Situation
High-Tech manufacturing companies are at the forefront of a transformative wave, driven by the vision of building the Smart Factory. In these advanced production environments, machines and systems are interconnected, allowing communication across the entire manufacturing process. This connectivity is a strategic move towards operational excellence and competitive advantage.
The approach results in the continuous collection of vast amounts of data from various stages of production, ranging from raw material handling to final product assembly. However, the true potential of a Smart Factory lies in its ability to transform this raw data into actionable insights and tangible business value. The challenge for High-Tech manufacturers is not just to collect data but to effectively leverage it.
Challenges
The manual workflow involves several cumbersome steps:
- Process experts manually download data from individual production machines.
- Data scientists prepare and clean the data using tools like MatLab or Python.
- Multiple batches of data are plotted and layered for comparison in Excel.
- The results are shared with the shopfloor expert via spreadsheets.
- Implementing changes based on the analysis is slow, and often the insights are not revisited.
This process not only takes approximately 2 hours but also poses significant challenges:
- The approach is reactive rather than proactive.
- Sharing results and analyses is difficult.
- Live-monitoring and alerting capabilities are non-existent, leading to a lack of timely interventions.
Solution
Implementing TrendMiner Self-Service Analytics revolutionizes the workflow by bringing all steps together in one tool:
- Data from all types of machines and other sources are visualized in seconds using TrendMiner.
- Data preparation: Identify time periods of interest and filter out not relevant events in seconds
- Data analysis quickly and easily performed: batches are compared in only a few clicks without the need for data science knowledge.
- Alerts and insights are immediately set up and insights are shared with colleagues.
Results
- Time Savings – The automated process reduces the time required for data analysis from 2 hours to just a few minutes, allowing the team to focus on actionable insights rather than data preparation.
- Efficiency Improvement – Achieving a huge increase in efficiency enables the company to react faster to production issues and implement changes more swiftly.
- Proactive Monitoring – The ability to set up alerts and continuously monitor production parameters helps in identifying and addressing potential problems before they escalate, e.g. in form of Predictive Maintenance.
- Scalability – The solution is scalable to other systems and parameters, making it adaptable to various production environments and requirements.
- Enhanced Collaboration – Immediate sharing of insights fosters better communication and collaboration among team members, leading to data-driven decision-making and continuous improvement.
By leveraging TrendMiner, the manufacturing company can achieve a higher level of operational efficiency, ensure better utilization of resources, and drive overall productivity.
Blog Posts
Low-code and No-code AI & Analytics Solutions Yield High-Value Use Industrial Use Cases13 September 2024 - 15:19- Building a Foundation for AI & Analytics with an Industrial Data Fabric30 August 2024 - 23:12
- TrendMiner’s 2024.R2 Release; A Complete Platform to Facilitate Event Analytics and Operationalize Industrial Data8 August 2024 - 16:38
