Behind the actual process of digitalization in production, there is a whole range of buzzwords floating around. Have you already introduced the IIoT? What about your smart factory and your position in Industry 4.0?

But to be honest: you don't need to worry about these terms, because they stand for concepts that are intended to summarize the actual digitization processes.

"Our processes have grown historically and are correspondingly complex." Many companies have to approach the digitalization of their own production processes with this in mind. And that is completely normal.

Only very few companies can keep up with every technological disruption immediately. However, the wealth of challenges that manufacturing companies face today makes sustainable digitalization unavoidable. These challenges include:

Step 1: Connected production

What does that mean?

It starts with the networking of assets on the store floor and networking from the store floor to the top floor. In most cases, this involves networking from physical to digital (e.g. by retrofitting existing machines with sensors). However, digital to physical (e.g. through the control of cobots) or digital to digital (e.g. through self-controlling AI) are also scenarios.

Why?

Real-time data streams between sensors, peripheral systems, databases, etc. enable the continuous collection of production and process-relevant data. This forms the basis for all further digitalization steps.

Step 2: Big Data

What does that mean?

As soon as data can be collected, it is generated - quickly and in large and not necessarily comprehensible quantities. Digitalization creates concepts for handling this data.

Why?

Data must not only be collected, but also managed. Big data projects involve structured data collection, storage, processing and evaluation.

Step 3: Visualization of data

What does that mean?

Up to this point, data is still just data. However, people are visually oriented. Therefore, the next step is to visualize collected data in a user-focused way, for example in dashboards.

Why?

Properly visualized, data creates transparency about production processes and enables production optimization, e.g. through machine or condition monitoring.

Step 4: Digital twin

What does that mean?

In addition to your physical machines, you have access to an exact digital image of them. This in turn is continuously fed with live data.

Why?

The digital twin gives you the opportunity to carry out simulations and analyses in order to discover potential for improvement. The best thing about it: the real asset in your production remains unaffected the entire time.

Step 5: Data Analytics

What does that mean?

From this point onwards, you analyze large volumes of data and historical data to gain insights into production processes and identify trends. The prerequisite for this is historical data that has been collected systematically and in a structured manner over a longer period of time.

Why?

The analysis reveals patterns that enable production processes to be optimized.

Step 6: AI in production

What does that mean?

AI technologies such as deep learning are at the end of the digitization of your production processes and require a high degree of structured data collection and processing. However, if this data is available, you can take a huge step towards intelligent and sustainable production using AI.

Why?

AI-supported processes enable a significant increase in efficiency and flexibility in your production and help you to reach the right conclusions quickly. Machines make their own decisions based on historical data. Predictive maintenance and self-controlling systems are possible.

Obstacles

• lack of financial resources
• data protection requirements (should software providers have access to my production and personnel data?)
• unclear IT security requirements (keyword: Cyber Resilience Act)
• lack of skilled workers
• complexity of the topic
• internal resistance from employees, e.g. due to concerns about their jobs

Risks

• high initial costs & lack of insight into ROI
• greater vulnerability to hacker attacks
• insufficient technology and interface standards
• incorrect assessment of priorities due to lack of digitalization experience
• technology dependencies / vendor lock-in

Opportunities

• creation of end-to-end information flows (IT and OT)
• data-based decisions for process optimization
• traceability of processes
• greater flexibility, transparency and efficiency
• reduction in costs (production, maintenance, commissioning, etc.)
• increase in product quality
• increase machine and system availability (zero downtime)
• better customer relationships thanks to greater individuality and shorter time-to-market
• strengthening your market position through new business models, digital services, etc.

When digitizing your production processes, the "how" is at least as important as the "what". The process must be focused on just as much as the solution. There is little chance that bad processes will lead to good solutions. Or as we like to say: "Bad processes digitized are still bad processes."

That's why we suggest a 6-step approach:

Step 1: Find a trustworthy digitalization partner

Work together with experts. In very few cases is there sufficient expertise in-house to evaluate the scope of digitization. You should therefore look for a partner who has the necessary experience, but who also has the right chemistry. You will be working together for quite some time and are dependent on mutual understanding.

Step 2: Maturity assessment along the maturity index

The first step is not implementation, but assessing where you stand and where the journey should take you. Your partner uses the maturity model, for example, to evaluate and advise you on what your actual and target status is.

Step 3: Identify profitable use cases (low hanging fruit)

Then it's time for the first project. This should never be the big answer to all digitization questions. Look for a project that can be brought to measurable success quickly. This will secure the status of your collaboration and also send a positive signal to your employees.

Step 4: Carefully planned roadmap and end-to-end digitalization strategy

In this step, you broaden your view of the big picture. You look at all processes end-to-end and identify potential problem areas. Draw up a roadmap and a digitalization strategy that focuses on holistic solutions. Isolated solutions and costs for unnecessary technologies are eliminated.

Step 5: Involve employees and stakeholders in the process

Once the overall goal is clear, the time has come to get your employees on board. After all, they should work with the solutions and feel comfortable doing so. This step is primarily about reducing internal resistance.

Step 6: Agile approach in sub-projects (sprints) instead of large projects

The concrete implementation is next. Do not set yourself project goals that are too big. Instead, work agilely from one sub-project to the next. This reduces the initial costs and allows feedback to be regularly incorporated into the ongoing process. You may not reach your goal tomorrow, but you will make sure that you actually get there.