For decades, the manufacturing gospel insisted on a trade-off: you could move fast or you could move efficiently, but never both. China’s carmakers are quietly tearing up that scripture. They are fusing speed with scale, treating cars like fast-evolving software products and factories as data-rich platforms. The result is not just evident on production lines or in code—it is increasingly visible on roads from Guangzhou to Gothenburg. 

In 2024 China accounted for more than 70% of the world’s electric-vehicle output. That volume gives its firms a vast domestic laboratory for rapid product iteration. The playbook blends “Agile” software cycles with “Lean-4.0” manufacturing disciplines—five interlinked moves that borrow from Japan’s quality control, Korea’s heavy-industry investment, and Silicon Valley’s software obsession, but recast for an era of connected machines and continuous updates.

1. The New Keiretsu - Where Japan’s post-war carmakers forged tight supplier alliances, China’s giants increasingly bring key parts in-house. BYD now makes its own batteries, motors and electronics, selling some 4.27m new-energy vehicles last year—profits and scale that fund yet more integration. The effect is a shortening of decision chains, insulation from geopolitical supply shocks, and capital deployment at speeds rivals find hard to match. Battery firms amplify this advantage: CATL alone supplied roughly 38% of the world’s EV battery installations last year, giving China leverage over chemistry, capacity and pricing.

2. Cars as Code - Chinese car firms behave less like Detroit in the 1970s and more like Shenzhen’s smartphone makers today. Products ship early and improve relentlessly through over-the-air (OTA) software updates. Xpeng (pronounced Zhaw-peng), NIO and BYD push fresh features, safety patches and AI-driving tweaks in weeks rather than years. Fleets serve as A/B-testing grounds; drivers’ dashboards become focus groups. The result is post-sale evolution: cars improve in owners’ driveways, not just in next-generation showrooms.

3. Factories as Flexible Algorithms - On shop floors, Industry-4.0 tech buttresses lean methods. Plants use digital twins, predictive maintenance and modular “island” lines that can switch models in hours. Robots handle the precision; humans orchestrate customisation and quality. This marriage of kaizen-style improvement with real-time analytics turns manufacturing from a fixed pipeline into a living system.

4. Data Loops as Competitive Moats - Millions of connected vehicles now double as roving R&D labs. Every kilometre feeds back data: on battery thermal profiles, autonomous-driving hazards, energy-management quirks. Engineers retrain AI models, patch bugs and monetise new services without a recall. Product development becomes not a series of launches but a continuous curve.

5. Ecosystem Lock-In - The infrastructure build-out is equally relentless. In 2024 China added around 830,000 public chargers, swelling a base in the millions. NIO’s battery-swap network crossed 3,000 stations, making “refuelling” as quick as coffee and creating subscription-style revenue. This fusion of vertical control, rapid software iteration, and infrastructure depth is not confined to EVs. AI chipmaking is undergoing a similar metamorphosis. 

For years, the model in advanced silicon was: design in-house, outsource fabrication, focus on software. That model is crumbling. AI chips demand constant iteration between design and manufacturing, precisely the advantage Chinese EV makers enjoy in hardware-software co-development. Tesla’s recent tie-up with Samsung illustrates the point. Facing brutal competition from Chinese EVs and a bleeding-cash Samsung fab in Texas, the two joined forces. Tesla designs its AI6 chips for its own needs; Samsung optimises production. Proximity matters: Samsung’s Taylor plant sits near Tesla’s Austin HQ, enabling Musk to drive to the fab and tweak designs in near real-time. The implications are threefold. First, vertical integration is becoming mandatory for leaders in AI, just as it has in EVs. Second, the once-cosy oligopoly in chipmaking is fragmenting. A $4.75bn injection from America’s Chips Act into Samsung’s Texas facilities has created excess capacity—something nimble AI start-ups can exploit. Third, geography is again destiny: when design and fabrication happen within commuting distance, iteration cycles compress dramatically. 

In both cars and chips, the winners will not be those with the prettiest models—whether automotive or algorithmic—but those who control the full industrial stack. China’s carmakers have already shown what happens when you erase the line between factory floor and software update. The next industrial revolution may depend on who else can follow suit.

Switching lanes: Is Europe the solution to China’s EV glut? and Have Chinese EV companies become a victim of their own success?

Not so long ago Chinese-made cars were considered badly designed and poorly made. But government subsidies, China’s rare-earth dominance and its famously nimble production processes have turned the country’s EV industry into the world’s largest. Nowadays Chinese EVs are well-made, chock full of great technology…and cheap. But there are roadblocks. At home, a price war rages and few of the country’s hundred or so manufacturers turn a profit. Meanwhile governments abroad are weary of Chinese competition.

The Anatomy of Industrial Renaissance: How strategic discipline and bold vision breathe new life into automotive giants

The automotive industry has rarely been more unforgiving. Legacy manufacturers face existential threats from electric upstarts, supply chain disruptions, and shifting consumer preferences. Yet amid this turbulence, some executives have proven that even the most entrenched industrial giants can be transformed through strategic clarity and operational excellence.

When Luca de Meo seized the steering wheel at Renault in 2020, the storied French carmaker was sputtering towards irrelevance. Bloated costs, fractured alliances, and a product portfolio caught between mass and premium had pushed the firm into a strategic ditch. Yet within five years, de Meo orchestrated a turnaround so dramatic it now features in Harvard Business Review case studies and the documentary "Anatomy of a Comeback". His "Renaulution" was no marketing gimmick but a masterclass in precision: operational rigour married to technological reinvention, wrapped in unapologetic strategic clarity. The plan consisted of three phases, initiated at the same time in January 2021: “REsurrection”, “REnovation” and “Revolution”. The transformation was neither cosmetic nor coincidental. De Meo resisted the siren call of flashy launches, choosing instead to dig deeper into fundamentals. Platform consolidation slashed complexity, unprofitable geographies were abandoned, and electrification bets were placed where they mattered most. The bloated product lineup was pruned, priorities re-ranked, and R&D refocused with surgical precision. By 2024, Renault had carved out €3 billion in costs while achieving record vehicle sales—a testament to the power of saying no to everything except what truly matters.

Critically, de Meo understood that revival required more than cost-cutting; it demanded coherence. Renault's identity had frayed over decades—neither fish nor fowl, caught between East and West, combustion and electric. Renaulution became an exercise in identity restoration: Alpine was elevated to premium performance, Dacia sharpened its value proposition, and the Renault brand itself was repositioned as accessible yet aspirational. The effect was palpable not merely on balance sheets but in boardrooms across Europe, where executives began studying the French firm's playbook.

On the other side of the globe, another automotive giant is staging its own act of reinvention. Call it Maruti 3.0. The analogy is no accident. Like Renault, Maruti Suzuki has been a totem of national mobility—dominating India's roads with affordable small cars and unmatched distribution. But as India's automotive landscape shifted towards SUVs, electric aspirations, and digitally-savvy consumers, the country's largest automaker found itself at a crossroads. The old playbook was no longer sufficient. Vision and velocity were needed.

Enter a new strategic compass. With initiatives spanning hybridisation, premiumisation through the Nexa channel, and a fundamental reconfiguration of its product mix, Maruti is realigning itself with 21st-century demand curves. Unlike rivals betting all-in on electric vehicles, Maruti's hybrid-first pragmatism mirrors Renault's selective electrification strategy. The company's renewed attention to export markets and deeper integration with Toyota echo the kind of alliance discipline that de Meo demanded in Europe—partnerships as strategic tools, not emotional entanglements.

Both transformations share architectural DNA. First, they prioritise operational discipline over innovation theatre. De Meo's success stemmed not from revolutionary products but from getting the basics right—manufacturing efficiency, cost control, and strategic focus. Similarly, Maruti's 3.0 strategy builds on the company's core competencies in mass manufacturing while extending them to new technologies. Second, both strategies demonstrate the power of clear strategic alignment. Rather than pursuing multiple directions simultaneously, each company identified its core strengths and built transformation plans around them.

Both transformations share common architectural principles. 

• First, they prioritise operational discipline over flashy innovation. De Meo's success at Renault stemmed not from revolutionary products but from getting the basics right—cost control, manufacturing efficiency, and strategic focus. Similarly, Maruti's 3.0 strategy builds on the company's core competencies in mass manufacturing and distribution while extending them to new technologies. 

• Second, both strategies demonstrate the power of clear strategic alignment. Rather than pursuing multiple directions simultaneously, each company identified its core strengths and built transformation plans around them. Renault leveraged its European engineering heritage and Formula 1 expertise to revitalise Alpine. Maruti is extending its mastery of affordable mobility to electric and hybrid platforms. 

• Third, both examples illustrate the importance of timeline discipline. De Meo's five-year transformation at Renault was neither rushed nor leisurely—it followed a methodical progression from stabilisation to growth. Maruti's 3.0 strategy similarly sets realistic timelines, recognising that sustainable transformation requires patience and persistence. 

The broader implications extend beyond automotive manufacturing. In an era of rapid technological change, the temptation for established companies is either to pursue radical reinvention or to cling to familiar strategies. Both approaches often fail. The Renault and Maruti examples suggest a third path: evolutionary transformation anchored in operational excellence and strategic clarity. 

The broader implications extend far beyond automotive manufacturing. In an era of relentless technological disruption, the temptation for established companies is either to pursue radical reinvention or cling to familiar strategies. Both approaches often fail spectacularly. The Renault and Maruti examples suggest a third path: evolutionary transformation anchored in operational excellence and strategic clarity. Legacy need not be a burden—it can be a potent advantage when paired with leadership that knows when to press the brakes, when to change lanes, and when to accelerate. 

Maruti's 3.0 strategy paints a promising picture for sustainable mobility in India, much as de Meo's Renaulution repositioned the French manufacturer for European leadership. Both transformations prove that even the most entrenched industrial giants can rediscover their edge through disciplined execution and visionary leadership. If turnaround tales have a blueprint, these two belong in the opening chapter—not as curiosities, but as templates for industrial renaissance in an age that waits for no one.

The automotive industry's next chapter will be written by companies that master this delicate balance. As de Meo moves from automobiles to luxury goods, his legacy at Renault stands as proof that even the most established industrial giants can be reborn through strategic discipline and visionary leadership. Maruti's 3.0 journey suggests that this lesson travels well across continents and cultures, offering hope for industrial renaissance in an age of disruption.

India's automotive journey represents one of the most remarkable transformations in global industrial history, evolving from royal curiosities to becoming the world's third-largest automobile market. This book traces every pivotal milestone across more than 130 years of automotive development, revealing how technological adoption, policy shifts, and market forces shaped India's transportation landscape.

Early Imports and Aristocratic Adoption (1890s-1940s)

The automobile era in India began as a technological novelty for the elite, gradually transitioning into a symbol of industrial progress. The first Indian owned vehicle was by ‘Sir Ranji’ in the UK in the 1880s. The first recorded automobile arrival in India occurred in 1892 when Maharaja Rajinder Singh of Patiala imported a French De Dion-Bouton steam car, predating most global automotive developments. This pioneering acquisition marked India's early engagement with motorised transport, though its operational history remains debated by historians. A watershed moment came in 1897 when British businessman J.B. Foster introduced the first gasoline-powered car to Calcutta, coinciding with the city's status as colonial India's capital. By 1898, industrialist Jamshedji Tata joined the ranks of early adopters, importing an Oldsmobile that demonstrated the growing interest among Indian entrepreneurs. These initial imports operated on rudimentary road networks called Macadam roads, often sharing space with bullock carts, horses and pedestrian traffic. These were more like “horseless carriages”. At the turn of the century, France was the leading car maker of the world. Soon, the British industry laid claim to the crown jewel of the empire.

The early 20th century (1900s) saw automotive culture take root through motorsport events, with the Motor Union of Western India organising the first official races (called reliability trials) in 1904, 1906 and 1908. Princely states emerged as luxury car collectors, notably the Nizams of Hyderabad, Rajasthani Rajputanas, Gujarati Kings, Punjabi Maharajas, Mysore’s rulers and Deccan’s kings, amassed Delaunay-Belleville, Rolls-Royce, Delahyde, Benz, Bugatti into their ‘stables’ using them as Throne/ state procession cars, Hunting (game) safari cars. Cars of this period were mostly soft-top tourers. Unlike the British officials, wealthy merchants, and professionals who started owning cars, the Maharajas, to show their pomp and status, customised their luxury cars from prominent French coachbuilders like Figoni et Falaschi with Hermes leather, crushed pearls paint, etc.. This period also witnessed foundational infrastructure developments, including the 1934 establishment of the Indian Roads Congress and the ambitious 1943 Nagpur Plan targeting a 532,700 km of asphalt road network.

Indigenous Manufacturing Foundations (1940s-1960s)

Post World War II, cars became hard-top closed saloons. Indian independence laid the groundwork for domestic automotive production through strategic industrial policies. Hindustan Motors, founded in 1942 by B.M. Birla, became India's first car manufacturer, later producing the iconic Ambassador. The 1944 establishment of Premier Automobiles Limited (by Walchand Hirachand) and Mahindra & Mahindra's in 1945 (then known as Mahindra & Mohammed) entry marked the beginning of diversified vehicle production, including trucks and jeeps.  India’s inclination towards the USSR’s communist framework led to the 1952 Tariff Commission policies initiating the "License Raj" era, restricting foreign competition to nurture domestic industry. India was inclined towards the USSR after the Sino-Indian War of 1962, when China took Aksai Chin. USSR and China shared borders and the USSR wanted a divided China. So, USA and China became friends to counter balance USSR. Accordingly, India adopted the socialist character and befriended USSR. After this, Pakistan was lured for proxy wars for USA and China during cold war period.

This protectionist framework enabled Tata Motors' 1954 entry into commercial vehicles and Hindustan Motors' 1957 launch of the Ambassador - a vehicle that would dominate Indian roads for decades. By 1960, indigenous manufacturers controlled 98% of the commercial vehicle market, though passenger cars remained luxury items with annual production under 20,000 units. HM, PAL and Telco were called the ‘Small Three’.

The Indigenous People’s Car (1970s-1980s)

The 1970s-1980s witnessed the transformation of automobiles from elite symbols to mass mobility solutions. Premier Automobiles' 1970 Padmini (Fiat 1100D, essentially an early 1960s Fiat 1200) and Hindustan Motors' Ambassador (1950s Morris Oxford) became ubiquitous as taxi fleets, covering 68% of commercial passenger transport by 1980, these were ancient vehicles from the ’50 and ‘60s.

MML :: Prime Minister Mrs. Indira Gandhi's younger son Sanjay, her political heir apparent, was a true-blue automobile aficionado. His interests were beyond academics, after Doon School, using his mother’s connections, he apprenticed at Rolls-Royce UK, in the UK for 2.5 years till 1967. On coming back to India, he wanted to build an indigenous ‘people’s car’, but had no business experience. He designed a few models with his airplane instructor ‘Tillu’ at a garage in Delhi. In 1971 Maruti Motors Limited was incorporated after the Parliament issued license to just 3 companies from a list of multiple applicants. With the 1971 Bangladesh Liberation War and victory over Pakistan muted the critical voices of accusations of nepotism. During the emergency 1975-77, Sanjay’s lost interest in the project but business was good for subsidiary Maruti Heavy Vehicles, but after emergency, business collapsed. After the emergency, Janta Dal coalition government was voted to power. A series of inquiries ensued for the grant of 300 acres of land to the company for its Gurgaon Plant. On 6 March, 1978, Punjab & HR High Court ordered winding up of Maruti Motors Limited. When his mother returned to power in January 1980, the people's car project was revived. However, on June 23, 1980, Sanjay’s Pitts aircraft crashed, with him producing just 21 cars before death.

MUL :: Sanjay's vision was endured, when on 24 Feb 1981, MML was acquired 100% by the Government as ‘Maruti Udyog Limited’. On advised by Mr. Arun Nehru, who suggested finding international partners for revival and technical knowhow. The triumvirate from BHEL, Dr. V Krishnamurthy’s, Mr. DS Gupta and IAS Mr. RC Bhargava were selected to head the company. American cars were gas guzzlers, so they looked towards European car manufacturers like Volkswagen to produce the Golf and the Jetta, but that led nowhere, as VW was interested in China not India. Fiat weren’t interested after their previous deal with Premier Automobiles. Peugeot and Renault were interested and Maruti started discussions with Renault. Both companies agreed the Renault 18 would be a car of interest, but then the Indian delegation visited the Japanese auto show and realized that actually a car that was bigger than the Hindustan Ambassador wasn’t the right thing for an Indian people’s car. Suez Crisis of 1956 triggered off a strong demand for low-consumption small cars like the Mini. The subsequent oil price shocks of 1973–74 and 1979 also bolstered this trend.

America’s people’s car was the Ford Model T – a cheap car that everyone could own. Germany’s people’s car was the Beetle - a cheap car that everyone could own. Britain’s people’s car was the Mini, in France it was the 2CV, in Italy it was the Fiat 500. Maybe the right car was one of these tiny “Kei jidosha cars” that were zipping around Japan. Maruti talked with Daihatsu (Toyota), Mitsubishi, Nissan and Subaru – all lead nowhere. But then an opportunity came from out of the blue and as last resort. Mr. Osamu Suzuki, director of Suzuki Motor Co. was in India to discuss JV with TVS motorcycles.  He read a newspaper article on his flight to India about Maruti's search for a partner. SMC acquired 26% stake in Maruti Udyog Ltd in 1982. SMC had launched the Alto (SS80) in Japan a few years earlier in 1979, and this car seemed perfect fit for the people’s car as the CKD value of the car was Rs. 30,000, which was below the target price of Rs. 50,000. The first Maruti Suzuki 800 (MB308) was presented to its owner by Mrs. Indira Gandhi on December 14, 1983 – what would have been Sanjay's 37th birthday.

Small car, Big impact! :: Before, the bigger, heavier, and the more elaborate a car was, the better it was considered. With Suzuki’s kei-jidosha cars like Maruti 800, Omni Van and Gypsy - lightweight, smallness in size and easy to drive were attributes that became popular amongst Indians. Small was not puny anymore, lightweight was not weak, and many preconceived notions of the automobile changed. The game-changing Maruti-Suzuki joint venture in 1981 revolutionized the market, with the 1983 Maruti 800 achieving unprecedented affordability at ₹47,500 (equivalent to ₹8.5 lakh today). This era saw car ownership grow at 11.3% CAGR, with Maruti capturing >75% market share by 1985. Simultaneously, infrastructure development accelerated, with national highway length expanding from 24,000 km (1947) to 34,000 km by 1980. The automotive workforce grew exponentially from 25,000 (1950) to 290,000 by 1985, signaling the industry's rising economic importance.

Government Largesse :: To start with, license was given to just one another insignificant carmaker, Sipani Automobiles, then import duties were reduced drastically, as was excise. Almost complete CKD and SKD cars  were allowed to be imported initially. Maruti was always behind its localization commitments, yet the govt turned a blind eye to that. Lower excise for fuel-efficient cars was just an excuse to justify minimal taxation. When Rajiv Gandhi came to power and decided to ‘broadband’ licensing, which was supposed to allow other 4-wheeler manufacturers to make cars too, their JV proposals were never given green signal, so that Maruti could be continuously protected and treated as a favored child. Govt largesse in terms of tax benefits gave Maruti at least 15% cost advantage over others.

For the first 3 decades of India’s nascent car industry, it was mostly under govt regulation, as India chose the planned economy route towards growth. Given the circumstances and international environment then, it must had made sense to make most of limited resources. It was a choice which several other democratic nations made too, countries like Japan and South Korea included. In Japan, automotive industry was a priority sector, with just 4 extremely strong manufacturers surviving and dominating the world even today. South Korea too followed the planned economy system providing necessary protection followed by Japanese style government-backed export-led growth, encouraging a select group of five chaebol carmakers, not unlike the oligopolistic system that the Indian economy evolved into. A more recent very good example of planned growth with intensive state intervention is China. Thus, planned economy was not the problem of the Indian Auto Industry. The problem was ‘Very Low Priority’ and the planning only on socialistic thinking in the form of License Raj, which in turn, curtailed growth potential, while benefiting certain oligopolistic groups.

Liberalization and Global Integration (1990s-2000s)

Economic reforms like Liberalization in 1991 dismantled the License Raj, triggering a foreign investment surge. Maruti realized that the good times of Government Largesse wont last. This galvanized into some serious action. Dr. Krishnamurthy wanted MUL to be a board-managed company without any single shareholder taking full control. Mr. Osamu Suzuki had realized that MUL was the ultimate ‘cash cow’ SMC could ever have had, and he was insistent in increasing SMC’s stake to over 50%, which is what Mr. RC Bhargava, as then MD, pushed for too. On 02 June, 1992, SMC increased its stake in MUL from 40% to 50%, making it a public company. SMC just paid Rs. 269 per share, a valuation with most feel was as good as gifted to SMC. Virtually nobody protested.

Maruti launched Zen in 1993 to face the upcoming competition. Peugeot Citroen was first to enter into an ill-fated JV with Premier Automobiles Limited. South Korean carmaker Daewoo followed in with Cielo and Matiz in 1994. Maruti responded with Esteem in 1994. Mercedes-Benz's 1994 entry as the first luxury brand post-liberalization marked a new era of market segmentation. GM tied up with Hindustan Motors and brought Opel Astra in 1996. Hyundai had failed in US market and placed bet on the Indian market in 1996 with its plant near Chennai. Hyundai’s marketing blitz was coupled with deep resources, good manufacturing fundamentals and export led growth. It was not until 1998-99, that Maruti saw serious competition from Hyundai Santro and Tata Indica, while Toyota's Qualis (2000) popularized UVs and Honda entered into a tie-up with Shriram Industrial Enterprises Ltd in 1998 introducing the City. Santro’s popularity since 1998 resulted in Maruti launching Wagon R in 1999 and Alto in 2000.

1997 Asian financial crisis (recession) had ripple effects and reached India, which led to many auto companies like Daewoo etc to close shop.

FY 1999-2000, MUL reported its first loss of Rs. 269.4 crore followed by Maruti’s first ever labor dispute. Environmental concerns emerged as priorities, with Bharat Stage - II emission standards introduced in 2001 by the Suo-moto cognizance of the Supreme Court and were progressively tightened. This resulted into drastic cost-cutting measures by Maruti like VRS etc. and embarked itself on model renewal program. In 2005, Swift was launched as stylish, funky and with attitude – as a symbol of transition. Next SX4 was launched in 2006, Dzire and A-star in 2008, and Ritz in 2009. Passenger vehicle production skyrocketed from 346,000 units (1991) to 1.4 million by 2005.

Over the years, the Indian government gradually reduced its stake in the company, culminating in a complete exit in 2007 when it sold all remaining shares to Suzuki Motor Corporation and other institutional investors. Suzuki Motor Corporation had progressively increased its stake in Maruti, gaining majority control as early as 2002 when its holding rose above 50%. By the time of the rebranding in September 2007, Suzuki held a 54.2% stake, making it the clear majority owner. Maruti Udyog Limited (MUL) was officially renamed Maruti Suzuki India Limited (MSIL) with effect from September 17, 2007. The decision to rebrand Maruti Udyog Limited as Maruti Suzuki India Limited was directly linked to significant changes in ownership and management control. This ownership shift was accompanied by a move to align the company’s identity with its global parent.

In 2008, Tata Nano launched for Rs. 1 Lakh, but had several quality issued. The best-laid plans, of mice and men, often go awry. Over the years Indian market had matured and consumers wanted more. The 2008 Tata Nano launch, priced at ₹1 lakh, attempted to redefine affordability though faced market challenges. By 2009, India became the 7th largest vehicle producer globally, exporting 1.8 million units annually.

Modernization and Electrification (2010s-Present)

The 2010s solidified India's position as an automotive powerhouse, surpassing Germany (2017) and Japan (2022) in market size. Stringent BS-VI norms implemented in 2020 reduced particulate emissions by 82% compared to BS-IV. The SUV revolution transformed consumer preferences, with UVs accounting for 55% of 2024 sales. Electric vehicle adoption gained momentum through FAME subsidies, reaching 8% market penetration in 2024. Hyundai had bought Kia in the 90’s and brought it to India in 2019.

Policy initiatives like Make in India (2014) and PLI schemes (2021) boosted manufacturing capabilities, with automotive exports valued at $27.1 billion in 2023. The industry's valuation reached $108 billion in 2023, supporting 37 million jobs directly and indirectly. Emerging trends include connected car technologies (19% penetration in 2024) and hydrogen fuel cell prototypes from Tata and Ashok Leyland (a 1955 JV between Chennai based Ashok Motors and British Leyland, now owned by Hinduja group which also owns IndusInd bank).

In late 2017, Toyota and Suzuki signed an MoU to cooperate on electric vehicles in India, and by August 2019 they formalized the capital tie-up to jointly develop advanced powertrains and autonomous technologies. Since 2018–19, Toyota has taken an equity stake of just under 5 percent in Suzuki Motor Corporation (for $900 mn) in order to share technology and gain market access in India, while Suzuki in turn rebadges its home-grown models for Toyota’s Indian joint venture, Toyota Kirloskar Motor (TKM) – a JV of Toyota (89 %) and Kirloskar Group (11 %). Suzuki took ~1.2 % of Toyota for about $500 mn. cementing a long-term technology and manufacturing alliance. This exchange has powered Toyota’s record sales of over 300,000 units in FY 2024–25, with about one-third of those coming from Suzuki-developed, Toyota-badged models.

Globally - From Big Three to Magnificent Seven :: Chrysler, General Motors and Ford are the Detroit's Big Three (Stellantis is now considered one of Detroit’s "Big Three" automakers, alongside General Motors and Ford. This status comes from Stellantis inheriting Chrysler’s legacy after the merger of Fiat Chrysler Automobiles (FCA) with PSA Group in 2021). It took America's car industry 20 years and a world war to recover the three-quarters of production wiped out by the 1929 Great Depression. But now the Magnificent seven control the American Auto Market i.e. General Motors (GM), Ford, Chrysler, Toyota, Honda, Nissan, Hyundai–Kia. The Japanese automakers could take cues from Hyundai-Kia, which overcame early reputations for poor quality in the U.S. by steadily improving their vehicles and investing in strong marketing. This transformation turned them into credible competitors. BYD, a Chinese company, began emerging as the next challenger with its electric cars and plans to launch its American sales network. Here, car enthusiasts tend to fall into one of two camps: those who fawn over the power and speed of German automotive engineering; and those who think Japanese cars are superior, admiring their reliability and value for money.

Automotive Global Value Chain: The Rise of Mega Suppliers

A trend of formation of powerful oligopolies (a market dominated by a few large suppliers) is being observed world-wide in the automotive component industry, specifically in tires, car seats, constant velocity joints (CVJ), braking systems, batteries and semiconductors through a mix of acquisition and organic growth. ‘Mega suppliers’ have grown, their impact on the overall automotive industry, and the decline of smaller component manufacturers.

· Tires: Bridgestone, Michelin, Goodyear : >40% market share (‘ms’)

· Seats: Adient, Lear, Faurecia : > 75% ms

· CVJ: GKN, NTN : >66% ms

· Braking systems like ABS, ESC, TCS: Continental, Bosch, ZF > 75% ms

· Automobiles contain 50 to more than 100 microprocessors, also known as ‘ECUs’ or electronic control units. Traditional ECUs 50nm, Advanced SoC ECUs 4nm. Emerging from consolidation tsunami of the past decades are the industry titans: NXP, Infineon, Renesas and STM >40% ms

· Battery:  CATL (Contemporary Amperex Technology Co. Limited), BYD (Beyond your dreams), CALB, Gotion High-Tech, EVE Energy produce LFP ((Lithium Iron Phosphate): Favored for safety, longevity, and cost-effectiveness) and NCM ((Nickel Cobalt Manganese): Used where higher energy density is needed (longer range)) batteries, and non-conventional Sodium batteries.

Future Projections and Sustainable Mobility (2020s-2030s)

Till 2010’s, Thailand was called the Detroit of South-East Asia, the country built a car-export powerhouse by combining Japanese auto-making know-how with a competitive network of Thai car-parts suppliers. Suzuki and Subaru, two Japanese carmakers, are closing down factories in Thailand. EVs built by Chinese firms in Thailand have elbowed out Japanese competitors, which tend to rely more on Thai parts suppliers.

Chinese cars are taking over the global south and how China became a car-exporting juggernaut:: Petrol engines, not batteries, are powering their growth. After 2008, the global automotive industry has been overhauled. China has taken a decisive lead as the world’s biggest manufacturer of cars. Despite its unpromising start, BYD has surpassed Tesla as the world’s largest maker of fully electric vehicles (EVs) by volume (and is far ahead when plug-in hybrids are included). The company has assisted in wresting China’s car market from once-dominant foreign competitors. At the same time, it and other Chinese firms such as BYD, Chery, Geely, Great Wall, Xiaomi and SAIC have turned their country into the world’s top exporter of vehicles, speeding ahead of Germany and Japan. A bulk of China’s car exports are ICE and are aimed neither at western Europe nor America, but at the rest of the world. Victory has come at a price, however. Creating a homegrown EV industry using subsidies and other government inducements has resulted in severe overcapacity. Oversupply has led to a vicious price war. Seeking an alternative outlet, Chinese carmakers have turned abroad.

Conclusion

From the De Dion-Bouton steam car to connected electric vehicles, India's automotive evolution mirrors its economic transformation. Each phase - colonial imports, indigenous manufacturing, liberalized growth, and electrified future - demonstrates adaptive responses to technological and policy landscapes. As the industry navigates sustainability challenges, its continued export growth remains pivotal.

Six Decades ago Mr. Peter Drucker, an American contributor to the modern management theory, dubbed automobile manufacturing as “the industry of industries.” Today, it still holds true as automobile manufacturing is the world’s largest manufacturing activity, with nearly 100 million new vehicles produced yearly. Most of us own one, many of us own several, and, although we may be unaware of it, these cars and trucks are an important part of our everyday lives. Yet the auto industry is even more important to us than it appears.

Twice in the previous century, we saw changes in our fundamental ideas of 'how we make things'. And how we make things dictates not only how we work but what we buy, how we think, and the way we live.

• After World War I, Henry Ford and General Motors’ Alfred Sloan moved world manufacturing from centuries of craft production-led by European firms, into the Age of mass production. Largely as a result, the United States soon dominated the global economy.

• After World War II, Eiji Toyoda and Taiichi Ohno at the Toyota Motor Company in Japan pioneered the concept of lean production. The rise of Japan to its current economic preeminence quickly followed, as other Japanese headquartered companies and industries in the West copied this remarkable system. Manufacturers around the World are now trying to embrace lean production, but they're finding the going rough. Many Western companies now understand lean production, and at least one is well along the path to introducing it. However, superimposing lean-production methods on existing mass production systems causes great pain and dislocation. Lean production is 'lean' because it uses less of everything compared with mass production.

India witnessed something extraordinary in January 2025, when Maruti Suzuki inaugurated Asia's largest automotive gigafactory in Kharkhoda, Haryana—a facility capable of producing one million vehicles annually. To put this in perspective, that rivals Tesla's famous California plant, the very facility that popularised the "gigafactory" concept. But this is more than just another big factory. Maruti's new plant represents a fundamental shift in how India approaches manufacturing. The plant aims to revolutionise Indian automotive manufacturing through Lean Industry 4.0 and sustainable practices. The facility will be 100% automated in welding operations, with artificial intelligence checking every weld spot for quality. Collaborative robots will work alongside humans to detect defects invisible to the naked eye. This isn't just automation—it's intelligent manufacturing. Automation and robotics for repetitive, low-value tasks. IoT for real-time monitoring and predictive maintenance (supporting TPM). The sustainability story is equally compelling. A 100-megawatt solar plant will power operations, with biogas and hydrogen production in development. This aligns perfectly with India's net-zero ambitions by 2070 and positions Maruti ahead of global environmental expectations. The company is essentially proving that large-scale manufacturing and environmental responsibility can coexist. Perhaps most intriguingly, Maruti is solving a uniquely Indian challenge: infrastructure reliability. The company has reserved land adjacent to the factory for key suppliers, creating an integrated ecosystem that reduces logistics costs and improves efficiency. It's a model that other manufacturers in emerging markets will surely study. The broader implications are profound. As Rajiv Gandhi from Maruti's executive committee noted, "If India is to compete globally, we need cost and quality." This gigafactory, powered by Lean Industry 4.0 technologies, delivers both. It signals India's transition from a low-cost manufacturing hub to a high-tech production powerhouse. The integration of lean and Industry 4.0 creates a powerful approach for operational excellence. The timing couldn't be better. As global supply chains fragment with ‘mega component suppliers’ and companies seek alternatives to traditional manufacturing centers, India is positioning itself not just as a participant, but as a leader in the next generation of industrial production.

The 

Endurance Race: 24hrs Le Mans, Reliability Trials 

Sprint Race: Grand Prix (Grand Prix motor racing, a form of motorsport competition, has its roots in organised automobile racing that began in France as early as 1894. Initially, the term was reserved for each country's largest or most prestigious F1 race, in which the winner would receive a “grand prize.” After the sport caught on around the world, the sport of Grand Prix racing would lend its name to all auto competitions raced on an open track styled like a real road.)

The 24 Hours of Le Mans, along with the Monaco Grand Prix and the Indianapolis 500, are considered part of the "Triple Crown of Motorsport".

You enter with ambition. With faith in merit. You believe hard work speaks. That truth carries weight. That doing the right thing earns its place. But slowly, the system teaches otherwise. Meetings are theatre. Decisions are made elsewhere. Speaking up draws attention, not change. The room rewards those who perform alignment over those who pursue clarity. You meet the Carpet Colleagues. Present in every call. Absent in every outcome. They master visibility without substance. Their genius lies in being noticed just enough, without doing enough to be questioned.

Bold ideas are handled like threats. Original thinking is paused, rephrased, or buried. Not because it lacks merit - but because it disrupts comfort. Performance reviews are perception games. Not what you did, but who narrated your story when you weren’t in the room. So you adapt. Speak less. Risk less. Add disclaimers. Smile more. Stay busy. Stay safe. Stay... silent. But that’s how it begins. Your edge fades. Your fire dims. Your voice starts to echo like everyone else’s. Corporate doesn't just take your time. It slowly edits your spirit. So say the thing. Challenge the room. Protect your sharpness. Because the goal isn’t to survive the system. The goal is to stay unmistakably you, within it.

survival instincts

1. ICAI INTT PT

2. Project Management Professional (PMP)

3. Lean Six Sigma Black Belt

4. MBA

The first is what kind of decision is being taken. the idea of one-way and two-way doors to separate decisions into two categories. A one-way door is a big decision which is hard to reverse and should absorb more thought. A two-way door is a decision where changing course is not that difficult and it’s better to move fast even if mistakes are made. (A revolving door is presumably for people who never come to a decision at all.)

The second question is who is taking the decision. Disagreements can quickly lead to an impasse unless someone has the authority to make the choice. There are lots of formal frameworks designed to specify decision-making roles. RAPID stands for Recommend, Agree, Perform, Input and Decide (this is not actually the order in which things happen but in management, acronyms always trump logic.) The RACI framework makes tremendously confusing distinctions between people who are responsible, accountable, consulted and informed. Even then, decisions can end up being countermanded by someone higher up the ladder (a framework informally known as BIG CHEESE). So an awful lot depends on whether bosses are willing to live with decisions if they disagree with them.

The final question is how to reach your decision. Should it follow a codified process? Should there be structured ways to gather opposing views? Should it involve a pre-mortem, which asks people to imagine the things that are likely to cause a decision to turn out badly? For strategic choices, the answer to all these questions and more is probably “yes”. However a decision is made, some rules are better than none. When you wash your hands, you don’t know specifically which virus or bacterium you are washing away, but you know it’s a good idea. A process makes big decisions more hygienic, too.

write a simple english linkedin tweet of about two paras.

The smallest components can cause the largest disruptions!

A modern vehicle is a library of technologies, a rolling monument to globalisation. Yet its most vital parts are often no larger than a fingernail. Late last month, the Dutch government, under pressure from U.S. officials, took control of the Chinese-owned chip maker Nexperia BV. The diplomatic skirmish that followed has sent ripples across the world, reaching the factory floors of India’s automotive boom.

The chips at the heart of this dispute are not the glamorous, high-value artificial intelligence processors that dominate headlines. They are the unsung workhorses of the electronic world: transistors, diodes, and logic devices. They regulate power, manage engines, ADAS, infotainment systems and control safety features in millions of vehicles. Their absence brings production lines to a halt.

Nexperia commands roughly 10% of the global market for basic automotive chips, but in certain niches—the building-block chips make engine control units hum or ADAS see—its share rockets to 40%. Major Tier-1 suppliers like Bosch, which provide pre-assembled components to OEMs, have warned that Nexperia chips are embedded within their modules as they sit soldered into their complex assemblies. These are not components one can swap out like lightbulbs.

This is the second such shock in six months. Earlier this year, rare earth magnets—critical for electric motors—became another flashpoint in the Sino-Western standoff. The pattern is clear: the automotive industry, for all its global heft, remains hostage to chokepoints it neither controls nor fully understands.

For Indian carmakers, this comes at the worst possible moment—when sales are soaring, fueled by festive season demand and supportive GST reforms, but chip inventories remain thin. The crisis exposes a painful dependency, yet this is also a moment of clarity. The rare-earth magnet shock earlier this year had already nudged automakers to rethink “just-in-time” supply chains. Now, the Nexperia crisis can cause “production delays, compliance risks, and costly relabeling efforts” and underscores the next frontier: semiconductor sovereignty.

What this episode reveals is uncomfortable yet undeniable: the automotive supply chain, for all its maturity, remains a Jenga tower of geopolitical dependencies. Can the industry wriggle free? In theory, yes, by diversifying suppliers. In practice, replacing Nexperia is no quick fix. Specialised chips require lengthy onboarding, testing, and vehicle re-homologation—a process measured in quarters, not weeks.

The industry is re-evaluating the principle of leanness that dominated the manufacturing dogma. Just-in-time once shunned inventory; now stockpiles are a strategy. The rise of "chiplets" is a promising development-modular, flexible, supplier-agnostic—that could be India’s opening to rebuild global resilience in semiconductors.

What's your take on this?

How the Car Industry Is Rebuilding Itself for a Fragmented World

For decades, carmakers worshipped at the altar of efficiency. They built sprawling supply chains, stitched together by cheap transport, liberal trade, and the gospel of “just in time.” Then came tariffs, wars, and chip shortages — and the realisation that efficiency, without resilience, is a luxury few can now afford. The global automotive industry, long a symbol of interconnected capitalism, is quietly rewiring itself to survive in a more divided world. The new mantra is localisation: fewer container ships, more control.

The industry faces not one disruption but several, arriving in quick succession. Geopolitical tensions are splintering supply chains built over decades. Software is eating the car. Chinese manufacturers, long dismissed as also-rans, have become formidable rivals. And the transition to electric vehicles (EVs), once thought inevitable, has hit unexpected speed bumps. For an industry accustomed to incremental change over long product cycles, the pace is dizzying.

"Stagformation", a term coined by automotive executives to describe their industry's difficulty, capturing a brutal reality: simultaneous stagnation and transformation. Carmakers are learning that doing everything everywhere is a recipe for exhaustion. Focus is essential. Profits from internal combustion engines, unfashionable though they may be, are funding the leap to electric and software-defined vehicles. Localisation brings higher costs and fragmented operations, yet the alternative is riskier still. Resilience, carmakers now realise, is not redundancy — it is strategy. Collaboration, too, is no longer optional. No single firm can master batteries, software, AI, autonomous driving, and manufacturing alone. The boundaries between carmakers, suppliers, and technology companies are dissolving faster than ever. Scenario planning has replaced long-term forecasts. Executives now game out quarterly contingencies rather than decade-long projections. In turbulent times, agility trumps precision.

By 2030, the industry will look unrecognisable from today. Those who adapt — embracing software, managing geopolitical complexity, fending off Chinese rivals, and financing electrification without burning cash — will thrive. The rest will serve as cautionary tales of what happens when established industries meet existential change. In business, as on the road, standing still is not an option. Stand still too long and you become roadkill.

How China Built the World's Most Formidable Auto Industry

Beijing saw what Detroit missed. The diagnosis was clinical. The prescription, ruthless. To build a Chinese automotive industry was always a clear target of Chinese industrial policy. While Western carmakers debated electric vehicles, the Chinese government offered tax subsidies worth $72 billion. The target has been hit. Now the question is whether anyone else can compete with what Beijing has built.

The learning curve started with Tesla. When Tesla's Shanghai-built Model 3 entered the Chinese market around 2020, it catalyzed a transformation—NEVs grew from bumping along on subsidies to capturing 47% of passenger vehicle sales by 2024. Chinese manufacturers absorbed the lessons: vertical integration, direct-to-consumer sales, software-first thinking. What Tesla pioneered, they perfected—then undercut.

The design of the tax subsidies was clever. The subsidies started small in the mid-2000s and scaled dramatically over 15 years. Domestic subsidies, available only to companies assembling vehicles in China were outside the purview of the WTO, while foreign companies exporting EVs to China faced a 25% tariff and were ineligible for subsidies. By the time the competitive threat became obvious, the industry had already been built.

The management playbook borrowed Toyota-style shopfloor discipline, German quality norms, Silicon Valley's speed and Beijing's scale. Politicians copied American-style industrial policy and Silicon Valley platform thinking and vertical integration. The result is a hybrid: lean production tuned by state direction and rapid digital iteration borrowed from Big Tech. The final model: own the supply chain, iterate faster, price aggressively.

Why they're winning comes down to three advantages. First, manufacturing scale that defies comparison. China produced 31.4 million vehicles in 2024, with NEVs reaching 12.9 million units—a 35% year-on-year jump. Second, battery supremacy. China controls 80% of global lithium-ion cell manufacturing, with companies like CATL and BYD commanding everything from raw material processing to pack assembly. Third, software sophistication. Major manufacturers now equip vehicles with advanced driver-assistance systems as standard, often without the additional fees Western competitors charge.

The technology improvements are relentless. CATL pioneered cell-to-pack battery technology and introduced Kirin batteries focused on high performance at low cost. BYD touts its in-house battery technology as safer than standard lithium-ion alternatives used in most EVs. Meanwhile, pricing has become predatory. BYD sells vehicles from $9,500 to $233,000, dominating the mass market while pushing upmarket.

From volume to value: Renault's remarkable pivot

When Luca de Meo took the wheel at Renault in 2020, the French carmaker was haemorrhaging cash. Three years later, it had engineered one of the most striking corporate turnarounds in automotive history.

The blueprint, aptly named "Renaulution", abandoned the industry's old gospel: chase volume at any cost. Instead, Mr de Meo bet on a radical reorganisation. Out went the monolithic structure; in came specialised business units, each with its own mission. Ampere would spearhead electrification. Alpine would chase performance enthusiasts. The shift was more than organisational—it was philosophical.

Rather than flooding showrooms with mediocre metal, Renault would build fewer cars with fatter margins. Classic nameplates like the R5 returned as electric icons, not mere nostalgic rehashes. Brand identity, long diluted, was sharpened. Technology, once an afterthought, became central.

The execution was surgical. Renault carved itself into autonomous divisions, each with its own balance sheet and accountability. Ampere, the electric vehicle unit, was designed for eventual separation—a pure-play EV business that could attract tech investors. Alpine became the performance brand, no longer just a badge on modified Renaults. This structure ended the internal conflicts that plague conglomerates, where premium and mass-market brands compete for the same resources and attention.

The product strategy was equally deliberate. Rather than chase Tesla with unfamiliar designs, Renault mined its own heritage. The R5, a beloved hatchback from the 1970s and 80s, returned as an affordable electric city car. The R4 followed suit. These weren't cynical retro exercises but carefully engineered EVs wrapped in emotional appeal—a combination of nostalgia and innovation that legacy carmakers can deploy but startups cannot. Electrification targets were accelerated. Partnerships were forged where building alone made no sense.

The gamble paid off. Profitability returned. The company that had lost its way in the volume wars found a new compass: value creation, not just unit sales.

In an industry convulsed by electrification and software disruption, Renault's transformation offers a lesson. Sometimes survival demands not doing more of the same, but doing something entirely different. The question now: can others muster the courage to follow?

What corporate transformations have impressed you recently?

How China Engineered Its Electric Revolution on Domestic Subsidies

When China first rolled out subsidies for “new energy vehicles” in 2009, few in Detroit or Wolfsburg paid attention. They should have. What began as a pilot programme to cut oil imports has morphed into a $70-billion state-sponsored transformation—one that now threatens to redraw the global map of the car industry.

When China first announced its “Ten-City, Thousand-Vehicle” pilot in 2009, the goal was modest: to test whether electric buses and taxis could ease its oil bill. Few imagined it would spark the largest industrial reordering since the birth of the internal combustion engine. Over the next decade, the Chinese state rewired capitalism’s favourite product — the car — using a mix of patient subsidies, sharp incentives and strategic withdrawal.

The architecture of support evolved in phases. In the early 2010s, Beijing moved from pilots to scale. Central ministries disbursed per-vehicle cash grants tied to battery range and efficiency, while provincial governments sweetened the deal with matching funds, soft loans and discounted land. Between 2010 and 2014, these dual incentives propelled domestic production and made BYD and SAIC household names. By 2014, the government layered in a fiscal measure: a full exemption from the 10 per cent purchase tax for qualified electric vehicles, effectively turning the tax code into a subsidy ledger.

Then came discipline. In 2016–17, the Ministry of Finance and Ministry of Industry and Information Technology tightened eligibility thresholds, linking aid to energy density, range and safety. Firms caught inflating numbers were fined; those that innovated climbed the rankings. In 2019, a planned “phase-down” began — local subsidies were curtailed and national ones reduced, steering the market from dependence to competitiveness. COVID briefly extended the life of these incentives, but each year saw further cuts: 10 per cent in 2020, 20 per cent in 2021 and 30 per cent in 2022. Subsidies became a taper, not a tap. Behind the consumer schemes ran a quieter industrial strategy. State banks offered below-market loans, provincial governments guaranteed offtake for municipal fleets, and R&D grants encouraged vertical integration from raw lithium to finished cells. The state provided the scaffolding; the firms built the structure.

Unlike the West’s scattergun tax credits, Beijing’s approach was methodical. It blended stick and carrot, factory floor and fiscal ledger. Consumers received direct purchase grants, tiered by battery range and efficiency. Local governments piled on cash bonuses and free number plates. Manufacturers enjoyed tax exemptions, subsidised land, soft loans, and access to cheap capital from state banks. Even suppliers were shepherded into line through “approved” battery lists that quietly favoured domestic champions such as CATL and BYD.

The trick lay not in the generosity, but in the design. Subsidies were linked to performance targets and capped over time, forcing firms to climb the technology ladder or die. Fraudulent claims were punished, eligibility thresholds raised, and local top-ups withdrawn as scale emerged. When COVID threatened the fragile market in 2020, Beijing extended the scheme but tapered it by 10-30 per cent a year—turning fiscal support into a glide path, not a crutch. The results are plain. China now produces over half of the world’s EVs and controls roughly 80 per cent of global battery cell capacity. Its firms iterate faster, localise more deeply, and export more aggressively than their Western rivals. What was once subsidy-driven is now efficiency-driven: vertical integration replaces cash transfers, and software margins replace tax rebates. Critics call this mercantilism. Yet it is also industrial learning at scale. China watched Toyota’s production discipline, copied Silicon Valley’s speed, and wrapped both in state coordination. The West, mired in legal wrangling over “level playing fields,” may find that the real race was never about subsidies—but about how cleverly one used them. Would you argue that the world should compete with or copy China’s model of state-guided innovation?

How China Taxed Its Way to the Top: Lessons for the Global Auto Industry

For years, the world dismissed China’s electric vehicle boom as a state-fuelled experiment—lavish subsidies propping up ambitious start-ups. The CCP wasn’t just subsidising cars; it was rewiring corporate behaviour.
China’s $72 billion in auto-related incentives were not direct cash handouts. They were tax-side accelerants: enterprise income tax reductions for EV makers, super-deductions for R&D, accelerated depreciation for capital expenditure, VAT refunds for battery materials, and targeted local tax holidays for qualifying “New Energy Vehicle” clusters. Instead of centralised grants, China used a tiered fiscal web—national, provincial, and municipal—to align every layer of governance behind one industrial mission.
The logic was simple but radical. Traditional automakers in the West cut taxes for profits; China cut taxes for progress. The state offered earned relief: pay less tax if you advance technology, scale battery supply chains, or export green vehicles. By fusing fiscal policy with industrial design, Beijing created an ecosystem where tax planning was strategy, not compliance.

However, its effects penetrated corporate taxation indirectly by reshaping comparative effective tax burdens, investment incentives, and inter-company pricing benchmarks. From a direct tax perspective, three features stand out.

First, there was an absence of explicit corporate income tax subsidies. The PRC did not issue NEV-specific corporate tax holidays akin to India’s Section 80-IA or 80-IB regimes. EV makers and component suppliers remained subject to the standard 25% enterprise income tax, except where they qualified for pre-existing High and New Technology Enterprise (HNTE) status, which conferred a reduced 15% rate. Thus, any relief was categorical, not sectoral—available to all qualifying R&D-intensive firms, not only EV producers.

Second, indirect tax foregone via purchase-tax exemptions reshaped consumer behaviour. The 10% vehicle purchase tax exemption for NEVs (a levy akin to India’s GST compensation cess) shifted the burden from buyers, expanding market volumes and boosting taxable profits downstream. This was fiscal expenditure in kind—not a deduction or credit under enterprise tax law.

Third, capital and cost-base distortions emerged through soft loans, state-provided land, and below-market financing, which lowered the cost of capital and compressed depreciation and interest deductions relative to a market environment. This created transfer-pricing complications where related-party suppliers or joint ventures operated under state-influenced financing, making Chinese tested-party margins incomparable with Indian or OECD standards.

For an Indian OEM, the comparative tax concern lies not in the direct incentives themselves but in the asymmetric after-tax return structure they create. Chinese EV producers enjoy scale and state-backed liquidity that neutralise what India treats as taxable income components—interest savings, grants, and capital subsidies. While India’s concessional taxation for EVs (for instance, GST rate cuts, Section 35AD investment-linked deductions, and FAME incentives) operates within its own direct-tax neutrality principles, China’s model blurs fiscal and industrial policy lines.

In short, China’s NEV incentives were not direct tax subsidies but fiscally backed expenditure programmes and consumption-tax exemptions that indirectly depressed effective corporate tax rates through scale, liquidity, and state co-investment. This distorts global benchmarking of profit allocation and signals why direct comparability in transfer-pricing or effective-tax-rate equalisation between India and China requires economic adjustments rather than nominal tax-rate parity.

The WTO silence is equally telling. China’s tax subsidies were domestically targeted, sector-agnostic on paper, and structured as general tax relief rather than export-linked incentives—cleverly sidestepping the Agreement on Subsidies and Countervailing Measures. The West saw distortion; Beijing saw design.

The question for policymakers elsewhere is no longer whether China subsidised its auto revolution. It’s whether anyone else can marshal their tax codes with the same strategic precision.

When Cars Became Code: How China’s Automakers Are Rewriting the Industry’s DNA

The global automobile’s journey mirrors that of semiconductors: born in the ateliers of late-19th-century Europe, it moved eastwards and up the value chain. Early petrol-engine vehicles emerged in Germany and France in the 1860s-70s, and by the 1890s Britain, Italy and the rest of the continent were racing to join. By the dawn of the 20th century, the United States—led by Ford Motor Company—scaled mass production and dominated manufacturing until the mid-20th century. In the post-war era Japan pioneered lean manufacturing and global export dynamics, followed by South Korea with high-volume integration and global brand ascension, and now China with software-defined vehicles, supply-chain dominance and the platform-playbook. The pattern: each region took the baton of production and competitive edge in turn, turning the car from niche craft into a globally traded software-enabled machine.

For decades, the automobile was an industrial artefact — steel, oil, and assembly lines perfected by Ford and Toyota. Then came the Chinese upstarts — BYD, XPeng, Nio, Xiaomi, Chery — who saw something the West didn’t: that the car was no longer a machine, but a platform.

They now build vehicles like smartphones on wheels. Every few weeks, over-the-air updates refresh the driving experience, upgrade autonomous functions, or unlock paid features. Agile software development — not piston count — drives value creation. Subscriptions replace sales. Cars learn, adapt, and evolve long after leaving the factory floor.

Behind this shift lies a manufacturing revolution. “Lean-4.0” disciplines merge vertical integration with modular island assembly lines — agile factories that can switch models in hours, not months. BYD makes its own batteries and semiconductors. Nio’s production lines resemble tech foundries, not car plants. The result is faster innovation, tighter margins, and unprecedented control over quality and cost.

For global incumbents still anchored to hardware-centric models, this poses a strategic shock. Chinese firms treat the car as a continuously improving platform — one that generates recurring revenue through software and data services. Western automakers, optimised for static product cycles, now find themselves chasing a moving target.

The question is no longer whether software will eat the car industry — it already has. The real question is: who will own the operating system of mobility in the 2030s — Detroit’s engineers, Stuttgart’s craftsmen, Hamamatsu’s Lean masters, Ulsan’s industrial integrators or Shenzhen’s coders?

The Dumb Design of Modern Cars - When disruption distracts - It's a car. It's not a phone

In 2012, Tesla's Model-S did more than popularize electric vehicles. It was an Iphone moment for the auto industry.  Its 17-inch touchscreen—as large as four to six conventional car displays—made the automobile feel like a consumer electronics product. Legacy manufacturers, terrified of irrelevance, scrambled to imitate Tesla's minimalist, autonomous driving, and software-first approach. The result has been a decade-long experiment in user interface design, conducted at 120 kmph, with car owners as unwitting participants.

The economics of gimmickry: The hypothesis was seductive - screens are cheaper to manufacture, more flexible, and convey technological sophistication. Physical buttons, levers, and knobs belonged to the era of carburetors and cassette decks. Yet this grand redesign overlooked a fundamental principle: cars are not smartphones. They are heavy machinery operated in dynamic, high-stakes environments where a moment's distraction can prove fatal.

The evidence is damning. Studies show that touchscreen tasks slow driver reaction times by over 50%—worse than the legal limit for alcohol. Researchers found that simple operations like adjusting climate controls took 44.6 seconds via touchscreen versus less than 10 seconds with physical buttons. During that interval, a car traveling at highway speeds covers nearly a kilometer. Researchers also concluded that selecting music on Spotify while driving impairs reaction times more severely than texting. This is not merely inconvenient; it is lethal.

Why did manufacturers persist with designs their customers disliked and that made their products more dangerous? The answer lies in misaligned incentives. Touchscreens signal innovation in showrooms, even if they frustrate in driveways. Higher-end vehicles with elaborate digital systems command better margins than affordable cars with conventional controls. The phenomenon resembles other instances where producers optimize for purchase decisions rather than use. Software subscriptions for heated seats—BMW's ill-fated experiment—attempted to import Silicon Valley's business model into an industry where ownership expectations differ. Like touchscreens, it was a solution in search of a problem.

The invisible cost: There is another concern, one that extends beyond safety. Modern vehicles have become surveillance devices. Research has revealed, cars now collect more personal data than any consumer technology—including facial recognition, voice recordings, location history, even sexual activity. Privacy policies reserve the right to sell this information to undisclosed parties. Drivers cannot meaningfully opt out without rendering their vehicles partly inoperable. This data extraction subsidizes the economics of connected cars. But unlike smartphones, automobiles cannot be easily replaced or left at home. The choice architecture is coercive.

Course correction: Market forces are finally asserting themselves. Surveys show 90% of drivers prefer physical controls; 60% say touchscreen-centric design would deter purchases. The European NCAP will deny five-star ratings to vehicles lacking physical buttons for critical functions from 2026. These reversals vindicate a principle that temporary enthusiasms often obscure: good design serves human capability, not corporate aspiration. The hand knows where a volume knob sits without eyes confirming it. Muscle memory works; menu navigation does not.

Lessons for other industries - Tesla deserves credit for proving electric vehicles viable and desirable. But the industry's slavish imitation of its design language—prioritizing aesthetic disruption over ergonomic reality—offers a cautionary tale for any sector tempted to conflate novelty with progress. True innovation solves problems. What the automotive industry pursued was innovation theater: changes that looked revolutionary in product launches but degraded the user experience and compromised safety. The cost of this confusion has been measured in lives, not just satisfaction scores.

When Beijing Bought Bavaria's Best – #ArticulatedRobots

Industrial robots are the silent workhorses of modern manufacturing. In automotive plants from Detroit to Stuttgart to Hamamatsu, machines from the "Big Four"—FANUC, ABB, KUKA, and Yaskawa Electric—perform the heavy-lifting of automation ranging from spot-welding car frames to painting car shells to handling assembly in continuous serial production with micron-level precision. These machines are valued for their speed, accuracy, repeatability. Their ability to integrate into large-scale lines drives millions of cars a year.

Unlike traditional CNC (computer numerical control) machines, which provide superior tool-path precision for machining applications excelling at fixed, repetitive cutting and milling operations along limited axes; six-axis articulated robots – which can move a tool centre point in six degrees of freedom – with their ability to move in multiple planes and mimic human arm movements, offer far greater offers dexterity and range of motion, accessing difficult angles and adapting to varying workpieces. This versatility explains why automotive manufacturers increasingly deploy both technologies in tandem: CNC machines for precision machining of engine components, robots for assembling them into vehicles. These industrial robots have become indispensable for complex assembly, material handling, and quality inspection.

In 2016, Midea Group, a Chinese appliance manufacturer, acquired KUKA, one of Germany's crown jewels in industrial robotics, for €4.5 billion—a transaction that sent shockwaves through European boardrooms and exposed the fragility of the West's technological sovereignty. The KUKA acquisition was no accident. As part of China's "Made in China 2025" strategy, Beijing identified ten key industry sectors where it aims to achieve global leadership, with robotics near the top of the list. By absorbing one of the Big Four, China gained immediate access to high-quality robotics technology, accelerating its transformation from low-cost manufacturer to high-tech powerhouse.

The transaction signals three key shifts: China’s push up-the-value-chain into high-end robotics, Western industrial ecosystems under competitive pressure, and a factory future where automation hardware, software and data converge across borders. From here, expect greater software-driven modularity in robot systems, regional automation hubs shifting eastwards, and manufacturing lines that waltz between CNC precision tools and articulated robot flexibility in hybrid cells.

The road ahead presents both opportunity and turbulence. The global industrial robot market reached $16.5 billion in installations in 2024, with future growth driven by artificial intelligence integration, sustainable manufacturing demands, and persistent labour shortages. AI-enabled robots can now analyze sensor data to optimize operations, while physical AI allows machines to learn from virtual simulations rather than explicit programming. Meanwhile, Robot-as-a-Service business models are democratizing access for small and medium enterprises previously priced out of automation. As supply chains fragment and nations pursue technological self-sufficiency, the robotics industry finds itself at the intersection of industrial policy and great-power competition—a place where efficiency and innovation must now coexist with national security considerations.

How China Rewired the Car Industry — From Steel to Software

A century-old industry is being upended—not by superior engineering alone, but by reimagining what a car actually is. For over a hundred years, the automobile industry has been dominated by a familiar cast: Detroit's giants, Germany's precision engineers, Japan's efficiency masters. Yet in the span of a decade, China has rewritten the competitive playbook entirely. The question is no longer whether Chinese carmakers can compete. It is whether legacy manufacturers can survive.

In 2012, Tesla's Model S did more than popularize electric vehicles. Its 17-inch touchscreen—as large as four to six conventional car displays—made the automobile feel like a consumer electronics product. Add autonomous revolution. Learning from this, Chinese automaker’s most profound shift is conceptual. Western carmakers still think of automobiles as hardware products perfected over decades of mechanical refinement. Chinese manufacturers view them as software platforms that happen to have wheels. This is the essence of the "software-defined vehicle" (SDV)—a car whose features, performance, and value can be updated remotely, transformed by code rather than confined by metal. Consider the implications. A traditional carmaker sells you a vehicle with fixed capabilities. A Chinese EV maker sells you a platform that improves monthly. New driver-assistance features arrive overnight. Battery management systems optimize themselves. The business model shifts from one-time transaction to continuous relationship—and recurring revenue. Western manufacturers, burdened by legacy architectures designed for internal combustion engines, face a painful truth: retrofitting software onto hardware built for a different era is like teaching Latin to a computer. Chinese firms, unburdened by such legacies, designed their vehicles as computers from the ground up.

The second pillar of China's advantage is ruthlessly vertical. While Western carmakers outsource batteries, Chinese manufacturers control the entire supply chain—from lithium mines in Australia to the refined cathodes, from cell production to vehicle assembly. BYD's "Blade Battery," a lithium-iron-phosphate innovation prioritizing safety and cost over pure energy density, exemplifies this approach. It is not merely a component purchased from suppliers; it is a strategic asset manufactured in-house. This vertical integration delivers three benefits that compound over time: cost leadership through economies of scale, security against supply disruptions, and the ability to capture margin at every stage. When CATL or BYD produces batteries, they are not merely supplying components—they are architecting the fundamental economics of electric mobility.

Speed as Strategy - Perhaps most unsettling for incumbents is the sheer velocity of Chinese product development. Where traditional carmakers measure development cycles in years, Chinese manufacturers measure them in months. New models launch with bewildering frequency. Features adapt to market feedback almost in real-time. This is not recklessness—it is industrial agility weaponized. The model borrows from consumer technology rather than traditional automotive practice. Iterate rapidly. Ship frequently. Improve continuously. Over-the-air updates mean that vehicles become better after purchase—a psychological inversion that traditional carmakers struggle to grasp. Based on Agile Platform like assemblies.

On autonomous driving, China has adopted an "all-in" posture that Western regulators would find alarming and Western shareholders would find expensive. Chinese cities serve as vast testing laboratories. Robotaxis operate in multiple provinces. Advanced driver-assistance systems, offered even in mid-market vehicles, normalize technologies that remain premium features elsewhere.

The advantage is not purely technological but ecological: Chinese carmakers integrate sensors, compute platforms, mapping data, and AI models into a unified product stack. Western carmakers still partner with multiple suppliers for these capabilities, introducing complexity and cost at every interface.

For Global car manufacturers, the threat is existential. Chinese EVs now account for over 10% of new car sales in several European markets—a figure that would have seemed fantastical five years ago. These vehicles offer comparable quality at lower prices, bundled with digital experiences that make other infotainment systems feel Paleolithic. The response from European policymakers has been predictable: tariffs, investigations into subsidies, tightening regulations. But protectionism is a palliative, not a cure. The fundamental question remains unanswered: can legacy manufacturers transform their organizations, their culture, and their economics fast enough?

Three scenarios seem plausible. In the first, Western manufacturers successfully reinvent themselves, investing massively in software capabilities, simplifying supply chains, and adopting Chinese-style agility. In the second, the industry bifurcates: Chinese manufacturers dominate in EVs and software-defined mobility while traditional players retreat into ICE vehicles and premium segments. In the third, consolidation arrives as weaker players collapse or merge, unable to fund the transition.

The final outcome will depend on factors beyond pure industrial competition: geopolitical tensions, access to critical minerals, regulatory divergence, and the willingness of Western governments to support their domestic champions. But one thing is certain: the playbook that built Ford, Volkswagen, and Toyota will not save them now. China did not simply electrify the automobile. It redefined what automobiles can become. That, more than any battery chemistry or sensor suite, is the revolution that cannot be reversed. The automotive century belonged to Detroit, Stuttgart, and Toyota City. The electric era may belong to Shenzhen.

E.V.s Force Carmakers to Reinvent the Wheel, and Brakes, and Mirrors …

Building electric cars, and repairing them, will require a huge change for the industry and usher in a new automotive era.

When General Motors declared that all its vehicles would go fully electric by 2035, it sent tremors through an industry accustomed to incremental change. Yet the shift to electric vehicles represents far more than swapping combustion engines for batteries—it requires reimagining nearly every component beneath the bonnet.

Consider the humble tyre. Electric vehicles deliver instant torque that can shred conventional rubber in months. Their batteries add thousands of pounds, demanding stronger materials. Yet manufacturers must simultaneously reduce rolling resistance to preserve battery range. Pirelli now engineers tyres with high silica content specifically for electric platforms, balancing grip against energy consumption in ways never before required.

The challenges cascade through the supply chain. Brakes must handle additional mass whilst brake dust must be minimised. Aerodynamics become paramount when every air current drains precious battery charge. Even side mirrors undergo scrutiny. "You have to realise that everything on the car you now take for granted—wheels, brakes, tyres—you have to optimise for efficiency," notes an Audi spokesman.

Then there's the question of sound. Electric motors whisper where engines roared. BMW commissioned Hans Zimmer to compose an "automotive soundtrack" for its i4. Audi's E-tron GT remixes sound effects based on speed. These aren't mere marketing gimmicks—acoustic vehicle alerting systems are now mandatory safety features.

Perhaps most consequential is the looming transformation of the aftermarket. America's 750,000 auto mechanics face an existential reckoning. Electric vehicles require no oil changes, spark plugs, or fuel filters. The grease monkey must become a software technician, trading wrenches for diagnostic computers.

The transition from petrol to electric isn't simply technological—it's cultural, economic, and industrial. Those who adapt will thrive in the new automotive era. Those who don't may find themselves as obsolete as the carburettor.

Central Asia's open road

For Indian automakers, five overlooked republics represent the next frontier in a shifting geopolitical landscape

As Donald Trump hosts Central Asian leaders this Thursday, India's automotive industry should take note. The "C5+1" dialogue signals a broader realignment: Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan are actively diversifying their economic partnerships away from traditional patrons. With a combined population of 80 million and growing middle classes, these markets offer Indian manufacturers an opportunity that Chinese and Russian rivals have long monopolised. More crucially, Central Asia holds deposits of 25 critical minerals essential for electric vehicle batteries and advanced manufacturing—precisely what India needs to build a self-reliant automotive supply chain.

The Middle Corridor trade route, bypassing Russia and Iran, presents Indian automakers with unprecedented logistics access to European markets whilst sourcing materials closer to home. Russia's Ukraine invasion has accelerated Central Asia's pivot toward new partners, creating an opening that didn't exist five years ago. Yet the window may be brief: China dominates regional infrastructure investment, whilst Western powers circle with chequebooks open.

Indian automakers possess distinct advantages—right-hand drive expertise, affordable engineering, and crucially, no colonial baggage. The question is whether Gurugram and Chenai will seize this moment, or watch from the sidelines as others carve up Central Asia's automotive future.

The Auto Industry's Impossible Triangle - Scarce Chips and Rare Earths Are Just the Tip of Big Auto’s Disruption

The chip shortage exposing carmakers' deepest contradictions. "We make cars, and everyone else makes money." Jacques Nasser's sardonic observation about Ford, uttered years ago, has aged like fine wine—or perhaps more accurately, like milk left in the sun.

The latest chip shortage, courtesy of a Dutch-Chinese standoff over Nexperia's semiconductor facilities, has once again brought Honda and Ford production lines to a crawl. That such basic "legacy" chips—the unglamorous workhorses comprising 95% of a vehicle's semiconductors—can still paralyze an entire industry merely four years after the pandemic's supply chain catastrophe tells you everything about automotive economics.

This is an industry caught in an impossible triangle. Governments demand reshoring for national security. Shareholders demand profitability. Customers demand affordability. Pick two.

The mathematics are unforgiving. Even Tesla, unburdened by legacy labour agreements and benefiting from China's manufacturing scale, has seen production costs stubbornly fixed around $36,000 per vehicle for four years. When the industry's celebrated disruptor cannot disrupt its own cost structure, the implications are sobering.

Carmakers have historically solved this equation through ruthless efficiency: just-in-time manufacturing, global supplier networks, squeezed margins. But that playbook assumed globalization would continue expanding, not reversing. Canadian aluminum now faces American tariffs. Chinese chips become geopolitical pawns. Redundancy—once a luxury—becomes mandatory, and inflation its inevitable companion.

The industry's proposed solution? Automation. Lear Corp's "lights-out" manufacturing facilities promise to replace assembly lines staffed by "dozens and dozens" of workers with robotic precision. Yet this creates its own contradiction: replacing variable labour costs with fixed capital investment may simply entrench the industry's structural rigidity.

Here lies the paradox. Reshoring was sold partly on the promise of manufacturing jobs returning to rustbelt communities. But economically viable reshoring may require precisely those jobs to disappear into automation. Politicians promised both national security and employment. Economics suggests they may deliver only the former.

The chip shortage is merely a symptom. The disease is an industry being asked to simultaneously reverse decades of globalization, maintain profitability in a brutally competitive market, and serve as a vehicle—quite literally—for industrial policy and employment creation.

Something will have to give. If history is any guide, it will be the workers.

The arithmetic of dependence – the prosaic threats that lurk in the unglamorous corners of global manufacturing

The automotive industry's recurring supply chain crises are merely symptoms of a structural condition: Automotive industry’s dependencies lie not in cutting-edge AI, but in the machinery that makes everything else possible.

The pattern is instructive. Carmakers spent four years recovering from pandemic chip shortages, only to find themselves vulnerable once more—this time to disruptions in "legacy" semiconductors, the mundane workhorses comprising 95% of vehicle electronics. Yet semiconductors, at least, generate headlines and policy attention. Far more binding are dependencies that escape notice entirely.

Consider the arithmetic of industrial reality. China State Shipbuilding Corporation (CSSC) built more tonnage in 2024 than America has produced in the eight decades since World War II. It’s share of global shipbuilding? 55.7%. Or examine the infrastructure that makes infrastructure possible. Seven of every ten tunnel boring machines (TBM) worldwide bear Chinese markings with ZPMC supplying 80% of global port machinery.

This is not the technological competition that dominates policy discourse. There are no breathless briefings about these companies' innovation capabilities. These are mature industries producing decidedly unsexy products: ship hulls, tunnel drills, bridge-building equipment, port cranes, heavy forgings.

Yet their dominance creates dependencies more binding than any cutting-edge technology. The automotive industry discovered this truth through bitter experience. Just-in-time manufacturing and global supplier networks delivered efficiency—until geopolitical tension transformed single points of failure into existential threats. Now apply that lesson to the physical infrastructure of global commerce itself.

A country can, with sufficient investment and patience, develop its own 5G infrastructure or social media platforms. Building an industrial ecosystem capable of producing heavy machinery at globally competitive prices requires not merely capital, but decades of accumulated engineering expertise, supplier networks, and—crucially—domestic demand sufficient to achieve economies of scale.

China's infrastructure spending provided that demand. Its industrial policy nurtured those suppliers. The result is a cathedral of capabilities that cannot be replicated quickly, regardless of political will or fiscal commitment. China First Heavy Industries' (CFHI) 15,000-ton hydraulic press, the world's largest, is booked through 2028. It produces just eight units annually of equipment essential for nuclear reactors and large-scale metallurgy. Good luck replicating that capacity in time for the next crisis.

The pharmaceutical dimension adds biological urgency to industrial dependency. Jinhe Biotechnology supplies over 90% of America's veterinary chlortetracycline. This is not a consumer product that can be foregone with modest inconvenience. Without it, American livestock operations would face existential crisis within months. Detroit worries about chip supplies. Agriculture depends on Chinese antibiotics.

Washington's response to Chinese technological advancement has largely focused on restricting China's access to American innovation—semiconductor equipment, software, advanced manufacturing tools. This assumes the competitive dynamic runs primarily in one direction. The reality of heavy machinery, shipbuilding, port infrastructure, and industrial biotechnology suggests something more complicated: mutual dependency, but with asymmetric leverage.

The automotive industry's predicament illuminates the paradox. Carmakers are simultaneously told to reshore for national security, maintain profitability through global efficiency, and keep vehicles affordable. Each chip shortage demonstrates the impossibility of this trilemma. Yet motor vehicles at least face competitive pressure to solve these contradictions. Who competes on tunnel boring machines or port cranes?

America cannot easily decouple from suppliers of port infrastructure or heavy industrial equipment without inflicting immediate economic damage on itself. China has successfully positioned itself not at the frontier of innovation, but at the chokepoints of production. Less glamorous than artificial intelligence, perhaps, but considerably more difficult to circumvent.

The ultimate irony? These industries were not stolen through espionage or forced technology transfer. They were largely ceded—abandoned by Western firms as unprofitable commodity businesses while China systematically mastered them. The West climbed the value chain, chasing software margins and intellectual property premiums. China claimed the foundations. Now the foundations are crumbling, and there's no competitive supplier to call.

One might call for industrial policy to address these gaps. But the timeline for developing competitive heavy machinery manufacturers measures in decades, not election cycles. Detroit is discovering that even reshoring automotive supply chains proves economically fraught. Rebuilding entire industrial sectors that have atrophied over generations? The digital threats from China can theoretically be contained through software restrictions and network architecture. The physical infrastructure of global commerce is rather harder to firewall.

Ford's production lines idle waiting for chips from a Dutch-owned, Chinese-operated semiconductor maker. American agriculture depends on Chinese veterinary drugs. Container ships offload beneath Chinese cranes. The machinery that bores tunnels, builds bridges, and forges nuclear components: Chinese. These are not future threats requiring preemptive action. These are present dependencies already constraining policy options.

Detroit worries about the next chip shortage. Washington worries about what China might do with advanced AI. Both should perhaps worry more about what America cannot do without Chinese cranes, drills, presses, and antibiotics. In geopolitics, as in automotive manufacturing, it's not the cutting-edge components that halt production. It's the basic parts you assumed would always be available.

Back then, there were similar concerns about cheap, fuel-efficient cars from Toyota and Honda flooding the U.S. market and destroying the Big Three. The Reagan Administration limited imported vehicles, but eventually allowed the Japanese companies to build plants in the United States. American carmakers copied some of their methods. “The U.S. automakers were able to learn and improve”. Analysts suggest that learning from China’s blend of state support and market rivalry—rather than walling it off—could accelerate America’s clean-energy transition, much like it once did with Japanese automakers in the 1980s. Yet domestic politics make such cooperation improbable, leaving U.S. consumers with few affordable EV options for now.

How Car Software Has Changed – Smartphone with wheels!?

Software is changing the world. We live in an era where vehicles are expected to be intelligent, connected, and continuously evolving. The in-car software revolution has been so rapid that it has given birth to an entirely new concept – the #SoftwareDefinedVehicle (SDV). Modern cars that are Software driven vehicle now contain up to 650 million lines of code, compared to just 15 million in a Boeing 737.

In the past, car software was functional but distributed and embedded with the hardware. Today, software defines the behaviour of almost every aspect of the vehicle – from infotainment to navigation, safety and automated driving. The transformation from a hardware-defined vehicle to a software-defined one has been extraordinary.

Gone are the days when software was mainly about individual control units, each one doing its own job, with minimal interaction. These systems handled tasks like engine management, transmission, ABS, and basic infotainment and navigation. Updates required a trip to a dealership and connectivity was no more advanced than a Bluetooth connection to a mobile phone. Cars that could hold a conversation were more Hollywood fantasy than reality.

As we moved through the decade, systems became more advanced, but not more efficient. To put this into context, did you know that, in 2022, the luxury car had more than 80 electronic control units (ECUs)? Although this multi-level system of individual components worked very well, it was too complex, which is why reducing the number of necessary units was the next logical step.

Fast forward to 2025 and latest vehicles, especially all-electric models, are now regarded as highly connected digital platforms and capable of being continuously updated, customised and improved over time. It wasn’t just a new infotainment system – it was the beginning of a software-defined driving experience. With natural language processing (NLP) and over-the-air updates - a move that delivers more connectivity, customization and high-tech-screen technology. It also gives customers access to deeper AI personalisation, third-party apps, content streaming, and an improved user experience.

Software updates over-the-air (OTA) have become the cornerstone of our desire to deliver a digital ownership experience that is immersive, evolving and responsive to personal preferences. In a nutshell, #OTA updates have revolutionised the way vehicles receive and install software upgrades, bug fixes and feature enhancements – all of which are designed to keep the vehicle up to date with minimal effort from the customer. OTA upgrades enable manufacturers to remotely deliver software enhancements directly to the vehicle, just like your mobile phone company does with your smartphone. As a result, there’s no need to take the vehicle to a workshop or waste time while the vehicle is off the road. Just a simple and convenient update over a wireless connection.

OTA capabilities are generally split into two types: SOTA (software over-the-air) and FOTA (firmware over-the-air) and both are essential. SOTA handles user-facing features: infotainment, apps, voice recognition, and interface changes. FOTA dives deeper – updating critical systems like powertrain controllers, battery management, or braking systems. To lead in software, the customer must be excited with new digital features by way of continuous software support and shorter release cycles. Security should be a top priority for any OEM, and it should employ several robust protocols and strategies to mitigate cyber threats and guarantee security, including end-to-end encryption, autonomous driving requirements, regular evaluation and updates, emergency response protocols, certification and compliance in accordance with the UN regulations R155 (cybersecurity) and R156 (software updates), and data privacy to safeguard customer data.

And, as software development never stands still, upcoming cars will take in-car software to the next level with the introduction of our all-new operating system (OS) which is very different from what we have done before in many ways and marks a fundamental shift in our software strategy. This OS are designed as a single system with full control over each part – and this includes all sensors and actuators – enabling creation of a fully connected digital ecosystem, speed-up development times and reduces reliance on third party-suppliers to bring new features into the vehicle. The OS reduces complexity through standardisation of electronic and electric hardware and software across all four domains: infotainment; automated driving; body and comfort; driving and charging.

The importance of Open-Source Software

Vast amount of programming code required to make modern-day software stacks function correctly. To illustrate, in 2015, a latest car had approximately 100 million lines of code per vehicle. In 2025, this number has increased to 650 million lines of code per vehicle due to the addition of features such as assisted and autonomous driving, artificial intelligence and more. That’s a phenomenal 550 percent increase in just 10 years. To produce this level of code quickly and efficiently, services provided by the global Free & Open-Source Software (FOSS) community are being used in the industry. Car makers are thinking to vertically integrate in-organically to produce everything for the OS i.e. chip-to-cloud architecture in house, but this would have been a very slow, complex and time-consuming process. Open-source components usually conform to high-quality standards and can be, therefore, instrumental to the success of such platforms.

However, to successfully implement this shift in strategy and software development, a change in culture and new internal workflows were also necessary.

As the industry moves to a more software-driven development strategy, that shall also drive a step change in the working mindset. Key to this is the implementation of #Agile frameworks, particularly #SAFe (Scaled Agile Framework), to help better manage large, complex projects. Agile frameworks streamline software development processes, ensuring efficient delivery of high-quality software and helping us prioritise rapid innovation, deliver continuous updates, and maintain a laser focus on the user experience. This mindset also helps attract the best talent from around the globe.

AI @ Product

#LLMs. These are large-language models, and they are the building blocks for creating personalised, secure, immersive and intelligent responses. LLMs, such as ChatGPT for example, understand conversational language, context and subtle nuances in speech. By acting as a personalised co-pilot with advanced reasoning across domains, LLMs help orchestrate intuitive interactions and generate contextually relevant responses. LLMs are trained purely on text data such as books, websites, code, and articles and since they are limited to language inputs, they are often referred to as unimodal models.

In contrast, #VLMs – Vision-Language Models – can handle both visual and textual input, creating a multimodal system. VLMs are trained on data that pairs images with corresponding text, and they are designed to perform tasks that require cross-model understanding, such as generating image captions, answering questions about visual scenes, retrieving relevant images based on a text prompt, or vice versa. VLMs are significantly more advanced than LLMs because they allow machines to reason jointly over language and visual data. We can take this a step further.

#VLAMs – Vision-Language Action Models – are not only capable of understanding visual inputs and language, but they can also make decisions and take actions in physical and simulated environments. Carmakers are currently working on implementing both VLMs and VLAMs into autonomous driving technologies.

The next chapter in ADAS and autonomous driving journey

There’s no doubt that VLMs and VLAMs have incredible capabilities. What makes these advanced models possible is their foundation built on Deep Neural Networks – or DNNs. A DNN is a type of machine-learning model that excels at learning complex patterns from large datasets – just like the human brain. This makes them particularly well-suited to handling the detailed perception, decision-making and control tasks required for semi-automated and automated driving.

Frontier Carmakers in the AI revolution are already shifting from single-sensor DNNs to full end-to-end (E2E) DNNs that can integrate inputs from multiple sensors and produce driving decisions directly, streamlining perception, planning and control. This is a key step towards building truly AI-driven vehicles capable of safe and intelligent autonomy. To ensure safety even if the AI model fails or behaves unpredictably, ADAS systems are built with a Safe Core – a certified fallback layer that monitors AI outputs, enforces limits and takes over if needed.

To use a variety of AI models, we need to host and manage them too. A unified platform for ADAS that allows the OEM to be in the driving seat of the full development flow – from data pipeline to model training and development. Using such a platform serves as the backbone of AI architecture because it allows the OEM to coordinate content and actions across different AI agents and applications. This is important as AI is not only transforming ADAS and autonomous driving technologies, but also the more tangible infotainment touchpoints inside the car.

Agents are the new apps

One prominent example for this is how we communicate with our cars. For the past two decades, digital apps have played a key role in defining the automotive in-car user experience. Now, we are entering the age of the AI agents. Unlike an app that is optimised for a narrow function – checking the weather or booking a flight – an agent-based experience is much more flexible and intuitive. Crucially, agents are goal-orientated: you tell them what you want, and they figure out how to make it happen. To put it simply, AI agents bring proactive intelligence to the driving experience. Natural conversation is central to this change, simplifying decision-making, reducing distractions and enabling the driver to stay productive, focused and hands-free.

Naturally, integrating AI at speed in a global giants is not without its challenges. A serious AI approach is data-driven, exploratory and iterative – moving away from linear implementation and process orientation. This requires focus and the need for all of us to be bold, creative and strong in our ideas and execution. And AI thrives on data. Not just any data – high-quality, well-structured and accessible data. Through radical standardisation all car makers will eventually have to, cleaning up systems to create a solid data foundation as “software is the new fuel, AI is the new horsepower”.

What excites me most about this software-centric revolution is how it will enable create functions that surprise and delight throughout the vehicle’s lifetime. I believe the innovations will set new standards in the industry, ultimately leading to safer and more personalized driving experiences.

SDV Revolution

68% of automotive CEOs believe their businesses won't be economically viable in 10 years without reinvention. Across these jurisdictions, the interplay between regulation, sustainability goals, and supply chain realignment is reshaping how global manufacturers and suppliers plan for the future.

Global supply chains remain under pressure as tariffs and geopolitical tensions drive automakers to consider regional production hubs and localized sourcing, particularly for batteries and critical minerals. At the same time, vehicles are evolving into software-driven platforms, creating new opportunities for connected services while heightening cybersecurity and data governance risks.

The automotive industry is shifting from hardware-centric design to software-driven architectures, where vehicles are increasingly defined by software rather than traditional hardware. Modern cars now contain up to 650 million lines of code, compared to just 15 million in a Boeing 737. The global SDV market is expected to achieve a compound annual growth rate of 22.1% between 2023 and 2032.

·         Over-the-air updates for continuous feature improvements

·         Centralized computing platforms replacing multiple electronic control units

·         AI integration for predictive maintenance and autonomous driving

·         Personalized driving experiences adaptable to individual preferences

·         Connected Ecosystems: Vehicles becoming integrated with smart infrastructure, homes, and IoT devices

Governments in the Middle East are aggressively positioning their markets as hubs for innovation, offering ambitious policies to attract foreign investment in electric vehicles (EVs) and autonomous vehicles (AVs).

How Hoshin Kanri (L1 X-Matrix) Turns Strategy into Reality

Most companies have great strategies and spend months crafting a “five-year plan”, but only a few manage to execute them. Hoshin Kanri—a Japanese strategy deployment system—solves precisely that problem. It links the CEO’s vision to the engineer’s daily task, through an elegant process of alignment and accountability.

At Level 1 (L1) (or top-tier) of the strategic planning phase within the Hoshin Kanri methodology, comes the corporate strategy deployment layer, where leadership decides what must change most to achieve long-term success with a few critical breakthrough objectives for a 3–5 year horizon. These are enterprise-wide priorities that must fundamentally change. These are not slogans; they’re measurable bets on the company’s future. The process typically involves: Translating the corporate vision and mission into measurable, strategic objectives. Selecting 3–5 Hoshins (strategic themes) such as market expansion, quality excellence, or innovation.

The genius lies in how these ambitions cascade. Through a tool called the X-Matrix, each L1 goal connects down to L2 functional plans (R&D, manufacturing, digital, HR) and L3 operational actions through a process called catchball — iterative goal negotiation between levels. Every link is discussed, tested, and agreed through catchball—an iterative dialogue where objectives are refined until every level understands how their work moves the corporate compass, or “True North.”

The result? No wasted motion. No misaligned KPIs. Just a disciplined rhythm of planning, doing, checking, and acting—what Japanese firms call hoshin-kanri nemawashi. Toyota, Komatsu, and even Western adopters like Mercedes-Benz and Danaher use it to ensure that strategic intent translates into daily execution.

Hoshin Kanri L1 is, in essence, the corporate nervous system: strategy in motion.

The Road to Self-Driving Cars: Understanding the Five Levels of Autonomy

The promise of autonomous vehicles has captivated Silicon Valley executives and traditional carmakers alike. Yet the path from today's driver-assistance systems to truly driverless cars is neither straight nor simple. Understanding the Society of Automotive Engineers' five-level framework reveals why.

The Starting Point: ADAS

Advanced Driver Assistance Systems—or ADAS—represent the foundation. These are the features already familiar to millions: adaptive cruise control that maintains safe distances, lane-keeping assistance that nudges vehicles back between white lines, and automatic emergency braking that intervenes when danger looms. Critical point: the human remains fully responsible.

Level 1 and 2: Assistance, Not Autonomy

Most modern cars operate here. Level 1 involves single functions—cruise control or lane assistance, but not both simultaneously. Level 2, where Tesla's Autopilot and GM's Super Cruise reside, combines these functions. The vehicle can steer and accelerate, but the driver must remain engaged, hands hovering near the wheel, eyes on the road. The technology assists; it does not replace.

Level 3: The Awkward Middle

Here lies the most contentious territory. The vehicle can drive itself under specific conditions—say, highway traffic jams—but must be able to return control to the driver when situations exceed its capabilities. Mercedes-Benz recently achieved regulatory approval for Level 3 in Germany, but the handover problem remains thorny: how quickly can distracted humans regain situational awareness when the car requests help?

Level 4: Freedom Within Bounds

True autonomy emerges, but with geographic or environmental constraints. A robotaxi operating in sunny Phoenix during daylight hours exemplifies this. Waymo operates here. No human intervention is needed within the defined operational design domain, but the vehicle may refuse to operate in snow or on unmapped roads. This is where commercial viability first appears.

Level 5: The Holy Grail

Full autonomy, anywhere, anytime, in any weather. No steering wheel required. This remains theoretical. The technical challenges—handling monsoon rains, construction zones with hand signals, or rural roads without lane markings—prove formidable. The regulatory hurdles may be even higher.

The Reality Check

The industry's dirty secret: each level up represents not incremental progress but exponential complexity. Level 2 requires thousands of engineering hours; Level 5 may require millions, plus regulatory frameworks that don't yet exist. Many companies that once promised Level 5 by 2020 have quietly recalibrated their ambitions toward Level 4 in limited domains.

The question is no longer whether autonomous vehicles will arrive, but which level will define their commercial future—and how long humans will remain in the driver's seat.

The Illusion of Autonomy: Why “Level 2” Still Rules the Road

When Maruti Suzuki launched Victoris with Level 2 ADAS, headlines celebrated it as a leap toward autonomous mobility. Yet, beneath the marketing sheen lies a sobering truth: most of the world’s “self-driving” cars—including Tesla, BYD, Kia, and Toyota—are still parked firmly in the same lane.

The Society of Automotive Engineers defines five levels of vehicle autonomy. Level 2 is where the machine can steer, accelerate, and brake—but only under the human’s watchful eye. Take your eyes off the road, and the illusion of autonomy evaporates. Tesla’s Autopilot, GM’s Super Cruise, Xpeng’s XNGP, and BYD’s DiPilot all fall here. Even Toyota’s “Advanced Drive” and Kia’s Indian ADAS systems—marketed as advanced—are Level 2 in regulatory reality.

The industry’s quiet confession is that every step up the autonomy ladder multiplies complexity, cost, and legal risk. Level 3 requires real-time situational awareness handovers; Level 4 demands flawless maps and weather resilience; Level 5, the dream of true driverlessness, remains science fiction for now.

India’s path may be pragmatic: focus on assisted safety, not autonomous fantasy. The next frontier isn’t removing drivers—it’s making them safer, more aware, and better supported by intelligent systems.

Question: As technology matures, should automakers chase full autonomy—or perfect the partnership between human and machine first?

Buybacks, Redemptions, and the Treaty Tug-of-War: Article 10 vs Article 13

The tax treatment of share buybacks by Indian companies from non-residents has quietly evolved into one of the more nuanced debates in international taxation. I recently discussed a live case with a colleague that brought this tension into focus: when an Indian company repurchases its shares from a non-resident individual, should the transaction fall under Article 10 (Dividends) or Article 13 (Capital Gains) of the applicable tax treaty?

Post 1 October 2024, Indian domestic law leaves little ambiguity — such buybacks are deemed dividends and taxed accordingly. The Indian company must withhold tax under Article 10, aligning with the law’s characterization. Yet, certain treaties tilt the balance by providing that gains from the alienation of shares are taxable only in the shareholder’s country of residence — a clear invitation to argue under Article 13 where it produces a better result.

The OECD and UN commentaries offer a referee’s whistle here. Both indicate that when a share redemption (or functionally similar buyback) is taxed as a dividend in the source state, Article 10 prevails. Paragraph 28 of the OECD Commentary on Article 10 and Paragraph 31 of the Commentary on Article 13 support this reading. In essence: the treaty follows the domestic law’s lead — if India calls it a dividend, the treaty treats it as one.

However, the theoretical tug-of-war between the two articles remains alive. The Netherlands Court of Appeal (ECLI:NL:GHSHE:2025:2121) recently ruled that if a transaction fits two treaty articles, the provision more favourable to the taxpayer should apply. While not binding on India, such reasoning could embolden non-residents to challenge withholding positions, particularly in non-OECD contexts.

As India tightens its domestic framing around buybacks, this Article 10 vs Article 13 puzzle will increasingly test treaty interpretation and taxpayer strategy alike.

Question for fellow tax professionals:

In your view, should domestic law characterization dictate treaty allocation in such hybrid cases, or should substance — the economic nature of alienation — take precedence?

China's quiet dominance in global chemicals - a story from one small island

Changxing Island, was just cluster of farmland and fishing villages off China’s north-east coast 20 years ago. Today, it's home to the world's largest producer of PTA (a key chemical for making polyester). Firms like Hengli Group, which started from a bankrupt textile mill, climbed the value chain with relentless efficiency and deep state support. China now makes over 60% of global PTA supply, while companies in Canada, Europe and Japan have scaled back or stopped production entirely.

How did this happen? A powerful mix: government support (tax breaks, cheap land, state bank loans), world-class infrastructure, access to state-funded research, and aggressive private companies climbing up the value chain. The lesson? China isn't slowing down its industrial expansion - it's moving into higher-value products. The world often looks at rare earths and batteries, but China’s quiet conquest of chemicals that go into everything we make may prove just as strategic.

Would you say this industrial model is replicable elsewhere

Mercedes-Benz's Strategic Miscalculation – The Luxury Gamble Unravels

Mercedes has quietly removed "luxury" from its homepage, replacing it with "the most desirable cars in the world"—a claim the market is systematically refuting.

When Ola Källenius pivoted Mercedes-Benz toward ultra-luxury in 2021, the strategy appeared inspired. Pandemic-era supply constraints and chip shortages created artificial scarcity that commanded premium prices. Margins swelled. Shareholders applauded. Yet what seemed like strategic brilliance was merely fortunate timing masking a fundamental misreading of market forces.

The reckoning has arrived. Factory 56 in Sindelfingen—Mercedes' showcase facility for flagship models like the S-Class and EQS—will reduce operations from two shifts to one after summer. The top segment collapsed 25% last quarter. One dealer reports not selling a single S-Class in nearly a year. EQS models languish unsold in dealer yards.

This is not merely a sales problem; it is an existential crisis for a company whose profit architecture depends on high-margin flagship vehicles. Operating margins have compressed from 13.5% to 10.2% in a single quarter. Mercedes now deploys discounts approaching 40%—a figure that would make a premium brand wince and a luxury marque shudder. Such discounting erodes the very scarcity that justifies premium positioning.

The diagnosis reveals multiple strategic failures compounding simultaneously. First, Mercedes mistook temporary pandemic dynamics for permanent market structure. Second, the company moved too slowly on electrification while competitors gained technical and perceptual advantages. Third—and perhaps most alarming—internal resistance persists. Managers in Sindelfingen reportedly still believe combustion engines will dominate and the "EV hype" will fade, with hydrogen emerging as salvation. Such thinking suggests institutional denial rather than strategic adaptation.

The confluence of errors is instructive. Pandemic stimulus inflated demand temporarily; its withdrawal, combined with Ukraine-related energy shocks, accelerated inflation and squeezed discretionary spending precisely as Mercedes positioned itself further upmarket. Meanwhile, dealer inventory masks demand signals, creating dangerous lag in recognizing the mismatch between product and market.

Tellingly, Mercedes has quietly removed "luxury" from its homepage, replacing it with "the most desirable cars in the world"—a claim the market is systematically refuting. The company appears to lack not merely a coherent strategy but even acknowledgment that fundamental recalibration is required.

The automotive industry's transition presents winners and losers not based on heritage but on adaptability. Mercedes, with its storied engineering culture, possesses the capability to compete. What remains uncertain is whether institutional inertia and strategic confusion will be overcome before the damage becomes irreversible. For a brand that once defined automotive aspiration, the urgency could not be greater.

Without data, you’re just another person with an opinion - American scholar W. Edwards Deming

The goal is to promote the importance and mindset of data-driven decision-making. This is an essential skill because in today’s fast-paced, highly competitive world, data turns insights into action – and action powers sustainable growth. In our organisation, we implement a four-stage approach:

1️⃣ Focus on decisions, not KPIs – KPIs are a means, not the end.

2️⃣Challenge dashboards – check if reports actually support decisions.

3️⃣ Tell stories with data – what works and what doesn’t? It’s important we understand how best to allocate resources.

4️⃣ Define success upfront – every feature and product require measurable customer success metrics.

By weaving analytics into every stage of the development cycle – from the first concept to full-scale series production – decisions become clearer, more consistent, and easier to execute.

The New Cartel of Convenience - Facing up to the automotive innovation dilemma

The rise of connected, autonomous, shared, and electric vehicles will reshape the industry. The challenge in the meantime is survival. When connected, autonomous, shared, and electric vehicles are finally ready for the mass market, too few of the automakers who are investing in them will be left in a position to actually benefit.

For an industry long famed for go-it-alone swagger, the sudden rush of alliances in Detroit, Wolfsburg and Tokyo has the whiff of a détente forged under duress. Honda’s pact with GM’s Cruise, Ford’s courtship of Volkswagen, and the quiet triangulations among BMW, Daimler and VW suggest that the great gamble on autonomous and electric mobility—once sold as the next Model-T moment—is proving far more ruinous than rosy forecasts implied. The numbers tell their own tale: billions sunk into connected, autonomous, shared and electric technologies have delivered returns so meagre that even the boldest incumbents now seek shelter in each other’s R&D budgets.

History offers uncomfortable analogies. Just as America’s railway barons once overbuilt in anticipation of a golden age that arrived too slowly, carmakers now face a future in which the spoils of autonomy and electrification may be captured not by them but by the sensor-makers, battery giants and software houses supplying their innards. Worse, the mass-market EVs meant to redeem their fortunes resemble the very small, low-margin vehicles many automakers quietly abandoned years ago. Collaboration, then, is less a strategic flourish than a mechanism for survival in a market where every new forecast of robotaxis or battery breakthroughs seems more distant than the last.

Yet partnerships alone will not save firms whose ambitions extend beyond mere endurance. As investment costs soar and the time line for adoption slides, the winning strategy may be the least glamorous: specialise narrowly, spend sparingly and abandon the illusion that one company can master the entire CASE alphabet soup. If the industry is to avoid becoming a museum of defunct badges—a fate that once befell Britain’s carmakers—each firm must decide whether it is an engineering house, a fleet operator, a software integrator or something stranger still.

The next decade will likely reward the patient and punish the profligate. Consolidation is certain; survival, less so. The real question for the industry’s titans is no longer who gets to invent the future of mobility, but who can afford to reach it intact.

chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.pwc.in/assets/pdfs/trs/automotive-industry-transformation-a-transfer-pricing-perspective.pdf

The OECD has released the 2025 Update to the OECD Model Tax Convention introducing a number of significant enhancements to the Model which bear direct relevance for cross-border tax planning and treaty policy. The most notable changes are the changes to the Commentary on Article 5 to clarify the circumstances in which an individual’s home could constitute a “place of business” of the enterprise for which the individual work.

First, under Article 5 (Permanent Establishment) the Commentary has been clarified to address home-office arrangements; in particular, the new paragraphs explain when an individual’s residence or remote working location can constitute a “fixed place of business” of the enterprise — reflecting modern working realities and reducing uncertainty around PE risk for remote employees.

Second, an optional provision relating to extractive industries permits Contracting States to adopt a lower threshold for PE creation in on- and offshore exploration/exploitation activity, thereby aligning treaty text with practice in natural-resource sectors.

Third, Article 25 (Mutual Agreement Procedure & Dispute Resolution) has been amended with a new paragraph 6 to clarify interaction between treaty competent-authority procedures and the General Agreement on Trade in Services (GATS) framework, thereby reducing ambiguity around dispute resolution stemming from services-trade obligations.

Fourth, in the realm of transfer pricing and interest deduction (Article 9) the Commentary now addresses the interplay of domestic interest-deduction limitation rules (in line with the BEPS Action 4 work) and the arm’s-length principle for intra-group financial transactions; also, sign-posting the role of “Amount B” for simplified TP methods in applicable cases.

Fifth, Article 26 (Exchange of Information) now explicitly states that information obtained via international exchange may be used for tax matters concerning persons other than those originally covered, and sets out clarified guidance on taxpayer access to, and disclosure of, derived non-taxpayer-specific information.

Multinationals and advisers should review: (i) remote-work-arrangements and cross-border staff deployment models in light of the updated PE guidance; (ii) treaty-networks (especially in jurisdiction negotiations) to determine whether the extractive-industry optional PE provision is relevant; (iii) dispute-resolution mechanisms in existing treaties to assess whether the new MAP/GATS interface may affect competent-authority exposure; (iv) intra-group financing, interest deduction planning and transfer-pricing policy to ensure alignment with the revised Article 9 commentary; and (v) information-exchange processes to ensure the organisation’s data-governance and access model is compliant with the expanded Article 26 commentary.

In light of the above, it is prudent to initiate a treaty-and-policy review of your global footprint and existing documentation in light of the 2025 Update.

Caveats: The 2025 Update largely amends the Commentary, with only limited amendments to the text of the Convention itself; many bilateral treaties will not yet reflect these changes in their negotiating history or in adoption; and local law, treaty protocol or administrative interpretation may take time to adapt.

Robots on the road

After three decades of development, self-driving taxis have arrived in American cities. Waymo now operates 2,500 autonomous vehicles across five locations, while competitors like Tesla and Amazon's Zoox are expanding their services. What started as a 1995 cross-country demonstration has evolved into a business serving over 1 million monthly riders.

Several innovations have combined to make that possible. Robotaxis rely on sensors such as cameras, laser-based LiDARs, microphones and radars to assess road conditions, judge distances and manage speed. They then use artificial intelligence both in the car and in the cloud to mimic the way human drivers process road conditions in real-time. The rise of multi-modal generative AI models, which weave together text, images and sounds, has also sped up progress, making it easier to train autonomous systems using simulations and teach them to react to unusual situations.

Safety data shows promise—Waymo's vehicles generate 88% fewer property damage claims than human drivers. But profitability remains elusive. Self-driving cars currently cost $7-9 per mile to operate compared to $2-3 for traditional ride-hailing services, mainly due to expensive hardware and fleet management costs.

The race is now on to dominate what could become a trillion-dollar market. Waymo leads with more advanced safety features, while Tesla bets on a cheaper camera-only approach. Meanwhile, Uber positions itself as the booking platform for all robotaxis, and Nvidia profits by selling AI chips to everyone. The question isn't whether autonomous taxis will succeed—it's who will control the road ahead.

Why Not Everything is Automated in Manufacturing (Yet)

Even in an era of AI and dark factories, automation is lagging behind promises. Why is this happening?

The hard part is not "robots", it is designing the whole system so their narrow reliability actually matters at the output. Most people underestimate how messy real factories are, even in the US. Parts are out of spec, fixtures are worn, upstream processes drift, operators constantly compensate in ways that never make it into a spec sheet. Humans quietly absorb that chaos, so the line still runs. Robots snap when the world is even a little off the nominal model. The real automation problem is less: can we teach a robot arm to do X. It is: can we stabilize the process, the parts, the fixturing, the data, and the failure modes enough that "doing X reliably" is even a meaningful concept. Only then do the unit economics and uptime math start to favor capital intensive automation instead of a slightly overstaffed, extremely adaptable human line. Curious how you think about the order of operations here: fix process variation first then add robots, or co design robot capabilities and process constraints from day one? New (modern) automation tech like that pioneered by pi, standard, the humanoid cos, and so on. The way forward to more robust systems seems plausible if by no means easy. But still we spend a lot of time engineering tasks for automation! Automation fails due to system brittleness, not complexity. Agents need consistent character judgment to close the loop on novel failures. Build minds, not Rube Goldberg machines. Complex systems are often brittle ones, and that complexity is often covering myriad weaknesses in the underlying automation. The real reason is that everything is a proprietary secret. You would have to make manufacturing from scratch again to be able to automate it. Automation only works when there are zero edge cases. If a process is 100% repeatable with no varations. That can be automated no problem. When business gets complicated. Little details come into play that destroys the ability to easy automate it. The idea that we can automate everything. Over simplifies the world. automation only works when the problem is repeatable. manufacturing is full of weird corners, custom specs, quality checks that need judgment. you can't automate away the variance.

https://itcanthink.substack.com/p/why-not-everything-is-automated-in?utm_source=share&utm_medium=android&r=3uj5js&triedRedirect=true

https://javalab.org/en/structure_of_an_atom_en/

What exactly is Industry 4.0? It’s when the worlds of IT and OT collied (enabled by IoT)

On a September morning in 2025, thousands of workers at Jaguar Land Rover's plants arrived to find their assembly lines silent. The culprit was not a strike, nor a supply-chain snarl, but something more insidious: a cyberattack. The JLR breach exemplifies a profound shift in manufacturing. Today's factories are no longer islands of mechanical precision; they are nodes in vast digital networks where information technology converges with operational technology, where business software communicates directly with robotic arms, and where a stolen password can halt production as surely as a broken machine. This convergence—termed Industry 4.0—has supercharged productivity. It has also created systemic fragilities that executives and policymakers are only beginning to understand.

To grasp what has changed, one must understand the distinction between two technological realms that historically operated in isolation. Information Technology (IT) is familiar territory. The software managing data and information—enterprise resource planning (ERP) systems running on servers, office computers, email. IT tolerates downtime with relative grace. Operational Technology (OT) inhabits a different universe. This is the domain of physical control: factory robots, conveyor belts, the articulated arms of FANUC and Yaskawa-Motoman machines that weld car bodies and spray paint with micron-level precision. OT downtime means production stops. Immediately. Expensively.

For decades, these worlds barely touched. Factory floors ran on proprietary control systems, isolated from corporate networks by design and obsolescence. That isolation, while limiting efficiency, provided security through obscurity. No more. The promise of Industry 4.0—real-time optimisation, predictive maintenance, just-in-time manufacturing at unprecedented scale—demands integration. The result is a technological stack of remarkable sophistication and equally remarkable vulnerability.

The anatomy of a modern factory

Consider a contemporary automotive assembly plant, with articulated robotic arms of FANUC and Yaskawa-Motoman coordinated across press shops, weld shops, paint booths, and final assembly lines. This orchestration operates through a hierarchical architecture.

At the foundation sits the physical layer: robots, CNC machines, sensors measuring everything from temperature to torque. Above this, the control layer comprises Programmable Logic Controllers (PLCs)—in Maruti Suzuki's case, Mitsubishi Q6 units, chosen for discrete manufacturing's demands of controlling individual machines and production lines. These industrial computers, smaller than a briefcase yet robust enough for 24/7 operation, execute the logic that transforms sensor inputs into actuator commands.

The supervision layer introduces SCADA—Supervisory Control and Data Acquisition. Here lies a semantic confusion worth clarifying. SCADA can denote either a system architecture (alternative to PLCs or Distributed Control Systems) or, more commonly in modern manufacturing, software that monitors PLC-based systems. Maruti Suzuki employs the latter: Elipse E3, a Human-Machine Interface (HMI) platform that visualises and coordinates operations across facilities.

Supporting this control hierarchy, Omron Remote I/O modules extend the reach of Mitsubishi PLCs across sprawling factory floors, collecting data from 6,000 input/output points—the nervous system connecting sensors and actuators to the controlling brain. Industrial networks, using protocols like DeviceNet, tie these components together, enabling the split-second coordination required when a car body moves from welding to painting.

At the apex sits the business layer: SAP ERP systems that translate physical production into financial reality, managing everything from raw material procurement to finished vehicle invoicing. The integration occurs through SAP-BAPI interfaces using SAP-RFC calls, creating a data flow that epitomises Industry 4.0:

Shop floor sensor data → Omron I/O → Mitsubishi PLC → Elipse SCADA → SAP ERP

This seamless progression from the tangible (a temperature sensor detecting paint-oven heat) to the abstract (an accounting entry booking production costs) represents manufacturing's digital apotheosis. When functioning, it produces vehicles with efficiency and quality impossible a generation ago. When compromised, as Jaguar Land Rover discovered, it produces catastrophe.

The rise of Industry 4.0 coincides with—and depends upon—another transformation: the migration of enterprise software from company-owned data centres to cloud platforms. This shift carries profound implications for both productivity and risk. SAP, founded in 1972, and Oracle, established in 1977, built empires on the on-premises model. Companies purchased perpetual licences and installed software on their own servers in their own facilities. Capital expenditures ran into millions; implementations stretched across years. But control was absolute. Data never left the building. The emergence of cloud computing, catalysed by Amazon Web Services' 2006 launch, challenged this paradigm. Upstart competitors—Salesforce for customer relationship management (1999), Workday for human resources (2005)—pioneered cloud-first models. Their pitch was compelling: no hardware investment, subscription pricing, automatic updates, access from anywhere, scalability on demand. Through the 2000s, SAP and Oracle resisted, protecting lucrative on-premises revenue. By the 2010s, market pressure proved irresistible. Both launched cloud offerings: SAP S/4HANA Cloud, Oracle Cloud ERP.

Today's reality is hybrid. Many large enterprises maintain core systems on-premises—particularly those handling sensitive data or requiring customisation—while moving less critical functions cloudward. The calculus involves trade-offs. Cloud offers lower entry costs, faster deployment (weeks versus months), vendor-managed security, and built-in redundancy. On-premises provides control, potential long-term cost savings for stable workloads, and compliance certainty for regulated industries. Yet cloud introduces concentration risk. When software runs on a vendor's infrastructure—AWS, Microsoft Azure, Google Cloud—a single vulnerability can affect thousands of customers simultaneously. A breach of SAP's cloud platform doesn't merely compromise one company; it potentially exposes every client sharing that infrastructure. This is the dark side of efficiency through standardisation.

The Jaguar Land Rover attack, attributed to a group calling itself Scattered Lapsus$ Hunters, followed a playbook catalogued in the MITRE ATT&CK framework, the de facto standard for understanding cyber adversary tactics. The initial intrusion exploited neither exotic zero-day vulnerabilities nor technical wizardry. Instead, attackers relied on social engineering's depressing effectiveness. The breach began with Infostealer malware—programs designed to harvest credentials from infected devices. These yielded access to Jira, Atlassian's project-management platform widely used in software development. Armed with valid credentials, attackers then deployed vishing (voice phishing): phone calls from individuals posing as internal IT staff, tricking employees into divulging additional passwords. This technique, banal in its simplicity, proved devastatingly effective.

Once inside JLR's network, the attackers executed lateral movement—the methodical expansion from initial foothold to broader access. They deployed custom malware for credential harvesting (stealing more passwords) and data exfiltration (copying sensitive information). Forensic investigators later identified specific IP addresses, domains, and file hashes as indicators of compromise. The exposed data catalogue makes grim reading: development logs, tracking information, source code, employee datasets.

But the real damage was operational. JLR's manufacturing infrastructure—that complex mesh of SCADA systems, robotics controllers, and SAP-based ERP modules—ceased functioning. Assembly lines stopped. Dealer management systems became inaccessible. Online sales platforms went offline. The company instructed staff to stay home. Production paused from September through early October: three weeks during which one of Britain's largest manufacturers built nothing.

The cascade extended far beyond JLR's gates. Modern automotive manufacturing operates on just-in-time principles, with supply chains synchronised to the hour. When JLR stopped ordering components, 5,000 organisations—Tier 1, 2, and 3 suppliers—felt the impact. Small machine shops in the Midlands. Semiconductor fabricators in Asia. Leather suppliers in Europe. The interconnectedness that enables global supply chains also propagates disruption at the speed of digital networks. The financial toll exceeded £50m weekly to JLR alone. The broader economic damage—lost wages, cancelled orders, opportunity costs—reached an estimated £1.9bn, making this Britain's most expensive cyberattack. When the Bank of England calculated third-quarter GDP, the JLR shutdown appeared as a measurable drag on national economic growth. Yet it was not unprecedented globally—merely the most visible instance of a vulnerability now endemic to modern manufacturing.

To understand why such attacks prove so devastating requires appreciating what ERP systems have become. They are not, as the name "Enterprise Resource Planning" might suggest, glorified accounting software. They are the nervous system of modern corporations—the infrastructure through which information flows and decisions propagate. A contemporary ERP installation manages not merely finance and accounting but human resources (payroll, hiring, performance tracking), supply chain (inventory, procurement, warehousing), manufacturing (production planning, quality control), sales (customer relationships, order management), and project management (resource allocation, timelines). Critically, these modules integrate. When a sales representative enters an order, the ERP system automatically updates inventory, triggers manufacturing if needed, creates invoices, notifies shipping, and adjusts financial forecasts. This is the "single source of truth" that executives prize: everyone sees the same data, in real time. The benefits are substantial. Manual data entry decreases, along with the errors that plague disconnected systems. Leadership gains instant visibility into business performance. Automation handles routine tasks. But the trade-offs are equally significant. ERP implementations consume 1-3 years, cost millions to tens of millions of pounds, and require reengineering business processes to match software logic. Once configured, systems prove inflexible; major changes risk destabilising operations. Implementing SAP, industry veterans say, resembles performing surgery on a company's entire operational structure while the patient remains conscious and working. This centralisation creates extraordinary efficiency. It also creates a single point of failure. When JLR's attackers compromised SAP, they didn't merely access financial data. They disabled the system through which the company coordinated global operations. Factories couldn't receive production schedules. Suppliers couldn't process orders. Dealers couldn't register vehicles. The business, quite literally, could not function.

The question facing manufacturers is whether the productivity gains from integration justify the systemic risk. There is no easy answer. Reverting to isolated systems would sacrifice competitiveness in markets where margins are thin and competition fierce. Yet the status quo invites existential crises from actors—criminal syndicates, state-sponsored groups, opportunistic hackers—who need not understand automotive engineering to cripple automotive production.

The Industry 4.0 revolution is irreversible. Global manufacturing increasingly depends on IT-OT convergence, real-time data flows, and cloud infrastructure. The competitive advantages—faster time-to-market, higher quality, lower costs, greater customisation—are too significant to abandon. But the security model must evolve to match the threat landscape. Some measures are technical: zero-trust architectures that verify every access request, network segmentation preventing lateral movement, immutable backups immune to ransomware encryption, regular penetration testing to identify vulnerabilities before adversaries do. The cybersecurity industry offers solutions; the challenge is implementation at the scale and pace required.  Other measures are organisational. Employee training to recognise social engineering. Incident response plans tested through regular drills. Cyber insurance to mitigate financial impact (though payouts rarely cover reputational damage or lost market position). Supply chain security assessments ensuring that integration with partners doesn't introduce vulnerabilities.  Perhaps most fundamentally, boardrooms must reconceptualise cyber risk. For too long, information security remained an IT department concern, divorced from business strategy. The JLR attack demonstrated what researchers have warned for years: in connected manufacturing, cyber risk is business risk. A compromised network doesn't merely expose data; it stops production, disrupts supply chains, and damages brands. Directors who would never delegate financial risk management to mid-level staff often do precisely that with cybersecurity—until catastrophe forces reassessment. The regulators are taking notice. Britain's National Cyber Security Centre has elevated the JLR incident to a Category 3 systemic event, the designation reserved for attacks affecting critical national infrastructure. The European Union's NIS2 Directive mandates cybersecurity measures for manufacturing firms. In America, the Securities and Exchange Commission now requires public companies to disclose material cybersecurity incidents within four days—a recognition that investors deserve to know when businesses face existential threats. Yet regulation alone cannot solve what is ultimately an asymmetric problem. Defenders must secure every potential entry point; attackers need find only one. Defenders must maintain vigilance constantly; attackers can probe patiently until defences lapse. Defenders operate under resource constraints and competing priorities; attackers often do not.

The convergence of information technology and operational technology represents manufacturing's latest revolution—as significant, perhaps, as the introduction of interchangeable parts or the assembly line. Like those earlier transformations, it promises extraordinary gains in productivity and quality. Like them, it introduces new risks and necessitates new thinking about how businesses operate and how societies govern their critical infrastructure. The JLR attack—with its stolen credentials, its lateral movement, its three-week shutdown, its £1.9bn economic toll—offers a preview of manufacturing's new reality. The connected factory is more efficient than its predecessors. It is also more fragile. The same digital networks that enable just-in-time production enable just-in-time paralysis. The same data integration that provides real-time business intelligence provides real-time attack surfaces.  Industry 4.0 has arrived. The question is not whether manufacturers will embrace IT-OT convergence—competitive pressures make that choice illusory—but whether they can secure what they have built before the next Scattered Lapsus$ Hunters, or their more sophisticated successors, demonstrate the limits of connectivity without security. The robots on factory floors now report to computers in distant data centres. That represents progress. It also represents risk. And in the autumn of 2025, on assembly lines from Solihull to Shanghai, 5,000 organisations learned precisely what that risk means when it materialises. The connected factory has transformed manufacturing. The connected threat has transformed risk. Industry 4.0's promise and peril are now inseparable.

The Anatomy of Connected Factory: Lean Industry 4.0

The history of industrial progress reads like a geopolitical scorecard. Britain held the crown in the 1760s, when steam and mechanisation birthed the first factories. America seized the lead a century later, electrifying production and perfecting the assembly line. Japan’s mastery of lean manufacturing and automation secured the third wave in the 1970s, when computers and programmable logic controllers crept silently onto shop floors. Now the world is in the grip of a fourth shift—one defined not by a new fuel or machine, but by the merger of two previously incompatible realms: information technology and operational technology. In this new era, software does not merely monitor production; it commands it. China, with its ferocious industrial ambition and willingness to rethink the rulebook, is now the front-runner.

Industry 4.0 is often summarised by jargon—cyber-physical systems, digital twins, IoT connectivity—but its essence is simpler: machines that generate data, systems that interpret it, and factories that learn as they produce. IoT provides the connective tissue. Sensors feed Omron I/O modules, which speak to PLCs such as Mitsubishi’s Q6, which in turn report to SCADA suites like Elipse. These systems no longer stop at the factory boundary. They now funnel into enterprise platforms such as SAP S/4HANA, where production schedules, purchasing decisions and accounting entries update in lockstep. The result is a remarkable chain: a temperature spike in a paint oven becomes an ERP alert; a machine-learning model forecasts failure; a spare part is ordered before the line goes dark. Mitsubishi robots and FANUC or Yaskawa-Motorman welding arms follow instructions that ultimately descend from cloud servers. Efficiency, once the product of physical engineering, now depends on software architecture.

No country has embraced this convergence with as much momentum as China. Its leading automotive plants run at automation levels approaching 97%, compared with roughly 60-80% in Europe and America. Digital twins allow entire production lines to be redesigned without metal ever touching metal. AGVs glide across warehouse floors where once forklift operators held sway. The transition is already evolving into something beyond its label: “Industry 4.0+”—where AI tunes production cycles autonomously, and 5G makes latency a memory. 2024 has been christened the first year of “smart mobility”: electrification was merely the prelude; intelligence is the main act. Indian manufacturers, including Maruti Suzuki and Tata Motors, are now scrambling to close the gap. They are building factories filled with robots, sensors and increasingly complex OT stacks. Yet many still run their operations on older ERP platforms, never designed to speak fluently to factory machinery. The upgrade path is as much strategic as technical. SAP, already dominant in global automotive manufacturing, offers the closest thing to standardisation. That is why Maruti Suzuki’s move toward SAP S/4HANA feels less a choice than a compulsory step in joining the international supply chain’s connected nervous system.

Connectivity, however, has introduced peril equal to its promise. In September 2025 Jaguar Land Rover learned that a factory network is only as strong as its weakest password. A stolen Jira credential—harvested by Infostealer malware and aided by low-tech phishing—proved sufficient to bring one of Britain’s largest manufacturers to a standstill. SCADA systems froze, robotic arms halted mid-stroke and SAP modules collapsed. For three weeks the firm produced nothing. Worse, the shutdown echoed across the supply chain: thousands of suppliers found their purchase orders paused, their schedules shredded. The total economic impact—nearly £1.9bn—appeared in Britain’s GDP figures. The lesson was stark: when IT and OT become one ecosystem, cyber risk evolves from nuisance to systemic choke point. The very forces that drive Industry 4.0—real-time data, automation, interdependence—also give attackers leverage once unimaginable.

The fourth industrial revolution is thus defined not merely by new machinery but by a shift in vulnerability. Factory downtime was once caused by broken parts; now it may be triggered by a database breach in a cloud data centre. Businesses will not abandon integration—competition forbids it—but they will have to rethink how such systems are governed, defended and audited. Zero-trust architectures, segmented networks, immutable backups and rigorous supplier scrutiny are likely to become as foundational as lean manufacturing once was in Japan. Regulators, insurers and investors will increasingly treat cybersecurity as operational infrastructure, not IT housekeeping. The future factory will be fast, adaptive and heavily instrumented—but, if its architects are wise, also architected for failure in a way its predecessors never needed to be.

The steam engine industrialised muscle. Electricity industrialised speed. Computing industrialised reasoning. Industry 4.0 industrialises decision-making itself. It is not yet clear whether nations leading this revolution will secure lasting dominance or inherit unmanageable systemic risk. The only certainty is that the next industrial contest will not be won solely on efficiency, robotics or artificial intelligence—but on the ability to secure the invisible networks stitching all three together. What happens when factories grow smarter faster than they grow safer?

My version: Everyone knows about IT (Information Technology), software that manages data and information like ERP running on servers, office computers, email, etc. and can tolerate downtime. On the contrary, one rarely hears about OT (Operational Technology) that controls physical equipment like factory robots and conveyor belts on assembly lines. This technology enables machines, sensors, controllers to do the physical work. Any downtime in OT means production stops.

This is the Fourth Industrial Revolution:

1. Industry 1.0 (1760s): Steam power, mechanization

2. Industry 2.0 (1870s): Electricity, mass production, assembly lines

3. Industry 3.0 (1970s): Computers, automation, PLCs introduced

4. Industry 4.0 (2010s): IT-OT convergence via IoT - cyber-physical systems

The Key Enabler: IoT, provides the connectivity infrastructure

China's auto industry is beyond Industry 4.0—they're pioneering what some call "Industry 4.0+" with the new initiatives calling for integrating AI, agile assemblies, big data, 5G, and IoT into factories, and establishing "smart factories" at levels exceeding most Western manufacturers. Chinese Auto industry is spearheading automation, using vertical integration, Digital Twins (Virtual simulation of entire production lines, and Optimization without physical testing) automated guided vehicles (AGVs-unmanned forklifts), robots, and intelligent warehousing for maximal efficiency. With the rapid rollout of 5G technology, China's Industry 4.0 market has a significant opportunity to expand through the Internet of Things (IoT). 5G enables faster data transmission, allowing real-time monitoring and control of factory operations. 2024 is widely recognized as the "Year of Smart Mobility," marking the beginning of the second phase of transformation in the automotive industry—after electrification, comes intelligence.

China's automotive industry is at:

✅ Full Industry 4.0 implementation (IT-OT convergence complete)

✅ Advanced AI integration (beyond basic 4.0)

✅ Leading global automation (97% in top facilities vs 60-80% Western average)

✅ Pioneering "Industry 4.0+" (AI, digital twins, 5G, humanoid robots)

Indian manufacturers (MSIL, Tata): Implementing Industry 4.0

Articulated robotic arms and CNC machines like those from FANUC and Yasakawa-Motoman have

PLC (Programmable Logic Controller) like Mitsubishi Q6 is best suited system architecture for industrial automation as it enables discrete manufacturing, i.e. controlling individual machines or production lines. SCADA-Elipse E3, is a Human-Machine Interface (HMI) software that runs on top of PLC systems to provide visualization and monitoring. These robots are controlled and coordinated by the Mitsubishi/Omron SCADA systems, all integrated with SAP ERP.

Manufacturing Control Systems, like SCADA, automate and control industrial manufacturing processes though components like Programmable Logic Controllers (PLCs) small industrial computers that control individual machines, Sensors that send data to the controllers, Actuators that execute commands, and Human-Machine Interface (HMI) the touchscreens/displays on shop floor. Industrial Networks connect all these components and allows coordination between systems. SCADA (Supervisory Control and Data Acquisition), acts as the "command center" that supervises and coordinates all the manufacturing control systems across an entire facility or multiple facilities. SCADA Systems are integrated with SAP through SAP-BAPI and Interface using SAP-RFC calls. This integration allows controlling press shops, welding robots, assembly lines, painting operations. Integration of OT with IT using IoT is what Industry 4.0 really is. All modern car manufacturers use Industry 4.0 technologies integrating ERP with SCADA to control Robotics using IoT enabling Real-time production monitoring.

Shop floor Sensor data → Omron I/O → Mitsubishi PLC → Elipse SCADA → SAP ERP

MSIL's Strategic Position:

├── Currently: Implementing Industry 4.0

├── Challenge: Oracle ERP not optimized for real-time OT-IT integration

├── Industry Standard: Global automotive uses SAP (BMW, Siemens, VW, Toyota)

├── Parent Company: Suzuki Motor Corporation uses SAP

└── Solution: Migrate Oracle → SAP S/4HANA

Why SAP S/4HANA for Industry 4.0:

├── Real-time in-memory computing (HANA database)

├── Native IoT integration capabilities

├── Better SCADA connectivity (SAP-BAPI, SAP-RFC)

├── Automotive-specific modules pre-built

├── Industry best practices embedded

└── Proven at scale (BMW, Siemens doing Industry 4.0 on SAP)

MSIL recognizes that achieving full Industry 4.0 requires an ERP system designed for real-time manufacturing integration, which Oracle isn't optimized for. SAP S/4HANA is the automotive industry standard for Industry 4.0, therefore migration is strategically necessary to complete their Industry 4.0 transformation.

With the rise of the smart factory, manufacturers are turning to ERP solutions that integrate IoT and machine learning for predictive maintenance and real-time data tracking. Infor and SAP S/4HANA are leading the charge.

The Complete Migration Logic 1. Industry 4.0 Requirements Driving ERP Choice What MSIL Needs for Full Industry 4.0: Real-time shop floor data → Business intelligence Seamless SCADA-ERP integration Predictive maintenance analytics Supply chain visibility IoT device management AI/ML capabilities for optimization Why Oracle Falls Short: Slower batch processing (not truly real-time) Complex IoT integration (requires more middleware) Less automotive-specific functionality Weaker manufacturing execution system (MES) Why SAP S/4HANA Excels: SAP S/4HANA is a standout, integrating machine learning, AI, and IoT to create smarter, more agile operations Cyber Press In-memory computing = instant data processing Built-in IoT platform Industry 4.0 ready out-of-the-box

A major driver of the global SAP S/4HANA application market is the impending end of support for legacy SAP ECC systems by 2027, prompting enterprises to migrate to S/4HANA for continuity and innovation.

https://www.linkedin.com/pulse/my-sap-implementation-journey-suzuki-farhan-muhammad-x4iof/

SAP modules:

·         Finance & Controlling (FICO)

·         SAP MDM (Master Data Management) - MDM ensures there is a “single source of truth” for master data, which supports accurate operations across procurement, inventory, sales, finance etc. - SAP’s dedicated tool for this is SAP Master Data Governance (MDG). - A framework/process (with dedicated SAP tools) to consolidate, clean, govern and maintain “master data” — core business data about products, vendors, customers, locations, assets etc. to ensure consistency across systems.

·         SAP Ariba - A cloud-based procurement/supplier-network platform focused on sourcing, supplier management, contracts, indirect procurement and supplier collaboration.

A standard interface/API within SAP that allows external applications or other modules to call SAP business-object functions (e.g. create orders, update master data). BAPI in SAP is basically a doorway that lets other software talk to SAP safely. It allows another system, like a website or custom program, to create or read things inside SAP. It is a standard and secure API provided by SAP to automate work and connect SAP with other systems.

RPG400 Programming

Primary Reasons for ERP Migration: 1. Better Industry 4.0 Integration Capabilities SAP S/4HANA is specifically designed for real-time integration with IoT and OT systems Better SCADA integration (which you noted: SAP-BAPI, SAP-RFC calls) Real-time data processing (in-memory computing) IoT enablement built-in Oracle ERP is powerful but historically: Stronger in financial/back-office processes Less optimized for shop-floor integration Slower real-time processing vs SAP S/4HANA 2. Automotive Industry Standard SAP dominates automotive manufacturing globally Volkswagen, BMW, Mercedes, Toyota, Honda, Ford → all use SAP Better automotive-specific modules Industry best practices built-in 3. Supply Chain Integration If MSIL's suppliers use SAP, integration is easier Parent company Suzuki Motor Corporation uses SAP Standardization across global operations 4. Real-Time Manufacturing Execution SAP MES (Manufacturing Execution System) is strong Better integration with SCADA/PLC systems Real-time production planning 5. Scalability for Growth MSIL targeting 4M vehicles/year SAP S/4HANA handles high-volume better Cloud-ready for future hybrid models

SAP (founded 1972) and Oracle (founded 1977) started as on-premises software. From 1970s-2000s, Companies bought licenses and installed software on their own servers in their own data centers. 2000s-2010s was the transition period where cloud computing emerged (AWS launched 2006). SAP and Oracle initially resisted cloud, protecting their profitable on-premises model. However, competitors like Salesforce (1999), a sales management software, and Workday (2005), a human-resource management software, launched cloud-first business models gaining market share. 2010s-Present saw a Cloud Push, SAP launched SAP S/4HANA Cloud and Oracle launched Oracle Cloud ERP. Both now aggressively push cloud versions, though many enterprises still run on-premises. Hybrid models are still common where some components are cloud based while others are on-premises. In the cloud model, the software runs on vendor's servers (AWS, Azure, Google Cloud) and the infrastructure is maintained by the vendor, data stored in vendor's data centers. The entity pays subscription, and vendor handles maintenance, this removes the initial hardware investment and makes it scalable, based on demand. The vendor handles installs, updates, patches, upgrades automatically, the entity always has latest version.

In September 2025, Jaguar Land Rover (JLR) experienced a massive cyberattack that involved a complete operational technology (OT) and IT systems compromise that Shut down SAP-ERP systems, disabled manufacturing control systems (SCADA) and paralyzed the entire global operation. A group calling itself Scattered Lapsus$ Hunters, exploited vulnerabilities mapped to MITRE ATT&CK techniques in their systems to gain access, using techniques such as valid accounts and public-facing application exploits breached through stolen Jira credentials harvested via Infostealer malware and posing as internal staff tricked employees into disclosing credentials over phone calls. Once inside, they moved laterally within the network, deploying custom malware for credential harvesting and data exfiltration. Indicators of compromise include specific IP addresses, domains, and file hashes. Exposed data includes development logs, tracking information, source code, and a large employee dataset. The breach affected 5,000+ organizations, all Tier 1, 2, 3 suppliers were impacted. This demonstrated how cyber risk equates to business existential risk. The automaker's connected manufacturing plants rely on a complex mesh of SCADA systems, robotics controllers, and SAP-based ERP modules ceasing core services like Assembly lines, dealer management systems became inaccessible and online sales platforms went offline.

ERP are the entire nervous system of a business (everything connected, coordinated). They are complete business operating systems that happen to include accounting as one module among many. That's why implementing SAP is like performing surgery on a company's entire operational structure. Single source of truth: Everyone sees the same data. Automation: Reduces manual data entry and errors. Real-time insights: Leadership sees business performance instantly. But takes 1-3 years to implement properly and can be inflexible once configured.

SAP T-Codes = Saving Time + Working Smarter Tired of endless menu navigation in SAP? Learn the power of T-Codes — quick commands that take you straight to what you need. From SE16 for data checks to SU01 for user management, T-Codes help functional consultants move faster, solve issues, and collaborate better with technical teams. 💡 Pro tip: You don’t need to code to be efficient in SAP. Knowing the right T-Codes is already a game changer.

How to command data?

The story of Data 4.0 is, in many ways, the story of human progress. It begins in 17th-century London, where John Graunt, a haberdasher with a scientific bent, compiled mortality statistics and unwittingly laid the foundations of modern data analysis. Over the centuries, others expanded on his idea: Florence Nightingale used charts to persuade generals and ministers of the need for sanitary reforms, while Edgar Codd’s invention of the relational database transformed how information could be stored and retrieved. As computers entered offices and homes, data evolved from simple record-keeping to a tool for decision-making—first descriptive, then prescriptive, predictive, and now generative.

Data 1.0, the digital dawn of the 1980s, democratized computing. Data became a corporate resource, though mostly confined to local databases and spreadsheets that generated routine reports. Data 2.0 arrived with enterprise resource planning. Firms sought a single version of truth, building warehouses and deploying analytical tools such as Teradata and Greenplum to consolidate information across departments. Data 3.0, the age of “big data,” saw an explosion in volume, variety, and velocity. Smartphones, sensors, and connected machines churned out torrents of information. Companies built data lakes and sophisticated algorithms to find patterns and automate decisions, but much effort still went into plumbing—governance, integration, and metadata—rather than insight itself.

Now comes Data 4.0, a more intelligent and holistic phase. Here, data is treated not as exhaust but as fuel—a strategic asset that powers digital transformation. Cloud-native and metadata-driven, this new architecture uses automation and machine intelligence to generate trusted insights at scale. Instead of rigid warehouses, modern firms rely on flexible “lakehouses” built on open standards, enabling multiple analytical engines—SQL, search, AI—to work seamlessly over shared, well-governed datasets. Data catalogs are now as vital as data stores, allowing organizations to locate and use information with precision. In this world, every application becomes a learning system, every decision an informed one. The evolution from Graunt’s mortality tables to today’s intelligent dashboards marks not just technological progress, but a profound shift in how knowledge itself is created and applied.

When you learn SQL, Python, and Power BI, your real goal isn’t coding — it’s automation and sharper insights.

🧮 SQL – The Data Librarian - SQL (Structured Query Language) is the language used to talk to databases and to pull data from different system and join all these datasets to create one consolidated view to built a single source of truth. Imagine your company’s database as a giant library with millions of books (rows of data). SQL is the librarian who knows exactly where to find what you want. For example, if you ask: “Show me all sales from Delhi last month where profit was above ₹1 lakh,” SQL fetches that precise slice of data in seconds. In short: SQL helps you ask questions to your data and get answers quickly. Analogy: SQL is like typing a Google search, but instead of the internet, you’re searching your company’s private data library. SAP holds everything — vendor details, invoices, tax codes, purchase orders, and GL entries — but finding the right slice of data when you need it can be painfully slow. With SQL, you can query this data directly.

You don’t need to wait for IT or pull 50 Excel dumps. Within minutes, you have a clean dataset — filtered, joined, and reconciled — ready for reporting or further analysis. SQL turns you into the data gatekeeper of the tax function: precise, independent, and fast. Topics: Basics of databases — tables, keys, joins, queries Filtering data (WHERE, GROUP BY, HAVING, ORDER BY) Combining multiple tables (INNER JOIN, LEFT JOIN) Creating tax exception reports (aggregations, CASE WHEN) Writing subqueries to extract insights (e.g., vendor-wise TDS summaries) ⚙️ Practical Projects: Extract TDS deduction data and flag missing vendor PANs Identify invoices where GST input credit was not claimed Build a monthly tax payment summary directly from SAP extracts. Tools: SQLite / MySQL Workbench for learning Later connect SQL to SAP or Power BI. Outcome: You’ll be able to pull your own tax datasets without Excel dependency — clean, accurate, and ready for analysis.

🐍 Python – The Problem Solver (Python helps you go from data → intelligence) - Python is a general-purpose programming language that’s simple to read and powerful enough to do almost anything with data. It can: clean messy datasets, automate calculations, predict and analyze patterns, detect anomalies, build machine learning models, and even automate boring office work (like renaming 100 files or sending weekly reports automatically). Because it’s easy to learn and very flexible, Python is the favorite tool of data scientists and analysts. In short: Python is the Swiss Army knife of data — it can analyze, predict, and automate, all in one tool. Analogy: If Power BI is the storyteller and SQL is the librarian, Python is the engineer who builds the entire data machine. Most work including Tax is repetitive by nature — reconciliations, validations, data formatting. Python helps you automate all of this. You don’t just ensure compliance — you anticipate non-compliance.

🧩 Power BI – The Storyteller - Think of Power BI as a smart dashboard maker made by Microsoft. You take all your SQL+Python insights and build stunning, interactive Power BI dashboards. It takes huge piles of numbers from Excel sheets, databases, or websites and turns them into colorful charts, graphs, and interactive reports. So instead of reading 50 pages of data, you can just glance at a dashboard and see—how much your sales went up last quarter, which region is performing best, or where costs are creeping up. In short: Power BI doesn’t create data; it makes data easy to see and understand. It helps decision-makers spot trends instantly. Analogy: Power BI is like Google Maps for your business— instead of showing roads, it shows how your data connects and moves.  The CFO doesn’t want a 40-page Excel report. He wants a one-page dashboard showing: Tax liability movement by category (GST, TDS, Income Tax) Refunds pending beyond 90 days Litigation exposure by jurisdiction Vendor-wise tax risk scores That’s where Power BI transforms your work. You connect your SQL and Python outputs to Power BI and build dynamic dashboards — interactive, auto-updated, and visually compelling. Now, during the monthly review, instead of defending spreadsheets, you simply open the dashboard. A few clicks show that “Input tax credit reversals increased 8% this quarter due to vendor non-filing.” Your leadership immediately sees the issue — and your proactive control over it. Most of your team’s time is lost in reconciliations, data validation, and Excel formula juggling. Python will help you automate all that. 🧩 Topics: Python basics — data types, loops, functions Working with Excel and CSV files using Pandas Cleaning and transforming tax data Automating reconciliations (e.g., purchase register vs. GSTR-2A/2B) Summarizing results and emailing automated reports ⚙️ Practical Projects: Create a TDS compliance tracker that reads expense GL data, checks deduction sections, and auto-flags mismatches. Build a GST input reconciliation tool that compares 2A/2B with your purchase register and generates an exception report. Develop a small email bot that sends daily summaries (e.g., pending refunds, litigation updates). 🧰 Tools: Python (Anaconda or Jupyter Notebook) Libraries: pandas, openpyxl, smtplib, matplotlib

For a tax manager at MSIL, learning SQL, Python, and Power BI isn’t about coding — it’s about reclaiming time, control, and influence. You move from: “Compiling reports” → to → “Driving decisions.” By mastering these tools, you: Reduce dependency on IT or analysts. Identify risks early — earning trust from finance leadership. Automate repetitive work — freeing time for strategic projects. Enhance your visibility — through insights that matter to top management. In short: You stop chasing data and start commanding insights. And in a firm like MSIL — where precision, compliance, and efficiency are non-negotiable — that’s what makes you not just a good tax manager, but an indispensable one. Topics: Power BI basics — importing data, cleaning, and relationships Building tax MIS dashboards (GST, TDS, refund trackers) Using DAX formulas for advanced calculations Creating automated reports linked to SQL and Python outputs Designing visuals for management (slicers, trend lines, maps) ⚙️ Practical Projects: CFO Dashboard: tax liabilities, refunds, litigation, and exposure GST Reconciliation Dashboard: ITC claimed vs. available Vendor Risk Dashboard: high-risk vendors flagged by Python scripts Litigation Tracker: state-wise case distribution and status. Tools: Power BI Desktop (free) Integrate with Excel / SQL / Python scripts. Outcome: You’ll present live dashboards that update automatically and drive management conversations — making you a visible “data-first tax leader.”

Scheduling Python scripts to run daily and push results to a shared drive Connecting Power BI dashboards to updated data outputs Building automated email reports for leadership Drafting SOPs for your team on using these tools.

Python basics for AI/ML, Unsupervised Learning / K-Means Clustering, Naïve Bayes, K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), Random Forest