Prescriptive Quality Analytics Service

Prescriptive Analytics for Quality Optimisation

The PREVENTION (Prescriptive Analytics for quality optimization) component addresses the prescriptive quality analytics needs of COALA.

PREVENTION aims at building predictive quality models and prescribing mitigating actions to optimize quality-related manufacturing performance indicators. To achieve its aims, PREVENTION utilizes 3 types of analytics: descriptive, predictive, and prescriptive analytics.

PREVENTION aims at covering the whole lifecycle of data analytics in order to derive insights on quality while enabling the development of several independent analytics processes with different scopes (descriptive, predictive, prescriptive) from multiple data sources and algorithms.

Main features of COALA Prescriptive Quality Analytics Service:

Exploratory Analytics

Statistics for the quality of the production as a whole and for its individual components, i.e. products and defects

Risk Assessment

Evaluation of the criticality of the defect with respect to the products and parts affected

Predictive Quality

Predictions about product defects and defects in the upcoming days

Quality Recommendations

Recommendations for actions in quality control

Videos

Human AI Collaboration in Quality Control with Augmented Manufacturing Analytics

COALA Webinar 1: COALA Prescriptive Quality Analytics Service

Public Deliverables

AI-support for predictive quality in manufacturing study

Prescriptive quality analytics service

Scientific Publications

M. Fikardos, K. Lepenioti, A. Bousdekis, D. Apostolou, G. Mentzas & E. Bosani. (2022). An Automated Machine Learning Framework for Predictive Analytics in Quality Control. International Conference Advances in Production Management Systems (APMS 2022).

A. Bousdekis, G. Mentzas, D. Apostolou & S. Wellsandt. (2022). Evaluation of AI-based Digital Assistants in Smart Manufacturing. International Conference Advances in Production Management Systems (APMS 2022).

T. Anastasiou, S. Karagiorgou, P. Petrou, D. Papamartzivanos, T. Giannetsos, G. Tsirigotaki, and J. Keizer. (2022). Towards Robustifying Image Classifiers against the Perils of Adversarial Attacks on Artificial Intelligence Systems. MDPI Sensors.

A. Bousdekis, K. Lepenioti, D. Apostolou & G. Mentzas. (2022). Data Analytics in Quality 4.0: Literature Review and Future Research Directions. International Journal of Computer Integrated Manufacturing.

Fouka, A., Bousdekis, A., Lepenioti, K., & Mentzas, G. (2021, June). Real-Time Equipment Health State Prediction with LSTM Networks and Bayesian Inference. In International Conference on Advanced Information Systems Engineering (pp. 155-166). Springer, Cham.

K. Lepenioti, A. Bousdekis, D. Apostolou and G. Mentzas, “Human-Augmented Prescriptive Analytics With Interactive Multi-Objective Reinforcement Learning,” in IEEE Access, vol. 9, pp. 100677-100693, 2021, doi: 10.1109/ACCESS.2021.3096662.

Bousdekis A. et al. (2021) Human-AI Collaboration in Quality Control with Augmented Manufacturing Analytics. In: Dolgui A., Bernard A., Lemoine D., von Cieminski G., Romero D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 633. Springer.

Mentzas, G., K. Lepenioti, A. Bousdekis and D. Apostolou: (2021) Data-driven Collaborative Human-AI Decision Making. 20th IFIP Conference e-Business, e-Services, and e-Society I3E2021.