Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT
Abstract
:1. Introduction
- DTs are dynamic virtual representations of real technical systems;
- DTs are connected to the real technical system over the entire life-cycle;
- Data are exchanged bidirectionally between DTs and the real technical system.
2. Literature Review
2.1. Bottleneck Detection in Manufacturing Systems
- At any given moment, the process with the longest uninterrupted active period is the bottleneck.
- During the overlap at the end of the current longest uninterrupted active period and the next one, the bottleneck shifts from one process to another.
Algorithm 1: Algorithm for the Active Period Method |
Input: time series of active periods |
Output: list of bottlenecks |
1: sort list by starting times |
2: for do |
3: if then |
4: pick entry with longest duration, add to |
5: update , |
6: else |
7: pick entry with longest duration |
8: if then |
9: add to |
10: update , |
11: else if then |
12: add to |
13: update , |
14: end if |
15: end if |
16: end for |
17: return |
2.2. Evaluation of Bottleneck Detection Methods
- Independent of production layout;
- Requires time series data only;
- Implementable in a small prototype;
- Delivers an interpretable metric.
3. Bottleneck Detection Method by the Active Period Method
3.1. Boundaries of Active Period Method
- Correct interpretation of the operating states. Often, operating states are not recorded correctly. As an example, concrete failure states are often not detected by the system automatically but entered manually at the end of a shift. These errors have to be corrected during preprocessing, which is a time-consuming task.
- Usage of the job schedule. In case of a task change, it is important to set the operating state to free capacity in case the next task does not start immediately. Unsubstantiated downtimes need to be investigated.
- Understanding of the production process. Without any understanding of the product profile, the production layout and structure or special features of the production process, the results of the Active Period Method can hardly be understood.
3.2. Results
- General result. The Active Period Method is an appropriate approach to supervise and measure the overall management of a production system. In addition to the sole/shifting bottleneck probabilities, the time series of active and inactive states constitutes a valuable input for production management. Together, these two results provide meaningful and expressive information to judge production quality. In general, the results mirror the experts’ personal opinion and experience on the production system.
- Expressiveness of results and visualizations. The results and presented visualizations are judged as expressive and a good foundation for discussions of potential production system improvements.
- Selection of relevant machines. It should be evaluated whether it is always the best solution to use all available machines as input to the Active Period Method, or if it is better to use an appropriately defined subset instead.
- Observation period. Different observation periods may well affect the results and the quality of the bottleneck prediction of the Active Period Method.
4. Process Heatmap
5. Integration of Analysis Results into a Digital Twin
6. Summary
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
DC43DG | Design Compiler 43 Design Language |
DT | Digital Twin |
FMI | Functional Mockup Interface |
FMU | Functional Mockup Unit |
GraphDB | Graph Database |
GraphML | Graph Description XML |
IoT | Internet of Things |
IIoT | Industrial Internet of Things |
MBSE | Model based systems engineering |
ML | Machine Learning |
OWL | Web Ontology Language |
SPARQL | SPARQL Protocol and RDF Query Language |
VDI | Verein Deutscher Ingenieure |
XAI | Explainable Artificial Intelligence |
XMI | XML-based Meta Data Interface |
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Input | Output | Bottleneck Detection Method |
---|---|---|
Time series data of active periods | Estimation of Sole and Shifting Bottleneck machines relative to observation period | Active Period Method [13] |
Time series data of blockage and starvation of machines | Identification of bottlenecks through starvation of downstream machines and blockage of upstream machines | Turning Point Method [14] |
Time series data of active periods | Average of active periods for each machine | Average Active Period Method [12] |
Observations of process, inventory states | Ranking of bottleneck sets | Shop Floor Bottleneck Detection [15] |
Arrival of Jobs | Identification of bottleneck machine pools through reinforcement learning | MINERVA: A Reinforcement Learning-based Technique [16] |
Percentage | Color |
---|---|
0–20 | blue |
20–40 | green |
40–60 | yellow |
60–80 | orange |
80–00 | red |
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Arff, B.; Haasis, J.; Thomas, J.; Bonenberger, C.; Höpken, W.; Stetter, R. Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT. Appl. Sci. 2023, 13, 3525. https://doi.org/10.3390/app13063525
Arff B, Haasis J, Thomas J, Bonenberger C, Höpken W, Stetter R. Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT. Applied Sciences. 2023; 13(6):3525. https://doi.org/10.3390/app13063525
Chicago/Turabian StyleArff, Benjamin, Julian Haasis, Jochen Thomas, Christopher Bonenberger, Wolfram Höpken, and Ralf Stetter. 2023. "Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT" Applied Sciences 13, no. 6: 3525. https://doi.org/10.3390/app13063525
APA StyleArff, B., Haasis, J., Thomas, J., Bonenberger, C., Höpken, W., & Stetter, R. (2023). Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT. Applied Sciences, 13(6), 3525. https://doi.org/10.3390/app13063525