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Recent key developments in MAKE│NZ
Last week we were still following up on staff sizes, this time asking what your plans were for the upcoming six months, and here’s what you had to say:
With the majority looking to expand, and then the rest split between staying the same and shrinking, it looks like we should be seeing a lot of progress over the next few months. And, presumably, a lot of job advertisements…
Speaking of, our beloved accountant of many years has finally decided it’s time to step back and enjoy some relaxation time. We’re sad to see her go but happy to know she’ll be getting some well deserved R&R. That does of course mean we’ll be joining some of you by posting an advertisement of our own:
As the poster says, we’re on the lookout for a part time accountant open to up to ten hours of work per week. If you or someone you know might be a good candidate, please reach out to dieter@makenz.org or sabine@makenz.org.
Make a note in your calendar: The next meeting of our Production Managers’ Working Group will be on the morning of Wednesday, Sep.17! We’ll be at Hamilton Jet, and the meeting will focus on opportunities and challenges when designing the lay-out of and processes in a new production facility.
Recent key developments in New Zealand
•There are on-going concerns about our ability – or lack of – to attract enough of the right (young) people to careers in manufacturing. There have been some laudable initiatives recently, like Earn-as-you-Learn and the FutureMakers campaign. The question remains of whether we are ‘pushing the right buttons’ with these campaigns to make enough of a difference, keeping in mind that this is not merely a numbers game. With the rapid changes in manufacturing processes and technologies in many areas, it will also be about attracting people with the right skills and, more importantly, the ability to acquire new specialist skills.
Could it be that one of the root causes behind the challenge is that manufacturing (in New Zealand) has an ‘image problem’? Not that there is a ‘bad image’, but that there is no image at all?
If we think of a list of professions – plumber, lawyer, teacher, pilot, builder, … in each case the label will immediately evoke a specific and concrete image in people’s mind, even if they have no specific knowledge of or affiliation with the industry in question. ‘Manufacturing worker’ …?! Try this at home: ask someone not familiar with manufacturing about the first image that comes up in their mind when they hear ‘manufacturing worker’. Or you can try yourself: Enter the word ‘teacher’ or ‘plumber’ into a Google Images search, and you get lots of symbolic images that pretty much show exactly the same thing. Now try ‘manufacturing worker’ …
Not a lot of uniform symbolism here …
The ‘no image’ problem is likely to be less of an issue where manufacturing as a career is more associated with the resulting product than with the activity itself: I work for Addidas, or Boeing, or Porsche … I make shoes, planes, or cars. Most of New Zealand manufacturing (outside of food & beverage) does not involve well-known and halo consumer products or brands, so there will be no (exciting) product-image association.
Is it likely to be much harder get young people to even ‘start thinking’ about a career in manufacturing when there is no compelling image to get them excited in the first place?
Another factor may well be the employer brand. Good employer brands can take a long time to build. Our contact in Festo in Germany tells us that each year their apprenticeship offers are over-subscribed many times. That may have something to do with the fact that the company has been operating in the same location for exactly 100 years and has been able to build a strong employer brand during that period.
And, last but not least, the inter-generational factor. Research done as part of an MBA degree at Canterbury University showed – albeit on a limited scale – that parental role models are an important factor in young people choosing a career in manufacturing. ‘Helping dad fixing the car’, or parents working in manufacturing themselves, seems to be a (strong) factor. It would be interesting to get a better understanding of this. We’ll organise an informal mini-survey at our next Production Managers’ meeting (see above): we’ll ask production managers to ask their team leaders to find out for how many in their team it is true that their parents were also manufacturing workers.
If you have any thoughts on any of the above, we’d like to hear from you! dieter@makenz.org
•Higher levels of unemployment tend to have a moderating effect on wage settlements. Having said that, Reserve Bank of New Zealand [RBNZ] labour market data show a slight increase in both unemployment rates and wage growth over the past four quarters:
The RBNZ’s projections, however, taking a more conservative approach and haven’t changed over the past six months (LCI= Labour Cost Index):
The question is whether wage growth can be capped at that rate when the rate of inflation for key components especially of lower-income household budgets – food and energy – keep rising much faster:
The last thing manufacturers need right now is a further squeeze on margins from labour cost increases driven by inflation well above the RBNZ’s target in areas that really matter most for most households.
Recent key developments in the World
In a Nutshell: The question was – whose jobs are most at risk from automation in manufacturing, and does / will AI change that picture? A very high-level answer to the first question is that jobs with a high component of manual repetitive simple tasks requiring low- to medium skill levels in long-run assembly operations are most at risk. Most of this automation has already happened, especially in high-wage economies, including China. AI can help to automate (much) more complicated tasks, but for jobs where cost is the only consideration – and not health and safety, for example – the cost of developing training models (and access to training data) may still make it uneconomic to automate. That is true in particular for functions that require relatively low skill levels but would be complicated to automate.
•Last week we wrote about the role of AI in automating a range of different functions and the downstream impact on jobs. For the latter, one of the key determinants is the complexity of the function to be automated vs the cost of human labour doing the job in question. In general, the more complicated the task, the more training data is required – think about Level 5 (full automation driving) in cars – and the more expensive it will be to automate. Digger operators was used as an example, but there will be any number of examples in manufacturing as well. In manufacturing, the problem is access to training data. It’s not like Large Language Models, where the internet is a vast source of training data. Hence a lot of effort has gone in creating an ‘industrial metaverse’ where synthetic training data is created with the help of digital twins and simulation platforms like NVIDIA’s GROOT, MIT’s PhysicsGen, Siemens’ SIMATIC Robotics AI, or Genesis, a GitHub open-source multi-party physics simulation platform designed for general purpose robotics, embodied AI, and physical AI applications, out of which Genesis AI was created as a commercial entity. Watch this space …
•Staying with automation: In manufacturing, the biggest wave of automation occurred in the last decade, when many assembly-related functions in particular became automated, especially in long-run manufacturing. Taking the automotive industry as the most prominent example:
To drill down to the next level:
•The above picture is the result of massive investment in robots, especially in some industries, over the past 20-plus years. However, at least for the automative sector and the period in question (2004 to 2019), in most cases automation did not lead to massive job losses, neither in component manufacturing, nor in final assembly. On the contrary, there was net employment growth in many cases, driven by rapidly growing market demand in both quantity and quality. More cars, and cars with more and more advanced features.
Across all manufacturing industries, however, during the two decades of 1995 to 2015, the deployment of robots went up, and the number of workers declined, in major manufacturing economies:
However, there is anything but a simple cause-and-effect relationship here, with different mechanisms at work in different countries. More details can be found here.
What automation did result in was a shift towards more roles at higher skills levels, with those at lower skills levels and unable to adjust being a higher risk of unemployment. That was demonstrated in a very thorough study of manufacturing industries in Germany and published in 2024. It showed that unemployment risk was highest in low-skill jobs, followed by medium-skill jobs, and lowest in jobs requiring a high skill level. Differences were more pronounced in industries that had higher levels of robotisation. Not surprisingly, robotisation affected jobs with a high component of manual repetitive tasks more than others, with, again, differences being greater in industries with high levels of robot deployment.
This appears to be at odds with what we reported in our newsletter two weeks ago, where we reported that, over the past decade in Germany, the number of people employed in low-skilled jobs has risen by 23%, almost as much as for specialist occupations and experts (31%). The number of people employed in skilled jobs in between the above categories, on the other hand, has only risen by 3.3%.
This goes to illustrate that the picture is more complicated and beyond simple cause-and-effect relationships across industries and types of manufacturing processes and operations. Contributing factors will be the ability to automate functions, the cost of doing so, both depending on the complexity of the function in question, the frequency of execution (long-run vs short-run manufacturing), and, most importantly, the return on investment for automating. What we can be reasonably sure about is that AI will continue to meddle with these dynamics – more about that next week.
And, finally, another quick snapshot: Germany’s car companies aren’t the only ones facing headwinds. Machinery and equipment manufacturers, one of the three pillars of German manufacturing (together with automotive and chemical), are similarly in decline:
The biggest threat is coming from China. China’s machinery and equipment industry is catching up – and in some cases surpassing – its German equivalent in functionality and quality, often at a lower price. The 15% tariff in the US, their biggest market, isn’t helping German manufacturers of machinery and equipment, either. The industry’s structure is quite different to that of the automotive industry, with some relatively large players like Trump, and the top end of their SME range still quite big, but there is also a long tail of small and highly specialised manufacturers whose best chance of survival will be that the niche market they play in is small enough not to be of interest to others.
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