Sustainable as standard: Transforming automotive supply chains with BSI

The automotive supply chain is transforming at a critical pace. The Covid crisis is forcing OEMs to improve visibility and digitalisation across operations. Connected and electric vehicle technologies are encouraging new business models and relationships. And sustainability is no longer a ‘nice to have’ for manufacturers, but a critical must to satisfy consumers as well as regulators.

In this podcast, Automotive Logistics editor-in-chief Christopher Ludwig talks to experts from the British Standards Institution (BSI) about how automotive companies are managing these changes including the role of standards in driving transformation, the circular economy of the end-to-end industry and understanding the potential for technologies like artificial intelligence and machine learning.

Christopher is joined by BSI’s Robert Brown, Global Head of Automotive, and Martin Townsend, Global Head of Sustainability & Circular Economy. Both have decades of experience in developing and implementing standards and best practices across the automotive industry and global sectors.

Guests

Robert Brown leads BSI’s global automotive services. He joined BSI coming from the IATF Oversight Manager at the UK’s Society of Motor Manufacturers and Traders (SMMT), where he was responsible for writing the new IATF 16949:2016 standard and its supporting rules. Rob was a founding member of the IATF training commission, responsible for the development of the “Automotive Process Approach” training and qualification of 400 Certification Body auditors.

Martin Townsend has worked as an environmental regulator, advised ministers, worked with city mayors and business across the world of all sizes and sectors, ensuring sustainability is an enabler of business success. He joined BSI as Global Head for Sustainability and Circular Economy in November 2019 and sits on several advisory boards for public and private sector organizations to support them in their success.

In Partnership

This Voice of the Industry podcast is in partnership with BSI.

BSI is the business improvement company that enables organisations to turn standards of best practice into habits of excellence. For over a century BSI has championed what good looks like and driven best practice in organisations around the world. Working with 84,000 clients across 193 countries, it is a truly international business with skills and experience across a number of sectors including aerospace, automotive, built environment, food, and healthcare. Through its expertise in Standards Development and Knowledge Solutions, Assurance, Regulatory Services and Consulting Services, BSI improves business performance to help clients grow sustainably, manage risk and ultimately be more resilient and trusted.

BSI looks to work with business of all sizes to ensure that a sustainable business is one that generates profit while improving societal and environmental impact it has. This includes what you do (your product or your service) as well as how you do it (the way you operate). Examples include ethical supply chains, and procurement, to the wellbeing of employees, corporate social responsibility and beyond.

www.bsigroup.com

Published at Mon, 01 Mar 2021 08:48:45 +0000

Winners and losers in the digital transformation of work

Milan – Perhaps no single aspect of the digital revolution has received more attention than the effect of automaton on jobs, work, employment and incomes. There is at least one very good reason for that — but it is probably not the one most people would cite.

Using machines to augment productivity is nothing new. Insofar as any tool is a machine, humans have been doing it for most of our short history on this planet. But, since the first Industrial Revolution — when steam power and mechanization produced a huge, sustained increased in productivity — this process has gone into overdrive.

Not everyone welcomed this transition. Many worried that reduced demand for human labor would lead to permanently high unemployment. But that didn’t happen. Instead, rising productivity and incomes bolstered demand, and thus economic activity. Over time, labor markets adapted in terms of skills, and eventually working hours declined, as the income-leisure balance shifted.

And yet, as augmentation of human labor gives way to automation — with machines performing a growing number of tasks autonomously in the information, control and transactions segments of the economy — fears of large-scale job losses are again proliferating.

After all, white — and blue-collar jobs involving mostly routine — that is, easily codified — tasks have been disappearing at an accelerating rate, especially since 2000. Because many of these jobs occupied the middle of the income distribution, this process has fueled job and income polarization.

As in the nineteenth century, however, labor markets are adapting. At first, displaced workers may seek new employment in jobs requiring their pre-existing skills. But, facing limited opportunities, they soon begin pursuing jobs with lower (or easily attainable) skill requirements, including part-time jobs in the internet-enabled gig economy, even if it means accepting a lower income.

Over time, a growing number of workers begin investing in acquiring skills that are in demand in non-routine, higher-paying job categories. This is generally a more time-consuming process, though it has been accelerated in some countries, including the United States, by initiatives involving government, businesses and educational institutions.

But, even with institutional support mechanisms, access to skills development is usually far from equitable. Only those with sufficient time and financial resources can make the needed investment, and in a highly unequal society, many workers are excluded from this group. Against this background, we should probably be worried less about large-scale permanent unemployment and more about an uptick in inequality and its social and political ramifications.

To be sure, technological adaptation may reduce the magnitude of the skills-acquisition problem. After all, markets reward innovations that make digital equipment and systems easier to use. For example, the graphical user interface, which enables us to interact with electronic devices via visual indicator representations, is now so pervasive that we take it for granted. As such intuitive approaches are applied to increasingly complex technological processes, the need for re-training — and, thus, the digital revolution’s distributional impact — will be diminished.

Progress on artificial intelligence will also have an impact. Until about ten years ago, automation relied on the codification of tasks: Machines are programmed with a set of instructions that reproduce the logic of human decision-making. But what about tasks that cannot be distilled into a series of logical, predefined steps? From understanding natural language to recognizing objects visually, a surprisingly large number of activities — even ostensibly simple ones — fit into this category. This has kept many jobs “safe” from automation, but not for much longer, owing to advances in machine learning.

Machine learning is essentially very sophisticated pattern recognition. Using large pools of data and massive computing power, machines learn to do things we cannot code. They do this using examples rather than rules-based logic. Advances in machine learning have opened vast new areas of automation: robotics, autonomous vehicles, and scanning technical medical literature for key articles. In many areas — such as pattern recognition in genetics and biomedical science — machines not only become capable of replacing human workers; in certain respects, their capabilities dwarf those of any human.

This is better news than it may seem. Yes, far more tasks and subtasks will be reallocated to machines. But the purpose and end point of the digital revolution must be to turn automation of work into digital augmentation. And when machines perform tasks humans cannot, augmentation is precisely what we are getting.

While it is impossible to say for sure at this early stage, there is reason to believe that the transition costs of this new round of work-related disruptions will be experienced more broadly across the income spectrum than the first. At the low end of the income spectrum globally, advances in artificial intelligence and robotics will disrupt and eventually displace labor-intensive manufacturing — and the development models that depend on it. At the high end, machine learning-based capabilities will have a major impact on scientific research and technological development, as well as high-end professional services.

The fact remains, however, that we are dealing with highly complex transitions, not equilibriums: And we cannot expect natural adaptation by workers and labor markets to produce equitable results, especially with huge differences in household resources as a starting point.

That is why policymakers (in partnership with business, labor and schools) must focus on measures to reduce income and wealth inequality, including ensuring broad access to high-quality social services like education and skills training. In the absence of this kind of intervention, there is a significant risk that the digital transformation of work will leave many people behind, with adverse long-run consequences for social cohesion.

Michael Spence, a Nobel laureate in economics, is emeritus professor at Stanford University and Senior Fellow at the Hoover Institution. ©Project Syndicate, 2021