European automotive companies can set benchmarks in the EV revolution by adopting an ecosystem approach and reinforcing digital capabilities to build resilient supply chains, writes Ruchir Budhwar
Nations at the COP26 summit pledged to substantially reduce greenhouse gas emissions to mitigate climate change. Automobile majors are scaling up e-mobility programmes to meet decarbonisation mandates by 2050. This pivotal shift is already reverberating around the world. In 2020, automotive sales plummeted in the aftermath of the COVID-19 pandemic, but sales of electric vehicles (EVs) registered a 40% growth, according to the Global Electric Vehicle Outlook 2021 report by the International Energy Agency.
As the world transitions to e-mobility and the global demand for EVs gathers momentum, automakers need to rewire and digitise the manufacturing ecosystem. The local auto industry must realign the product lifecycle management (PLM) construct with the dynamics of EVs, and incorporate digitisation into each stage of the production cycle to sustain market leadership.
Generative design for optimisation
Computer-aided design, engineering and manufacturing (CAx) models of the 1980s fired the imagination of product designers and effected a shift in automobile design and development. CAx platforms integrated functional areas and manufacturing activities to streamline PLM processes and drive engineering excellence. Generative design transforms CAD software with artificial intelligence (AI), machine learning (ML) and virtual simulation. It replaces linear design and engineering processes with parallel runs of design, evaluation, validation and optimisation. This accelerates research and development, product engineering services, homologation, and time-to-market for EVs.
As the world transitions to e-mobility and the global demand for EVs gathers momentum, automakers need to rewire and digitise the manufacturing ecosystem
EVs need advanced design and engineering frameworks to overhaul legacy manufacturing and leverage emerging practices. Generative design algorithms abstract textual and visual data from input files and apply deep learning to align core systems with the performance and quality requirements of EVs. The solutions address structural constraints, technical parameters, functional specifications, and aesthetic considerations, with the primary objective of minimising the weight of the vehicle. A lightweight chassis helps improve the driving range of EVs.
In 2018, General Motors pioneered the adoption of generative design. GM partnered with Autodesk to generate multiple combinations of an automotive part leveraging cloud computing and AI-based algorithms. The technology generates hundreds of organic and high-performance design options based on user specifications such as durability, material and fabrication type. For instance, an AI-generated design solution for a seat bracket enabled a 40% reduction in weight and a 20% increase in strength.
Digital twin for scenario analysis
Generative algorithms generate a multitude of potential solutions for design and engineering goals, be it the chassis weight, load capacity or end-of-life value. Digital tools enable product teams to optimise and test design variants in real-world conditions. For instance, scenario modelling may be used to analyse the impact of innovative materials on product performance. A digital twin simulates the entire product lifecycle as well as the manufacturing environment, enabling AI-generated design options to be tested without prototyping and trial runs.
Besides creating design variants for permutations of specifications and variables, and simultaneously verifying and optimising potential options, generative AI empowers automakers to explore alternative manufacturing techniques. 3D printing, for example, can be a cost-effective replacement for injection moulding or machining to produce parts. Further, generative design and advanced manufacturing technologies enable mass customisation.
Robotic automation for repeatability
Traditional computer numerical control (CNC) machining systems used by OEMs in make-to-stock operations are primed for an upgrade. Robotic automation powered by AI and computer vision enables manufacturers to establish a make-to-order ecosystem with lean production configurations. Robotic control systems seamlessly integrate design, engineering and production processes to boost manufacturing efficiency. Advanced automation minimises downtime and enhances the efficacy of EV production sites. EVs can be assembled faster, at lower costs. Further, lean manufacturing systems rationalise investment in inventory, reduce waste, and maximise utilisation of production resources. Hyundai Motors leveraged electrification and automation to gain entry into the Japanese automotive market.
Industrial robots can be easily programmed to undertake a range of tasks, including welding, trimming, spray painting, material handling, packaging, and scrap removal. Moreover, the programmability of robotic automation systems based on real-time operating conditions lends itself to enhancing critical functions such as quality control. Robots combine machine intelligence and computer vision to evaluate materials on the assembly line as well as finished products vis-à-vis specifications. Notably, inspection and testing by robots is more accurate than statistical quality control systems, irrespective of production volumes..
Blockchain for product history
Automakers phasing out internal combustion engines need to minimise the environmental footprint of batteries powering electric vehicles. In this regard, precious minerals and metals in used batteries can be retrieved for reuse and recycling. It requires manufacturers to incorporate circularity in their business models and ensure transparency for closed-loop supply chain management of batteries.
Blockchain technology acts as a digital ledger that enables immutable traceability, disclosure and validation across the extended supply chain—from raw materials through the service life of a battery and its second life via recovery and reuse. This traceability encourages sustainable practices in the EV value chain; it incentivises stakeholders to accelerate ethical sourcing practices and minimise the carbon cost of operations.
The distributed ledger offers a mechanism for supply chain provenance to mitigate risks. It facilitates a holistic system for compliance with regulatory frameworks and industry initiatives for sourcing and sustainability, such as the Responsible Minerals Initiative (RMI), International Council on Mining and Metals (ICMM), and Copper Mark. Industry-wide programmes such as the Battery Passport, an initiative of the Global Battery Alliance (GBA), and ReISource, a consortium for end-to-end cobalt traceability, ensure accountability and resource-efficiency of EVs. In addition to fulfilling environmental and social obligations, manufacturers can address ‘range anxiety’ for end users. They should ensure the feasibility of e-mobility by designing battery packs that can be swapped, and building battery-swapping stations as well as battery charging infrastructure.
Ruchir Budhwar is Senior Vice President and Industry Head of Manufacturing—Europe at Infosys