The Impact of AI in Manufacturing on Employment ...

INTRODUCTION:

These days, there is a lot of excitement globally about AI: It seems that AI has quickly become the expected panacea to address and solve forecasting, operational and logistical issues across the economy. At the same time, there are a lot of people expressing concerns about the impact AI “may” have on employment!

Being involved in the AI-in-Manufacturing (“AI4M”) Industry since 1985, I have seen many opportunities, applications, successes, and failures – as well as a lot of confused people wondering exactly how AI “works”: I have noticed that to many people it seems that if they understand how AI works they will then be able to understand how the AI paradigm will impact the world of employment in the future. Unfortunately, this is usually not true!

My involvement with AI started in 1989 when I founded Integrated Wood Research in Canada – in partnership with GE’s Factory Automation Group, to develop and patent an AI-driven secondary wood processing application to optimize wood fiber utilization and redefine the economics of adding value to “trees”.

Then, in 2016, I founded Canadian Industrial Hemp Corporation (“CIHC”) to perform the R&D needed to develop a proprietary, patentable AI-driven hemp stalk “optimizing” and processing application for hemp and flax stalks to produce natural fibers at a lower cost and with higher quality.

My involvement expanded further in 2021, when I was invited to sit on the Board of NGEN’s AI-in-Manufacturing Association with 7 other Canadian leading Ai4M / Machine Learning experts who have created some of the top Digital Twin and Manufacturing applications and solutions used in Canada and abroad. In fact, NGEN is an acronym for “Next Generation Manufacturing” and is Ontario’s contribution to the Canadian government’s national SuperCluster Program – designed to accelerate the R&D and commercialization of Advanced Manufacturing opportunities and to keep Canada competitive.

I founded Advanced Bio-Material Technologies Corp. (“ABMT”) in 2021 to commercialize the successful results of CIHC’s R&D: The world’s first and only Smart Factory application based on CIHC’s patented “Smart Stalk System”.

THE SIGNIFICANCE OF MANUFACTURING IN THE GLOBAL ECONOMY TODAY

In 2010, Dr. Jon Rynn, the author of "Manufacturing Green Prosperity" wrote that:

(a) Manufactured goods are necessary for trade. According to the World Trade Organization, 80% of interregional trade is in goods, and only 20% is in services.

(b) According to the U.S. Economic Policy Institute, each manufacturing job supports three other jobs in the wider economy, through something called “the multiplier effect.

(c) Economic growth depends on manufacturing. Manufacturing productivity, that is, the goods that are output from a specific amount of input, increases by about 3 percent each year in the U.S., year in and year out, because of technological advances are always being made for factory machinery.

(d) National power depends largely on manufacturing power. Over the last 100 years, the “Great Powers”, or most powerful four or five countries, have controlled about 75 percent of global industrial machinery production. This is primarily because industrial machinery is used both to generate national wealth and to produce military equipment.

(e) A world in which all regions had a strong manufacturing base would go far to eliminate poverty and war. Manufacturing creates middle class jobs that anchor a middle-class economy.

It is worth noting that he made these comments just one year before the Fourth Industrial Revolution (“Industry 4.0”) surfaced into the commercial Manufacturing world in 2011!

Given the key advances in the advanced Manufacturing tools and platforms available since then, I am sure you can appreciate just how much more important Manufacturing is today – and why AI in Manufacturing is gaining in relevance and importance globally.

In fact, in 2018, the UK’s Dean Group wrote: “ Manufactured goods are necessary for trade and ￡6.7 trillion ($US7.5 trillion) to the global economy. And then, in January 2022, McKinsey & Company advised in an article they produced: “Want to drive inclusive economics growth? Start with Manufacturing.”

Manufacturers – all over the world are pivoting to Ai4M, because they know they must: For sustainability; Environmental friendliness; and, in a world of fast-rising wages – to lower costs to improve productivity and to remain competitive, both to protect their domestic markets as well as to safeguard whatever markets they have been serving outside of their national boundaries.

In the end the relevant question will be: “How will this new approach to redefine the economics of Manufacturing? Will it simply just replace humans with technology?”

THE “BIG-PICTURE” IMPACT OF AI-in-Manufacturing

I believe that AI4M will have a definite impact on employment – but not necessarily to its detriment. I believe that this is the case because AI will not just change the opportunities and dynamics of employment, but more significantly, because it will change global culture – much the same way that the Industrial Revolution did back in 1760.

Many of the jobs that will be lost to AI4M will not be missed: In many cases, these jobs involved working in very difficult conditions, with very low wages. They were generally unskilled, dead-end jobs, usually difficult to keep operating without the need to turn to low paid immigrants and foreign workers: But these jobs did feed their families.

Not well - but they were fed. What of them?

There will be certain trades where technology will have little impact – at least in the short term. These will include specialized trades who perform their tasks in different locations, most days, such as plumbers, carpenters, and electricians. However, over time, the development of Sentient robots may change even this: Think of fully automated modular home construction with installation performed by AI-driven ultra-strong and agile robots.

This may all sound very negative, but I think we need to expand our context of interpretation: The way we regard things today will, I believe, be very different over the next decade of “transition” to the operating paradigm focused on human capabilities up until now! For example …

When Henry Ford determined that physically moving a car being assembled to the workers at each subsequent “work-station” - instead of having men all work on building the car at the same time – I expect that there were some people who thought it was a bad idea: That being more productive (ie: making more cars in less time) would inevitably result in fewer jobs for people in Detroit, Michigan, in the then fast-growing automobile manufacturing industry. However, there is another side to this coin.

Consider that when Ford started selling his Model A Ford in 1904 it was priced at $US750, equivalent to about $26,320.24 in buying power today. Meanwhile, the average income in the USA in 1904 was about $US500 – worth about $US 17,634.63 in buying power today. That means that in 1904 an average individual in the USA would have had to save the equivalent of 1.5 years of their TOTAL wages to be able to buy a car. Remember that in 1904 there certainly was no leasing – or financing available. Now, let’s turn to 1921, 10 years after Mr. Ford invented the assembly line.

In 1921 a new Model A cost $US275 - which today would be equivalent to $US 4,362.35 - while the average American’s annual income in 1921 was $US3,269.40 a year – which in today’s terms would provide about $US 49,341 in buying power. Suddenly, almost every working man could afford – and justify - buying an automobile. And they did.

The point is: Improving productivity and introducing new levels of cost efficiency often redefine the economics of production enabling new products to COMPETE ON LOWER PRICES AND MORE CONSISTENT HIGHER QUALITY very effectively against traditional materials. And, the new lower costs and larger production volumes will generate larger scale, sales and margins, facilitating the cashflow needed to invest in ongoing R&D, to further improve the overall Manufacturing process - and bring the costs down even more, over time.

I believe that it is not meaningful to make predictions without knowing all the relevant variables – and possibilities. Some of the relevant variables will change over time, however, there are certain facts to consider – both Pro and Con:

PRO ....

? AI4M reduces costs, improve quality control & delivers a scalable solution

? Generates higher productivity & material consistency, & Customer satisfaction

? AI4M provides easier, low cost, digital, ML-based job & skills training solutionsCON ...

? The skills and wage scale for AI technicians will go up

? Higher productivity and higher margins

? Improved Economies of scale will definitely be generated

? The system can use open orders and trends to automatically determine the balanced volume between demand and supply.

? A stable market minimizes “commodity” pricing swings

? AI4M increases productivity and minimizes the impact of wage increasesCon ...

? Will create a short-term shortage of supply of appropriate workers

CON ...

? Capex and technology costs are higher than the existing Manufacturing paradigm

? Some workers will lose the specific jobs they have been used to working

? Many currently working individuals will need to be re-trained

? There will likely be a shortage of skilled AI technicians for at least 5 years

? Will create a short-term shortage of supply of appropriate workers

THE BRAVE NEW WORLD: “Adaptation”

There will be jobs: Many new jobs. And, many of the old jobs will disappear. And there will be a lot of “new” jobs requiring individuals to be trained – both practically and emotionally - which has historically been a problem.

However, the access to information – and knowledge – will continue to explode over the web in the new AI world, and training will become easier and more “digitally” pro-active; with built-in tests and objective real-time evaluations of progress becoming standard fare. Inexpensive and effective retraining will play a significant role in upskilling the unskilled who may lose their existing job to AI4M.

People will be retrained faster and at a lower cost simply because many of the necessary skills can be taught online - step by step - and explained. This statement will be especially true if an individual is given access to an intelligent bot which can virtually interact with them to “determine” the most effective method for them to learn whatever it is that they need to know. The bot will also be able to provide unannounced real-time “spot” tests to ensure that the individual has actually studied, learned and understood the material required. AI4M will bring about a quantum leap in cultural changes.

HOW PEOPLE ADAPT TO CULTURAL CHANGES

Throughout history, mankind has had to adapt to many cultural changes. These were brought about by the never-ending changes in the economic, political, technological changes applied around the world, over the centuries.

In 1760, the Industrial Revolution was introduced to mankind by the coal-fired steam engine. Suddenly, heavy work could be addressed with the power of nature – rather than having to rely on strong men or horses. At that time it certainly must have seemed like meaningful progress had been made towards improving mankind’s circumstances of life. However, it also would have impacted the employment realities of many people doing manual labour who would have inevitably been concerned about their livelihoods.

The Industrial Revolution impacted only hourly paid blue-collar workers. However, the same impact on job security impacted office workers in the 1990s when computerized systems redefined their lives.

Just as in the 1760s, people in Manufacturing today are watching their jobs being replaced by “technology” and “machines” which can produce more output, faster and better and thereby achieve meaningfully lower unit production costs.

What this means to future employment opportunities is fundamental: Companies will earn more; people with practical OPERATIONAL or DIAGNOSTIC skills will earn more ; there will be many more of these “skilled” jobs; and, with reduced real-time pressures, these jobs should be less stressful and more satisfying.

THE “NON”- EMPLOYMENT OPTION

Back in the summer of 2020, when the USA was preparing for their Federal Elections, one of the contenders in the Democratic Party’s Primaries was Andrew Yang who introduced the idea of providing a “Universal Basic Income”.

Yang attended Brown University and Columbia Law School and found success as a lawyer and entrepreneur before gaining mainstream attention as a candidate. His signature policy was a monthly Universal Basic Income which proposed that the US government give every adult citizen a set amount of money on a regular basis, typically without conditions. The goals of the program is to alleviate poverty and other need-based social programs that would otherwise potentially require greater bureaucratic involvement. The intention is to offset the job displacement anticipated by the ongoing progress in advanced digitized solutions: To give an individual the ability to survive financially during a period of unemployment and retraining. At the same time, by making it universal, little administration would be needed to administer the program.

I foresee that “UBC” will ultimately become a significant part of the cultural shift that AI will require to offset some of the uncertainties associated with transitioning from an economy based on human “thinking” to one which is primarily AI-based. Yang called UBI a "Freedom Dividend".

Other cultural changes will most likely create a shift in gender hiring preferences as well: Jobs once seen fit only for stronger men, for example, will become meaningfully gender neutral – bringing with it the elimination of any possible rationalization for gender bias in pay scales!

RE-SHORING MANUFACTURING JOBS

Another very positive thing that AI4M will help achieve is the re-shoring of manufacturing jobs and their allied industries - from logistics and trucking to equipment and facilities maintenance and local parts distribution. All of this will happen because of the resurgence of manufacturing that will be created by AI4M’s incredibly high productivity performance.

This type of “local” Supply Chain integration will not only provide more jobs within each country, by localizing it the associated shipping and handling costs will be reduced. This will not only improve the overall productivity of manufacturing, helping to keep costs down to the end consumer, it will also ensure greater stability throughout individual Supply Chains.

SUMMARY:

Nobody “KNOWS” what the future will bring. However, if AI4M does not deliver at least some of the benefits I have described above, and unemployment ends up running rampant the only thing that will prevent social chaos will be cultural change. There is basic logic to my comments above, but it is certainly not guaranteed. Let’s just hope that mankind’s ingenuity will continue to go beyond his current abilities to create new solutions which will serve to make this a better world with fewer inequalities.

NGen Canada