برنامه های کاربردی برای مدیریت پیچیدگی تولید
Abstract
1- Introduction
2- Definition of AI, ML and DM
3- Application of DM methods for managing production complexity
4- Findings and conclusion
References
Abstract
Production complexity has increased considerably in recent years due to increasing customer requirements for individual products. At the same time, continuous digitization has led to the recording of extensive, granular production data. Research claims that using production data in data mining methods can lead to managing production complexity effectively. However, manufacturing companies widely do not use such data mining methods. In order to support manufacturing companies in utilizing data mining, this paper presents both a literature review on definitions of data mining, artificial intelligence and machine learning as well as a categorization of existing approaches of applying data mining to manage production complexity.
Findings and conclusion
At the interface of rising production complexity due to shifting market demands and vast amounts of production data, DM can be a valid tool to support managing complexity. Most applications of DM in production management have so far been related to quality management. There are very few applications of DM directly related to production complexity. However, other applications of DM in other fields of production management serve the purpose of managing production complexity very well. We have presented some of these applications and plan to extend the categories in future work to present a holistic framework of DM, as well as other ML and AI applications able to cover all relevant aspects of managing production complexity.