Human-machine collaboration has never called for as much flexibility and adaptability as in the era of Industry 4.0. Companies see Industry 4.0 as an opportunity to gain a competitive advantage to become market leaders or strengthen their market leadership.
However, more than half of companies surveyed by the German National Academy of Science and Engineering said they are lagging behind with digitalization. They explained this is mostly due to a lack of guidance and positive experience examples from industry leaders.
Our client—an innovative leading European food producer with manufacturing facilities across different countries—has been undergoing a digital transformation. The company wanted to move from reliable plants to smart plants equipped with a data-driven digital nerve system. This would enable faster and better decisions and, using advanced automation, allow plants to self-optimize at incredibly high levels.
The main issues our client faced were:
- Conventional industrial controllers required frequent input from process operators as each machine’s controller was tuned individually. This forced skillful workers to tune low-level process variables instead of focusing on higher-level metrics.
- No centralized control of the factory to optimize its performance—each process/machine performance was optimized individually.
- A shortage of skillful workers in the industry and long onboarding time put significant pressure on the manufacturer. As a result, much effort was necessary considering personal experience with machinery.
- Lack of historical data storage for machine telemetry and operation data caused issues with handling similar manufacturing challenges over time or across different factories (for example, a year with low rainfall, or raw material quality changes).
- Significant challenges in optimizing industrial processes, due to lack of configuration options, difficulty adding new sensors, applying intelligent control algorithms, and simultaneously controlling multiple actuators.
- Seasonal changes in raw material quality caused frequent tuning of conventional industrial controllers by external vendors and led to operational cost increase.
