Digitalization in Production describes transforming traditional, frequently paper-based workflows into data-driven processes. It goes beyond the idea of a “paperless factory”, also includes the precise collection and processing of relevant process data in real time, in order to make production steps more efficient and transparent.
On top of that, digitalization enables a continuous flow of data that connects all stages of production, from raw material delivery to secondary packaging and shipping. The term Industry 4.0 is often used in this context. However, the key aspect is the close integration of production and IT, which enables companies to respond more quickly to market changes and secure their long-term competitiveness.
Key Technologies in Production Digitalization
Anyone who wants to digitalize needs a stable technological foundation. The implementation relies on several core technologies that form the basis for modern, intelligent production systems.
Internet of Things (IoT) / Industrial IoT (IIoT)
The Internet of Things (IoT) is a network of sensors and machines that gather information like extruder pressure, sterilization times, or fill volumes automatically. This is known as Industrial IoT (IIoT) in industrial contexts. In this context, industrial IoT platforms act as a central hub that directly connects the shop floor with the IT level. The goal is to ensure complete data integrity and reduce manual documentation efforts while increasing process stability.
Big Data & Analytics
Raw data alone is of no use to anyone. It only becomes valuable once algorithms filter these massive data streams into process insights and a solid basis for decision-making. The vast amounts of data generated by the IIoT are analyzed using Big Data techniques to uncover hidden patterns. In this way, quality deviations can be predicted before a batch is put at risk. Without the early detection of such hidden patterns, batches may exhibit quality defects such as incorrect dosages, material faults, or contamination. This can lead to product recalls, increased waste, or safety risks for end customers.
Cloud & Edge computing
Where does all this data go? This is where the combination of Cloud and Edge technologies shows its strengths. While the Cloud provides scalable computing power for long-term, cross-site analysis (for example: for ERP integration across locations or long-term data storage), Edge computing processes critical data directly at the machine. Together, they enable efficient operations with minimal latency in production environments.
Artificial Intelligence (AI) & Machine Learning
AI acts here as a smart assistant, identifying complex relationships and proactively adjusting production parameters before issues occur.
Typical applications include:
- Predictive maintenance
- Automated quality control
- Process optimization
- Collaborative robots (in short: cobots)
Digital twin
A digital twin is a virtual replica of a real machine or process. It allows engineers to simulate and test different operating scenarios in a virtual environment before implementing them in reality, which helps significantly reduce both costs and risks.
Opportunities and Challenges
The use of digital technologies and AI in production, especially in GMP fields, offers significant potential but also comes with its own set of challenges.
Opportunities
- Real-time optimization: Analyzing production lines allows for precise market demand forecasting.
- Predictive maintenance: Reducing unplanned downtime and maintaining sterility through timely, data-backed servicing.
- Higher precision & efficiency: Cutting raw material waste and energy consumption through smarter extrusion processes.
- Flexibility: Rapidly adapting production to meet specific customer requirements.
Challenges
- High investment costs: Significant expenses for infrastructure, software, and cybersecurity (especially critical for SMEs). New systems must be seamlessly integrated into existing legacy equipment (retrofitting), which is often complex and costly.
- Skills shortage: Need for professionals who are well-versed both in specific production processes and in digital data analysis.
- Risk management: Requiring clear strategies are required to manage technical and financial risks. The integrity and reliability of the data must also be ensured.
- Workforce adaptation: Strong need for reskilling and upskilling employees to thrive in a digital work environment.
A step-by-step guide to digital transformation
Digitalization in Production doesn’t happen overnight. It’s a gradual process often presented using a maturity model:
1. Data standardization & integrity
Before using complex AI tools or advanced algorithms, the basics must be in place. That means processes need to be validated, and data structures clearly defined. Data should be transferred into centralized, validation-ready systems (e.g., MES or SCADA), rather than stored in unreliable spreadsheets.
2. Asset connectivity (IoT Foundation)
There’s no need to overhaul the entire facility at once. Instead, we should start by implementing suitable sensors and measurement systems at key production stations. Monitoring machine availability provides the initial foundation for data-driven control.
3. Data analysis and local optimization
After collecting data over 3-6 months, basic analytics tools can be used to identify bottlenecks. For example:
- Are there correlations between specific raw material batches and fluctuations in extrusion pressure?
- Can CIP/SIP cycles be optimized without increasing risk?
Solving these targeted issues using data paves the way for more advanced AI applications later.
4. Building digital skills within the team
Technology is only as effective as the people using it. Even the best technology won’t deliver much value if people aren’t willing or able to use it. Therefore, targeted training measures are necessary to ensure acceptance and safe operation of digital systems. Strong team alignment is essential for the success of any digitalization initiative.