Industry 4.0: Manufacturing Post-Pandemic
By: Prince Canuma
Tags: Industry 4 0, Manufacturing, Deep Learning, AI, Manufacturing Processes

In this article, we are going to discuss the benefits, challenges and ethics of automation at scale.

Image credits to Forbes

Last December the world celebrated and we all were anxious to get started with 2020, as a new year comes with the promise of better fortune but little did we all know that we would be in this situation. Highly social beings being forced to keep social distance, wear masks and develop hygiene routines better than hypochondriac — basically going against our nature.

I would like to take a moment to express my condolences to the families who lost their loved ones to COVID-19, I can personally relate because I too lost a dear family member to it.

Now, after being in the eye of the tornado we the survivors are rebuilding. In the wise words of Napoleon Hill:

“When defeat comes, accept it as a signal that your plans are not sound, rebuild those plans, and set sail once more toward your coveted goal.”

The pandemic made us completely rethink how we live and do business, there are many examples of changes that COVID-19 enforced across multiple industries but we are going to focus on manufacturing as it provides an interesting test case of an industry where the players in it will have to re-create themselves or they will not survive as well as an industry that due to the pandemic everyone now is on a level playing field and now the underdogs, the Davids of it have a chance to take on Goliath.

The Evolution of Manufacturing

Let’s backtrack and understand the history of the evolution of manufacturing. According to the article “Industry 1.0 to 4.0: The Evolution of Smart Factories” by Apics and “Industrial Revolution — From Industry 1.0 to Industry 4.0” by Desoutter, the evolution manufacturing is as it follows:

Industry 1.0

The First Industrial Revolution began in the 18th century through the use of steam power and mechanisation of production. Water and steam-powered machines were developed to aid workers — big emphasis on “aid workers”. From the beginning, the goal was never to replace humans with machines but to aid.

Continuing…

While before we used to produce threads on simple spinning wheels, the mechanised version achieved eight times the volume at the same time. Steam power was already known.

As production capabilities increased, the business also grew from individual cottage owners taking care of their own — and maybe their neighbours’ — needs to organizations with owners, managers and employees serving customers.

The use of steam power for industrial purposes was the greatest breakthrough for increasing human productivity. Instead of weaving looms powered by muscle, steam-engines could be used for power. Developments such as the steamship or (some 100 years later) the steam-powered locomotive brought about further massive changes because humans and goods could move great distances in fewer hours.

Industry 2.0

The Second Industrial Revolution began in the 19th century through the discovery of electricity and assembly line production. It was easier to use than water and steam and enabled businesses to concentrate power sources to individual machines. Eventually, machines were designed with their own power sources, making them more portable.

This period also saw the development of a number of management programs that made it possible to increase the efficiency and effectiveness of manufacturing facilities. Division of labour, where each worker does a part of the total job, increased productivity. Mass production of goods using assembly lines became commonplace. Lastly, just-in-time and lean manufacturing principles further refined the way in which manufacturing companies could improve their quality and output.

Industry 3.0

The Third Industrial Revolution began in the ’70s in the 20th century through partial automation using memory-programmable controls and computers. Since the introduction of these technologies, we are now able to automate an entire production process — without human assistance. Known examples of this are robots that perform programmed sequences without human intervention.

This period also spawned the development of software systems to capitalize on electronic hardware. Integrated systems, such as material requirements planning, were superseded by enterprise resources planning tools that enabled humans to plan, schedule and track product flow through the factory. The pressure to reduce costs caused many manufacturers to move component and assembly operations to low-cost countries. The extended geographic dispersion resulted in the formalization of the concept of supply chain management.

Industry 4.0(Post-Pandemic)

We are currently implementing the Fourth Industrial Revolution. Industry 4.0 connects the Internet of Things (IoT) with manufacturing techniques to enable systems to share information, analyze it and use it to guide intelligent actions.

It builds on the developments of the Third Industrial Revolution. Production systems that already have computer technology are expanded by a network connection and have a digital twin on the Internet so to speak.

It also incorporates cutting-edge technologies including additive manufacturing, robotics, artificial intelligence and other cognitive technologies, advanced materials, and augmented reality, according to the article “Industry 4.0 and Manufacturing Ecosystems” by Deloitte University Press.

This is the next step in production automation. The networking of all systems leads to “cyber-physical production systems” and therefore smart factories, in which production systems, components and people communicate via a network and production is nearly autonomous.

Why is it crucial?

As problems soar the market so as the opportunities, entire industries and the players in it no matter the size are being forced to rapidly adapt technology to ensure their success as well as their survival. In the wise word of Jean Piaget:

“Scientific knowledge is in perpetual evolution; it finds itself changed from one day to the next.”

According to this Forbes article by Rohit Arora, we can clearly see that most of of the organizations that fared best before, during and will continue post-Covid-19 were the ones that were either prepared with their digital transformation or were forced undergo one during the pandemic and embraced it completely. And this theory holds true not only for fortune 500 companies.

From this statement above we can infer that organizations need to learn how to acquire data, process it and derive actions from the insights discovered.

Data is growing exponentially to the point that we can surely say that there is no other point in time in the history of mankind that data was produced this way.

While before the different countries focused their economies on mining raw materials from the earth, now we are heading to a world where these same countries are shifting their focus to a more valuable and perpetually growing raw material(data). Absurd amounts of data are produced daily, and we have only started to scratch the surface with what we can do with it.

“We’ve been merging with tools since the beginning of human evolution, and arguably, that’s one of the things that makes us human beings. “— Franklin Foer

This also serves to say that even legacy businesses that either aren’t publically identified as tech companies or don't identify themselves as one, have to change the way they see and present themselves. A great example is auto shops, they are not seen as a tech company but as cars become more and more technologically advanced and electric vehicles become more common auto shops need to change the tools they use from pure manpower to data-driven digital tools.

Major Benefits

The major immediate and long term benefits are many but we will only focus on a handful of them:

Automated human and machine monitoring

Increased production

Improved quality

Reduced costs and defects.

According to this article by Tommy Palladino, GE aviation has a great story that sums the points above. In collaboration with their software partner Upskill, they gave their mechanics Google Glass paired with a smart wrench to assist them in the assembly and maintenance of aircraft engines which resulted in a 16% speed increase. Upskill has software called Skylight that provides hands-free instructions, in the form of process flows, images, videos, and animations, via the Glass display as well as video

Challenges

Just like any change, this one doesn’t come without resistance. Organizations are going to face one or more of the following challenges:

Lack of skillset

Performance

Privacy & Security

Over automation

Infrastructure

In my opinion, the very first thing organization should be looking into is re-skilling of their current staff(for those areas which are dying) and acquisition of new talent pool among which are data scientists and developers so they can take maximum advantage of data they produce or get and software that can help interpret the insights derived from data.

One key component is performance, some data have a limited useful lifetime so real-time processing is a must. An example of the importance of performance is Google’s initiative to leverage an AI system from its child company Deep Mind on their data centres which they claim the use of this AI system led to 40% reduction in energy used to keep servers cool. This system uses IoT edge devices to gather that and the algorithms learn heat patterns and predict how hot the servers will be hour-by-hour and then the system uses its insights to supply the right amount of cooling. Here performance matters any delays can equal bigger costs.

Another challenge is privacy and security because now we have IoT devices scattered throughout the factory or building and the old school Private Network infrastructure doesn’t work anymore. We have an infrastructure made of a network, OS, applications, data and physical devices sending and receiving data from public servers to protect. This setting naturally raises various privacy and security issues, that's where I believe Cloud and Cloud Hybrid(synced cloud and on-premises infrastructure) services shine the most because they provide you with a way to connect everything together in a safe, secure and cost-effective way.

Finally, I want to express my concern about the risk of over-automation. The idea is to help humans work more efficiently and not to replace them, I know it’s enticing for businessman and managers to maximise profits — AI and Automation are words highly associated with those 2 words. For more information, I go over the topic of over-automation in detail in my previous work: “AI & Automation: A Take Over or Symbiosis?”.

Ethics of Automation

Why organizations should be careful with biased production processes.

AI can help reduce bias, but it can also increase and scale bias.

Bias is present everywhere and it's well documented in humans. We all have bias and we act on it either consciously or unconsciously. For example, diagnostic errors are the most common cause of medical errors reported by patients. They account for the largest fraction of malpractice claims, the most severe patient harm, and the highest total of penalty payouts. It can be caused by multiple factors the doctor might be tired, under a lot of stress and so on that might lead him towards making a certain diagnosis.

Much of the conversation about definitions have focused on individual fairness, or treating similar individuals similarly, and on group fairness — making the model’s predictions or outcomes equitable across groups, particularly for potentially vulnerable groups.

Bias has been identified in facial recognition systems, hiring programs, the algorithms behind web searches, recommendation systems and so on.

In contrast there multiple examples of great implementation of AI systems that improved decision making and also increased fairness. For example, Jon Kleinberg and others have shown that algorithms could help reduce racial disparities in the criminal justice system.

Bias comes mainly from the data used to train the AI system and not from the AI system itself. Organizations have to make sure that their data has the least amount of bias that allows for great decision making and fairness.

Several approaches to enforcing fairness constraints on AI models have emerged. The first consists of pre-processing the data to maintain as much accuracy as possible while reducing any relationship between outcomes and protected characteristics, or to produce representations of the data that do not contain information about sensitive attributes

But the effort shows that removing bias from AI systems remains difficult, partly because they still rely on humans to train them. In the wise words from Olga Russakovsky, Princeton:

“Debiasing humans is harder than debiasing AI systems.”

Closing Thoughts

I believe that the future looks even brighter than the current times.

But first, they have to become thoroughly versed on technologies such as cloud, IoT, data science, software engineering and so on.

The year of 2020 put organizations of all sizes on a level playing field and all must fight to survive and thrive and by changing how they see themselves, how they interact with their customers and finally by becoming more data-driven because data is the new gold, diamond and oil.

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