It’s Time to See What Industry 4.0 Can Really Do

Industry 4.0 has been on business leaders’ lips for nearly a decade now.Its definition has become more nebulous in recent years — a catch-all for any move intended to improve manufacturing operations — but the term originally referred to a very specific vision for modern manufacturing.

Brought onto the public stage by WEF founder Klaus Schwab in 2016, Industry 4.0 was the brainchild of Germany’s Federal Ministry of Education and Research (BMBF). It detailed the country’s plans to prepare German industry and infrastructure for the future of manufacturing, citing the emergence of cyber-physical systems, machine-to-machine (M2M) communications, and the Internet of Things (IoT) as drivers of the shift.

The BMBF posited that the set of disruptive innovations emerging in the 2010s—like steam power and electricity before them — would drive an economic shift so significant that it would profoundly reshape not only business operations but also culture. These disruptive innovations are:

• Machine-machine connectivity

• Human-machine cooperation

• Advanced engineering

• Artificial intelligence

Investments in new equipment and advanced tracking and monitoring systems that support new human- and machine-machine interactions have helped businesses boost productivity, quality, and customer satisfaction to new heights. They’ve also, no doubt, fostered lasting social changes.

However, in my view, manufacturers have yet to make the most of Industry 4.0 because many are still missing one critical piece of the puzzle: artificial intelligence (AI).

AI: The missing link, found

Though analytics software has led to significant advancements, few facilities have achieved Schwab’s 4.0 vision. The industry remains in a middle ground — Industry 3.5, if you will — because AI has, until very recently, been inaccessible to most organizations. To clarify, I’m not talking about AI assistants or chatbots; I’m talking about more subtle AIs that work in the background to anticipate needs and optimize production.

Take production scheduling. Manufacturers have been able to outsource this to computers for some time by feeding order, supply, and production capacity information into algorithm-based software. These tools certainly improve and expedite scheduling strategies, but they don’t offer the flexibility that today’s modern facilities demand. With traditional scheduling, manufacturers might be locked into a given framework for a whole month, even if conditions change.

With AI integrations, algorithmic production scheduling (APS) tools can adjust schedules in real time and make updates throughout the system to ensure that related processes — like material sourcing or fulfillment operations — are adjusted. The increased agility afforded by algorithmic production scheduling tools drives efficiency on and off the factory floor, helping to shorten lead times, improve resource utilization, boost capacity and support continuous improvement.

AI tools are also changing human-machine collaboration in qualitative areas of production. For example, computer vision software uses AI to “watch” video feeds and identify subpar products as they move throughout production — and do so more precisely and consistently than human workers. Critically, these systems can interface with one another and use analytics to streamline root cause analyses so workers can focus on action rather than research.

Both examples illustrate a core difference between the “3.5” model and true 4.0 models: the time it gives back. Yes, the “3.5” model highlights opportunities for improvement, but “4.0” allows manufacturers to take advantage of them now, saving precious time and materials. Additionally, automated adjustments, reporting, and monitoring give time back to workers, so they can devote their energy to bigger-picture objectives.

Now that AI is widely available and increasingly integrated into manufacturing environments, the fourth industrial revolution is poised to come to fruition.

Prepping production for AI

It’s worth noting that just because AI is more accessible doesn’t mean the 3.5-to-4.0 shift will happen overnight. A recent survey from my company, Parsec, revealed that most North American manufacturers (66%) aren’t ready to use AI tools, citing personnel expertise (46%) and trust in the tools (39%) as top barriers — both of which are critical to the 4.0 model’s function.

Since human-machine collaboration is the heart of the model, manufacturers maturing their connected facilities will need to invest in organizational change as well as tools. They’ll need to build:

• AI-savvy workforces: Since a shift in human-machine cooperation sits at the center of Industry 4.0, ensuring that workers are ready to work in true 4.0 environments should be top of mind. Offering workers upskilling, reskilling, and general training on how AI works and how to work with it should be a top priority.

• Trust in the technology: Upskilling will help staff put more trust into the tech, but building confidence in its value will also mean communicating openly about the motivations for the changes, the path forward, and the project’s overall objectives. Starting with the tangible outcomes — whether financial, productivity-related, or otherwise — and then finding tools to support them will help workers understand how these tools provide value and 4.0 transformations stay on track.

In our survey, cost (33%) and data availability (23%) were other commonly cited obstacles to implementing AI tools, and they are similarly entwined. AI tools are only as valuable as the data that feeds them, so leaders should start by leaning into automated monitoring and reporting—especially since 69% of manufacturers shared that they are still collecting most of their data by non-digital methods. Investing in automated monitoring will not only set the stage for AI-enabled tools but will increase the value of existing analytics systems.

The Final 0.5

The analytics widely used in manufacturing today provide vital visibility into operations and address production bottlenecks. However, the Industry 4.0 model’s true value lies in its ability to give time back to manufacturers, by both enabling rapid adjustments and allowing people to pursue strategic initiatives that drive exponential value.

Achieving digitalization will give manufacturers a taste of Industry 4.0, but introducing AI will let the model really shine. With AI more accessible than ever before, manufacturers are now able to bridge the gap between theoretical aspiration and practical realization — giving the industry as a whole a front row seat to 4.0’s unbridled potential.