Industry 4.0

New technologies are arriving

More than ever, the emphasis is on data-driven processes. These are processes governed by hard data taken straight from the production floor. This approach calls for efficient data collection, whether centralized or decentralized, and it opens up room to apply data extraction technologies, IoT, and sensor systems.

Know-how is being digitalized

Experience from manufacturing itself is starting to be put to use. Models are built on the know-how of the workforce and applied to the collected data. Evaluating that data leads to more efficient production. Most know-how is locked away in the experience of the workers and in the way processes are configured, which drives demand for methods and tools capable of extracting that know-how and putting it to work.

Automation

The rise in automation itself can be traced to conditions in the labor market (a shortage of skilled workers) as well as to the efficient collection and evaluation of data streams. Projects are emerging that make it possible to configure processes automatically, based precisely on the evaluated data, along with sophisticated alerting systems that flag deviations from limit values and thereby improve production quality.

Replacing human labor

Although Industry 4.0 may look like a threat to the human workforce, which faces extinction at the hands of robotization, replacing human work is to a large extent a matter of replacing it with other human work — more specialized and more highly skilled. Digitalization creates a need for data analytics, for understanding data and evaluating it competently. That calls for knowledgeable people, and demand for them is now on the rise.

Rolling out Industry 4.0

The methods being introduced include machine learning, auto-configuration and self-diagnostics, and networking machines and components together. Products and machines are fitted with chips that make it possible to monitor and operate them over the internet. Cloud storage, 3D printing, data centers, automatic problem reporting, and "smart warehouses" that report low stock on their own will also come into play.

What influences the deployment of automation and I40 elements? 1. Inadequately defined problems

It is the employees directly involved in the process who face the problems. They need to be identified and named. Only then can you start looking for effective solutions. Poorly mapped problems lead to waning interest in improvement and change.

2. Centralized IT

This is a common issue, especially in multinational companies, in the parent-versus-subsidiary relationship. Management pushes to centralize IT and to use tools intended for the entire group.

3. Lack of know-how in the process

Companies rely on hands-on experts. That is the right approach. But an expert in production processes and their practical problems is not an expert in implementing automation elements and advanced analytics.

4. Absence of S.M.A.R.T. goals

A frequent problem in implementing I40 projects is the absence of so-called "smart" goals. If we set out to implement something, we should know what to expect and be able to make it concrete. Large, abstract goals demand a great deal of effort and time to achieve and leave people disappointed with the project. Concrete, small goals, on the other hand, deliver a visible effect quickly and spark growing interest in similar projects.

Centralized solutions

These mean implementing I40 as a whole. It can essentially be implemented "turnkey." The starting point is analyzing the processes of, say, an entire production line. Once the needs and the room for improvement have been mapped, a centralized solution is put in place that automates the line, consolidates the data streams, evaluates them with analytics, and presents them to consumers — or automatically adjusts the settings of the line itself.

Decentralized solutions

Decentralized solutions pinpoint specific problems and proposals for solving them, then put those into practice quickly. This method again requires process analysis — but in this case only of the processes related to the identified problem. The analysis is faster and more targeted. It yields a concrete blueprint for digitalizing a certain part of the process and applying specific analytical methods to it to improve the outcome. The result is a small dashboard with evaluated data, or the automation of a specific machine's settings.