For the better part of the last 75 years of the automotive industry, efficiency has been one of the major battlegrounds in the quest for better margins. Keeping costs down during development whilst avoiding downstream quality costs has been the ongoing focus with major shifts in industry-wide cultures. Yes, there certainly have been moments of innovation that spark temporary, market-driven pricing, but much of automotive pricing and margins have been managed around costs, e.g., upfront development, Bill of Material pricing, manufacturing costs, etc.
Focusing on the engineering and re-engineering costs, there have been multiple shifts throughout the decades which have swept across competitors and continents. These ages, if you will, have not been discrete and fully separated, but rather a gradual shift with frequent overlaps. Nevertheless, they can be understood within the context of macroeconomics and the after-effects of world events.
The First Age: Stability
In the 20+ years after World War II, the Gross National Product (GNP) grew tremendously around the world, e.g., in the United States it jumped fourteen (14) times as fast as the population — which was “booming” with post-war births and associated spending — and seven (7) times the inflation. With that, the average income grew from $2,200 in 1942 to $8,000 in 1965 (when adjusted for inflation), meaning the average household earned 3.6 times as much by the end.
Companies saw the way to efficiency was maintaining a stable workforce that could produce new designs and revenue without the need for recruitment, training, etc. Employment-based health coverage tripled, and between 1945 and 1950, new participants in pension plans doubled.
The Second Age: Offshoring and Onshoring
Outsourcing or offshoring began in the late 1960’s and early 1970’s as large corporations began transferring labor to lower-cost countries. This practice by multiple manufacturer-based countries began with manufacturing in each instance (e.g., United States sent production to Mexico) since a 30-50% labor cost reduction overcame other expenses such as shipping. Decades after this blue-collar introduction, engineering jobs followed as to empower manufacturing teams to exact evolutionary changes to the existing platforms under their care post-production’s launch.
Simultaneously, countries sought other ways to shed the historical expenses of post-War workers by importing workers. In 1952 in the U.S., the H-1 visa was created for immigrants with “distinguished merit and ability.” In 1990, the United States Congress created the H-1B visa, which began as 65,000 slots, but interest ballooned such that it received a record 780,884 applications in 2023, a 61% increase from 2022.
The Third Age: Process Control
From 1987 to 1997, Carnegie Mellon University create the Capability Maturity Model (CMM) that demonstrated greater efficiency could be achieved by better process control even within engineering development. Coincident with this, the International Standards Organization (ISO) first published the ISO 9001 standard in 1987. Both organizations sought to reduce costs and risk via quality management around standards that assessed engineering practices. Eventually, those standards evolved and, at times were replaced (e.g., Automotive SPICE PAM 4.0 will be released in the coming weeks, which is used by nearly every automotive manufacturer instead of CMMI), but still with the premise of methodical rigor equating to lower overall costs and, ironically, quicker delivery due to fewer messes to clean-up.
The Fourth Age: Connected Data
In the early 90’s, three companies – General Motors, EDS and Hughes Aircraft – recognized the convergence of cellphones, airbags and data centers as part of an effort originally named “Project Beacon,” which eventually became OnStar. The subsequent sharing of information to and from the vehicle and a server was not the first connected car, but began an automotive revolution that’s grown from very little collected data per vehicle to “… between 380 and 5,100 TB every year.”
10-15 years after the birth of the connected car, manufacturers started to realize that data could help with warranty reduction and, therein, both reduce downstream costs and inform the upfront engineering. “What we are striving for is to fix the issues as fast as possible so that those adjustments are as small as possible,” Kumar Galhotra, the now-President of Lincoln told Reuters in 2020.
The Fifth Age: Flexible Workplace
The pandemic was viewed by many as the rise of the hybrid workplace since working from home only accounted for 5% of white collar workdays prior to Covid (and now 40% in 2023), but remote worker efficiency was measured and widely known beforehand. As the personal computer became more widely available in the 1990s, nearly 1% of American employees worked from home with studies suggesting such flexibility increased productivity, increased hours worked, decreased the campus footprint required and decreased turnover. Now three years after the pandemic, multiple studies have either confirmed or denied such findings, but two of the most renowned are the Microsoft study of 60,000 employees (finding a 10% boost in weekly hours), and the 27-country study (that found workers saved 72 minutes of average, daily commuting and reapportioned two hours per week to additional productivity).
The Sixth Age: Added Intelligence
The sixth age is the application of Artificial Intelligence (AI) to various parts of the product development instead of just to the product itself. This “added” intelligence makes the tech guru more efficient by arming him/her with machine learning that finds quality concerns, creates first drafts or suggests efficiencies such that the intelligent engineer isn’t supplanted but rather further equipped. For instance, AI-driven analyses can find requirements defects, which on “… larger projects, 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 (e.g., Monolith AI software) help engineers determine optimized test plans based upon the requirements. “Our north star is to help companies get something to market faster,” states Dr. Richard Ahlfeld, the founder and CEO of Monolith, an AI software provider to the world’s leading engineering teams. “[Companies] run battery aging test campaigns that take three years, run across thousands of cycles and cost more than $10 million. AI can reduce that by up to 70%.’”
“AI will unleash a new level of productivity and innovation,” predicts Ludovic Hauduc, CTO of Envorso and former VP of Engineering for Meta (in charge of their AI infrastructure). “Just like a large percentage of jobs from the 1950’s no longer exist today, it’s likely that 30% of all current jobs will be reshaped somehow in the next 2-3 years through automation.”
The reader probably has noticed that several of the ages are still happening concurrently; some declining, some accelerating, some resurrected anew. In all probability, the winner of the Efficiency Game shall be the CEO who figures out the correct mix between them. For instance, does Stability with sufficiently trained engineers on Added Intelligence (and an understanding of how those support evolving process standards) create a better efficiency than Offshoring? Will the Hybrid Workplace support that Stability by creating team morale, or will it hinder it by diminishing a sense of team and co-creativity?