The world has recently entered a new age of manufacturing and this technological revolution will change the industry forever. This next step is called digital transformation and it uses digital data, connectivity, and processing to enhance every aspect of manufacturing activities.
From rapid R&D and prototyping to performance and production analytics, digital transformation in manufacturing will have an impact on all aspects of businesses from how they generate revenue to their organizational structure.
In this article, we’ll be discussing several key trends as well as various core principles applied in the digitalization of manufacturing that are moving manufacturing into the future.
Advantages of Using Digital Transformation in Manufacturing
There are many benefits of digitization in manufacturing and these can be summarized under various categories:
Quality – Production parameters of the product are monitored with high-definition sensors along the whole production line. Machine learning algorithms are applied to production data, deciphering defects’ root causes automatically, as well as predicting issues relating to waste before they occur. This is sometimes known as Quality 4.0.
Productivity – Processes such as design and development are done quicker and are better informed by using tools like augmented reality and 3D printing, and by leveraging users’ real time behavioral data. Production is optimized with negligible downtime due to machines sending crucial maintenance data that is leveraged to optimize output and prevent malfunctions.
Customization – Nowadays, a key selling point for customers is often customization. Digitized manufacturing lines enable attractive customization options for customers, while still performing at a high level of efficiency on a mass scale, resulting in prices remaining competitive.
Cost – Capturing and analyzing data across every manufacturing process stage, including machine and production line data, and transportation and logistics, enables the identification of new opportunities to reduce costs. Inventory can be managed better to meet demands more accurately, while machines have a high degree of flexibility that enables quick changes between products.
Safety – Robots can handle work in dangerous environments. Thanks to dedicated sensors placed throughout the plant or factory, employees can be warned about potential hazards in advance.
The Most Valuable Players of Digital Transformation in Manufacturing
There are several “Valuable Players” that have already started shaping the digital transformation in manufacturing
- Asset Performance Management
Asset Performance Management’s (APM) definition has changed with Industry 4.0 emerging to include a wide function set. APM uses a number of tools to improve the overall reliability and availability of physical assets within the manufacturing environment. These tools are used for collecting, organizing, visualizing, and analyzing assets’ data, and then leveraging it by using reliability maintenance, condition monitoring and predictive forecasting.
- 3D printing (Additive Manufacturing)
Additive manufacturing is commonly known as “3D printing”, and it uses incorporates a broad range of materials and processes that have one thing in common –transformation of 3D data into physical items. This type of manufacturing enables freedom of design that was not possible previously, and as the technology progresses, new applications are emerging in sectors like consumer/lifestyle, medical, automotive and aerospace.
- Connected Services and Products
Manufacturers can position themselves as innovators within their markets by digitizing the supply chain through connected products. The customers’ exact requirements can be met constantly by making enhancements to the features of the product. This continuous connectivity enhances the manufacturer’s reputation for delivering a service.
- Cloud Platforms and Applications
The current approach of management systems based on client-server data is fast being replaced by industrial cloud applications. This method to develop and deploy software has several advantages over the older server approach which is more complex and heavier, and allows for low-cost maintenance and easy updates.
- Fog Computing
Fog computing, also known as the fog, lives in the space between the cloud and IoT endpoints. It is a network that connects the data creation and input points to data storage. The fog processes data from the edge and manages tasks that don’t need the cloud, but can’t be done on the device.
- Edge Computing
More tasks can be handled by on-site devices as computing power improves. This lightens the load placed on the cloud and the IoT network, thereby reducing latency, reduces connectivity costs and mitigates data security risks. Processing by field devices is known as edge computing and it enables many possibilities within the IoT environment like face recognition, object detection, language processing, obstacle avoidance and other machine learning applications.
- Advanced Analytics and Machine Learning
Industrial IoT rests on the premise of receiving and analyzing constant data from sensors and other data collection points in real time, and the ability to respond with action immediately. To leverage IoT to benefit industrial production, machine learning therefore becomes a very powerful tool.
Through predictive maintenance and machine learning, the performance and behavior of machines used in manufacturing is learned and “understood,” while algorithms use new information to adapt. This enables the identification of unusual behaviors and for the prediction of malfunctions and errors with high accuracy.
- Industry 4.0 and IIoT (Industrial IoT)
The German government coined the term “Industrie 4.0” in 2011 and digital disruption in manufacturing has moved rapidly from there. Industrial IoT is moving forward at a rapid rate as manufacturers begin to understand the massive potential this technological approach has for changing their operations. Industry 4.0’s leading use cases include fleet management, data-driven R&D, digital twin, predictive maintenance and condition monitoring.
It is common today to use robotics in industrial manufacturing and nearly 2 million industrial robots work globally. Robotics offer unparalleled efficiency and they can do dangerous, unpleasant and monotonous work instead of humans.
The IoRT (Internet of Robotic Things) takes robotic technology up another notch and will form a major part manufacturing in the future. Decisions regarding performance and synchronicity on the factory floor are made by using connected production robots that are fed with real-time data. The IoRT will enable manufacturers to meet the requirements of their customers better, and respond accurately to supply chain changes.
- Open Process Automation
A Distributed Control System (DCS) and programmable logic controllers (PLCs) control many of the automated manufacturing systems used today. These types of systems aren’t well suited to the latest technological climate, due to the architecture being customized to a specific production line, difficult to change and proprietary in nature. Open Process Automation provides a new generation of infrastructure for automation that can be adapted and implemented easily for use in consumer and industrial IoT scenarios.
The Smart Factory
Although we have described various individual technologies above, things get really interesting when we imagine a complete smart factory that uses Industry 4.0 technology, together with VR, AR and wearables.
In these scenarios, all manufacturing activities, be they human-machine interaction, machine, or human, are coordinated and synchronized to achieve optimum output, and ensure the operation’s sustainability.
Digital Transformation Strategy
A strategy for digital transformation that is well-defined is crucial for achieving overall success when implementing IoT in a manufacturing environment. This strategy should cover all aspects of business activity – from product development and production, to advanced analysis, delivery and quality control.
A company’s legacy systems should be looked at when identifying potential challenges. Before implementation of a new system is started, all machine data should be collected for their past and current states.
As IoT offers manufacturers a myriad of potential directions for development, the sheer volume of options may be confusing. A clear strategy is therefore critical to ensure focus. A portion of this focus should obviously be on customer requirements as this should be the overriding goal of the digital transformation process.
A typical digital transformation strategy would look as follows:
- Creating a roadmap. Set targets for the next 5 years taking the current status of the company’s digitization into account. The goals with the best ROI should have the highest priority, and steps should be implemented to get top management involved.
- Deciding on Proof of Concept projects. This phase is essentially experimental and it should consists of several pilot projects to establish cross-functional teams’ performance, and to determine if the process is agile.
- Defining target functionality. Use the knowledge gained in the previous step to decide which capabilities within Industry 4.0 will deliver the most value for your firm. You should be better informed about your teams’ abilities to implement new technologies at this stage, and know if additional recruiting is required.
- Learning to leverage data analytics. Moving to Industry 4.0 does not make sense if you are not able to analyze the data collected. The results of this analysis should be fed into the decision making process immediately.
- Adopting digital transformation as a company. Moving to Industry 4.0 is not simply a temporary adjustment phase. Adoption of this new approach must be company-wide to reap the benefits, and should be led by top managements with financial stakeholders and the C-suite setting the tone.
- Development as an integral part of your culture. Keep a wide vision of your position within your business ecosystem in mind as you start using IoT to create better solutions for customers. Explore new possibilities for further collaboration to improve your products’ and services’ scope and quality, and share knowledge with suppliers and partners.
Challenges of Digital Transformation
There are a number of challenges when you embark on digital transformation. There are however already many services and tools available to help manufacturers to make the process of digital transformation successful, predictable and structured.
When implementing digitization in manufacturing, the following are some of the challenges you are likely to encounter:
Lack of appropriate knowledge
If you don’t have the relevant knowledge when introducing technology it won’t be enough to make it work. An important part of integrating IoT into manufacturing is to invest in staff’s knowledge. Management will have to consider hiring new employees or partnering with consultants if the expertise level within the company is not sufficient. Even if this is the case, the implementation of IoT should never be only the responsibility of a single department or employee, but should be a shared goal.
A substantial investment is required to lead a manufacturing plant through the digital transformation process. Although both the short and long-term rewards are numerous, it should be kept in mind that businesses are all different, especially as far as expense and revenue structures are concerned. The process needs planning and customization as digital transformation programs will always be different. The requirements of each factory or plant are different, as are the resources available.
IoT is however extremely flexible and not a tool where one-size-fits-all. Manufacturers with limited budgets should however think big from the start as it is important to have a long-term vision to reach a valuable goal at the end of the line. Once the vision has been defined, a solution with an acceptable ROI should be selected as a proof-of-concept. This involves selecting the most influential and important information for that specific operation, collecting data via the network and performing analytics and resultant actions based on the data as the first phase in the process. Once these central parameters have been leveraged, decisions can be taken about how to further the network’s capabilities.
Development processes that are not suitable
Manufacturers have to realize that their development processes and technology stack will have to undergo various changes to be suited to Industry 4.0’s agile nature. They will need to replace release cycles based upon lengthy and rigid iteration schedules. The aim is to use the data in completely new ways. This will demand changes to how data is leveraged, what content is presented and business rules.
This represents a dramatic change in manufacturing. As the release of product becomes continuous, the IoT development process will have to support this by using analytics and data from user feedback to reach a higher level of digital performance.
This is achieved by making data read and write-accessible via robust, secure APIs. This is virtually impossible to do with old technology, which means more than 5 years old for core business systems.
Company structure is rigid
Introducing of IIoT to a manufacturing facility is a paradigm shift rather than a slight improvement. In order for this technology to be implemented properly, the firm itself needs to change. Although this could be intimidating, it may lead to many positive outcomes as the organization’s structure is reset and re-tested. This creates opportunities for improved employee placement and other enhancements.
An approach often used is forming a multi-disciplinary team including service professionals, data analysts, product designers and engineers to act as the primary negotiators for the digital transformation. The team incubates new technologies, implements POCs, and rolls out successful iterations to the company once these have been approved.
From the beginning of any digital transformation project, cyber security should be taken into account. Vulnerability points should be identified and various fail-safe mechanisms and defense layers need to be put in place, ensuring the system is completely secure.
Resistance from Employees
Most people will resist change, especially in their work environment. Many employees experienced the current digital disruption in manufacturing as a threat.
Although the future is always uncertain, change is not something to be afraid of. Commitment to the digital transformation process should start with top management and be passed down to each and every employee. Transparency and clear communication is crucial, and getting every staff member excited about the potential of this new technology will go a long way.
Identifying opportunities for improvement in performance that will deliver significant benefits to customers, directly or indirectly, is a good approach to launch digital transformation in manufacturing. This puts the focus on areas like the business model itself, engineering and support, customer service, operations and the supply chain.
To start a digital transformation process:
- Define the business’ objectives, and design a clear strategy to reach them.
- Launch short-term projects that will deliver a ROI that’s measurable.
- Start moving data and applications to the cloud, and extract machine data to a local gateway.
- Once you’ve tested a single asset or production line, start scaling up.
- Make use of digital technology experts to stay up to date with the latest solutions and how these can be implemented.
Some companies in the manufacturing sector show clear signs of digital transformation, while it is completely dormant in others. Ask yourself two simple questions as an initial step in designing your digital transformation plan: How do you see your operation currently? Where would you like your operation to be in the future?