Execution: The Differentiator of Industry 4.0

Industry 4.0 Technology Trends

An essential aspect of Industry 4.0 is that the technologies implemented should not be isolated. The strength lies in their interconnection and how they integrate into all aspects of your business, from R&D to production, logistics, and services.

In the past, a new computerized or automated solution was sufficient. It could improve operational efficiency in one area, even if it was siloed. There were limits to what it could do and actually have an impact.

The digital transformation of manufacturing comes with infinite possibilities for connected systems and data sharing. Data obtained on supply chain disruptions or inventory levels can lead to production increases or decreases. And while this seemed impossible before Industry 4.0, it can now be done in real-time with little or no human intervention.

Connected machines can communicate with each other, regardless of their proximity, and share data at incredible speed. These machines can analyze this data, predict outcomes, and make decisions, such as whether to increase production.

What’s even more impressive is that these machines can learn automatically. Each data point leads to better predictions and better results. So if the decision to increase production resulted in surpluses in certain locations, this data is fed back into the system to achieve better outcomes.

We can therefore see that the main concepts supporting digitization are:

• Interconnectivity
• Automation through elimination of human-caused bottlenecks
• Optimization through continuous information flow

Big Data Analytics

Data is everywhere. From management systems to production equipment sensors, to your suppliers’ systems and everything in between, data is collected and analyzed in large quantities.

While data use in manufacturing is not new, the scale and speed of its use have increased exponentially. Previously, you could analyze data only a few times per year; today, it’s extremely easy to do in real-time through systems that continuously seek to increase operational efficiency and quality while reducing downtime and costs.

An example of analytics in manufacturing is using this method to predict when a piece of equipment may need maintenance. By collecting and analyzing sensor data, the system can then recognize signs of a problem before it actually occurs. Preventive maintenance can then be performed on this equipment instead of seeing it break down and cause significant downtime.

Internet of Things (IoT)

Industrial IoT creates the infrastructure that facilitates the sharing, collection, and analysis of all this data. This is what makes the digital factory possible.

With sensors and chips embedded in machines, and these machines connected to a cloud, they become smarter. If we had to integrate all the computing power needed to store and analyze massive amounts of data into each piece of equipment, the cost would be astronomical. However, public clouds (AWS, GCP, Azure) allow us to create high-performance infrastructure for data sharing and then react to commands.

With IoT, everything is connected. This not only allows you to eliminate repetitive tasks but also to integrate with other systems. Data collected by these sensors can be used to inform other equipment, integrated into your ERP system, or sent to your suppliers or distributors.

Machine Learning (ML)

Machine learning is artificial intelligence that allows a system to learn from data rather than relying on programming to execute functions. The more data the system receives, the more it continuously improves. And at the foundation of machine learning, data is always required.

The preventive maintenance example we used earlier is an excellent illustration of machine learning. Using a multitude of data, the system learns what can lead to failures or has identified signs that a failure is imminent.

Augmented Reality

Augmented reality is an aspect of virtual reality where digital images are projected onto physical objects or environments. Through its camera, you see both the real world and the digital image, which can become a powerful tool for providing training on the factory floor rather than in a classroom.

Imagine being able to see a prototype in its real environment without having to produce a physical version. Changes and iterations can happen quickly, with staff immediately seeing the effects of these changes. Projects you wouldn’t normally attempt due to cost can be pursued through digital means, increasing your innovation speed.

Benefits and Challenges of Being a Smart Manufacturer

Digital transformation presents many benefits, but it can also bring its share of challenges, especially if you’re starting your journey.

Benefits

A recent PWC study shows that most executives focus on improving operational efficiency to drive revenue growth. As we’ve seen, this is one of the main benefits of digital transformation:

• Increased operational efficiency:
  • Improved access and data analysis allows you to quickly spot operational problems and see how processes can be improved.
  • Centralization of monitoring and control systems.
  • Rapid adaptation to changing market and supply chain conditions.
  • By learning from a constant data stream, machines can reduce the need for human interaction and make decisions previously limited to humans.
• Cost reduction:
  • Preventive maintenance reduces downtime and extends equipment life.
  • Data is easy to obtain, access, and analyze.
  • Prototypes can be produced and tested entirely or partially through digital means.
• Increased capacity to innovate:
  • Rapid prototyping is possible. It’s easier and less expensive to fail and quickly return with a new iteration.
  • The more you do, the more data you collect and the more you can use it to optimize and predict outcomes.

Challenges

Digital transformation projects are new and unfamiliar to many organizations. The following points are not necessarily disadvantages of digital transformation, but simply challenges to consider:

• Cost of equipment and systems:
  • You’ll need to either convert existing machines or purchase new IoT-compatible equipment.
  • With less experienced internal resources, implementation projects may need to be outsourced, at least initially.
  • Your current workers will need to be trained on new systems and processes.
  • There’s an enormous amount of data to determine what’s relevant, and even the most beautiful algorithm is worthless without quality data.
  • Machine quality depends on data quality. Poor data will mean poor results.
• Personnel:
  • Much of the staff you need is probably not on your payroll.
  • You’ll increasingly depend on specialized resources such as data scientists and architects, software engineers, and cloud specialists.
  • These specialized resources are in high demand across all sectors of the economy, and you’ll face competition to obtain these resources.
  • Your current workers may be resistant to the change that digital transformation brings.
• Cybersecurity:
  • With all this data circulating, cybersecurity becomes a concern that must be managed and mitigated.
  • Even if you don’t store the data, cyberattacks are very frequent and can harm your credibility.

Human Resources for Digital Transformation

While some overlap exists, the skill sets and roles required for Industry 4.0 will be markedly different from those needed for traditional production. As indicated, manufacturing companies will increasingly rely on specialized personnel such as software engineers, technical project managers, data and cloud specialists. Some resources need to be recruited, while others need to be outsourced for specific projects.

Companies that adopt agile and iterative approaches to their projects and merge operations and IT will be particularly advantaged for Industry 4.0 adoption. Companies will need to analyze their needs closely, project by project, to find the best resources for the tasks to be distributed.

And it’s important not to forget your current workers during your digital transformation. The World Economic Forum predicts that 54% of manufacturing workers will need significant upskilling by 2022. While many of your current workers understand and accept the need for continuous training, others will be more resistant to change. It’s important to implement good change management practices to facilitate staff transition and ensure the success of any digitization project.

The Pandemic’s Effect on Manufacturing

No conversation is complete these days without mentioning the massive impact that COVID-19 has had on manufacturing and the entire world. Nearly 80% of manufacturers believe the pandemic has had a negative impact on their company’s finances. In many ways, and across all sectors of the economy, the pandemic has accelerated the pace of digital transformation. Companies are striving to maintain the status quo with a suddenly remote workforce. Of course, social distancing measures have been particularly difficult to implement in manufacturing, where the majority of workers must be on-site to maintain production.

While we hope not to see an event of this magnitude again soon, it underscores the need to change our business practices. Solutions such as autonomous forklifts and cranes could significantly reduce the number of workers needed on the floor and thereby increase automation of repetitive tasks. Centralized monitoring and control systems could be managed from anywhere. Preventive maintenance reduces downtime and, again, reduces the number of workers needed on-site in case of problems. Systems that communicate with each other and can adapt to real-time changes would mitigate potential supply chain disruptions.

The financial impact of the pandemic cannot be ignored, and investment projects are often interrupted when finances tighten. However, the more you continue to postpone this type of project, the more vital they become.

Best Practices for Digital Transformation

To increase the success of your digital transformation, regardless of where you are in your journey, there are several best practices you can implement:

• Adopt agile execution strategies: at the heart of agile execution is its iterative approach. While a project may be large-scale, delivery is done in iterative stages, allowing you to quickly see value, adapt to new data and lessons learned, and fail fast.
• Start with smaller pilot projects and learn from them: a pilot project can quickly demonstrate proof and thus show concerned teams how to learn to work together. Project success will help increase buy-in from the entire organization.
• Adhere to a targeted approach for data analytics: You may already be doing some form of data analytics, but it’s probably more ad-hoc activities that occur as needed with limited scope. Setting up a versatile expert team is an excellent start.
• Evaluate before starting and reevaluate often: before you can decide where to go, you must determine where you are. Where are you in your journey toward digital maturity? Where do you want to be in one year and five years, and what are the steps to get there? Do you have the right people? If not, who do you need? And this evaluation shouldn’t be one-time. Your competitor is already digitizing and the digital landscape is constantly changing and evolving.
• Create and maintain a digital culture: successful transformation can only occur if the organization fully embraces it. This starts at the top, with the leader driving initiatives and support from all management members.

Conclusion

The fourth industrial revolution is here, and it will continue to significantly disrupt the manufacturing industry. Looking at previous industrial revolutions, few question the necessity of adopting these technologies. We can also look to other long-standing industries, like taxis and hotels, to see the impact technology can have. It’s happening, and it’s happening quickly. Ignoring it has a cost.

While digital transformation has challenges, the benefits are more numerous. Cost savings, operational efficiency, and increased innovation can all lead to improved growth.

While your competitors are already well advanced in their own digital transformations and are already planning the use of new technologies. For your future success, ignoring or postponing them risks making you irrelevant. And lack of relevance is never a business success model.

Source: https://console.virtualpaper.com/edition-2021/01_mci_fev-mar-2021/?utm_source=edition&utm_medium=facebook