For most of the last 75 years of the automotive industry, efficiency has been one of the key battlefields in the quest for better margins. Keeping costs down during development while avoiding downstream quality costs has been a constant focus, with major changes in industry-wide cultures. Yes, there have certainly been moments of innovation that fostered ad hoc, market-driven pricing, but most automotive pricing and margins have been managed around costs, e.g., upfront development, material pricing. Bill of lading, manufacturing cost, etc.
Focusing on engineering and re-engineering costs, there have been many changes over the past decades that have spread across competitors and continents. These eras, if you will, are not distinct and completely separate, but rather gradual changes with frequent overlap. Nevertheless, they can be understood in the context of macroeconomics and the after-effects of world events.
First Age: Stability
In the 20+ years following World War II, Gross National Product (GNP) grew tremendously around the world, for example, in the United States it grew fourteen (14) times faster than population – which was the lowest since the war. After was the “boom”. War births and related expenses – plus seven (7) times inflation. With this, the average income rose from $2,200 in 1942 to $8,000 in 1965 (when adjusted for inflation), meaning the average family earned 3.6 times more by the end.
Companies saw that the path to efficiency was to maintain a stable workforce that could innovate and produce revenue without the need for recruiting, training, etc. Employment-based health coverage tripled, and between 1945 and 1950, the number of new participants in pension plans doubled.
Maintaining workforce stability was paramount, and the adage “Take care of the company and the company will take care of you” was born.
Second era: offshoring and onshoring
Outsourcing or offshoring began in the late 1960s and early 1970s as large corporations began to move labor to lower-cost countries. This practice by many manufacturer-based countries began with manufacturing in each instance (for example, the United States sent production to Mexico) because the 30–50% reduction in labor costs outweighed other expenses such as shipping. Decades after this blue-collar introduction, engineering jobs empowered manufacturing teams to make precise evolutionary changes to existing platforms under their care following the launch of production.
Additionally, countries looked for other ways to reduce historic post-war labor costs by importing workers. In the US in 1952, the H-1 visa was created for immigrants with “distinguished qualifications and ability”. In 1990, the United States Congress created the H-1B visa, which started as 65,000 slots, but interest grew so much that in 2023 it received a record 780,884 applications, a 61% increase from 2022.
Third era: process control
From 1987 to 1997, Carnegie Mellon University created the Capability Maturity Model (CMM) which demonstrated that greater efficiency could be achieved by better process control even within engineering development. Additionally, the International Standards Organization (ISO) first published the ISO 9001 standard in 1987. Both organizations sought to reduce costs and risk through quality management around standards that evaluated engineering practices. Ultimately, those standards evolved and, at times, were replaced (for example, Automotive SPICE PAM 4.0 will be released in the coming weeks, which is used by almost every automotive manufacturer instead of CMMI), but still Lower total costs based on equal methodological rigor and, ironically, quicker delivery due to less mess to clean up.
Fourth era: connected data
In the early ’90s, three companies – General Motors, EDS and Hughes Aircraft – recognized the convergence of cellphones, airbags and data centers as part of an effort originally called “Project Beacon”, which eventually became OnStar. Went. The subsequent sharing of information from vehicles and servers was not only the first connected car, but began an automotive revolution that grew from very little collected data per vehicle to “…between 380 and 5,100 TB each year”.
10-15 years after the birth of the connected car, manufacturers began to realize that data could help reduce warranty costs and, in turn, reduce downstream costs and inform upfront engineering. Kumar Galhotra, Lincoln’s current president, told Reuters in 2020, “What we’re trying to do is fix the issues as quickly as possible so that those adjustments are as small as possible.”
The fifth era: flexible workplace
The pandemic was seen by many as the rise of the hybrid workplace as working from home accounted for only 5% of white-collar workdays before Covid (and now 40% in 2023), but remote worker efficiency was measured and widely reported before. Was known since. As personal computers became more widely available in the 1990s, approximately 1% of the American workforce worked from home, with studies showing that such flexibility increased productivity, increased work hours, campus Reduction in required footprint and reduction in turnover. Now three years into the pandemic, several studies have either confirmed or denied such findings, but two of the most famous are the Microsoft study of 60,000 employees (10% increase in weekly hours), and a study of 27 countries. (which found that workers saved an average of 72 minutes in daily commutes and reallocated two hours per week to additional productivity).
Sixth Age: Extra Intelligence
The sixth era is the application of Artificial Intelligence (AI) in various parts of product development rather than just the product itself. This “extra” intelligence makes the tech guru more efficient by equipping him with machine learning that detects quality concerns, prepares earlier drafts or suggests capabilities that do not replace the intelligent engineer but rather further equip him. is done. For example, AI-powered analysis can detect requirements flaws, which “…on larger projects, lead to rework.” [can be] About 60% of the total cost.” AI code assistants help software developers write code faster and more accurately. And AI-testing solutions (for example, Monolith AI software) help engineers determine customized test plans based on requirements. “Our north star is to help companies get something to market faster,” says Dr. Richard Ahlfeld, founder and CEO of Monolith, a provider of AI software to the world’s leading engineering teams. ,[Companies] Run battery aging testing campaigns that take three years, run through thousands of cycles and cost more than $10 million. AI can reduce this by 70%.”
Ludovic Houduk, CTO of Enverso and former vice president of engineering for Meta (in charge of their AI infrastructure), predicted, “AI will bring a new level of productivity and innovation.” “Just as a large percentage of the jobs of the 1950s no longer exist today, it is likely that 30% of all existing jobs will be reshaped in some way through automation over the next 2-3 years.”
The reader has probably noticed that several eras are still happening simultaneously; Some are declining, some are accelerating, some have been revived anew. In all likelihood, the winner of the efficiency game will be the CEO who finds the right mix between them. For example, does sustainability with engineers adequately trained on additional intelligence (and an understanding of how they support evolving process standards) create better efficiencies than offshoring? Will the hybrid workplace support that stability by building team morale, or will it hinder it by reducing the sense of team and co-creativity?