How Smart Factories Driving Efficiency in Auto Manufacturing

In an exclusive interaction with Thiruamuthan, Assistant Editor at Industry Outlook, Vivek Gosain, Vice President – Production Engineering at Force Motors, shares his insights on how India’s auto sector is accelerating smart factory adoption to boost efficiency and quality while managing integration and workforce challenges. He also highlights digitalization, policy support, and capability-building as key to creating a more resilient, sustainable, high-performance manufacturing ecosystem. Vivek Gosain is a senior automotive manufacturing leader with 32+ years of experience across Honda, General Motors, MG Motor, and Tata Motors, specializing in greenfield plants, EV/ICE portfolios, digitalization, sustainability, capacity planning, smart manufacturing, and high-performance team building.

With India’s auto sector adopting smart factory technologies, what measures are enhancing production efficiency while overcoming integration and workforce adaptation challenges?

India’s automobile sector is returning to the fast track with the GST amendment, and the need for swift adoption of smart technologies has become even more critical. Companies are investing in robotics and automation to boost manufacturing efficiency through robots, cobots, and specialized solutions that enhance quality, speed, accuracy, and repeatability. IoT and data analytics enable real-time monitoring and predictive maintenance, reducing downtime and improving machine efficiency, while digital twins, as virtual models of production lines, help visualize and optimize layouts and processes and are used for continuous improvement, significantly reducing optimization time.

AI and machine learning are being deployed for quality inspection, defect detection, and process optimization. MES/MOM (Manufacturing Execution Systems / Manufacturing Operations Management) integrates shop-floor data with business systems for better scheduling, traceability, and real-time decision-making, and this integration is being extended to strategic and critical vendors in the value chain. Smart supply chains are improving productivity at OEMs by reducing WIP, line-side, and finished goods inventories, while real-time tracking of vendor quality performance strengthens built-in quality.

The industry has implemented a standard approach to establish RCCP (Rough Cut Capacity Planning) with vendors, helping companies plan resources in advance, reduce variations, and improve operational efficiency. At the same time, energy and resource management have improved, with smart sensors monitoring energy and materials, reducing waste, and fostering sustainability, making it a win-win from both ESG and financial standpoints. Government and industry initiatives such as Samarth Udyog Bharat 4.0 further boost digital adoption and skill development across the sector.

In light of rising automation and robotics deployment, which strategies are manufacturers using to optimize assembly line productivity without compromising quality?

Automation and robotic applications deliver consistent, repeatable results for the operations they are deployed for. This is enabled through proximity sensors, vision cameras, and other integrated field devices that ensure there is no significant variance in child parts or assemblies and any variance can disrupt operations until the non-conformity is resolved. These controls must not be altered or bypassed to ensure quality. For productivity, uninterrupted production depends on material availability, maintenance, and technically skilled personnel.

Assembly-related automation opportunities exist in handling through ergo assists, manipulators, and AGVs/AGCs, as well as in human–robot collaboration where co-bots manage repetitive tasks. Smart manufacturing, IoT, flexible lines, digital twins, automated quality control, workforce upskilling, and Lean/Six Sigma practices collectively enhance efficiency and quality.

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As digital twin and IoT solutions gain traction, what steps are being taken to ensure real-time monitoring improves uptime and predictive maintenance across plants?

To make machines smart and capable of understanding their own health, IoT sensors are installed on equipment to capture real-time data such as temperature, vibration, and energy use. This data is streamed via IoT edge devices to a central cloud platform for processing and storage. A digital twin, or virtual model that represents the equipment, is built and continuously calibrated utilizing real-time information. This data will be examined by analytics and AI to find anomalies or deviations and forecast possible failures. Before issues arise, the system sends out notifications or suggests maintenance. This is critical in areas such as press shops, robotics, and EOT hoists, where predictive maintenance of machines, dies, and moving systems is essential.

With advanced data analytics transforming operations, how are auto manufacturers leveraging insights to reduce waste, energy consumption, and production bottlenecks?

As automotive manufacturing becomes more complex, competitive, and sustainability-driven, manufacturers can no longer rely on periodic checks or manual oversight to stay efficient. They are increasingly turning to connected machines, sensors, and analytics platforms that convert shop-floor and supply chain data into real-time insights, enabling them to systematically cut waste, lower energy use, prevent unplanned downtime, and keep production lines running smoothly and predictably.

As connected supply chains become standard, in what ways are smart factories addressing logistics coordination and material flow inefficiencies to meet market demand?

Automated transport uses self-driving robots (AGVs/AMRs) to deliver parts exactly when and where they are needed, minimizing manual labor and errors. Sensors on parts and bins enable “talking” parts, which automatically trigger replenishment orders to prevent assembly line shortages. Additionally, AI-driven predictive planning analyses real-time and historical data to forecast demand and ensure materials are ordered proactively.

Digital testing using virtual simulations, or digital twins, helps identify and resolve workflow bottlenecks before they impact physical production. By seamlessly integrating design, manufacturing, and supply chain systems via cloud computing, real-time communication is enabled, allowing holistic optimization of material flow. Product sequencing, tracking, and part traceability are then efficiently managed through MES/MOM platforms integrated with ERP systems.

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Looking forward, what innovations or policy measures could enable India’s auto sector to scale smart factory adoption while driving sustainable, high-efficiency manufacturing?

Targeted government incentives and extended Production-Linked Incentive (PLI) initiatives, which benefit companies in managing the high initial cost of new technology, tend to be examples of policy measures to speed up smart and sustainable manufacturing. In order to close current skill shortages in the workforce, specialized training programs in data analytics, robotics, and artificial intelligence are vital.

Technological integration facilitated by the widespread usage of AI, IoT, digital twins, and robotics accelerates workflows and promotes data-driven decision-making. At the same time, innovation in green production and recycling increases environmental responsibility and energy savings. Future smart transportation is built on strong digital connectivity as well as networks of alternative fuels and public charging stations.

On the shop floor, technological innovations such as the integration of AI, IoT, and robotics, the use of digital twins and simulations, advanced supply chain management, sustainable manufacturing processes, and MES/MOM-based smart digital manufacturing plants collectively enhance productivity, visibility, and long-term competitiveness for manufacturers.