Principles for Intelligent Decision Support Systems in Industrial Manufacturing Processes
[ 1 ] Instytut Zarządzania i Systemów Informacyjnych, Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ SzD ] doctoral school student | [ P ] employee
2024
chapter in monograph
english
- intelligent decision support
- digital transformation
- industrial management
- manufacturing processes
EN Adopting intelligent decision support systems to resolve the increasing complexities and uncertainties in manufacturing processes requires certain principles to optimize the created value. Based on, among others, artificial neural networks, genetic algorithms, artificial intelligence, and expert systems, intelligent decision support systems play a critical role in the digital transformation of the manufacturing sector. The current business environment entails intense market competition, highly dynamic customer needs, and unprecedented innovations. As such, manufacturing entities are required to look for strategies to ensure sustainable operations continuously. Decisions made during manufacturing processes management go a long way to impact the sustainability of the enterprise. The paper proceeds from the premise that optimal decision-making, when managing manufacturing activities, reduces wasted resources and drives performance. As the proliferation of intelligent support tools in manufacturing continues, the need to ensure efficient use of such systems remains. This paper identifies and explores three principles for integrating intelligent systems into manufacturing processes. These are human control, synergism, and interpretability. Human management entails leveraging critical human aspects such as emotional intelligence to ensure cohesion between people and the systems. The study establishes that reducing bias improves the effectiveness of intelligent support system adoption. Furthermore, the study demonstrates that synergism plays a vital role in ensuring that the systems do not replace but amplify human intelligence for even more significant value addition. Finally, the deployed systems should be easy to understand, use and interpret to ensure optimal usage in all designated processes. The paper recommends the principles to be incorporated into general guidelines for designing, training and deploying intelligent decision support systems in industrial manufacturing processes.
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