At the risk of sounding a little melodramatic, we’ve passed the threshold and have entered a new age of manufacturing; a technological revolution that will change industry forever. The term “digital transformation” describes this next step, where the utilization of digital data, connectivity, and processing encompasses every aspect of manufacturing activity.

From rapid prototyping and R&D to production and performance analytics, digital transformation in manufacturing is poised to impact all aspects of business from the organizational structure of companies to how they generate revenue.

In this article, we’ll be highlighting some of the core principles behind the digitalization in manufacturing as well as some key trends taking manufacturing operations into the future.


What You Get – The Advantages of Digital Transformation in Manufacturing

The benefits of digitization in manufacturing are many, but can be summarized under these 5 categories:

Productivity – Design and development processes are faster and better informed using tools such as 3D printing and augmented reality, and by leveraging behavioral data from users in real time. Production is streamlined with minimal downtime due to connected machines sending vital maintenance data that can be leveraged to prevent malfunctions and optimize output.  

Quality – High-definition sensors monitor production parameters of the product along the entire production line process. Sometimes referred to as Quality 4.0, machine learning algorithms are applied to production data to automatically decipher root causes of defects as well as to predict waste-related issues before they occur.

Cost – Capturing and analyzing data across all stages of the manufacturing process, including production line and machine data, logistics and transportation, makes it possible to identify new cost reduction opportunities. Inventory can be better managed to meet demands in a more accurate manner while machines offer a high level of flexibility that allows for quick changes between products.

With IIoT, inventory can be better managed to meet demands in a more accurate manner while machines offer a flexibility that allows for quick changes between products.

Customization Customization has become a key selling point for customers. Digitized manufacturing lines can offer customers attractive customization options while still operating on a mass scale and at a high level of efficiency so that prices remain competitive.

Safety – Work in dangerous environments can be handled by robots. Staff can be alerted about potential hazards well in advance thanks to dedicated sensors placed throughout the plant/factory.


Meet the 10 MVPs of Digital Transformation in Manufacturing

If you thought you were getting a list of CEOs here, sorry to disappoint you, but you’ll probably find this much more useful.

Here’s a list of the 10 “Most Valuable Players” who have already begun shaping the digital transformation in manufacturing…in alphabetical order for your reading pleasure!

1. Additive Manufacturing (3D printing)

Commonly referred to as “3D printing”, additive manufacturing incorporates a wide variety of processes and materials that share one common characteristic – the direct transformation of 3D data into the physical realm. This form of manufacturing allows for a freedom of design that was never before possible, and as this technology advances, we’re witnessing the emergence of applications in sectors including, but not limited to, aerospace, automotive, medical and consumer/lifestyle.

Prototyping for additive manufacturing

2. Asset Performance Management (APM)

With the emergence of Industry 4.0, the definition of Asset Performance Management has changed to include a broad set of functions. APM pools together a number of tools in order to improve the availability and overall reliability of physical assets within the manufacturing ecosystem. These tools collect, organize, visualize, and analyze data from the assets, and leverage it by performing predictive forecasting, condition monitoring and reliability maintenance.

3. Cloud Applications & Platforms

The older approach of client-server data management systems is rapidly being replaced by industrial cloud applications. This new method of developing and deploying software has many advantages over the older heavier and more complex server approach, and allows for easy updates and low-cost maintenance.

4. Connected Products and Services

Digitization of the supply chain in the form of connected products positions manufacturers as innovators within their markets. The precise needs of customers can be constantly met through enhancements made to a product’s features. This continuous connectivity strengthens the manufacturer’s stance as a service.

5. The Edge

As computing power improves, more tasks can be handled by devices “on-site”. This reduces latency by lightening the load placed on the IoT network and the cloud, mitigates data security risks, and reduces connectivity costs. This processing by devices in the field is known as “edge computing” and opens up many possibilities within the IoT realm such as obstacle avoidance, language processing, object detection, face recognition, and other machine learning applications.

6. The Fog

The fog, or fog computing, inhabits the space between IoT endpoints and the cloud. In other words, it’s a network that connects the points of data input and creation to where that data is stored. The fog is a midway processing zone that takes care of data from the edge, managing tasks that don’t require the cloud, but can’t be taken care of on-device.

7. Industrial IoT (IIoT) and Industry 4.0

Beginning with the term “Industrie 4.0” which was coined by the German government in 2011, the digital disruption in manufacturing is now well underway. Industrial IoT is advancing at a rapid pace as manufacturers understand the immense potential of this technological approach to completely change how their operations work. Leading use cases of Industry 4.0 are condition monitoring, predictive maintenance, digital twin, data-driven R&D, and fleet management.

8. Machine Learning & Advanced Analytics

The most significant premise of industrial IoT is the ability to receive and analyze constant updates from sensors and other data collection points in real time – and to be able to respond with immediate action. This makes machine learning a very powerful tool in leveraging IoT to benefit industrial production.

Through machine learning and predictive maintenance, the behavior and performance of machines in a manufacturing setup is learned and “understood” while algorithms adapt based upon new information. This allows for unusual behaviors to be identified and for errors and malfunctions to be predicted with high accuracy.

9. Open Process Automation

Many of the automated manufacturing systems in operation today are controlled by what is commonly known as a Distributed Control System (DCS) alongside programmable logic controllers (PLCs). This type of system isn’t very well suited to the current technological climate as the architecture is usually very proprietary in nature, difficult to change, and extremely customized to a specific production line. Open Process Automation aims to provide a new generation of automation infrastructure that can be easily implemented and adapted for use in industrial and consumer IoT scenarios.

10. Robotics

The utilization of robotics in industrial manufacturing is common today with close to 2 million industrial robots working around the world. The motivation behind robotics is clear. The efficiency they offer is unparalleled and they can do monotonous, unpleasant, and dangerous work instead of humans.

The Internet of Robotic Things (IoRT) takes robotic technology to the next level and will be a major part of the future of manufacturing. Production robots will be connected and fed with real-time data that will be used to make decisions with regards to synchronicity and performance on the factory floor. The IoRT will allow manufacturers to better meet the needs of their customers, and accurately respond to changes in the supply chain.

The Internet of Robotic Things (IoRT) takes robotic technology to the next level and will be a major part of the future of manufacturing.


Digital Transformation in Manufacturing: The Complete Smart Factory

The list above describes individual technologies, but imagining a complete “smart factory” which applies Industry 4.0 technology, along with wearables, AR and VR, is where it starts to get really interesting.

In such a scenario, all aspects of manufacturing activity – human, machine, and human-machine interaction – are synchronised and coordinated to achieve optimal output, and ensure sustainability for the operation, and for the people that make it work.


Digital Transformation Strategy

A well-defined digital transformation strategy is critical for the overall success of IoT implementation in a manufacturing setting. The strategy should cover every aspect of business activity – from development and production, to advanced quality control, delivery, and analysis.

The state of the company’s legacy systems should be taken into account to identify potential challenges. As much data as possible should be collected from the machines in their current and past states before implementation of the new system begins.

A well-defined digital transformation strategy is critical for the overall success of IoT implementation in a manufacturing setting.

IoT offers manufacturers so many potential directions for development that the myriad of options can be confusing. This is why a clear strategy is imperative to ensure focus. Part of this focus is of course the needs of the customer which should be a central goal of the digital transformation process.

Let’s take a look at a breakdown of a step-by-step digital transformation strategy:

  1. Create an Industry 4.0 road map. Take into account the current status of the company with regards to digitization, and then set targets for a 5-year period. Goals with the most significant ROI should take top priority, while measures should be taken to get leadership on board.
  2. Decide upon projects that establish POC. This somewhat experimental phase should use a variety of pilot projects to establish the performance of cross-functional teams, and gauge how agile the process is.

SEE ALSO: Top Industry 4.0 Use Cases

  1. Define target functionality. Based on knowledge gained from Step 2., decide which Industry 4.0 capabilities will drive the most value for the company. At this stage, you should be better informed about the abilities of your teams to implement the new technology, and whether additional recruiting is necessary.
  2. Learn to leverage data analytics. Progressing to Industry 4.0 means nothing without being able to analyze the collected data. This analysis should be immediately fed into the decision making process.
  3. Adopt digital transformation as a company. Implementing Industry 4.0 is more than a temporary adjustment phase. To reap the benefits, adoption of this new approach should be company-wide, led from the top with C-suite and financial stakeholders setting the tone.
  4. Develop as an integral part of your ecosystem. As you use IoT to create better solutions for your customers, keep a broad vision of your position within your business ecosystem. Share knowledge with partners and suppliers, and explore potential avenues for further collaboration to further the quality and scope of your products and services.


Digital Transformation Challenges

The roadmap for digital transformation does include a number of challenges. The good news is that there are already many tools and services in place to assist manufacturers in making the digital transformation process structured, predictable, and successful.

Here are some of the challenges to look out for when implementing digitization in manufacturing:

Budget limitations

Leading a manufacturing facility through the digital transformation journey requires a substantial investment. The rewards are numerous, both short and long-term, but it’s important to keep in mind that every business is different, especially when it comes to revenue and expense structures. This process requires planning and customization as no two digital transformation programs should ever look the same. The needs of every plant or factory are different, and so are the available resources.

The good news is that IoT is flexible and not a one-size-fits-all type of tool. Manufacturers with a more limited budget should think big initially since having a long-term vision is important for reaching a truly valuable goal down the line. Once this vision has been explored, a solution with a solid ROI should be sought out as a proof-of-concept. This means that as a first phase in the process, data collected by the network – and the analytics and resultant actions based on that data – should be the most important and influential information for that specific operation. Once those central parameters are being leveraged, decisions about how to further the capabilities of the network can be taken.  

Lack of relevant knowledge

Introducing technology alone is not enough without the relevant knowledge to make it work. Investing in employees’ knowledge is an important part of integrating IoT into manufacturing. If the level of expertise within the company is insufficient, management will have to consider partnering with external consultants or hiring new employees. Even in such cases, the introduction of IoT shouldn’t be the sole responsibility of one employee or department, and instead should be a shared goal.

Rigid company structure

The introduction of IIoT to a manufacturing facility is more like a paradigm shift than a slight improvement. The organization itself will need to change in order for this new technology to be properly implemented. While this can be daunting, it can lead to a lot of positive outcomes as organizational structure is reset and re-tested, creating the opportunity for better employee placement and other improvements.

One approach is to form a multi-disciplinary team that includes engineers, product designers, data analysts, and service professionals to act as the primary agents for the digital transformation. This team will incubate new technologies, implement POCs, and then roll out successful iterations to the company after they’ve been approved.

Unsuitable development processes

Manufacturers need to understand that their technology stack and development processes will need to undergo numerous changes to suit the more agile nature of Industry 4.0. Release cycles based upon quarters, or other lengthy and rigid iteration schedules, will need to be replaced. The goal is to make use of the data in completely new ways, which naturally demands changes to business rules, the way that content is presented, and how data is leveraged

This represents a real sea-change in manufacturing. As product releases become continuous, the IoT development process will need to support this behavior, utilizing data from user feedback and using analytics in order to achieve a high level of digital performance.

To do this, updates will be needed to make the data read and write-accessible through secure and robust APIs. This is difficult to do with outdated tech, which unfortunately means more than 5 years old when it comes to core business systems.

Employee reluctance

Not everybody is open to change. In fact, most people don’t welcome changes to their work environment. The current digital disruption in manufacturing is experienced as a threat to many employees.  

While no one can be certain of what the future holds, change is not something to fear. Commitment to the digital transformation process should start with executive management and be passed onto individual employees. Clear communication and transparency is key, and getting everyone excited about the potential of this new technology can’t hurt either.


Cyber security should be taken into account at the start of any digital transformation project. Points of vulnerability should be identified and a number of defense layers and fail-safe mechanisms need to be in place to ensure that the system is completely secure.


Ok, I’m in. Now what?

A good approach to launching a digital transformation in manufacturing is to identify improvement opportunities in performance that will result directly or indirectly in significant benefits to the customer. This places the focus on areas such as the supply chain, operations, customer service, engineering and support as well as the business model itself.

Additional tips for beginning a digital transformation process are to:

  • Define your business objectives, and set out a clear strategy to reach them
  • Start with short-term projects that can deliver a measurable ROI
  • Begin moving applications and data to the cloud, and extracting machine data to a local gateway
  • Test a single production line or asset, and then scale up
  • Partner up with experts in digital technology so that you’re aware of the latest solutions and how to implement them

The digital transformation in the manufacturing sector is very evident in some companies and completely dormant in others. How do you view your operation at the moment? Where would you like to be in the future? These two very simple questions are important to answer as an initial step in laying out your digital transformation plan.

To learn more, pick up our free handbook on how to build a successful digital transformation strategy.

When you’re ready, start your free trial with the Seebo Industrial IoT platform.

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