In most mechanical engineering companies, digital innovation unintentionally fails halfway through.
Do these problems sound familiar to you?
Your company has been recording machine data for years, but all your customers get from it are dashboards.
Your AI research projects each time end with "promising results." However, how to bring them to production remains unclear.
Your project ideas for digital innovation keep getting pushed back because the necessary data are missing.
Your customers do not understand the added value of your digital services and are not willing to pay for them.
The three most fundamental reasons for your digital innovations lagging behind are:
Insufficient Data Quality
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The system handling your data is blind for its origin in your process and fails to correctly integrate key information like machine parameters and service data.
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Customers are hesitant to share the data necessary for developing robust diagnostic tools.
The generated insights barely scratch the surface of what's possible.
Limiting Data Architecture
Having been developed with different requirements, the data architecture struggles to accommodate long-term needs and lacks critical capabilities required to deploy, montor data-driven algorithms and adapt the to the specific operating context of every machine. It's sufficient for pilot projects, but in production, performance drops and costs skyrocket.
The architecture more often hinders than helps, preventing your diagnosis tools from reaching your customers.
Inaccessible Data
Without data-driven insights, prioritization of use cases relies on guesswork. And your R&D and service engineers do not have appropriate access to work with the data.
You're focusing on secondary issues and miss the chance to make your process expertise your solutions' USP.
If you successfully overcome these challenges, you can...
Digitally capture and make available your vast experience to your customers.
Model the complex relationships between sensor, production, and quality data that only your process experts and the most experienced machine operators know.
Empower your customers to always operate the optimal process based on informed decisions.
The three most important solutions to the described challenges are:
Scalable Data Platform
Gathers all necessary data for diagnostic algorithms—machine data, maintenance histories, process expertise—into a structured format. Scales to any number of connected machines and supports flexible data-sharing modes, processing, and storage according to customer requirements.
Structured Algorithm Development Process
Facilitates data-driven prioritization and selection of use cases through defined decision logic. Creates clear interaction points for process experts to enhance solutions with their unique expertise, enabling automation of data processing and AI model training.
MLOps: Automated AI Deployment and Operation
Tailors algorithms to specific machines and their operational contexts. Continuously monitors data and algorithm performance changes, incorporating expert feedback and implementing feedback loops for perpetual learning.
