5 Key Manufacturing Industry Trends (2022)

by Josh Howarth
May 12, 2022

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The manufacturing sector isn’t as prominently in the public eye as industries like tourism and hospitality.

Nonetheless, the global pandemic has seen the industry endure its most severe disruption in over a decade.

Fortunately, 2021 promised a strong recovery — data from the UN Industrial Development Organization indicating a global growth rate of 18.2% last year.

As the industry recovers, resilience is the key theme giving rise to the trends that are altering the global manufacturing landscape.

In fact, a poll of over 200 C-suite decision-makers at global manufacturing companies found that 68% listed improved resilience and agility as a top business priority in the coming years.

Read on to gain insights on the biggest trends shaping the future of manufacturing.

1. Smart Factories are Changing How We Make Things

The “smart factory” or “smart industry” concept has been around for a long time. But has witnessed a surge in interest over the last few years.

Searches for "Smart Factory" have risen by 92% over the last decade.

Smart factories are part of the broader “Industry 4.0” movement that’s transforming operations and shop floors in production environments across Europe, North America, and China.

The first and second industrial revolutions were associated with mechanization powered by steam and the rise of assembly lines. The third saw the adoption of computers in manufacturing.

Industry 4.0 continues the push towards automation, employing technologies such as IIoT (industrial internet of things), big data, machine learning, artificial intelligence (AI), and advanced analytics.

In the automotive industry, for example, it’s estimated that smart factories could create $160 billion in value by 2023.

Representation of an automotive smart factory.

In fact, automotive manufacturers predict that about a quarter of their plants will be smart factories by the beginning of 2023.

The industrial internet of things (IIoT) is a major component of Industry 4.0.

IIoT uses connected sensors and machinery to build a complete data picture of the entire manufacturing process, to improve decision making.

At the start of 2020, only an estimated 10% of manufacturing enterprises had implemented IIoT.

This number is expected to rise dramatically to 50% by 2025, with some the market already worth north of $200 billion.

Searches for "IIoT" have increased by 4,250% over the last 10 years.

With the increase in connected devices will come an explosion in the amount of data generated by businesses.

Already, enterprises with advanced IIoT setups find that a single assembly line can generate as much as 70 terabytes of data per day.

The need to transmit such large volumes of data is also fueling the explosive growth of 5G.

Searches for "5G" have grown by 2,400% in 5 years.

With speeds significantly faster than Wi-Fi or 4G (up to 10 Gbps), near-zero latency, and wireless connectivity, 5G brings an unprecedented degree of flexibility to high-speed manufacturing environments, with the potential to connect virtually any sensor or device.

A recent forecast projects a minimum of 22 million 5G units in the manufacturing sector by 2030.

And another study pegs the value addition of 5G to manufacturing businesses at $605 billion in unlocked revenue.

The manufacturing sector is expected to demand more 5G IoT devices than any other sector.

Audi is an example of an early adopter of 5G, having contracted with Ericsson to implement advanced automation powered by 5G at their production labs.

2. AI, Machine Learning, and Advanced Analytics Drives Efficiencies

A typical oil rig has more than 80,000 sensors, providing data on everything from process parameters (temperatures, pressures and flow rates, for example), to machinery and equipment status.

This allows for remote monitoring and focused manufacturing, which is both fed by data.

However, those many sensors put out, on average, about two terabytes of data per day — well beyond the scope of what can be effectively analyzed with conventional methods of data analysis.

This partly explains the rise of advanced analytics methods powered by AI and machine learning.

Searches for "Machine Learning" have risen by 745% over the last decade.

Earlier projections envisioned an 8x increase in investment in AI in manufacturing in the 5 years leading up to 2021, and a 3x increase in advanced IT such as cloud computing and analytics.

By the beginning of 2020, nearly a quarter of top-tier manufacturing businesses had adopted AI-based solutions, and by 2021, this increased dramatically to 76%.

Adoption of AI in day-to-day manufacturing operations is especially high in Europe, with 80% and 79% of manufacturers going this route in Italy and Germany, respectively.

AI use by manufacturers in different countries.

Other advanced manufacturing economies like the US, Japan, and Korea, lag behind at 64%, 50%, and 39%, respectively.

The widest deployment has so far been in the automotive, heavy machinery, electronics, chemicals, and metals industries.

In these industries AI-based decision making is being used for everything from quality inspection, to supply chain management, production line checks, and inventory management.

And it’s no wonder these sectors are racing to implement AI in their manufacturing processes. A 2021 Harvard Business Review report estimates the total value created by AI to be close to $13 trillion.

Adoption of machine learning and advanced analytics (defined as the autonomous or semi-autonomous examination of data through methods beyond traditional business intelligence) is at even higher levels than that of AI, with two-thirds of manufacturing companies attributing significant revenue savings or generation to these technologies.

Searches for "AI Analytics" have increased by 2,375% since 2012.

By 2026, the manufacturing analytics market is projected to be worth $28.4 billion, up significantly from its current value of $8.45 billion.

3. Predictive Maintenance and Digital Twin Technology Reduce Errors

In manufacturing, equipment failures can be extremely expensive.

For example, a compressor failure at a petroleum plant can cost between $1 and $2 million per day, while studies show that unplanned downtime costs manufacturers $50 billion per year.

IIoT combined with big data analytics makes it possible to use sophisticated algorithms to predict such failures with great accuracy — before they actually occur.

Search interest in "Predictive Maintenance" has grown by 315% over the last 10 years.

Data indicate that predictive maintenance can reduce downtime by 30% to 50%, increase machine life by 20% to 40%, and cut costs by more than 30%.

Last year’s pressures of shutdowns have only accelerated implementation, with 35 % of large-scale manufacturers in the US already employing predictive maintenance techniques.

Airbus has been an early adopter, helping its airline customers in implementing advanced predictive techniques.

This year, Airbus partnered with GE to deploy sophisticated AI- and ML-based predictive maintenance software to anticipate part failures in aircraft and perform timely repairs and replacements to improve safety and profitability.

The market for predictive maintenance solutions was valued at $3.8 billion at the end of 2020 and is expected to grow to $13.9 billion by 2026.

The growth of IIoT, AI and ML has set the stage for another important sub-trend of the smart factory evolution: digital twins.

Much like the entertainment industry makes use of virtual models instead of real characters, props, or sets, in animation and CGI films, digital twinning allows the manufacturing industry to use a virtual representation of an object to improve decision making through data-driven simulations.

Searches for "Digital Twin" have increased by 244% over the last five years.

In the context of smart factories, a digital twin can be a single component (like a car tire) or a product (like an entire car, with all of its subsystems, including software and thousands of components).

It can even be a whole manufacturing process itself (like a factory with several workstations, assembly lines, and part stores).

Applications of digital twins range from predicting the life of a product based on a design (like GE did with aircraft jet engines), to managing entire factories and optimizing their operations through simulations and what-if analyses (as demonstrated by Siemens).

A Siemens digital twin of a Jet Engine

Forecasts predicted that close to half of all large industrial enterprises would have implemented digital twins at some scale by the end of 2021.

And businesses are expected to save as much as $1 trillion through the use of digital twins.

4. Supply Chain Restructuring is Changing Where Products are Made

The same need for resilience that is spurring on the Industry 4.0 phenomenon is also driving dramatic shifts in shoring and sourcing, especially across North America and Europe.

While globalization and the relentless quest for greater cost-efficiency drove the offshoring and lean supply chain movements for several decades, a number of factors are now swinging that pendulum in the opposite direction — the disruptions due to Covid-19 not being the least of them.

The reshoring trend was one that began as far back as 2010, but it has drastically accelerated in the past year.

Search interest in "Reshoring" over the last 10 years.

In addition to the pandemic-induced supply chain shocks, the reshoring trend has also been bolstered by increasing labor costs in offshoring countries, rising shipping costs, and growing social and regulatory pressures related to sustainable sourcing and logistics.

These forces have altered the sourcing landscape significantly.

In a surprising study, KPMG revealed that Canada actually offers one of the lowest costs of manufacturing operations in the world, well ahead of the usual suspects such as China, India, and Mexico.

Last year, 83% of manufacturing companies in the US confirmed that they would be reshoring, as opposed to just 54% in 2020.

The economic impact of reshoring is estimated to be $443 billion in the US.

The strategic shifts in supply chain management aren’t limited to the location of the manufacturing facility.

They also include new strategies when it comes to where raw materials and components are sourced — variously described as near-sourcing, multi-sourcing, local sourcing, and “China, plus one”.

Part of the reason for this has been the over-reliance (especially in automotive manufacturing) on “just-in-time” inventory management, or — depending on who you ask — a complete misunderstanding of the principles of lean manufacturing.

Just-in-time is an inventory management philosophy focused on ensuring that parts are available where they’re needed in a manufacturing line, at just the right time, and no sooner.

The intention is to reduce wastage by avoiding expensive inventory carrying costs.

Whether by design or by misapplication, in the end, pre-Covid sourcing practices ended up plunging more than 169 industries into a global shortage of computer chips, which is likely to last at least until the end of 2023.

As a result, data now indicate that 50% of manufacturers are looking for alternative and backup suppliers, and up to 25 percent of manufactured goods are expected to be reallocated or moved globally, valued at $4.5 trillion.

5. Microfactories Emerge as Commerce Evolves

The widespread effects of the global pandemic have also begun to impact how manufacturers make the things they make.

This change in thinking has led to the growth of the microfactory — small, highly modular setups that make use of leading-edge technology like artificial intelligence, robotics, and big data, to enable hyper-autonomous manufacturing.

Searches for "Microfactories" over the last five years.

The microfactory trend is a perfect amalgamation of the two megatrends (smart factories and supply chain restructuring), and all the smaller trends already discussed.

The hunt for resilience has seen manufacturers try to improve their flexibility to accommodate small part runs, and to rapidly switch production lines to assemble new models.

In 2020, an electric vehicle manufacturing startup named Arrival made waves when it signed a $110 million deal with Hyundai and bagged an order for 10,000 electric vans from UPS.

Searches for "Arrival Limited" have grown by 66% over the last 10 years.

Arrival is building its vans in microfactories, which are just a fraction of the size (and cost) of traditional factories.

Arrival’s Microfactory

The company has even announced that it will build more than 1,000 microfactories around the world by 2026.

By harnessing the power of IIOT, robotics, and advanced automation through AI, as well as by allowing the point of production to be moved closer to the point of trade, microfactories cut down time-to-market, reduce inefficiencies, and enable agility to change production runs like never before.


The overlap of IIOT, advanced analytics, and automation has set the stage for trends such as predictive maintenance, digital twins, and intelligent automation to take off in 2022.

Together, all of these add up to the smart factory megatrend.

In addition to technical innovation, pressures related to supply chain considerations have been equally important catalysts of change.

These have triggered landmark shifts in supply chain design, globalization, and new models of production, such as the microfactory.