At a glance
- Packaging equipment manufacturer enhances its products with data-based services
- AI-supported forecasts reliably predict machine downtimes and defects
- Efficiency gains range from warehousing to overall productivity
The business situation of our client
In global competition, efficient production processes are a decisive factor for success. Against this backdrop, digitalisation offers manufacturers of machines and production lines many opportunities to inspire their customers with data-based services from the field of Artificial Intelligence (AI). For example, we implemented prognosis scenarios for predictive maintenance for a globally operating machine manufacturer, which significantly increase the efficiency of the manufactured plants.
The objective of our client was to:
- Prevent machine downtimes
- To exclude machine damage
- Minimise downtimes
- Identify typical malfunctions
- Optimise maintenance intervals
- Improve productivity
The solution for our client
The starting point for our data-based forecasting services is historical as well as current machine data. On this basis, a prognosis model is set up and continuously improved:
- We continuously collect the sensor data of the machines
- We supplement current measured values with quality-assured master data
- We generate diagnostic data on machine problems
- We train a machine learning model on the basis of the diagnostic data.
- We continuously compare our optimisation algorithms with real-time data.
How data turns into new values
By using our AI-based prognosis model, the machine operators can specifically prevent possible malfunctions and defects. The availability of the machines in operation has improved significantly as a result. But our customer also benefits from the optimisations at its customers: Testing costs and costly recall campaigns are avoided, and warranty costs are significantly reduced. At the same time, customer satisfaction and loyalty have increased.
The improvements for all involved in concrete figures:
- Machine defects and failures are detected 75% proactively
- Downtimes have been reduced by 50 to 80%.
- Maintenance costs have been reduced by 50 to 80%
- Warranty costs have been reduced by over 50%
- Inventory costs reduced by 20 to 30%
- Overtime costs reduced by 20 to 50%.
- Overall productivity increased by 20 to 30%.
turn your data into value.