Darwin - Identifying causes of performance losses
Uncover bottlenecks in your production lines and machines and create transparency about the causes of performance losses - down to the signal level.
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Identify the causes of performance losses
Our analysis algorithms get to the root cause of performance losses of your complex, automated production lines and machines
Optimize production lines in the right place
Only if you know the real bottleneck of a linked production line, you can increase the output in a targeted way.
Darwin continuously determines the real bottleneck based on various factors and thus helps you to perform focused optimizations:
- Breakdown of the real cycle and process times of the individual production stations
- Combination with states of production stations incl. waiting and blocking situations
= Increase of the output of production lines
Identify the cause of performance losses
When looking for the cause of performance losses, the devil is often in the details. A multitude of influencing factors makes it difficult to identify the real cause of cycle time deviations.
Darwin analyzes manufacturing operations at the process step level and allows you to drill down to get to the root cause of timing deviations:
- Process step analysis as drill-down of process time deviations
- Benchmark of similar production lines and machines to identify potential for optimization
= Reduction of performance losses (short stops, process fluctuations)
Detect changes in your production lines and machines at an early stage
A manufacturing process is subject to a variety of influencing factors. If these change and negatively influence the manufacturing process, it is important to react quickly.
With our trend analysis, you can reliably detect changes in process times and in malfunction behavior and react to them quickly:
- Identification of trends in disturbance behavior and process times
- Notification about new trends
= Optimization through targeted, rapid rectification of deviations
Darwin's Added Value
6-18%
Reduction of cycle time per machine
Up to 38%
Reduction of the average productivity variation
10 - 30%
Shortening the start-up and relocation phase
Darwin solves these production problems
- No transparency about actual & ideal cycle time behavior
- Causes of output fluctuations that are difficult to identify due to complex, fully automated manufacturing processes
- Output increase only possible with multiple resources
- Differences in Productivity Among Similar Machines
- High fixed and unit costs for fully automated machines
All features of Darwin
Identification of trends
Identification of trends
A manufacturing process is subject to a variety of influencing factors. If these change and negatively influence the manufacturing process, it is important to react quickly.
With our trend analysis, you can reliably detect changes in process times and in malfunction behavior and react to them quickly:
- Identification of trends in disturbance behavior and process times
- Notification about new trends
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Benchmark of similar machines
Benchmark of similar machines
Similar manufacturing machines often have different levels of productivity. Due to the complexity of the manufacturing process, it can be difficult to identify the cause of different KPI values.
With the machine benchmark, you can compare the performance of similar machines in detail at process step level to identify optimization potential.
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Downtime statistics
Downtime statistics
In order to optimize a production line in a targeted manner, you need good information at hand that clearly identifies problem areas.
To do this, use the statistics on faults to understand which faults caused how much downtime over time. Get detailed statistics to understand the disturbance behavior in detail.
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Process time statistics
Process time statistics
Only if you know the real bottleneck of a linked production line, you can increase the output in a targeted way.
Darwin breaks down the real cycle and process times of the individual manufacturing stations, enabling analysis of the performance of the individual components of a manufacturing plant.
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Process Step Analysis
Process Step Analysis
When looking for the cause of performance losses, the devil is often in the details. A multitude of influencing factors makes it difficult to identify the real cause of cycle time deviations.
Darwin analyzes manufacturing operations at the process step level and allows you to drill down to get to the root cause of timing deviations.
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State statistics
State statistics
Only if you know the real bottleneck of a linked production line, you can increase the output in a targeted way.
Darwin determines the states of production components and thus enables, for example, the waiting and blocking behavior of production stations to be analyzed.
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KPIs
KPIs
Key performance indicators are an important component of manufacturing optimization to identify problem areas.
Darwin continuously generates key figures for the considered production lines and machines. You can display and group these flexibly to generate the statements that are appropriate for your use case.
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Action Tracker
Action Tracker
Plan, document, and verify optimization measures.
Most factories have a continuous improvement process in place to increase the efficiency of their production lines and machines. Darwin gives this process a permanent place—right next to the data on which it is based.
Darwin directly links actions to analysis: With a single click, last week’s error statistics are used to generate an optimization measure, including a description of the planned change and a comment feature for team review. After implementation, Darwin provides a before-and-after comparison and shows whether the change actually had an effect.
The board view groups tasks by label, production unit, or area of responsibility, making it clear at a glance who is responsible for what.
Interface
Security
Add-ons
What our customers & partners say
Shannon® & Darwin at M10 Solar Equipment
- Virtual commissioning of a fully automated solar module production plant with plus10 tools (hardware-in-the-loop simulation) with seven newly developed processes
- Full transparency regarding system behavior and optimization potential in terms of availability and performance
- Faster start-up and smooth handover to operating personnel thanks to virtually trained plus10 tools
Impact: Rapid ramp-up and full transparency into system performance
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Darwin at Schwarzkopf
- Fully automated and interlinked filling and packaging line for consumer goods (0.17 sec. cycle time)
- Detailed transparency of downtime per shift at station level for effective shift handover and basis for conceptual optimization
- Faster root cause identification in the optimization process and in the event of downtimes thanks to visualization of the signal behavior
Impact = up to 17% potential for throughput increase identified
Darwin at Haselmeier
- Fully automated assembly line for the production of self-injection pens (cycle time under 5 seconds)
- Identification and quantification of the causes for availability and performance degradation
- Trend analysis on fault occurrence and problem resolution times for each individual cause of loss
Impact = Uncovering of OEE potential greater than 10%.
Contact now
- Personal Product Presentation with Live Demo
- Initial Assessment of Your Use Case
- Help with Calculating Your ROI
More products from plus10
Do you have questions? We have answers.
And if you have any further questions, please feel free to contact us.
These are often confused because the terms are used in different contexts. Predictive Maintenance refers to the technical availability of the machine— Darwin and Shannon® are the right tools for this. Hopper is quality-oriented, i.e., Predictive Quality. We make this decision based on your OEE breakdown: Where are your biggest losses today? For automated assembly and manufacturing lines, starting with Shannon® usually offers the greatest leverage; however, the decisive factor is what most significantly impacts your production during multi-shift operations.
Yes. Trends can be identified and analyzed from the collected asset data, and these have proven effective in practice as a pragmatic approach to predictive maintenance—without the effort required by traditional condition monitoring projects.
The same as for Shannon®: a local virtual machine for the DataCollector, outbound data traffic via port 443 to the plus10 backend, targeted firewall rules allowing access to the control addresses, user management via LDAP, and the option to operate as Software-as-a-Service within the EU or entirely on-premises.
The high-frequency raw signals (e.g., step chain signals) are stored for three months. This allows users to go back to any point in time within the last three months in the plus10 SignalViewer—for example, to analyze in detail a malfunction that occurred during the last night shift the following day.