 3, 2025

The White House has called the recent foreign investments the “Trump Effect in action.”
However, the US government announced steep tariffs on Chinese and Taiwanese imports in early April. Unexpectedly, the Trump administration exempted tariffs on smartphones, computers and other electronic parts, including chips, on April 13.  The Washington Post reported that US Commerce Secretary Howard Lutnick said that semiconductor and other electronics tariffs are still in the works.

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Tata Consultancy Services (TCS) on Thursday announced a collaboration with Salesforce to enhance the use of artificial intelligence in the manufacturing and semiconductor sectors.

As part of this partnership, TCS has launched three key initiatives to improve sales and service efficiency. The Semiconductor Sales Accelerator will help businesses increase sales by providing data-driven insights. 

The Seller for the Future initiative will offer real-time updates, predictive analytics, and personalised recommendations to enhance sales strategies. Meanwhile, Digital Field Service will assist field technicians with real-time data, predictive maintenance tools, and better scheduling.

TCS and Salesforce aim to help companies make better use of their data, which is often scattered and unstructured, making AI adoption challenging. By combining their expertise in AI and cloud computing, the two companies seek to transform how manufacturers and semiconductor firms sell and service their products.

Prashant Shirgur, global head of enterprise solutions at TCS, highlighted the need for accurate, real-time data in the fast-moving semiconductor industry. He emphasised that TCS is committed to providing businesses with the insights necessary for growth.

The company’s TCS Crystallus on the Salesforce platform is designed to give sales teams the tools to engage customers confidently, reduce sales cycles, and boost revenue.

Meanwhile, in its latest Q3 FY25 earnings call, TCS reported that its clients were actively investing in generative AI and agentic AI while building robust data foundations.

According to the statement issued by the company,TCS is  actively engaged with clients on AI/GenAI-led software engineering, legacy modernisation and AIOps. This has resulted in an increase in successful production deployment of AI/GenAI engagements leading to greater business certainty and confidence for  clients.

TCS has also launched its TCS 5A Framework for Responsible AI in partnership with AWS to address and mitigate AI risks holistically. TCS claims that it is the first of its kind in the industry.

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India’s CRIUS Group has made history in the nutraceutical industry by becoming the first contract manufacturing organisation (CMO) to fully implement AI-driven project execution. This global milestone is powered by the innovative NutrifyGenie integrated AI platform that promises to transform the way products are designed, developed, and delivered to market.

For CRIUS, the journey began with a vision. “As a pharmacist and founder, I’ve always believed in applying the gold standard of pharmaceutical precision to our nutraceutical endeavours,” said Subbarao Chinni, founder and MD of CRIUS Lifesciences. 

The AI platform can automate the entire process, from ideating scientifically advanced formulations for global clients to mapping the most efficient supply chains and identifying ideal manufacturing partners. 

For Rao, this integration wasn’t about replacing human expertise but amplifying it. “NutrifyGenie lets us focus on our strengths and world-class manufacturing while AI handles the complexities.”

The impact of NutrifyGenie goes beyond efficiency. By tapping into a proprietary databank of over 3.6 million scientific data points, the platform accelerates go-to-market timelines by 200%. This isn’t just about speed; it’s about rethinking what’s possible in ingredient discovery, safety analysis, and product innovation.

But it’s the platform’s “plug and play” nature that stands out. It’s not merely a system; it’s a partner, automating every step from ideation to compliance. The outcome is that CRIUS can focus more on innovation and scale. 

“We’ve shifted our focus to process innovation and quality, leaving the heavy lifting of formulation and sourcing to NutrifyGenie,” Rao added.

Why This is Needed

Contract manufacturers worldwide are struggling with the need for speed, efficiency, and sustainability. In such a scenario, AI platforms like NutrifyGenie are becoming essential as optional tools. Studies suggest that CMOs embracing AI can optimise processes by 40-50%, reducing waste and costs while shortening production cycles.

CRIUS is committed to fostering a collaborative ecosystem that bridges academia, industry, and government to navigate complex regulatory landscapes and bring safe, high-quality products to global markets faster than ever.

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Machine monitoring systems solutions startup Leanworx that works on reducing machine capacity wastage raised ₹8.3 Crore. The round was led by YourNest Venture Capital and the funding is part of the YourNest-SanchiConnect Velocity Program 2024, aimed at supporting high-growth startups through funding, mentorship, and market access.

Leanworx, founded in 2017, provides an Industry 4.0 machine monitoring system that delivers real-time data from shop-floor machines. The plug-and-play IoT solution significantly improves decision-making efficiency by reducing the traditional 24-36-hour data collection process to just one minute. The system addresses inefficiencies in machine utilisation, which often hovers between 30%-50%, by offering actionable insights in near real-time.

The startup looks to leverage its AI-powered solutions and cloud-based SaaS product, to address a rapidly expanding market which is valued at ₹3,000 crore annually in India and ₹1.8 lakh crore globally. 

Leanworx was founded by Dasarathi GV, Srihari D, and Bhagavan SK, who come from decades of experience in the manufacturing sector. 

Global Growth

The startup plans to utilise the funds to expand into high-density manufacturing regions such as Thailand, Vietnam, Indonesia, and Mexico. Additionally, it aims to target sectors such as FMCG, including packaging and plastics. To support this growth, Leanworx is focusing on hiring key personnel in their leadership team as well. 

The funds will also drive marketing and lead-generation efforts in India and Southeast Asia and support product development, including hardware and software certification. With connections to over 2,000 machines, Leanworx is looking to tap into a market that includes 12 lakh machines in India’s metalworking and FMCG manufacturing sectors, with a global market 60 times larger.

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In manufacturing, automation is considered an indispensable force, serving as the key to functioning. However, surprisingly enough, this is not the case. Automation may not be a universal solution. 

“Automation isn’t for everyone. Sometimes, the process a customer is following is already solid, and they don’t need to spend heavily on robotics,” said Hari Parthasarathi, global manufacturing conglomerate 3M’s application engineering leader in India, Southeast Asia, Australia and New Zealand, while interacting with AIM. 

It’s safe to call Fortune 500 and 3M the crusaders of automation. Parthasarathi spoke about the importance of assessing automation’s relevance on a case-by-case basis, with the goal being to address genuine needs rather than becoming a costly exercise in over-engineering. 

India’s Manufacturing Landscape 

3M, short for Minnesota Mining and Manufacturing Company, has been in the automation space for 122 years. The company entered India in 1987. With over three decades in the country, 3M’s presence in India is only solidifying over time. Last year, the company opened an abrasive robotics lab in Bengaluru, which is the first in India and 17th in the world. 

India’s industrial automation market is estimated to hit $15.2 billion in 2024 and is expected to touch over $29 billion by 2029. 

Interestingly, India’s manufacturing ecosystem, which is considered diverse owing to small-scale to large global operations, presents unique challenges for automation, as many enterprises prioritise cost-effective, scalable solutions over advanced, high-cost systems. 

“If you look at India as where we are – let’s say manufacturing or industrial space – that’s where we operate. We are trying to position ourselves globally as a manufacturing hub,” Parthasarathi said. 

He goes on to explain that over the past 20 years, India has established itself as a leader in the IT and service industries, while manufacturing was often viewed as secondary. However, initiatives such as ‘Make in India’, shifting geopolitics, and increasing global reliance on India have significantly transformed this perception. These factors have positioned India as an emerging manufacturing hub, with the country aiming to demonstrate its growing capabilities in the sector. 

“We want to flex our muscles in a few years and show that we are a much bigger manufacturing hub today than what we used to be,” he added.  

Collaborative Approach for Automation

There are two types of automation that are predominantly run in manufacturing facilities. “We are working with both fixed automation guys and robotic automation guys,” said Raghavendra Koneri, Application Specialist, Robotics and Automation, 3M, to AIM. “If there is a big volume with a similar shape, size and everything, then they will go with fixed automation. If there is a complex part, the customer uses a robotic arm, and they will say yes.” 

3M partners with robotic arm manufacturers, compliance system providers, and customers to create automation systems tailored to specific industries and processes. For example, in the abrasives segment, 3M has demonstrated that automation can boost productivity tenfold. However, the company ensures proof of concept before scaling such solutions, focusing on long-term benefits rather than immediate returns.

The team believes that automation adoption is slower in India, not because of reluctance but due to a lack of skilled manpower and high implementation costs. Furthermore, many businesses are hesitant to invest in automation without a clear understanding of its long-term value.

3M acknowledges that the transition to automation must be gradual and strategic. The company emphasises that automation should complement, not replace, human skills. By focusing on upskilling the workforce and integrating automation thoughtfully, businesses can achieve a balance between efficiency and inclusivity. 

3M Finesse-it Robotic at Bengaluru lab

Humanoids for Automation? 

As we already know, the adoption of humanoids in manufacturing facilities is no longer a figment of our imagination. There are humanoids and semi-humanoids already being implemented by big tech companies. BMW manufacturing plants are already using Figure 02 humanoids on their production lines. Similarly, Amazon is leveraging Digit robots at their warehouses to support safety. 

Considering the possibility of humanoids at 3M, it definitely is not implausible. “Humanoids are already playing a huge role, especially when there’s a huge level of safety that comes into the picture. Here, I think it could happen, but not in the immediate future,” Parthasarathi said. 

He goes on to explain 3M’s automated paint repair solution for automotive original equipment manufacturers (OEMs), where they aim to replace manual inspection of paint defects, a task prone to errors due to operator fatigue. Parthasarathi believes the automation at 3M might pave the way for a future where humanoids or automated systems can handle such tasks seamlessly.

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Space42, a UAE-based AI-powered space tech company, and ICEYE, a global leader in Synthetic Aperture Radar (SAR) satellite operations, have announced a joint venture to manufacture SAR satellites in the UAE. 

The partnership aims to meet the increasing demand for high-resolution SAR satellite imagery and bolster the UAE’s Earth Observation (EO) capabilities.

We're pleased to announce a joint venture with @iceye_global to manufacture SAR satellites in the #UAE, building on our successful cooperation, which includes the recent launch of the UAE's first SAR satellite, Foresight-1.

Focused on satellite manufacturing and deploying SAR… pic.twitter.com/OYgXEOg2l9

— Space42 (@space42ai) December 9, 2024

The collaboration builds on the successful launch of Foresight-1, the UAE’s first SAR satellite, in August 2024. The new venture will focus on satellite production, deploying SAR missions, and supporting the UAE’s EO Program, which seeks to enhance national remote sensing and EO capabilities.

Space42’s Assembly, Integration, and Testing (AIT) and low-Earth orbit (LEO) mission operations facilities in Abu Dhabi will serve as the hub for the joint venture’s activities, as mentioned on the official blog.

The initiative will also act as a platform for technology transfer and localisation, fostering the UAE’s space R&D capabilities and encouraging the development of sovereign SAR payloads.

“The joint venture is a continuation of our strategic partnership with ICEYE to advance Earth Observation capabilities,” said Hasan Al Hosani, CEO of Bayanat Smart Solutions, Space42. 

“It aligns with our goal of unlocking the full potential of space technologies and supports the UAE’s ambitions by localising SAR manufacturing, placing the nation at the forefront of global space advancements.”

The partnership also aims to localise the supply chain, sourcing components and services from UAE-based industries. Emirati talent will play a significant role in furthering Emiratisation goals and contributing to national innovation and economic growth.

Rafal Modrzewski, CEO and Co-founder of ICEYE, stated, “This venture will bring efficiency in deploying SAR constellations, leveraging the combined expertise of Space42 and ICEYE.” 

“Extending our production line to the UAE enhances our technology’s global reach while supporting the country’s growing space programme and capabilities.”

This joint initiative is the latest in a series of collaborations between Space42 and ICEYE, including the ongoing development of the UAE’s SAR constellation, set to expand over the next three years.

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UptimeAI, a startup specialising in AI-powered solutions for manufacturing plants, has successfully raised $14 million in a Series A funding round. The investment was led by WestBridge Capital, along from existing investor Emergent Ventures and new investor Aditya Birla Ventures.

UptimeAI Expanding AI Solutions for Industry

Founded in 2019 by Jagadish Gattu and Vamsi Yalamanchili, UptimeAI has developed an AI-driven platform that monitors and optimises manufacturing plant operations. The company’s technology uses advanced machine learning algorithms to predict equipment failures, improve efficiency, and reduce downtime in industrial settings.

Strategic Growth and Market Expansion

With this new influx of capital, UptimeAI plans to:

1. Accelerate product development
2. Expand its market presence in North America and Europe
3. Strengthen its team by hiring top talent in AI and machine learning

UptimeAI’s solutions have already been adopted by several Fortune 500 companies across various sectors, including energy, chemicals, and heavy industries. The platform has demonstrated significant improvements in plant reliability and performance, with some clients reporting up to 20% reduction in unplanned downtime.

Future Outlook

This investment will allow UptimeAI to further enhance our AI capabilities and bring cutting-edge solutions to more manufacturing plants globally. We’re excited to partner with WestBridge Capital and our existing investors to drive the next phase of our growth. The funding round highlights the growing importance of AI in industrial operations and positions UptimeAI as a key player in the digital transformation of manufacturing. As industries continue to seek ways to improve efficiency and reduce costs, AI-driven solutions like those offered by UptimeAI are likely to play an increasingly crucial role in shaping the future of manufacturing.

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Bengaluru-based space technology startup Pixxel has signed the 350th contract under the iDEX (Innovations for Defence Excellence) program to manufacture miniaturised multi-payload satellites for the Indian Air Force.

The contract, awarded as part of the iDEX Prime Space grant, marks a significant milestone in Pixxel’s mission to revolutionise the space industry in India.

The contract was signed between Awais Ahmed, CEO of Pixxel, and Anurag Bajpai, Additional Secretary (Defence Production) and CEO of IDEX-DIO, in the presence of Defence Secretary Giridhar Aramane, the Vice Chiefs of the Armed Forces, and other officials of the Ministry of Defence.

“We are delighted to receive iDEX’s grant and utilise our expertise of building microsatellites in-house to manufacture satellites externally for the first time,” said Ahmed. “This recognition highlights Pixxel’s dedication to pushing the boundaries of space exploration and innovation.”

Under the multi-crore contract, Pixxel will develop small satellites weighing up to 150 kg for electro-optical, infrared, synthetic aperture radar, and hyperspectral applications. The company will leverage its indigenous hyperspectral satellite technology and manufacturing expertise to build these satellites, enabling ease of manufacture, low cost, and ease of launch.

As Pixxel sets out to launch six commercial-grade hyperspectral satellites, ‘Fireflies’, this year, the company remains committed to harnessing its indigenous expertise and the power of hyperspectral satellites for a sustainable future. Building on its expertise, Pixxel now offers high-performance, cost-effective satellite manufacturing solutions, empowering clients to drive meaningful change with space data.

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In a significant announcement at the 10th Vibrant Gujarat Global Summit, Tata Sons Chairman N Chandrasekaran announced plans to construct a semiconductor factory in Dholera, Gujarat, according to Tata Sons Chairman N Chandrasekaran. 

“We are about to complete negotiations for the semiconductor fab, and start in 2024,” Chandrasekaran stated.

Chandrasekaran highlighted the significant presence of Tata Group in Gujarat, with 21 of its companies operating in the state, employing over 50,000 people. The group has recently made substantial commitments to expand its footprint in Gujarat, including the development of a 20 GW gigafactory for lithium-ion batteries in Sanand, set to begin construction in the coming months. Additionally, Tata Group is building the C295 defence aircraft in Vadodara and DOA.

In addition to these developments, Tata Group is actively engaged in manufacturing C295 defence aircraft, with operations initially underway in Vadodara and soon to expand to DOA. Moreover, Chandrasekaran revealed, “The T group has also made a commitment and is on the verge of concluding and announcing a huge semiconductor Fab in Dholera.”

The group’s vision for Gujarat extends beyond manufacturing facilities. Chandrasekaran disclosed their partnership with the Indian government and the Gujarat government to establish an Institute of Skills spanning 3.2 lakh square ft. This institute aims to train more than 25,000 professionals annually in advanced manufacturing, electric vehicles, advanced electronics, and hospitality. The first phase of this project is slated to launch in March 2024.

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Intel joins the Make in India brigade. The company has unveiled a strategic partnership with eight prominent Electronics Manufacturing Services (EMS) companies and Original Design Manufacturers (ODMs) to bolster laptop manufacturing in India.

The move aims to harness Intel’s extensive industry knowledge to lay the groundwork for a robust laptop manufacturing sector in the country, in alignment with the Make in India initiative.

The collaborative effort with Intel involves firms like Bhagwati Products Ltd, Dixon Technologies India Ltd, Kaynes Technology India Ltd, Optiemus Electronics Ltd, Panache Digilife Ltd, Smile Electronics Ltd, Syrma SGS Technology Ltd, and VVDN Technologies Private Ltd.

For some of these companies, this venture signifies their inaugural foray into laptop manufacturing, reflecting Intel’s commitment to empower the Indian manufacturing ecosystem to cater to both domestic and global demand.

As part of this collaboration, Intel will leverage its expertise to facilitate the production of complete entry-level laptops in India, employing state-of-the-art Surface Mount Technology (SMT) assembly lines, implementing quality control processes for components, and benchmarking finished products. Intel also offered support to ODMs across both Semi Knocked Down (SKD) and Completely Knocked Down (CKD) manufacturing processes.

“It is our Prime Minister’s goal that the Indian Electronics Ecosystem should have deep and broad capabilities, and that Indian Electronics Manufacturing Companies should grow, scale, and expand their footprint as trusted players in the Electronics Global Value Chains,” stated Rajeev Chandrasekhar, Minister of State for electronics and IT, skill development, and entrepreneurship.

“By enabling the laptop manufacturing process – from surface mount technology assembly to finished product – we are not only meeting the demands of the Make in India initiative but also contributing to the technological progress of the nation,” remarked Santhosh Viswanathan, VP & MD, India region, Intel.

Intel is set to host the India Tech Ecosystem Summit in November, which will bring together numerous local manufacturers to showcase a broader range of devices manufactured in India. 

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Infosys, the second-largest IT services company in India, announced its financial results for the first quarter. The company reported a notable 11% increase in net profit, amounting to ₹5,945 crore, compared to ₹5,360 crore during the same period last year. However, Infosys had to lower its FY24 revenue guidance from 4-7% to a much lower range of 1-3.5% due to challenges in the demand environment.

In terms of regional revenue growth during the June quarter, Infosys experienced a positive 10.9% year-on-year growth in Europe and a 9.3% growth in India. However, revenue growth in North America was only at 2.3% YoY, while the ‘rest of the world’ segment faced a decline of 4.5% YoY. The company added 99 new clients during Q1FY24, reaching 1,883 active clients as of June 30, 2023.

Among the verticals, the financial services and communication segments reported a 4.7% and 6.1% YoY drop in revenue, respectively. In contrast, the manufacturing segment showed a healthy growth of 21.3% YoY, while the life sciences segment rose significantly by 14.9% YoY. The ‘others’ segment revenue also increased by 28% YoY. However, the retail and hi-tech segments saw relatively sluggish growth in Q1FY24.

Infosys’ CEO and MD, Salil Parekh, expressed satisfaction with the company’s solid Q1 performance, highlighting the 4.2% growth and the success of large deals worth $2.3 billion, which set a strong foundation for future growth. Additionally, he mentioned the development of generative AI tools based on an open-source model, with 40,000 employees trained in these areas.

“Our generative AI capabilities are expanding well, with 80 active client projects. Topaz, our comprehensive AI offering, is resonating well with clients. We see this being transformative for clients and enhancing our overall service portfolio” said Salil Parekh, CEO and MD. 

“We have expanded the margin improvement program with a holistic set of actions for the short, medium and long-term, working on five key areas, supported by our leadership team”, he added.

Effects on the Market

However, the sharp downward revision of FY24 revenue guidance led to a significant drop in Infosys’ ADRs in the pre-market session on the NYSE, falling nearly 9%.

The company’s management aims to improve its operating margin in the future. The attrition rate improved to 17.3%, but the headcount of employees declined compared to the previous quarter.

During the opening deals today, the company’s stock experienced a significant decline of 9%, reaching Rs 1,311.60 on the BSE.

Analysts have mixed opinions on Infosys’ stock, with some expressing concern over the steep cut in the FY24 revenue outlook. Nevertheless, the company’s Q1FY24 revenue growth is expected to be positive, with moderate improvement in upper-end sales growth for 2023-24.

For instance, Motilal Oswal Securities stated in its earnings review that while the guidance cut is concerning and could have a negative impact on the share price in the short term (partly due to the 11% gain in the last month), they believe the miss is more related to perception rather than operational issues, as the earlier guidance was overly optimistic given the current business environment.

Conclusively, Infosys reported an increase in net profit for Q1FY24, but due to challenges in the demand environment, the company lowered its revenue guidance for FY24. Despite this, the company secured significant deals and expressed optimism about its generative AI capabilities. The ADRs experienced a sharp decline in response to the revised guidance. The management aims to enhance the operating margin, and analysts have varying opinions on the company’s stock performance.

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India will commence construction on its inaugural semiconductor assembly plant and aims to start manufacturing its own microchips within the country by the end of 2024, the Financial Times reported on Wednesday.

Ashwini Vaishnaw, India’s minister of electronics and information technology, said that Micron Technology, the company establishing a chip assembly and testing facility in Gujarat, is scheduled to commence construction on the $2.75 billion project in August. The project, which has received government support, is set to move forward, the report added.

In the interview with Financial Times he said “This is the fastest for any country to set up a new industry. “Eighteen months is when we have targeted [the first] production to come out of this factory — that is, December of ‘24.” he added. 

On Monday, Microchip Technology inaugurated its newly established research and development (R&D) center in Hyderabad. The company also revealed its intention to expand the workforce at the facility, aiming to double its headcount to 1,000 in the coming years. This new office is a crucial component of Microchip Technology’s long-term initiative, which involves investing $300 million over several years.

Earlier,  the Indian government decided to accept new applications to build Semiconductor Fabs and Display Fabs in the country, starting from June 1, 2023, through the Modified Semicon India Programme with an aim for development of semiconductors and display manufacturing ecosystem in India.

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Autonomous vehicles have been in the media glare ever since their inception. Today’s autonomous vehicles are technological marvels, from the hardware deployed on the vehicle to the models they use to drive themselves. They are also a hotbed of AI innovation, as autonomous vehicles have to solve real-world problems using machine learning algorithms.

The best-known manufacturer in the autonomous vehicles space is Tesla, and for a good reason. It has innovated extensively for powering capable on-vehicle computers through its collaboration with TSMC. Along with this, it has also trained models in-house for use in its autopilot feature using a supercomputing cluster powered by NVIDIA GPUs.

Google’s autonomous driving company, Waymo, is the only other manufacturer to achieve fully-autonomous driving. Using Google’s silicon chops, Waymo has developed and manufactured a full set of chips and sensors in-house to use with their cars, dubbed Waymo Drivers. The team behind the car has also created a simulation they call ‘Carcraft’ which allows them to train models in a completely virtual environment. 

The hardware powering autonomous vehicles

Tesla has worked closely with ARM and TSMC to create their own chips called Tesla FSD chips. This silicon is custom-built to run inference with low-latency while being extremely power efficient. Modern Teslas are equipped with eight cameras which provide a 360-degree of the car’s surroundings. This information is then fed into the FSD chip to make driving decisions in real-time.

In addition to their supercomputing cluster powered by NVIDIA GPUs, Tesla has also built another supercomputer called the Dojo system. Custom-built from the ground up for machine learning training tasks, this uses Tesla’s datasets to train their full self-driving algorithms.

Waymo, on the other hand, developed a full suite of hardware for use in their self-driving cars. The current 5th generation Waymo Driver starts as a stock Jaguar I-PACE, an all-electric SUV. This car is then modified for use with Waymo’s suite of sensors and compute, consisting of radar, LIDAR, and cameras paired with enterprise-grade CPUs and GPUs. Waymo’s algorithms are trained on Google’s cloud platforms powered by TPUs and the TensorFlow ecosystem. 

The brains of self-driving cars

Both these companies have also invested heavily into creating AI and ML models to be deployed in their vehicles. Tesla was one of the first companies to use neural networks for self-driving applications. Using datasets collected from their FSD beta test fleet, the team behind Tesla Autopilot trained over 48 networks to deploy in their vehicles. A single build of the Autopilot system reportedly takes over 70,000 GPU hours to train. 

Apart from the neural networks that iteratively learn from new scenarios recorded every day from Tesla cars, Tesla also uses a high-fidelity representation of the world around their vehicles to develop autonomy algorithms. These algorithms can reconstruct a complete computer-readable version of the car’s surroundings using data from cameras in the car. This is then used as the ground truth for the algorithms, which perform inference using the car’s onboard FSD chip. 

Waymo, on the other hand, has a 115-acre training facility in California known as Castle. The team behind the self-driving car has created a fully realistic closed-course testing facility that mimics a variety of urban settings. By testing their vehicles in this facility, Waymo is able to train its algorithms to react to emergency situations that humans face every day. 

In addition to testing at Castle, Waymo has also trained its algorithms by driving over 20 billion miles in a simulation. Here, they can accurately identify the most challenging situations a Waymo Driver will encounter, as well as create virtual scenarios to better train their algorithms.  They also work closely with Google Brain to integrate state-of-the-art AI and ML algorithms in their vehicles. 

Behind the scenes: AI in manufacturing

Artificial intelligence creates unique designs that cut down on the amount of material required to create a part while still maintaining structural stability. A prime example of this is Audi’s new AI network, called FelGAN, which uses a generative adversarial network to generate rims for their cars. Trained using self-supervised learning, this model can generate fresh kinds of rims while keeping them lightweight. This is then made into a prototype and tested, with Audi’s engineers praising the model’s capability to ‘think outside the box’. 

While big auto manufacturers are looking to introduce AI as a value add for the manufacturing process, a company has turned to state-of-the-art algorithms to design a car from scratch. Czinger, an American-based automobile manufacturer, has created the 21C, a hypercar designed with AI. 

By using AI, Czinger was able to apply Pareto optimization to every single one of their components, thus making sure that not a single gram of the car has gone to waste. Due to the complex nature of certain components, they had to also invest heavily into additive manufacturing techniques to synthesise them in house. 

AI will soon become another tool in the arsenal of manufacturers that will bring them into a new age of automobile design and creation. Bringing together novel 3D printing techniques, powerful generative AI, and advancements in robotics, the cars of the future will leave the cars of today in the dust. 

The post Revealed: The AI Systems and Training Models of Tesla, Waymo appeared first on Analytics India Magazine.

“Human beings rarely make mistakes. ML model gets 95% precision, whereas most human beings would have a 99.9% precision,” said Devashish Shankar, Principal Architect at Drishti. California-headquartered Drishti provides AI-powered video analytics technology to optimise the manual assembly processes in the manufacturing industry. With over eight years of experience in AI to boot, Devashish has been instrumental in developing Drishti’s data platform and core action recognition model. 

During his talk at the MLDS Conference, ‘New developments in Deep Learning for unlikely industries’, Shankar outlined Drishti’s industrial applications of AI in manufacturing. The company leverages deep learning and computer vision to automate the analysis of factory floor videos. Essentially, the company has installed cameras on assembly lines that capture videos on which the company runs object detection, anomaly detection and action recognition. Then, the data is sent to industrial engineers to improve the line. During the talk, Shankar discussed Drishti’s major AI use-cases.

Cycle detection and action detection

The first use-case is in cycle and action detection in the assembly line. Essentially, the cycle is a unit of work. “(The unit) comes into the station, some actions are performed on it, and then that unit leaves. The entire sequence is called a cycle,” Shankar said. Action detection is done within these cycles. Shankar illustrated how repetitive cycles are accompanied by data on the actions being performed, time taken to complete the cycle and time taken to complete each action.

The cycle starts and ends with the unit coming and going, and is solved using standard object detection techniques. “The neural network is operating at a frame level. Each frame detects if the unit is present and where it is present. On top of that, you do heuristics to define the cycle,” he added.

Anomaly detection through configurable heuristics FSM

Manufacturers are expected to follow ‘standardised work’, a specific sequence of actions, and an incorrect sequence or missed steps can lead to product defects. Anomaly detection identifies outliers in the system. The neural network tracks the unit in its field of view, detects the actions and attaches them to the cycle. Shankar explained the machine’s responses to the cycles through a toy experiment, where, if the cycle is correct, the actions are shown as ‘successful’ and tagged in green. In the opposite case, the device red flags it. 

Given the complexities in the cycle, Drishti’s FSM can be configured based on conflict changes to define custom rule engines for each station. Additionally, the semi-supervised model allows training on minimal object data.

Variation in unit detection

A common issue in manufacturing is the variation on the factory floor, from unit sizes, locations, irregular trajectories, multiple units in the field of view, variations in hands and clothes of operators, background changes, lighting changes and more. This leads to different frames in the video showing conflicting images. So, the deep learning model is trained on different variations and sampling to reach the desired level of accuracy.

Action detection through 3D convolution models

Along with the problems of unit detection, the problem of action detection occurs when the meaning of the action can not be understood without motion or in a single frame. Additional issues such as a left-handed or a right-handed operator or different ways of picking things up can make detection even more difficult.

Multiple planes are required to mitigate this problem. Drishti leverages 3D convolution models that have several planes stacked together in a spatiotemporal cube with images together, on top of a 3D cone. This architecture is built for action localisation over video classification through semi-supervised models.

Overcoming machine inaccuracies

To alert the operators of errors, a  tablet is placed right in front of them. “While the workers are performing a cycle, if a mistake happens, they are warned right there,” Shankar said. “This is the way in which Drishti is augmenting human beings. We are making them more efficient.” However, humans are extremely accurate in work, whereas machines tend to raise loads of false alarms. 

To overcome the issue, the FSM based validation engine is used to define these anomalies. The neural network’s accuracy is increased through continuous loops of retraining by human annotators that correct data and input it back into the network. Another technique uses NLP bean search that splits the model’s actions into ‘confident’, and ‘non-confident’, followed by trying possible combinations to pick the one closest to the standard sequence. The language model then takes the sequence with the highest lock probability as the final sequence. “This allows us to tune anomaly precision versus anomaly recall”, he added.

ML infrastructure at Drishti

Drishti’s in-house infrastructure, ML Gym, is responsible for continuous training and evaluation. The various steps in the ML infrastructure include: A collection of raw video from the factory to be randomly fed into the ML gym as datasets; the datasets then go to ‘Drishti Annotation Service’ for data labelling; datasets finally go through ML training work through pipelines.

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A data scientist transitioned from an electronic communication engineer, Sahana Prabhu‘s research interests include diabetic retinopathy image analysis, meibomian image segmentation, emotion recognition via deep learning approaches, and retail analytics via RFID and stereo cameras. Analytics India Magazine caught up with Sahana, who is currently serving as a research scientist and technical architect at Bosch Engineering and Business Solutions, to understand her insights on deep learning, machine learning, etc.

AIM: As a research scientist, what factors do you believe contribute to your research success? Please throw some light on it for us.

Sahana Prabhu: Proper dataset collection that is representative of the problem to be solved is vital. Once the problem statement is formulated, one approach to innovating is to reduce the restrictions in previous research papers. An example of this is to add data augmentations to tackle variations in data from open settings instead of having a controlled setting for capturing data. Another valuable research method is combining two related problems and developing a comprehensive framework that optimises both solutions. For instance, one of my papers involves a framework to solve image matting and super-resolution simultaneously. Maintaining a daily log of useful web links and attempted experiments helps in structuring research work. Displaying the results of the intermediate steps of the algorithm and checking if it is along expected lines reduces erroneous assumptions.    

AIM: Is the problem of unsupervised learning more difficult to solve? What is your view?

Sahana Prabhu: Unsupervised learning does not have pre-existing patterns to learn from in labelled training data. Nevertheless, it is necessary to harness the potential of huge unlabeled data and recognise unknown insights without the limitations of human bias. Since purely unsupervised learning is difficult, an approach to dealing with unlabeled data is self-supervised learning, an active area of research.   

AIM: How can larger firms that invest in deep learning ensure that their efforts benefit others in the field?

Sahana Prabhu: Larger firms have more capacity to do experimental research for problems that are not obvious.  

They can achieve greater operational efficiency from their proprietary data for real-world problems. Their existing customer network can look into various areas and formulate ideas to improve automation processes. Many large firms provide start-up incubation support, such as the Bosch Accelerator program, which goes a long way to further progress in this field.

AIM: What applications of deep learning excite you the most right now or soon?

Sahana Prabhu: Material recognition using texture analysis is a topic I am working on now, and it has applications in track-and-trace across automotive manufacturing, food produce, and pharmaceutical domains. Explainability is another research area emerging for many domains such as medical imaging, assisted driving, and manufacturing defect detection. 

AIM: Which industry do you believe will be most disrupted in the future by deep learning?

Sahana Prabhu: Autonomous retail and autonomous driving are two emerging industries that will be made possible in the future. Smart automation of retail stores for self-checkout, tracking the flow of customers, and monitoring inventory can be facilitated by deep learning, and this is already under the experimental phase. Several automotive companies, including Bosch, have made significant advancements in autonomous driving. In addition, deep learning for computer vision, including semantic segmentation and image retrieval, is used extensively for assisted driving applications.    

AIM: What are your opinions on the recent rise in interest in deep learning in the media?

Sahana Prabhu: There are two sides to it – while it has gained proper attention for automation in some domains such as manufacturing, it is also necessary to recognize its limitations. For example, deep learning can be an additional aid for routine screening in many medical image-based diagnosis tasks, but medical image-based diagnosis cannot fully eliminate doctor supervision. Moreover, there are many applications wherein conventional computer vision methods already provide the required accuracy for large-scale deployment, and deep learning may not be useful. There is also a perception that deep learning can give better predictions for cases where humans cannot, but deep learning gives only close to human accuracy in many applications.      

AIM: What are some of the difficulties that someone new to machine learning might encounter? How should they approach them?

Sahana Prabhu: Information overload is a common problem that both newbies and researchers in machine learning face. It is tempting to apply machine learning for problems where conventional computer vision methods would suffice. We have a lot of online resources available, but it is important to focus on the problem and limit oneself to appropriate methods. New state-of-the-art algorithms keep getting added, and these can be found in paper listing sites such as “Papers with code” and “Awesome Deep Learning Resources”, rather than doing random searching on the internet.  

AIM: Who is your role model in machine learning research?

Sahana Prabhu: Prof Andrew Ng has facilitated many research students (including myself) from related core fields such as computer vision to transition smoothly to machine learning. The way he straddles academic theory and industrial applications is inspiring. 

AIM: Are there any research papers you think every data scientist should read, irrespective of whether they are just starting or have years of experience?

Sahana Prabhu: I will mention three breakthrough deep learning papers in computer vision for the three broad topics – classification, detection, and segmentation, respectively:

[1] Simonyan K, Zisserman A. “Very deep convolutional networks for large-scale image recognition.” 2014.

[2] Girshick R., et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.” CVPR 2014. 

[3] Ronneberger O., et al. “U-net: Convolutional networks for biomedical image segmentation.” MICCAI 2015. 

The post Maintaining A Daily Log Help In Structuring Research Work: Sahana Prabhu, Robert Bosch appeared first on Analytics India Magazine.

Amazon recently announced the general availability of its computer vision (CV) appliance, the AWS (Amazon Web Services) Panorama. The AWS Panorama Appliance is a machine learning appliance and software development kit (SDK) that allows bringing computer vision to on-premises cameras to make predictions locally with high accuracy and low latency.

Using the appliance, one can automate tasks that traditionally require human inspection to improve visibility into potential issues such as evaluating manufacturing quality, identifying bottlenecks in industrial processes, and monitoring workplace security even in environments with limited or no internet connectivity. The software development kit allows camera manufacturers to bring equivalent capabilities directly inside their IP camera.

“Organisations across all industries like construction, hospitality, industrial, logistics, retail, transportation, and more are always keen to improve their operations and reduce costs. Computer vision offers a valuable opportunity to achieve these goals, but companies are often inhibited by a range of factors, including the complexity of the technology, limited internet connectivity, latency, and inadequacy of existing hardware. We built the Panorama Appliance to help remove these barriers so our customers can take advantage of existing on-premises cameras and accelerate inspection tasks, reduce operational complexity, and improve consumer experiences through computer vision,” said Swami Sivasubramanian, VP of Amazon machine learning at AWS. 

The appliance is an edge device, so it runs applications locally on optimised hardware instead of sending images to the AWS Cloud for processing. The appliance only requires an internet connection to report its status, upload logs, get software updates and deployments.

Amazon says that the Panorama Appliance is already being used by companies, including Accenture, Deloitte, and Sony.  

AWS Panorama supports models built with Apache MXNet, DarkNet, GluonCV, Keras, ONNX, PyTorch, TensorFlow, and TensorFlow Lite. Integrated with Amazon SageMaker, Amazon’s own service for building machine learning models, the Panorama Appliance can be updated and deployed with new computer vision models. Companies who opt not to create their own models can choose from solutions offered by Deloitte, TaskWatch, Vistry, Sony, Accenture, and other Amazon partners. The Panorama Appliance is now available for sale through Amazon’s AWS Elemental service in the US, Canada, the UK, and the EU.

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Sensor fusion (SF) is in high demand due to the availability of sensor data from a variety of sources. Due to the inherent advantages and disadvantages of various sensor types, a good algorithm will also prioritise certain data points over others. SF techniques combine sensory input to assist in reducing ambiguity in machine perception when appropriately synthesised. They are tasked with the responsibility of integrating data from many sensors. Bayesian methods like Kalman Filters are frequently used to perform the fusion. There are a few more algorithms that are employed in the fusion process.

Existing Sensor Fusion Algorithms

SF algorithms combine all inputs and generate accurate and dependable output, even when individual measurements are incorrect. Let’s have a look at some of the existing SF algorithms.

• The Kalman Filter: It is the most extensively used prediction-correction filtering method in sensor fusion and is especially effective in navigation and positioning technologies.

• Bayesian Network: These methods, which are based on Bayes’ rule and emphasise probability, predict the likelihood of contributing components from many hypotheses.

• Central Limit Theorem (CLT): Based on the law of large numbers, CLT algorithms collect several samples or readings in order to calculate the most accurate average value for the dataset, which is generally represented by a bell curve.

• Convolutional Neural Network: These algorithms classify findings by fusing image recognition data from multiple sources.

• Dempster-Shafer: Often referred to as a generalised form of Bayesian theory, these algorithms employ uncertainty management and inference techniques that closely resemble human thinking and perception.

What data type does SF deal with?

The type of data utilised as inputs to algorithms can also be used to define the degree of sensor fusion.

• At the data level, the fusion algorithm is fed raw data from a variety of sources.
• At the feature level, the fusion algorithm is fed data or features from a range of individual sensors.
• After data and feature-level sensor fusion, decision-level sensor fusion occurs when a hypothesis is chosen from a set of hypotheses.

How does sensor-to-sensor communication happen?

• Complementary: When “sensors are not directly dependent on one another but can be coupled to produce a more complete image,” which is advantageous for motion detection jobs.
• Competitive or Redundant: When each sensor “provides separate measurements of the same attribute,” this is advantageous for error correction.
• Cooperative: When data from separate sensors is used to “deduce information that would not be available from single sensors,” as is the case when analysing human motion in science and medicine.

SF has a wide range of functions and applications. It is used in the following levels:

Level 0: Alignment of data

Level 1: Assessment of entities 

Level 2: Situational analysis

Level 3: Impact evaluation at the third level

Level 4: Process optimisation

Level 5: Refinement of the user

Where is Sensor Fusion most frequently used?

Sensors are employed in an infinite number of applications across a wide variety of industries and sectors. Some of the industries that benefit from SF are the automotive industry, climate monitoring, computer software, consumer electronics, healthcare, home automation, industrial control, Internet of Things, manufacturing, military, oil exploration, etc.

A 19.7% CAGR is expected to take the Sensor Fusion market to a global value of $19.84 billion by 2030, as per Allied Market Research. There are extremely few contributions to this field of research. Many AI-based approaches to sensor fusion have been created in recent studies to determine multiple sensor information contributions based on unique requirements, conditions, and tasks. Moreover, new sensor technologies are being integrated with AI-based solutions in real-world applications on a daily basis, now that Industry 4.0 has been introduced. The cost of software and processing power will rise in tandem with algorithm complexity. 

The post Can AI Transform The Global Sensor Fusion Industry? appeared first on Analytics India Magazine.

NASSCOM Centre of Excellence IoT & AI, an initiative of the Ministry of Electronics & Information Technology (MeitY), and the Gujarat government organised a Manufacturing Innovation Challenge 2020 (MIC2020) from 15th July 2020 to 21st August 2020. MIC2020 received overwhelming response as 88 matured startups from across the country registered for the challenges and they went through a rigorous screening process of four rounds.

The objective of this challenge was to build a first of a kind platform for enterprise and start-up collaboration to solve key challenges manufacturing industry is facing today. In an online award ceremony, the three startups– AirVlabs, Alluvium, Chistats were announced as winners of the three challenges. The winners will now get an opportunity to work on a remunerative Proof of Concept with companies who nominated their use cases. In addition, start-ups will also get one-year membership to the NASSCOM CoE Acceleration Program.

The NASSCOM Center of Excellence is the largest collaborative innovation platform in the emerging technologies of IoT, Analytics, AI/ML, AR/VR and Robotics for Digital Transformation. It is part of the Digital India Initiative to jumpstart and drive the emerging technologies ecosystem in India, by leveraging India’s IT strengths and assist the country attain a leadership role in the convergent area of hardware and software. The main objective of the CoE is to help Indian Deep-Tech Startups& Companies leverage cutting edge technologies to build market-ready products. Through the Startups Program, CoE aims to build industry capable talent in an entrepreneurial ecosystem by facilitating Incubation, Funding, Acceleration, Industry Connect and Mentoring.

NASSCOM CoE in conjunction with JK Lakshmi Cements Ltd, Bayer Corpscience, and Tata Chemicals Ltd nominated three use cases i.e.Workers Health and Safety, Solution to Promote Safety of Workers in Large Plants; Predictive maintenance of high-value assets; and 360° Virtual Tour of plant for which startups were encouraged to solve any of the challenges by submitting a proposal. IIT Gandhinagar was the Research Partner and Gujarat Electronics & Software Industries Association (GESIA) was the Outreach partner. Twelve top startups presented to Jury that had representation from senior leaders from industry, academia and government.

Speaking on the occasion, Saurabh Gaur, Joint Secretary, Ministry of Electronics and Information Technology, Government of India, said, “In current times, the significance of self-reliance is at an all-time high. Even the Government of India is cognizant of this and made the clarion call for an AatmaNirbharBharat. India has high calibre potential to drive incremental change using technology, and the time is now to build capabilities that will bolster self-reliance and innovation. This requires a collaborative effort – of enterprises, startups, policy makers – to identify the right problems and solution providers. The Manufacturing Innovation Challenge 2020 is a perfect example of this cross collaboration to further innovation & build self-reliance.”

Platforms like MIC can be a great potential to accelerate to kick-start a Digital Transformation journey in the country’s formidable industries. In current situation when companies are doing best to maximize the capacity utilization, such initiatives can be of great support for them to embrace digital technologies to increase productivity, efficiency and create a safe work environment for workers.

“NASSCOM CoE is building an ecosystem for manufacturing companies to work with technology companies and innovative start-ups to drive adoption of Industry 4.0 solutions. MIC2020 was launched to accelerate this mission and received exceptional response. This challenge created value for everyone as it helped enterprises find the best solution partner and start-ups got an opportunity to work with their dream companies. Success of this program has motivated us to run these challenges every quarter and we look forward to getting similar support in all our future initiatives”, said Amit Saluja, Sr. Director & Vertical Head, Manufacturing, Gandhinagar, NASSCOM CoE.

Vinita Singhania, Managing Director, JK Lakshmi Cement said, “We are delighted to be a part of such an initiative from NASSCOM CoE. Manufacturing Innovation Challenge has helped us connect with top class start-ups who can be of big help in creating our future innovations. We encourage other manufacturing companies also to leverage this platform as a means to make your plant smarter and safer with digital. We believe that the use of digital technology like AI, IoT, AR etc for manufacturing processes optimize sustainable development i.e. productive maintenance, reduced cost and saves time.”

The post Indian Startups Leverage AI, IoT and AR/VR For Government Manufacturing Innovation Challenge appeared first on Analytics India Magazine.

Dr Sheela Siddappa is the Chief Advisor for Data Science at Bosch, where she leads the design and implementation of AI and data science solutions at multiple plants across the globe. With 15 years of experience in analytics and machine Learning, Dr Sheela has addressed business problems in various industry domains for multinational companies.

Before her current role, Dr Sheela worked as Global Head- Data Science at Bosch, and was responsible for building and steering the organisations for various projects in domains like digital, agriculture, health care, smart living etc. In this role, she helped acquire and lead global business, review and guide challenging projects and built data science accelerators. Previous to that, she had also led data science research and projects at Infosys and General Motors. 

In the following interview, we connected with Dr Sheela to know more about her data science career and the work she has been doing at Bosch to transform the manufacturing processes using AI/ML solutions. Dr Sheela also shared some useful tips for aspiring data scientists.

How did your journey in data analytics begin?

My interest in Data Analytics started during my engineering days. The course Operations Research for two semesters’ and the elective of Design of Experiments offered by the Industrial Engineering discipline did all the magic. The eagerness to know more about the subject encouraged me to go for higher education. I did MS, and PhD from Texas, USA and the journey began.  

What are the opportunities you see by implementing IIoT and Analytics in the manufacturing and industrial scenario?

There is a bonanza of opportunities. In fact, at Bosch, we have implemented several Industrial IOT embedded with Artificial Intelligence solutions at multiple plants across the globe. The benefits are enormous and we have been releasing them for a few years now. Our teams in India, Germany and the USA are supporting this activity for more than seven years. We are also providing solutions to our clients. Some of the company types range from a Dairy Company in Norway to Wheel Manufacturing Company in Turkey and OEMs in Germany, Japan and India. 

How are you solving operational efficiencies for Bosch using different data analytics and AI?

AI helps in omitting some steps in the process. It also helps with the predictions and estimates to make better decisions than before. Some tasks are outsourced, while other tasks are automated by the Analytics tools and packages.

How can data analytics solutions help the industrial and manufacturing sector gain deeper insights? Can you share a few examples? 

The AI solution has not only helped in getting insights but also aid in better business decisions. Some of the use cases are: 

Quality: For instance, a typical tyre manufacturing company employs twice the number of associates for quality check in comparison with the associates involved in the production of tyres. By implementing Deep Learning Techniques on the Images of the tyres produced, we were able to identify the tyres that had defects, point the defect area on the tyre, and measure the defect size. Going forward, by adapting Multi-label Image Classification Technique, we were even able to detect tyres with more than one type of defects on them. DL enabled the decision to reject or repair tyres in seconds,. The number of employees supporting the quality check also reduced significantly.

Warranty: It is critical to learn the reason for high warranty costs for any OEM. Adopting Big Data technologies, bringing data from multiple sources to a common Database and developing visualisation dashboards (using Tableau) helped our customers in the UK to discover top components leading to high warranty costs. It could detect it region-wise on a time series model. It enabled business decisions and actions that minimise the warranty costs. 

In yet another case, assignment of the warranty issue (technical) generated at the service station to the correct subject matter expert was manual and it caused massive delays. The assignment process was automated by adopting advanced Natural Language Processing Techniques. The instantaneous assignments helped eliminate the backlog.

What, according to you, has been the most important breakthrough in the last five years when it comes to AI?

Deep Learning has helped significantly in solving business problems. Some of the issues, which were challenging to solve earlier are now solvable with good accuracy and reliability.

For professionals, how challenging is it to prepare for an ever-changing data science field and how can they succeed?

For someone who has a strong knowledge of Data Science will not find it challenging to incorporate the advancements, as the underlying concepts would be either known or understandable. For those who are new to data science, they can consume the benefits using automated tools. Now, with academia incorporating Data Science as a subject in most of the disciplines will benefit youngsters in the long run. 

Thus, I do not see any major challenges and find the future filled with AI solutions in almost every field. The days of employees saying, “what I studied in Engineering is of no use, as what I do at work does not need an Engineering degree. It is a routine job with no excitement” will soon vanish.   

Finally, any tips for the Data Science aspirants?

Data science is a subject rich in mathematics, reasoning, logic etc. It requires one to use intelligence at work, write every constraint as an equation, process/steps as logic etc.  If one is curious and filled with reasoning skills and like mathematics, do take up Data Science. Try to understand the fundamentals of the technique before implementing. Knowing the concepts at a high level does not suffice for someone wishing to pursue their career in Data Science for a long time.

Just applying tools and packages to data and getting the results is not the role of a data scientist. They should be able to identify the problem statement, draw inference from the model, and create an ensemble approach to develop a robust model. The most important aspects are being able to first identify the problem that needs to be solved and secondly envision the future, thereby identifying the correct AI-based enhancing features for the product —  in alignment with the business.    

The post Interview: How Bosch Uses AI To Transform Its Manufacturing Processes appeared first on Analytics India Magazine.

With the outbreak of COVID-19 pandemic, the supply chain has seen a significant disruption with declining revenues as businesses are shutting down, and customers are changing their buying patterns. Also, with a lesser number of employees working at the site, it became difficult for manufacturing and construction aggregate supplying companies to continue their business amid this crisis. The subsequent impact has led to several job losses of workers, and therefore businesses are relying on automation in order to have a smoother logistics process.

Concretia Rock Products is one such mining, manufacturing and construction aggregate supplying company that has realised the potential of leveraging technologies to survive this pandemic and maintain business continuity in the post-pandemic world. With its own leased quarries and a fleet of trucks, this Madurai-based company manufactures crushed aggregates and rock products for district’s construction companies and manages to procure orders from approximately 400 customers including engineers on a regular basis. 

The company has seen an immense potential of expanding and streamlining their business in the whole Madurai district, and therefore decided to restructure with technology-led solutions to take advantage of it. Concretia also realised that their business process required a holistic approach with scalable models customised to Madurai district, which can later be scaled up across other geographies.

In the traditional logistics management, Concretia was working under several human interventions as middlemen in their business processes, which, in turn, slowed down the whole supplying operation of the company. With sales personnel manually jotting down critical details of customers on papers, it made the process even more vulnerable to errors. This manual intervention was also creating insufficient datasets on customers, and therefore creating hassles for the company to monitor the loopholes and bring out efficient sales conversation customised to targeted customers. 

Further, these manual datasets on paper were also taking up a considerable amount of efforts by their employees to understand and bring out key insights beneficial for the business. Consequently, the company was looking to automatise this tiresome process of analysis and streamline its supply chain management. Alongside, in order to survive in this competitive landscape, the company was also looking to create transparency in their logistics process by optimising every department of the company starting from lead generation and sales to routing process and delivery management. 

The site in Madurai, Tamil Nadu

The Solution & Benefits 

The company was looking for a holistic new-age solution that can help the leaders monitor their business and employees better and interact with stakeholders. Taking everything into consideration, Concretia decided to implement Salesforce’s AI-powered analytics platform — Einstein Analytics to make smarter decisions for their business. Einstein Analytics is a platform powered by artificial intelligence, deep learning and predictive analytics by combining dynamic and interactive data visualisation with the power of machine learning, which helps businesses with data-driven insights.

With Einstein Analytics, Concretia managed to eliminate the human intervention in their business processes, starting from lead generation, creating order to preparing the dispatch and collection of the orders. Einstein provides a single view dashboard for every customer, which again helped their sales employees to have relevant data while pitching their business. Alongside, the team also used the solution to geo-tag their construction sites and created a real-time upload of site images, which generated a comprehensive dataset for the sales personnel to take advantage of. 

Such extensive data — including the status of the site, type of customers and also the materials used — aided the company, sales personnel and business leaders to understand their customers better and strategise smarter ways to enable sales conversations. Einstein further uses predictive analytics on the granular customers’ data that allowed sales employees to pitch the right product to the right customers, depending on their requirements and previous buying patterns. Other aspects of the business such as booking, ordering, invoicing, as well as customer complaints have also been automatised with the help of Einstein Analytics platform, which provided transparency for their customers while doing business with Concretia. 

“These well defined robust and streamlined processes started providing business benefits for the company,” stated Rajesh K, Founder & Managing Director, Concretia. Post the implementation the company managed to onboard 70-100 customers without human intervention and foresees to convert 400 customers every month in the future. Concretia also utilised the collected data and its analysis to ensure vehicle availability for the supply of the aggregates and also optimised their route management process to speed up their delivery schedule.

Another critical aspect that has been revolutionised post the implementation of Einstein Analytics is employee monitoring. With the advanced solution, business leaders of the company were able to micro-manage their employees and also monitor the activity and efficiency of their salespeople. The company noticed that with these defined processes, “customers are influenced positively and are more likely to convert since customer data is leading to more relevant conversations,” stated Rajesh.

“Digitisation of processes has tremendously improved the productivity and morale amongst the sales team. The time taken to train our sales team has also drastically reduced,” said Rajesh. “The implementation of Salesforce has also enabled us to serve our customers better, as we’re able to have a smarter and more relevant conversation based on granular customer data.”

Apart from utilising the platform for enhancing daily customer relationship, Concretia is also leveraging the collected data analysis to forecast the potential business benefits of a particular area in the district and map out future sales budget across sites.

Wrapping Up

Post automating the business processes, Concretia managed to speed up its supply chain and logistics process without human intervention, which again freed up their sales team to focus on creating critical business strategies. By the coming year, the company is aiming to expand its business with more products and solutions to scale up the business module to multiple geographies.

The post Case Study: How This Madurai-Based Manufacturing Company Streamlined Its Logistics Process With Augmented Analytics Amid Crisis appeared first on Analytics India Magazine.

We know that the world has been depending on Asian regions like China and its neighbouring state Taiwan for a vast majority of the supply of electronics hardware manufacturing. With the coming of COVID-19 pandemic, the supply chains have been constrained, and many large nations are worried about the lack of electronics chips manufacturing. The US too has been considering building up factories within the domestic border and moving away from its dependence on Asia, according to reports.

If you look at India, the government attaches high priority to electronics hardware manufacturing as it is one of the fundamental tenets of programs such as Make in India. But there is clearly a dependence on other nations for semiconductor chips as there is a lack of electronic components manufacturing ecosystem in India. But, the government has recognised the development of the supply chain is critical for the manufacturing of electronic products with substantial domestic value creation.

Attracting Investments Into India’s Electronic Manufacturing 

To attract investments in the space, the Modified Special Incentive Package Scheme (M-SIPS) was announced, give monetary incentives support to counterbalance the high upfront expenses in building electronic component manufacturing units, thereby attracting investments from interested companies.

This scheme was free to receive applications till 31 December 2018 for new projects and provided a subsidy for investments in capital expenses for setting up electronics manufacturing facilities. M-SIPS had, in fact, has played a decent role in promoting investments in electronics manufacturing in India so far.

Now, there is another program to boost investments in the electronic manufacturing sector. Known as Scheme for Promotion of Manufacturing of Electronic Components and Semiconductors (SPECS) introduced in April 2020, the program is aimed to offer monetary stimulus of 25% of capital expenditure for the manufacturing of goods that form the supply chain of electronic goods.

The scheme will balance the lack for domestic manufacturing of components and semiconductors, and aims to grow the electronics manufacturing supply chain ecosystem in India. There are various classes of goods eligible for incentive under SPECS, ranging from a Minimum Investment Threshold Limit of INR 5 crore to Minimum Investment Threshold of INR 1000 crore, including semiconductor wafers and Semiconductor Integrated Chips (ICs). 

With the newly announced era of Internet of Things (IoT), directing that the new generation of interconnected devices be capable of advanced computing, the Indian semiconductor industry is set for a solid rise with fresh prospects provided India’s infrastructure and funding issues for electronics chip manufacturing are met.

At the moment, almost all the semiconductor demand is satisfied by imports from nations like the USA, Japan, and Taiwan. In the semiconductor area, India has a large human-capital pool, which is focused on the design side, not manufacturing. Hopefully, this may change in the coming years.

The post India May Soon Boost Manufacturing Of Electronic Components & Semiconductors appeared first on Analytics India Magazine.

With more than 350 people working in Gurgaon and Mumbai office, BEUMER India is a manufacturing company with expertise in designing intralogistics systems for conveying, loading and distribution processes. The company’s employees, due to their nature of work (remotely and on-field) had regular travels to be taken care of. Before automating the process, employees of BEUMER India were using traditional formats like excel sheets and hard copy documents in order to record their travel logs, travel expenses and claims for reimbursements. Such a process was not only time-consuming for the employees but also created hassles for the finance department in keeping a track and analysing the data for further business insights. 

Keshav Dubal, IT Head of BEUMER India explained the process — considering the whole process of expense management was earlier done manually on an excel sheet, and via emails, the process of analysing the data used to take a huge amount of time for the finance department.

The company was urgently looking for an automated, digital expense management solution in order to meet and ease their requirements. An efficient travel and expense management solution became crucial for the business, which could improve the efficiency of the process and omit out the loopholes and glitches that could potentially be created due to manual intervention.

The Solution

The company required an advanced travel and expense tool that can automatise the whole process and can help the company to have a single dashboard view of their employees’ travel and expenses. BEUMER India decided to partner with SAP Concur, which is a software-as-a-service company specialising in designing travel and expense management solutions for its customers. 

The company Concur Technologies was acquired by SAP in 2014, and since then, SAP Concur has been working towards creating seamless travel and expense management solutions. Beumer India decided to opt for SAP Concur solution, however, with a high level of customisation according to the company’s requirements.

SAP Concur travel and expense management solution has been designed to automate the process of reimbursement, which proactively manage the employee spends with accounts payable automation, and help companies to have all their data in place to obtain business insight. According to SAP Concur, the travel and expense management is a one-stop solution, which integrates travel and expense processes of the customers and provides real-time visibility into different types of company data.

The cloud solution helped the customer make a smarter data-driven decision with complete data of employee spend. Alongside providing data to the customers, the solution also flags policy compliance violations and provides simplified budgeting for the company.

Benefits

The advanced travel and expense solution automated the company’s recording and reporting process of travel claims, which, in turn, saved a lot of time and effort for the finance department. Besides, the solution completely omitted the majority of human intervention in the process of managing travel expenses. Apart from these, the solution has been designed extremely employee-friendly, accurate and smart, which helped in increasing the control and visibility of the company’s travel and expense reporting. 

Also, within six months of deployment, the company could see a drastic improvement in their travel expenditure. Not only the solution streamlined the process, but because of its high regulatory compliance, the company managed to save approximately 2.5% of their spendings.

Apart from saving the employee’s significant time in filing the claims, the solution also provided a single dashboard view for all the related data. Additionally, SAP Concur also provided a mobile-based application for the company, which was designed only for the on-field employees and the employees having regular travels, to submit their claims online without any delays.

Additionally, Mankiran Chowhan the Managing Director – Indian Subcontinent, SAP Concur, said that the T&E management solution had been designed to help their customers manage their employee spend. “Such solutions would enable them to simplify their processes, gain visibility to spend data, establish control and compliance, make smart decisions and enhance employee experience all at the same time,” said Chowhan.

She further added, “We are delighted that just like BEUMER India, using our solution, organisations can make strategic decisions based on insights and the consolidated view of their budget.”

Post the implementation; the company realised that the workflows have become smooth, and it has become way easier for the finance department and administrators to check the details of the employees. The solution has helped the company in saving approximately 30% in terms of time and productivity.

The post Case Study: How This Manufacturing Company Simplified Their Travel & Expense Management Workflows appeared first on Analytics India Magazine.

Manufacturing industries were the early adopters of automation with distributed control systems (DCS) and programmable logic controllers (PLC) but are way behind in embracing cloud computing platforms.

Consequently, they are devoid of cutting-edge technologies like AI that can enhance their production workflows.

Manufacturing industries often think the latest technologies are for IT firms and are tedious to implement them in production processes. However, these companies need to be forward-thinking and deploy machine learning capabilities through cloud computing. 

To help production companies in integrating the latest technologies into their workflows, various companies are collaborating and offering services through the cloud. Most recently, Baker Hughes, an oil field services company, collaborated with Microsoft and C3.ai to develop and offer AI services for oil and gas manufacturing firms.

The Current Landscape

While the adoption of DCS and PLC has helped the manufacturing sector to increase their efficiency and productivity, only a few companies have been able to completely automate their production processes. 

One of the primary factors that impede the implementation of DCS and PLC for automation are abrupt breakdowns and malfunctioning of sensors, thereby, in most cases slackening the automation initiatives.

To mitigate such pressing issues, companies have now moved on to embracing solutions from different vendors for forecasting sensor breakdowns. However, with such practices, firms are only able to witness incremental advancements as those solutions do not address the specific needs of different production industries. Mostly, these solutions are one-size-fits-all and fail to deliver the desired results.

Manufacturing processes are strenuous in nature and require an in-depth understanding to determine correlations between several factors for deploying best practices. 

Therefore, the implementation of machine learning capabilities is the best way forward for companies in obtaining insights into data to make informed decisions.

How Can They Leverage Cloud Computing

Cloud computing has become the backbone of IT organisations success, and it has the potential to transform the manufacturing industries too. Firms can utilise services such as the AWS IoT Analytics for analysing and managing services to enhance their processes. This and other similar solutions from the cloud providers also offer notebook instances that can be used to address personalised problems and deploy machine learning capabilities.

As per reports, machine learning is able to enhance product quality by up to 35% in discrete manufacturing industries. Integrating advanced innovations will allow companies to train machine learning models with their historical data and evaluate them for further improvements. 

As we have not reached a point where we train a model with specific data and evaluate on another, individual manufacturing companies should trust the ML models that are trained with their data rather on some other companies data.

Imbibing above discussed practices can result in advantages that can be summarised as follows: 

Outlook

Production companies need to adopt these new technologies through cloud computing to gain operational resilience and have a competitive edge in the manufacturing landscape. Companies across the world are slowly striving towards equipping machine learning abilities into their workflows, and in turn, revolutionalising their production facilities to achieve smart manufacturing.

The post The Recent Baker Hughes & Microsoft Collaboration Shows Why Manufacturing Should Embrace Emerging Tech appeared first on Analytics India Magazine.

Manufacturing and production systems have a lot of catching up to do with the world of software. The manufacturing ecosystem has seen a lot of upgrade and innovation but it still lags in terms of software application. With the onslaught of artificial intelligence, new opportunities have opened for the sector to leverage new technology and improve productivity.

In the recent decade, deep learning is driving most of the innovation in AI. Deep learning systems have found applications in a variety of fields such as healthcare, aviation, agriculture and many others. The ability to experiment with different architectures that specialise in specific tasks has also been a special feature in modern-day deep learning systems.

Deep Improvements In Dispatching 

One of the most important and critical parts of manufacturing is the dispatching rule. It minimises inventory costs and ensures that goods are delivered on time. With the internet allowing consumers to express their wishes in real-time, the manufacturing world is switching to a more on-demand framework where goods are produced. With a large base of consumers, the trend is also moving towards low volume products designed and semi-customised for a particular consumer. The manufacturing sector has no choice but to leverage advanced technologies to adjust to modern realities. The modern marketplace also demands new orders are verified quickly and the production settings are assembled as soon as possible.

With these demands in mind, researchers at Industrial AI Lab at Hitachi America have tried to solve the acute problem of dispatching in manufacturing using reinforcement learning. They propose to design the shop floor state as a 2D matrix and optimise for dispatching of a good taking into many ignored factors such as job slack time and tardiness. The researchers cleverly avoid maintaining an RL task or model for every product floor and have made improvements in deep RL models to suit their needs. Their approach and experiments have shown to decrease total lateness and the newly proposed policy transfer has reduced training time for the whole deep RL system.

Changing How Dispatching Works 

Older methods mostly relied on heavy heuristics and decision processes that didn’t handle complexity very well. The contribution of researchers Shuai Zheng, Chetan Gupta, and Susumu Serita is to design and represent the shop floor as a matrix and incorporate many important details into the dispatching optimisation function. Apart from this, they have also developed a transfer approach for dispatching policy using manifold alignment.

The overall objective of the deep reinforcement learning method is to optimise the product dispatching in terms of tardiness. The system tries to decrease the tardiness on overall jobs. The design of this improved system includes a job queue which readies components and a processing machine. 

The RL agent observes a state S which consists of a job queue state and machine state and the gives out a probability vector and acts on it. As it is an RL system it receives a reward. A simulation with such an environment is created and repeated multiple times to get different possibilities. The reward is observed for each possible setting a favourable trajectory is chosen for dispatch of the product.

The production settings are incredibly hard and complex and hence the results of the deep RL simulation can’t be transferred to many scenarios. This is unfortunate, as running deep RL experiments for each factory setting is not feasible. Hence the researchers in their Deep Manufacturing Dispatching (DMD) framework, have taken steps where it will be easy to generalise this model for other factory settings. 

The following features are generalised:

1. Factory setting parameters, such as job queue states and machine states, etc
2. Job characteristics parameters, such as job length distribution, job arrival speed, etc.

The researchers state in the paper, “To apply a trained policy in a new factory setting or when job characteristics changes, knowledge transfer would greatly improve the performance of learning by avoiding expensive data collection process and reducing training time.”

Outlook

The state of manufacturing, product ordering and dispatching has been a focus area from a long time. The design of factories and shop floors is a critical aspect when it comes to improving the future efficiency of manufacturing. Deep learning methods can provide a way out of complexity because of their proven track record of handling large amounts of data and features. The complex world of production systems will be helped greatly by a model family such as deep learning which thrives in complex problems with millions of variables.

The post How Deep Reinforcement Learning Can Make Factories Efficient & Dispatch Products Faster appeared first on Analytics India Magazine.

For long, Bengaluru has been a back-office function for international tech firms. However, that is changing now with Drishti, a California-based AI company that  is driving disruption for manufacturers of all stripes. In fact, it is among the top 56 early growth stage companies recognised by the World Economic Forum for developing a cutting-edge technology that keeps humans in the loop in the manufacturing sector. Drishti is one of the first AI companies producing deep learning solutions which will not automate factory workers out of their jobs, but augment their productivity. 

It is one of the first companies to commercialise action recognition technology specifically for manufacturing. Now, their Bengaluru-based engineering team led by Dr Krishnendu Chaudhury is solving some of the toughest problems in AI, up and down the entire stack, as Dave Prager, Head of Marketing and Business Development at Drishti puts it. 

Some of the critical solutions Drishti is solving are in the area of Industry 4.0. “Computer vision is playing an increasingly key role in manufacturing, as it is in other industries. At Drishti, we use vision technology to augment human workers,” said Prager. Citing an example, he shared that similar to technology like spell check, where the computer sees a mistake and suggests a correction to the typist, one of the many things that Drishti’s computer vision solution sees is if an assembly line worker misses a step, it triggers a correction. 

In that way, we are able to keep the benefits that humans provide – cognition and adaptability – and marry them to the benefits of machines, augmenting and extending the efforts of the human worker, he shared. 

Bengaluru Engineering Team Produces Cutting-edge Solutions

Drishti has drawn some of its most talented, hard-working individuals from India. Bengaluru office boasts of double-digit numbers of employees who are working on developing Drishti’s signature action recognition technology, a new deep learning architecture that generates real-time analytics on highly variable human actions using a video stream. “It’s orders of magnitude more difficult than object recognition, in the same way that parsing a spoken sentence is much more difficult than analysing the written word,” said Prager.  

The solution’s architecture goes beyond Deep Learning. The team built a vertically integrated solution from the sensing layer all the way to change management in collaboration with customers. “Our engineers love working at Drishti because, unlike with other Bengaluru offices, they’re not QA’ing at the periphery of other people’s innovations – here, with Drishti, they are the real innovators,” he said. 

How Computer Vision Can Revolutionise Assembly Line Work

Of late, the scope of AI in factory automation has increased significantly. Manufacturers are increasingly recognising that AI has the potential to transform their business. “One piece of advice we give companies who are implementing AI is to bypass the lab; it’s important to test AI applications, as with any new technology, in their destination environment, with all of the variability and challenges that come with it,” said Prager. Laboratory deployment doesn’t provide a real world equivalent, and therefore many AI solutions that succeed in the lab ultimately fail on the floor. 

Factories Are Not Ruled By Robots 

We usually believe that factories are full of robots with a few humans in lab coats wandering here and there. The truth is the exact opposite. The company conducted primary research with AT Kearney, the global strategic management consulting firm wherein the findings indicated that 72% of tasks in the factory are still performed by humans. 

Drishti addresses a longstanding manufacturing problem that is pervasive across the industry: manufacturers are using outdated manual time and motion studies to gather data on tasks performed by humans. Drishti changes that by automatically collecting data on every task that’s performed on the floor, and providing that data to manufacturers. 

The market for factory automation is huge globally. Manufacturing represents $12 trillion of an $80 trillion global economy; in India, manufacturing makes up nearly $3 trillion of the national economy. With the country’s Make In India initiative, more investments are being made to push those numbers up, which opens up even more opportunity. 

AI Is The Best Fit For Factory Automation 

AI is well-suited to factory automation. Manufacturing is defined by predictable and repetitive systems. Because AI can be applied more easily in the factory than in highly variable environments, such as self-driving cars, the possibilities to solve real-world problems in real-world environments are greater. Also, because of the massive scale of manufacturing, and its impact on global economies, the companies who find an edge in AI can be extremely competitive, providing a huge incentive to invest in new technology.

Of late, the manufacturers have also started thinking about data. In India specifically, AI has been less of a focus for the government than other technologies. “Despite this and given the large need, you will see a very large “annotation industry” growing in India, just as we saw the BPO and call center businesses in the past,” said Prager. 

The post Why Has California AI Company Drishti Based Its Engineering Office In Bengaluru? appeared first on Analytics India Magazine.

With AI reaching a tipping point, countries across the globe are building up their capabilities. A widely cited Accenture report highlighted AI has the potential to add US$957 billion, or 15 percent of India’s current gross value in 2035. One of the key takeaways was that the combination of technology, data and talent that make intelligent systems possible has reached a critical mass, driving extraordinary growth in AI investment, but is India ready to cash on this opportunity. Leading AI practitioners and the C-suite believe this and with funding for AI startups growing at an unprecedented rate. Another highlight was AI’s potential to add US$957 billion, or 15 percent of current gross value added, to India’s economy in 2035. We cast a look at top industries in India — manufacturing, healthcare and ITES that are leading in terms of AI deployment. Also, the Make In India and Digital India campaign have also spurred new-tech adoption among Indian companies.

Here’s how India has evolved into one of the leading adopters for AI

Automation on a rise: So, is India the leading adopter of AI. In terms of automation, India has outgrown Japan and US, a new study by Automation Anywhere noted. An academic study conducted by Goldsmiths, University of London and commissioned by Automation Anywhere showed 71 per cent of Indian respondents revealed employees used RPA and AI-based augmentation to its full potential — the highest proportion in the four markets surveyed. According to a press release, Dr Chris Brauer, Director of Innovation in the Institute of Management Studies (IMS) at Goldsmiths, University of London noted that while the hyped potential of AI generates endless headlines, technologies such as RPA are quietly being rolled out in many of the most productive companies around the world – humans and bots are already working alongside each other across the globe and in every sector.

Manufacturing sector sees a share rise in profits due to AI: That’s one indicator of India being a prime market for AI. Accenture report shares how the manufacturing sector could see a significant increase in the share-of profit thanks to AI-powered systems that learn and evolve over time. Another study, The Ghost in the Machine: Artificial Intelligence in the Factory of the Future, conducted by BCG, ranked India third after the USA and China in terms of artificial intelligence (AI) implementation. The global management consulting firm surveyed manufacturing and technology managers from 1100 industrial companies worldwide about their applications of and willingness to invest in AI.

Uptick in healthcare: With AI getting embedded in diagnostics and in overhauling the overall patient experience with AI-powered solutions, healthcare providers are warming up to the idea of implementing AI to assist physicians and improve patient outcome. For example, Bengaluru-headquartered Manipal Hospitals, leverage IBM Watson for Oncology to help physicians identify personalized cancer care options across the country.

In a recent interview, Sundar Srinivasan, General Manager, Artificial Intelligence & Research team at Microsoft India, talked about Microsoft’s partnership with LVPEI, wherein MSR launched the Microsoft Intelligent Network for Eyecare (MINE) to apply AI to help eliminate avoidable blindness and scale delivery of eye care services across the planet. “This initiative harnessed the combined power of data, cloud, and advanced analytics to drive strategies to prevent avoidable blindness.  The initiative which had also been adopted by the state of Karnataka has now expanded into the AI Network for Healthcare to create an AI-focused network in cardiology, in partnership with Apollo Hospitals,” he said.

Srinivasan also shared details about an AI powered Interactive Cane, which will function as a visual aid for the visually impaired. MSR is experimenting with adding sensors to existing canes and adding gesture recognition to enable the cane to provide the user with information that would not otherwise be available. Interestingly, MSR is trying to do this by using low resource sensors that are also intelligent, so that real-time information and feedback is available to the user.Similarly, Bengaluru’s Columbia Asia Hospitals too started using startup Cardiotrack’s AI algorithms to predict and diagnose cardiac diseases better.

Meanwhile, Accenture study indicates that it has developed AI-powered smartphone solution called Drishti to help the visually impaired improve their experience and enhance their productivity in the workplace. The solution, was developed and tested through a collaboration with the National Association for the Blind in India.

The post Is India A Prime Destination For AI? Check Out Sectors That Are Leading In Digital Transformation appeared first on Analytics India Magazine.

In a bid to lead the world in the area of artificial intelligence, Indian Prime Minister Narendra Modi this week asked the NITI Aayog to acquaint all the ministries with high-end technologies.

“China is way ahead of India in a lot of areas but the government does not want to miss the bus this time when it comes to research and adoption of this technology… NITI Aayog has been asked to undertake all possible pilots happening elsewhere in the world, if it addresses India’s problems, so government departments and states are willing to use AI in their day-to-day functioning,” a source claimed.

According to a report in a financial newspaper, the Indian think tank has also been asked to describe how artificial intelligence will be beneficial to address the most of country’s socio-economic problems. In fact, all central ministries have been asked to set up dedicated AI cells as soon as possible.

This directive comes only weeks after the national AI Task Force submitted a report on exploring the possibilities of AI in development across various fields, including industry and research. Following this directive from PM Modi, the NITI Aayog will likely periodically review the progress made by the respective ministries.

The AI Task Force has already recommended the following points about AI to the government:

• Leverage AI for economic benefits
• Create policy and legal framework to accelerate deployment of AI technologies
• Create concrete five-year horizon recommendations for specific Government, Industry and Research programs

In the report, the AI Task force has also identified 10 specific domains that need attention with respect to AI. They are, manufacturing, fintech, healthcare, agriculture, education, retail and customer engagement, public utility services, aid for differently-abled persons and accessibility technology, environment and national security.

“With rapid development in the fields of information technology and hardware, the world is about to witness a fourth industrial revolution… Driven by the power of big data, high computing capacity, artificial intelligence and analytics, Industry 4.0 aims to digitise the manufacturing sector,” Nirmala Sitharaman, former minister for Commerce and Industry, who had set up the task force, had said in August 2017.

The post PM Modi Asks Union Ministries To Embrace AI To Track Socio-Economic Growth appeared first on Analytics India Magazine.

Rinalytics Advisors is rated as India’s top talent search firm, focused on Analytics and Data Science. The firm recently announced the appointment of Mr. John Irudayaraj as the Managing Partner. This new job role will see Irudayaraj John spearheading Rinalytics Advisors. The newly appointed Managing partner also plans to forefront next level of growth in analytics leadership hiring across global markets.

The firm was founded in 2012. Since then, the organization has grown leaps and bounds. Irudayaraj comments, “Besides Technology, Captives and GIC organizations, I see a mounting upsurge for the need of analytics strategic talent across the traditional sectors such as Industrial Engineering, Automotive and Manufacturing.”

Irudayaraj serves as an executive search veteran and trusted advisor in building C-level teams. Besides, he has accumulated two decades of market experience serving clients in leadership hiring across the industry sectors. He was previously engaged with TRANSEARCH as Senior Partner, where he built the Automotive and Manufacturing practice, besides managing South India operations.

It’s not an easy task to address the needs of analytics leadership hiring in this highly competitive market. Rinalytics’ addresses this challenge through its ‘Knowledge Management Center,’ which is a captive talent research center. This has helped the firm to tap almost every strategic talent in the global Analytics market. “It’s a sheer pleasure to have John onboard; it affirms our commitment to excellence in Analytics leadership hiring. We look forward for an exciting journey under his stewardship,” concludes Narasimhalu Senthil, the Founder Partner, Rinalytics Advisors.

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IT firm Happiest Minds Technologies has been on lookout for acquisitions in the IoT space for some time now. The firm plans to expand revenue through this move. This week, the firm acquired Cupola Technology for an undisclosed sum. Besides expanding revenue, the firm wants to strengthen its position in the IoT space.

Cupola Technology was founded in 2011, and the firm has since then delivered high-value IoT projects for a US-based chemical factory, a large telco in India, and a Hong Kong-based consumer electronics company. Ajay Agrawal and Huzefa Saifee are the Co-founders for Cupola Technology.

In an IoT ecosystem, sensor-fitted gadgets or things placed within a network communicate with each other, swapping real-time data and information to perform specific actions. Sashi Kumar, CEO and MD, Happiest minds Technologies comments, “We have found that organisations are rapidly moving from proof of concept to enterprise-wide IoT rollouts. This has brought about the need for a dedicated IoT operations centre with a solution-centric approach.”

Happiest Minds was founded in 2011 by industry veteran and former Mindtree Chairman Ashok Soota. The organization has an impressive revenue run rate of USD 75 million. The firm is currently on track to reaching USD 100 million run rate. Happiest Minds has announced a three-year window, within which it plans to go public.

Happiest Minds has clients for its IoT business from across several industry verticals, such as manufacturing, transportation, utilities, and retail. “IoT has been an integral part of the Happiest Minds Technologies business strategy in the last 3 years, based on which the firm has gained considerable market traction,” concludes Ashok Soota, Executive Chairman, Happiest Minds Technologies.

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