Automation in Manufacturing: Emerging Trends and Solutions 

Over the past four decades, automation has been slowly transforming the manufacturing industry. Industrial automation has gone from novelty to routine — no longer can companies increase productivity and throughput, relying solely on manual labor. They seek to take advantage of robotics, on the one hand, and artificial intelligence (AI) and machine learning (ML), on the other, to improve the manufacturing process and become more competitive.

According to McKinsey research, manufacturing has the second highest potential for automation across 20 industries. Actually, ~90% of predictable physical activities on the factory floor can be automated, thus increasing production, reducing cycle times, improving quality, cutting manufacturing lead times, and more.

Let’s take a closer look at how manufacturers adopt and implement specific automation solutions and explore emerging trends in automation, from vertical integration of manufacturing and business operations to continued reshoring.

Industrial Automation Trends in the US

#1 Vertical Integration: IIoT, Data, and the Cloud

Free-flowing information increasingly becomes a cornerstone for achieving a variety of goals in manufacturing, from cost reduction and increased efficiency to product innovation and improved safety.


Nowadays, a seamless connection between all layers of a manufacturing enterprise, including IT and OT, is achieved due to a combination of three factors:

  1. Widely available Internet access, including 5G

  2. Advance of cloud computing

  3. Smaller, more efficient IIoT devices


IIoT devices and sensors collect various data points, which are pushed via Internet to the cloud for storage, processing, and analytics.

(1) Automation in Manufacturing Emerging

In this ecosystem, data is immediately connected to the BI and ERP solutions, allowing for an integrated and holistic view of manufacturing and business operations. No wonder that manufacturers are expected to have invested $267B in IoT by 2020.

#2 From Data Collection to Analysis

Data is a critical asset for manufacturers, and data collection is hardly a new practice for most enterprises. However, as more and more data is getting extracted from manufacturing processes, the focus has to shift from data stockpiling to analysis, to enable quality, maintenance, and process optimization improvements.


Fortunately, cloud providers such as AWS, Microsoft Azure, and Google Cloud have all the tools and services needed to process and analyze unlimited amounts of data. They employ their advanced ecosystems to help businesses build, manage, and support a wide range of AI solutions and ML use cases.

(2) Automation in Manufacturing Emerging

In manufacturing, cloud can facilitate the adoption of such use cases as predictive maintenance, quality inspection, manufacturing process optimization, automated physical security and worker safety, supply chain optimization and material handling, access control, and resource optimization. 


Because automated reporting and analytics become standard, the key is to streamline data collection and data analysis processes — be it batch analysis or real-time analytics — to effectively interpret data and generate insights. In many ways, it is data analysis that helps manufacturers address issues before problems arise.

#3 Predictive vs. Preventive Maintenance

In manufacturing, downtime is the single largest source of lost production time and revenue. The average manufacturer deals with 800 hours of downtime per year. In the automotive industry, one hour of downtime may cost up to $3 million. It is critical to keep all equipment functioning optimally.

Historically, enterprises moved away from reactive to scheduled maintenance to increase the reliability of equipment, reduce the amount of breakdowns, and extend machinery life. Regular checks and fixes were administered regularly to prevent, not react on equipment failure.

However, with the advance of IoT, cloud computing and machine learning, manufacturers can not only prevent but predict equipment failure. Data from sensors (e.g. performance metrics) is processed by predictive analytics solutions in the cloud to let engineers know in advance when to stop machinery for maintenance.


The benefits of predictive maintenance include:

  • Reduced maintenance costs

  • Reduced unplanned outages

  • Reduced mean time to repair

  • Extended lifetime of manufacturing equipment

  • Increased overall equipment effectiveness

#4 Reshoring to the US

Over the last decade, manufacturers have moved dozens of factories back to the US shores. Multiple factors are contributing to reshoring, including:

  • Go-to offshoring countries like China and Mexico are doing well and increase wages for industrial workers, making their labor less competitive internationally

  • Countries with inexpensive labor lack infrastructure and cannot maintain manufacturing operations on a greater scale

  • Skilled labor, advanced supply chains, and customer proximity in the United States


Advances in automation also encourage manufacturers to bring production back to the US soil. They can automate a considerable portion of manual tasks to improve productivity, increase efficiency, and increase spending. 

Overall, according to BCG, the share of automated tasks will grow from the current average of 10% to 25% by 2025; manufacturers will see up to a 30-percent increase in production.

Other Trends to Keep Track Of

3D Printing: Product designers can take advantage of faster, less expensive prototyping to test the products. Manufacturing tooling, molds, jigs, and fixtures can be 3D-printed on-site instead of being imported.


VR/AR: Though it is still not technically feasible to implement assistive technologies like augmented reality (AR) and virtual reality (VR) at scale in manufacturing, the positive effects of a human-machine collaboration are clear: more efficient product design, more accurate quality inspections, faster and more effective worker safety training, etc.

Robotics: Industrial robots have been creating efficiencies in manufacturing for decades, from raw material handling to finished product packing. With AI under the hood, robots can potentially take on more complex functions and customize their activities (e.g. an inspection robot fixes a flawed part of a product on the assembly line instead of rejecting it).

Automation and AI Solutions for Manufacturing

Examples of automation in manufacturing are many, from automatic assembly machines and industrial IoT to automatic inspection systems for quality control and data-driven enterprise resource planning systems.


Implementation of any automation solution is a long-term capital and innovation investment, and manufacturers approach it without unnecessary hassle. Fortunately, the rise of AI makes it much easier to start taking advantage of automation solutions. 


Let’s have a look at a few AI use cases for manufacturing.


Checking each worker’s ID manually is inefficient and takes too much time. AI can significantly accelerate worker access to the factory itself and to its selected zones while offering manufacturers new ways to monitor and track workers. Powered by computer vision, image analysis, and face recognition technology, AI analyzes video streams from high-resolution cameras and recognizes employees to check their qualification, access level, working hours, skills to operate and fix specific equipment.


Worker safety

On average, a worker injury costs manufacturers from $20K to $200K, including compensations, lost production time, OSHA penalties, equipment damage, etc. AI worker safety solutions allow manufacturers to reduce the number of worker injuries on the factory floor by enforcing PPE compliance in real time. Live video streams are captured by cameras and fed to image analysis algorithms that identify missing PPE items and report violations to safety engineers.


According to the ASQ report, manufacturers can lose up to 20% of their sales revenue due to scrap and waste caused by defects. In complex manufacturing lines, the defect rate can reach 90%. With AI, they can now move away from manual and automated optical inspection to ML/DL-enabled quality control systems that accurately capture and analyze product images for defect, allowing to scrap flawed details earlier in the production cycle.

Automation and AI Solutions for Manufacturing

Automation has been prevalent in manufacturing for a few decades already, but with advances in AI, cloud computing, and wireless connectivity, it is to explode in the coming years. Automation solutions will become more smart, more reliable, and more efficient. Manufacturers need to jump on the AI bandwagon not to be left behind.


Automation is to contribute to further increases in productivity and throughput, and it will likely speed up reshoring to the US. However, it will also replace many people. Hopefully, they are going to move into new, more complex roles, which will result in a higher average standard of living in the long run.

Thank you for reading! 

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