# Actionable Cognitive Twins for Decision Making in Manufacturing

arXiv: https://arxiv.org/abs/2103.12854 · HTML: https://mlsanity.com/paper/2103.12854/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 166 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [Cognitive Twins for Supporting Decision-Makings of Internet of Things Systems](https://mlsanity.com/paper/1912.08547/) (2019)
- [Building Knowledge Graphs About Political Agents in the Age of Misinformation](https://mlsanity.com/paper/1901.11408/) (2019)

## Built on by (most cited first, 5 of 5)

- [Ontologies in Digital Twins: A Systematic Literature Review](https://mlsanity.com/paper/2308.15168/) (2023)
- [XAI-KG: knowledge graph to support XAI and decision-making in manufacturing](https://mlsanity.com/paper/2105.01929/) (2021)
- [Enriching Artificial Intelligence Explanations with Knowledge Fragments](https://mlsanity.com/paper/2204.05579/) (2022)
- [Knowledge Modelling and Active Learning in Manufacturing](https://mlsanity.com/paper/2107.02298/) (2021)
- [The use of Synthetic Data to solve the scalability and data availability problems in Smart City Digital Twins](https://mlsanity.com/paper/2207.02953/) (2022)

## Abstract (arXiv)

Actionable Cognitive Twins are the next generation Digital Twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users. The knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a manufacturing setting. It also contains information on possible decision-making options that can assist decision-makers, such as planners or logisticians. In this paper, we propose a knowledge graph modeling approach to construct actionable cognitive twins for capturing specific knowledge related to demand forecasting and production planning in a manufacturing plant. The knowledge graph provides semantic descriptions and contextualization of the production lines and processes, including data identification and simulation or artificial intelligence algorithms and forecasts used to support them. Such semantics provide ground for inferencing, relating different knowledge types: creative, deductive, definitional, and inductive. To develop the knowledge graph models for describing the use case completely, systems thinking approach is proposed to design and verify the ontology, develop a knowledge graph and build an actionable cognitive twin. Finally, we evaluate our approach in two use cases developed for a European original equipment manufacturer related to the automotive industry as part of the European Horizon 2020 project FACTLOG.