Industry 4.0 Innovations at the Time of Crisis

Progress is unstoppable even at times of crisis, and Industry 4.0 is in full swing, making manufacturing less wasteful, more efficient, and more productive. Learn how to adapt to the rapidly changing market conditions, secure competitive advantage and raise the value of a product from DataArt's Industry 4.0 expert, Maxim Invannikov.
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By Maxim Ivannikov
IoT specialist
Industry 4.0 Innovations at the Time of Crisis

Industry 4.0 Challenges in 2020

The concept of Industry 4.0 refers to the use of smart technologies to make manufacturing less wasteful, more efficient, and more productive. Whether it is to adapt to changing markets, to secure competitive advantage, or to raise the value of a product – innovative manufacturing processes are changing the manufacturing landscape dramatically.

But how do we continue innovating in an ongoing health and economic crisis with significant uncertainty and likely limited resources? 

Moving Forward with Innovations

The strategy depends on a number of factors, including the company’s goals and its financial position. Since our planning horizon is relatively short, a development roadmap and an implementing methodology play a crucial role.

Typically, the digitization of manufacturing begins by introducing the connectivity layer - sensors, devices, and gateways that collect data. In other words, we transform physical objects and processes into a digital environment. The second stage involves converting the collected data into meaningful information and storing it. The next step is the development of a digital twin that enables us to simulate real conditions, analyze potential scenarios, and make optimal decisions based on real data. 

So what is the usual roadmap for introducing a custom Industry 4.0 solution? The first step is always the analysis of requirements and solution design. Depending on the project, it can run in parallel with the market and technical research, development of proof-of-concept prototypes and feasibility studies, as well as validation by stakeholders, and documentation. Developing a Minimal Viable Product shows first results before the entire solution is developed, thereby mitigating numerous risks. An iterative approach is recommended to add and test new features gradually. Finally, the production release does not mean that development should halt. The solution should be supported and developed further.

Let’s talk about some real use cases. I find the following areas to be the most popular among the projects we delivered recently:

  • Predictive maintenance;
  • Equipment modernization;
  • Digital twin solutions.

These directions are particularly suited to the current environment because these projects focus on solving specific problems and results are visible relatively quickly, while they may be a part of a bigger picture. Let’s look at them in more detail. 

Predictive maintenance

Breakdowns can halt operations and result in lost revenues. Preventive maintenance may be one way to avoid disruptions, but not an optimal one - you don't know if the degraded part needs to be replaced immediately or it could continue to function normally. So there’s a good chance of wasting resources. Also, the cost of downtime may be high, substantially higher than developing a predictive maintenance system. On the other hand, predictive maintenance enables early detection of equipment degradation or abnormal behavior, and allows for it to be fixed or replaced without disruption to operations. One of our clients saves millions by implementing an AI-driven predictive maintenance solution.

Equipment modernization entails adding new features and services to the equipment that was designed and manufactured before the age of “smart” devices. It may be an elevator, a conveyor, a boiler, a turbine, a smelt-furnace, or any other equipment. The important part is that this equipment functions perfectly well; the only thing it lacks is connectivity and other IoT stack technologies. Usually, it’s not optimal to replace this equipment due to high costs and potential disruption to integrated infrastructures, but it doesn’t mean that upgrading it is not possible. Innovations and even high-level AI-powered analytical platforms can be introduced by adding sensors, controllers, and actuators. 

A digital twin is a general category for developing virtual copies of objects and processes. It may be warehouses, retail stores, power grids, whole factories, infrastructural objects, among others. Digital twins make it possible to simulate, analyze, and predict potential scenarios of system behavior. Most importantly, they enable automation of operations, optimization of resources, and their remote management, which is crucial in our new reality.


There are many use cases for Industry 4.0 innovations. Implementing them during difficult economic conditions is challenging and requires an experienced team, agility, fast reaction to market changes, cost-effectiveness, and, most of all - a clear purpose. Crises tend to reveal weaknesses in organizations, and while painful, it gives us vision and focus going forward.

Selecting an appropriate strategy and a reliable technological partner, like DataArt, will help to both overcome the crisis and prepare the organization for forthcoming developments. Find out more about our experience in IoT and contact DataArt experts to discuss your needs!

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