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Top-7 technology trends accelerating digital transformation in manufacturing

November 17, 2021
Andrii Rybakov

The coronavirus crisis has accelerated manufacturing digitization significantly since companies are obliged to address supply chain disruptions, create new delivery methods, and deal with remote work. The most important thing manufacturers have learned from this experience is that investing and applying the latest technologies is crucial for improving operational processes and safeguarding the business. 

According to a Gartner Smart Manufacturing and Implementation Trends survey, 83% of companies "agree that their leadership understands and accepts the need to invest in smart manufacturing." Meanwhile, IBM's 2021 Digital Transformation Assessment states that 67% of organizations in the manufacturing industry have already “accelerated digital projects as a result of COVID-19.” That makes digital technologies drive the market; they allow reducing costs, optimizing processes, enhancing the products' quality, and increasing the overall productivity. 

So what are the key technology trends accelerating digital transformation in manufacturing? And what are the main benefits and challenges in manufacturing digitization? Read in our blog post. 

Given the numerous technologies manufacturing companies consider as critical, it is incredibly encouraging to see that many of them have already been applied or will be soon. According to IBM’s 2021 Digital Transformation Assessment, among the top seven tech trends in manufacturing are the following: 

Source: IBM’s 2021 Digital Transformation Assessment

1. Cybersecurity

The increasing number of cybersecurity attacks serves as a significant challenge for the modern manufacturing industry. To address the issue, manufacturers can use artificial intelligence (AI) technology. That helps create protection against sophisticated and malicious malware or multiple social engineering attacks. Also, implementing machine learning (ML) algorithms provides an important part for cybersecurity regarding threat intelligence. Both technologies allow monitoring and detecting anomalies in the network and identifying new threats. 

2. Advanced data analytics

Companies in the manufacturing sector can leverage data analytics and ML’s predictive power to analyze logistic and market data. That allows forecasting future trends and meeting demand with appropriate restocking. Manufacturers can also use advanced data analytics for processing information related to the entire factory workflow, evaluating KPIs, and spotting bottlenecks or processes that need to be improved or harmonized. 

In addition, ML-based data analytics is a critical component of asset performance management (APM). Traditionally, organizations adopt it for scanning machinery performance data provided by sensors and detecting inconsistencies that may serve as signs of malfunction. The particular approach offers better asset reliability and longevity since ML-powered data analytics improves maintenance operations and overall worker safety. 

3. Cloud

Manufacturers will continue to realize the vision of a “smart factory” thanks to deploying cloud services and cloud-based applications. That allows for expanding the adaptability and improving the flexibility of the factory’s digital infrastructure. In turn, making the digital infrastructure more agile and resilient provides conditions for increasing automation of enterprise IT, operating procedures, and critical business operations. Lastly, migrating data to cloud-based platforms enables organizations to analyze information in real-time and make informed decisions. 

4. Automation/robotics

Implementing new robotic technologies in the manufacturing industry can increase productivity, improve product quality, and enhance the safety of operational processes. For instance, cobots (collaborative robots) and AI-enabled robots will cooperate with humans on different tasks that may provide potential risks to employees or manually repetitive tasks. After all, according to McKinsey's research, “60 percent of manufacturing tasks could be automated.” 

But modern robots are also involved in data collection, sharing, and analysis. It means that their actions may depend on real-time data insights and predictions. Besides, the emerging robotic technologies can enhance their performance due to the ability to learn through experience. 

5. IIoT/IoT data from devices 

Industrial IoT (IIoT) serves as a critical element of manufacturing digital transformation that requires using IoT-enabled sensors for connecting manufacturing equipment with IT systems. That helps drive valuable insights related to the organization’s operations and productivity. With the appropriate sensors and analytics tools, companies can collect and analyze information from all points in the manufacturing process. 

Thus, applying IIoT enables manufacturers to discover new data and make informed decisions related to their operations or supply chains. For example, the information from IIoT devices allows reconfiguring production lines or assets more easily. That makes it possible to support the company’s variable contracts, along with client customization. 

6. Artificial intelligence and machine learning

Combining AI technology and ML models allows proactively detecting and correcting anomalies before they occur. Besides, coupling AI and ML algorithms can help manufacturers in optimizing maintenance schedules, spotting production glitches, and monitoring the quality of products. Both technologies will result in an emerging hybrid workplace that utilizes tools provided by augmented intelligence - an efficient combination of employees’ and machine cognitive abilities. 

Companies also use AI to find or create new information sources and fill in missing links that help solve challenging issues. Meanwhile, several examples of ML may include suggesting the most appropriate course of action for workers, predicting waiting and shipping times, or preventing risks by identifying behavior models. 

7. Augmented and virtual reality

Through augmented and virtual reality (AR/VR) applications, manufacturers create experiences for improving the production floor’s performance. AR/VR technologies can guide employees in assembling products or repairing machines thanks to a real-time feedback loop. That allows confirming everything is done correctly. Also, such technologies may guide inspections for ensuring that all locations are inspected correctly, all assets are in proper places or positions, and the required quality criteria are passed successfully. 

Apart from that, manufacturers will embed AR/VR in the training process, which helps workers attain the necessary expertise rapidly. With augmented and virtual reality, employees can obtain the on-premise and virtual experience that involves walking through the factory floor, finding required assets and checking their health instantly, and so on. Therefore, implementing AR/VR provides a community of experts with efficient problem-solving and a better understanding of design and planning. They can see what technicians, floor managers, or operators are doing. 

Benefits of manufacturing digital transformation

When it comes to digital transformation in the manufacturing industry, organizations deal with holistic changes in running a business that involves appropriate investments and efforts. But the results are worth it. According to PWC’s Digital Factories report, “90% of industrial company leaders believe digitization offers more opportunities than risks,” while “98% say efficiency gains are the main reason for investing in digital transformation.” 

Fortunately, the advantages that companies can achieve at the end of the digitization journey meet expectations. Among the critical benefits identified by organizations that have applied the smart factory approach are:

  • Increased productivity. The Deloitte and MAPI Smart Factory study states that investing in digital transformation has provided manufacturers with “a 12% growth in labor productivity” and “10% in total production output.” The particular tendency may skyrocket in the next decade and promote the manufacturing industry to record the increase in labor productivity three times faster compared to the previous ten years. 
  • Reduced costs. As a result of the increased productivity, manufacturing digitization also allows cutting down the overall operating costs. Companies can achieve this objective by identifying cost-cutting opportunities regarding the production workflow due to data analytics or automating numerous time-consuming processes and tasks. That may fall under RPA (robotic process automation) and robotics use cases. 
  • Better quality. Apart from increasing productivity, digital transformation in manufacturing improves quality. With predictive analytics in place, manufacturers can detect relevant defects, which allows ensuring better product quality and requires lower efforts. After all, according to the McKinsey report, “machines’ superior accuracy results in 10-20% decreases in the cost of quality-related operations.” 
  • Safety. Thanks to applying IoT-enabled sensors and combining them with ML-based prediction models, organizations can analyze machinery operation, detect or even predict dangerous failures that may affect employees’ health. Besides, manufacturers can achieve higher safety by providing workers with wearables that monitor their conditions and spot signs of unease or fatigue. 
  • Customization. Since customers have become more demanding, companies should consider greater product personalization among the key approaches to coping with the particular pickiness. At the same time, increased flexibility and productivity of digitally transformed manufacturing lines allow creating a large number of customized products and maintaining competitive prices. 

Challenges of manufacturing digitization

Source: IBM’s 2021 Digital Transformation Assessment

The IBM’s 2021 Digital Transformation Assessment identifies the most significant barriers to adopting new technologies in manufacturing. Almost 60% of the industry’s decision-makers consider time as the biggest issue. They say that employees are incredibly focused on daily activities and have no time to learn more efficient ways to perform tasks or think more strategically. Ultimately, the top three most critical obstacles do not relate to the technology factor. Instead, they depend on the organizational culture and established working practices. 

At the same time, many manufacturers find it challenging to choose the right stakeholder that will support their technology project. Besides, companies may face difficulties scaling pilot projects successfully or taking something with the already proven value in relevant workflows or cells. It can also become hard for them to roll such an initiative out through the organization. 

In addition, the coronavirus pandemic has affected the manufacturers’ opportunities to secure the funding necessary for conducting tech explorations or pilot projects. That only reinforces the need for successful business cases. Sometimes, barriers may relate more to the existing infrastructure, including the network, connectivity, or systems that enable organizations to collect data. Upgrading it may not be too costly, and the relevant improvements will benefit the entire organization, but companies need to have the right employees to support such infrastructure. 

Finally, numerous manufacturers rank “Adoption/Culture'' among the most significant obstacles to digital transformation in their industry. They realize that the current resistance to changes can be stronger than in previous years. Now, employees may experience pay cuts, reduced working hours, or lost bonuses, which provokes them to ignore new initiatives. Therefore, business leaders should work harder to convince these employees that their paychecks are spent on critical things to make their lives easier and protect the companies' future. 

The bottom line

To sum up, modern manufacturers should take action in some critical areas for surviving and flourishing:

1. Lead, engage, and enable employees in new ways. For instance, you can provide more flexible work options, improve the workforce’s wellbeing, and encourage skills development. That helps drive trust and builds retention.

2. Implement AI, automation, and other emerging technologies for making workflows more intelligent. You have to focus on cybersecurity, operational effectiveness, or supply chain resiliency. 

3. Enhance innovation scalability through operations by adopting the correct architectural choices. That allows your innovations and pilot projects to scale across the organization. Often, the cloud serves as the best option, but sometimes companies may combine cloud, on-premise, and edge. Applying the appropriate toolset provides manufacturers with absolute flexibility in chosen deployment models and does not undermine the business case. 

Today, both risks and opportunities are significant. Thus, business leaders should prepare their organizations for constant uncertainty, inevitable disruption, and ongoing changes. 

How AgileVision can help

Over the past several years, AgileVision has helped many manufacturers accelerate their digital transformation and increase overall efficiency. Are you considering adopting new technological solutions or need advice on your current ones? 

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