Ashok Leyland is an Indian multinational automotive manufacturer, headquartered in Chennai. It is owned by the Hinduja Group. It was founded in 1948 as Ashok Motors and became Ashok Leyland in the year 1955. Ashok Leyland is the second-largest manufacturer of commercial vehicles in India, the third-largest manufacturer of buses in the world, and the tenth-largest manufacturers of trucks.With the corporate office located in Chennai, its manufacturing facilities are in Ennore (Tamil Nadu), Bhandara (Maharashtra), two in Hosur (Tamil Nadu), Alwar (Rajasthan) and Pantnagar (Uttarakhand). Ashok Leyland also has overseas manufacturing units with a bus manufacturing facility in Ras Al Khaimah (UAE), one at Leeds, United Kingdom and a joint venture with the Alteams Group for the manufacture of high-press die-casting extruded Aluminium components for the automotive and telecommunications sectors. Operating nine plants, Ashok Leyland also makes spare parts and engines for industrial and marine applications.Ashok Leyland has a product range from 1T GVW (Gross Vehicle Weight) to 55T GTW (Gross Trailer Weight) in trucks, 9 to 80-seater buses, vehicles for defence and special applications, and diesel engines for industrial, genset and marine applications. In 2019, Ashok Leyland claimed to be in the top 10 global commercial vehicle makers. It sold approximately 140,000 vehicles (M&HCV and LCV) in FY 2016. It is the second largest commercial vehicle company in India in the medium and heavy commercial vehicle (M&HCV) segment, with a market share of 32.1% (FY 2016). With passenger transportation options ranging from 10 seaters to 74 seaters (M&HCV = LCV), Ashok Leyland is a market leader in the bus segment. In the trucks segment Ashok Leyland primarily concentrates on the 16 to 25-ton range and has a presence in the 7.5 to 49 ton range.
The electrification of road transport is expanding demand for dedicated battery-pack assembly capacity, and manufacturers increasingly seek to design, verify and rebalance such lines virtually before committing capital. This paper reports the design of a digital-twin layout for a modular cell-to-module-to-pack (C2M2P) assembly plant for a prismatic lithium-iron-phosphate (LFP) electric-vehicle battery pack of 3.2 V per cell, sixteen cells per module (16S) and three modules per pack (3S). A three-dimensional virtual entity was built in DELMIA against the actual two-dimensional plant layout and organised as a five-dimension digital-twin model comprising physical entity, virtual entity, twin data, services and connections [9,10]. A provenance-labelled data layer classifies every quantity as source-derived, engineering-reference, calculated or estimated, so that confirmed project data is not conflated with assumptions — the manual analogue of the data-integrity discipline that full digital twins enforce at scale. A discrete-event throughput model localises the line constraint to the cell-stacking robot (≈30 modules h⁻¹) and the six-axis busbar-welding station (≈55 modules h⁻¹); a gravitational-load and NIOSH-based ergonomic screening justifies overhead handling for the 64 kg module and 192 kg pack; and a qualitative failure-mode assessment maps dominant risks to design mitigations. The study shows how a defensible, service-oriented digital-twin layout can be specified from incomplete concept-stage data, and it defines the connections that must be instrumented to advance the model from a digital shadow to a fully closed-loop twin [13].
Battery pack manufacturing is a safety-critical, energy-intensive and economically sensitive process in the Indian manufacturing landscape. Operational data on production, quality, maintenance, cost, energy, inventory, traceability and environment are typically scattered across separate functional reports, delaying executive review and weakening cross-functional accountability. This paper presents the design and development of a plant-level Management Information System (MIS) dashboard for a commercial battery pack assembly plant. Using a Business Requirement Document (BRD), benchmarking and exploratory secondary research as primary inputs, the proposed dashboard integrates eight management dimensions: production/OEE, quality, maintenance, safety, cost/energy, traceability, inventory and environment, health and safety (EHS). Adopting a management-oriented design-science approach, the study benchmarks recent peer-reviewed literature (2021–2026) to derive KPI commonality, battery-specific measures, formula governance and Sustainable Development Goal (SDG) linkages. The central argument is that dashboard value is created not by visualization alone but by clearly defined KPI formulas, ownership, data-freshness rules, exception handling and traceability to source records. The result is a practical framework that turns distributed shop-floor data into a one-page decision-support system, with future scope for AI and digital-twin integration.
Transportation sector in India accounts for 12% of total energy consumption. Demand of energy consumption is being met by the imported crude oil, which makes transportation sector more vulnerable to fluctuating international crude oil prices. India is mindful of its commitment in 2016 Paris climate agreement to reduce GHG emissions intensity of its GDP by 40% by 2030 as compared to 2005 levels. To fast track the decarbonization of transportation sector, commercial vehicle manufacturers have been exploring other viable options such as battery electric vehicles (BEVs) as a part of their fleet. As on today, BEV has its own challenges such as range anxiety & high total cost of ownership. Range anxiety can be certainly addressed by optimum sizing of electric powertrain, reduction in specific energy consumption (SEC) & use of effective regeneration strategies. Higher SEC can be more effectively addressed by doing vehicle energy audit thereby estimating the energy losses occurring at each powertrain component of an electric vehicle. The work illustrated in this paper involves drive cycle-based energy audit & range estimation for 4X2 rigid electric truck using simulation approach. It involves strenuous exercise of simulation specific input data generation by doing rigorous component level tests for battery, motor, tires & auxiliaries. Duty cycle data was acquired for 3000 km & condensed cycle of 32 minutes was formed which represents real world usage pattern. Data recorded in component and vehicle tests was used to build robust simulation model in GT-DRIVE. Simulated SEC was validated within 4% with on road trails. 73.5% of battery discharge energy was used to overcome rolling resistance loss, aerodynamic drag loss, electromechanical conversion loss, auxiliary losses, braking losses & differential losses. Effective power at wheels observed to be 26.5% of total battery discharge energy. Sensitivity analysis for RAR, RRC, coasting & braking regeneration limits was carried out and effect of each parameter on final SEC was studied and optimum set of parameter combination was suggested to the OEM. Outcome of this project has also laid down the sophisticated methodology to carry out energy audit of any electric vehicle, which in turn will help to bring simulation predictions much closer to the real-world scenarios.
The rising demand for smart manufacturing has accelerated the adoption of Digital Twin (DT) technology, enabling real-time monitoring, simulation, and process optimization in industrial settings. This research focuses on developing a Digital Twin model for a Single Cell Manufacturing Unit (SCMU) using DELMIA, a widely recognized digital manufacturing platform. The proposed virtual model accurately represents the manufacturing cell, analyzing key factors such as cycle time, workstation utilization, material flow, and energy consumption. Through iterative simulations and data-driven optimization, the Digital Twin framework enhances decision-making, reduces inefficiencies, and improves overall productivity. The research methodology involves creating a 2D layout, transforming it into a 3D model, and conducting process simulations within DELMIA to assess different configurations. A comparative analysis of multiple iterations helps determine the most efficient and operational-effective manufacturing setup. This study underscores the importance of Digital Twin technology in enhancing single-cell manufacturing processes, lowering operational constraints, and fostering data-driven, intelligent production systems in alignment with Industry 5.0 principles.
The goal of the project is to create an optimised layout design for imparting training at training centre using Revit software. The training center deals with identifying and rectifying mistakes made by workforce in the industry by improving their skills. The model focuses on space utilization, improved space allocation for workstations, class rooms and emergency exit areas. It’s also designed considering futuristic scope of integrating new technology in the training center.This study highlights the importance of effective space planning in skill development centers (otherwise called training centers) to create a productive and learner-friendly environment while maintaining compliance with safety and accessibility standards
