Over the past few years, since Industrie 4.0 (Industry 4.0) came to light in Germany, we have been asking ourselves the same question — why are companies now developing and adopting all the fancy, handy, smart stuff?
Automation, robotization, digital tools, and even machine learning were well known before then, so why are companies now investing in and putting so much effort into the transformation process? Where will it all end? On the one hand we have 30- to 40-year-old factories and on the other there are futuristic visions of the fully automated, lights-out, and human-free decision-making supply chains.
From the start, most companies believed Industry 4.0 would help them to improve efficiency and reduce costs on the shop floor, rather than being just another tool. Manufacturers were talking about the digitization of production sites targeting operations KPIs, but mostly with no broader concept.
By combining enablers like the industrial internet, IoT, and cloud, a whole new area for business has opened up — digital business models. Manufacturers themselves have started to transform from B2B to direct-to-consumer (D2C). For manufacturers, the new business models are more disruptive and much more complex with D2C than with the traditional B2B approach. Now we have remote asset condition monitoring and predictive maintenance utilizing the real-time data-based digital twins of the product, process, and assets. We have mass customization services based on automated configure–produce–deliver–pay processes, which is extremely challenging in terms of connected supply chains, system integration, production automation, and seamless and touchless data flows.
The digital twin becomes the center of the data-hungry company, bringing manufacturers back to the initial idea of Industry 4.0 — the cyberphysical system, represented by a digital twin where autonomous collaboration between smart product and smart equipment is happening. Enhancing digital twins with AI and machine-learning engines is opening up a wide range of industrial applications. This is not just represented by the predictive algorithms used in the monitoring of production process or asset performance, but also in utilizing machine learning in supply chain planning, non-conformity pattern recognition, service based on sound recognition, etc.
By the end of 2020, IDC predicts that a third of all manufacturing supply chains will be using analytics-driven cognitive capabilities, increasing cost efficiency by 10% and service performance by 5%. This is not easy to achieve, of course, especially in legacy environments, but this is the future of manufacturing.
The digital transformation of manufacturing has already started, and no company wants to be left behind. Manufacturers find themselves pressured to digitize and automate, as most countries are already adopting Industry 4.0 or smart manufacturing programs to increase their competitiveness.
We see not only the significant development of the technology itself, but also a change in mindset among C-level company representatives — a key success factor when thinking about any kind of transformation. The positive news is that 46.8% of respondents to an IDC survey of manufacturing organizations indicate they plan to establish digital transformation in the next 24 months.
From a current IT and technology development point of view, the sky is the limit. But manufacturers still face numerous challenges when it comes to digitization. Three key areas include technology, the human aspect, and IT/OT security.
- Technology. The complexity of the technology landscape, the integration of the legacy systems, and the implementation costs (ROI) could be seen as obstacles when building the factory of the future. Another challenge, especially for global footprint manufacturers, is keeping data transparent, real time, online, and safe. Because of the implementation costs and effort, many manufacturers try to avoid implementing the fully integrated IT landscape and opt instead for “island solutions.” These isolated solutions might be easier to implement and might bring faster ROI, but in the long term they create complex vertical and horizontal integrations of enterprise systems and can have a significant impact on the company’s future performance.
- The human aspect. The human aspect of industrial digitization and automation is often underestimated. Lack of in-house IT competence and poor understanding of modern tools and approaches can significantly impact digital transformation and even the implementation of smaller digitization projects. The real challenge is the lack of people able to handle the digital technologies on the production plan level, such as designers, integrators, and end users. Another significant challenge is the aging workforce, especially in Western Europe and the U.S., which results in the loss of knowledge within the company. The number of people that can transfer their experience to junior colleagues is decreasing, while at the same time the systems are getting more complex and the life cycles are becoming shorter.
- IT/OT security. IT/OT system security in the integrated and online environment is often cited by manufacturers as one of the main challenges when adopting Industry 4.0 and digitization in general. An integrated and connected shop floor and connected supply chain are seen as a significant threat to companies’ security and safety. Take the cloud approach: many manufacturers are already using cloud-based services, believing this to be a safe environment that could bring significant competitive advantage; others are still hesitant and prefer to stick with on-premise solutions, fearing that data on the cloud could be vulnerable or that systems could be hacked.
Based on research and discussions with manufacturing-related customers, both vendors and end users, IDC offers the following recommendations to tackle the main DX challenges in manufacturing:
- Have a real strategy in place. Your digital strategy should be in line with the business strategy, so define your business needs and support them with your IT and technology landscape. Make the landscape scalable and transparent. Keep in mind that there are likely to be some significant benefits as soon as the whole technology landscape is in place — you can invest in IIoT deployment, for instance, so you can see production and asset performance data. The full benefits, however, will come when the ML-based predictive maintenance models have been running for a while.
- Drive top-down DX change. Get board-level commitment to ensure that DX becomes part of the organization’s DNA.
- Do not reinvent the wheel. Try to get as much inspiration as possible from other industries, vendors, and competitors.
- Remember that the technology itself is still just a tool, and that it needs to be assessed by people who also need to perform corrective action if needed.
- Without integration of process and technology, the information or recommendations from the cyber environment are unlikely to have much of an effect.
- Get the best analytical and enterprise IT talent. This can be done through industrial hackathons and by cooperating with start-ups.
- Deploy an augmented worker strategy. This can be done using AR and VR technology for skillset development and job training. Maintenance staff, for example, could be trained using VR to provide assisted training at the right time and place.
- Build the vendor ecosystem. Digitization projects are becoming increasingly multidisciplinary, which makes the full scale of technologies and services challenging when delivered by a single vendor or system integrator.
- Collaborate across industry. Some of the best ideas could come from outside your professional network.
- Respect the cybersecurity threat but don’t let it slow down your DX activities. Robust cybersecurity solutions for industrial enterprises to monitor OT networks for cyber issues and monitor the OT cybersecurity risk are included in industrial cybersecurity vendors’ offerings.
- Make sure you are really digitally transforming your company. Individual digitization activities and projects might help to increase some operational KPIs in the short term, but the real winners will be those that transform both operations and business models.
To learn more about IDC’s research for the manufacturing industry, contact IDC Manufacturing Insights Europe.