Industry 4.0 Insights

A Policy Brief from the Policy Learning Platform on Research and innovation. September, 2019

Americans have not yet grappled with just how profoundly the artificial
intelligence (AI) revolution will impact our economy, national security,
and welfare. Much remains to be learned about the power and limits
of AI technologies. Nevertheless, big decisions need to be made now
to accelerate AI innovation to benefit the United States and to defend
against the malign uses of AI.

The Global Risks Report 2021, 16th Edition, is published by the World Economic Forum.

The Global Competitiveness Report SPECIAL EDITION 2020. How Countries are Performing on the Road to Recovery. Klaus Schwab, Saadia Zahidi. World Economic Forum.

Collaboration with business

In4act team applies analytic techniques to business problems such as statistical, machine learning and deep learning algorithms for classification, continuous estimation, clustering, anomaly detection, and producing recommendations.

As part of Kaunas University of Technology, the In4act team collaborates with business companies regarding:

– Conducting R&D activities as partners in EU funds’, MITA, LMT funded projects

– Directly providing services for companies

– Automated recommendations for forms’ fields auto-complete

– Automated recommendation for task assignments

– Prediction of task accomplishment duration

– Company-specific ERP-embedded recommendations

– Order-to-cash, Purchase-to-pay, Manufacturing-to-release process discovery, conformance checking and improvement insights (e.g., long cases, bottlenecks, over-exploited resources)

– Order-to-cash, Purchase-to-pay, Manufacturing-to-release process real-time case prediction based on historical data

– Company-specific use cases of process mining

– Employee productivity and health improvement insights

– Customer habits insights

– Process improvement insights

– Company-specific use cases of wearables-based data collection and analytics

– Sales and demand forecasting

– Predictive maintenance

– Yield optimization

– Procurement and spend analytics

– Inventory and parts optimization

– Statistical process control

– Company-specific use cases of analytics for manufacturing and supply chain

– High quality and transparent data flows to monitor progress with respect to supply chain goals.

– Data hub for circular economy monitoring and reporting connected to circular economy data and software.

– FAIR principles

– Machine-actionability of data collection

– Analytics on custom business use cases

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