Applying clustering and classification data mining techniques for competitive and knowledge‐intensive processes improvement. (17th February 2019)

• Record Type: Journal Article
• Authors: Khanbabaei, Mohammad; Alborzi, Mahmood; Sobhani, Farzad Movahedi; Radfar, Reza
• Is Part Of: Knowledge and process management. Volume 26:Number 2(2019)
• Page Start: 123
• Page End: 139
• Publication Date: 2019-02-17
• Subjects: Data mining -- process improvement -- competitive process -- knowledge‐intensive process; Organizational change -- Periodicals; Industrial management -- Periodicals; Corporate reorganizations -- Periodicals; Production management -- Periodicals; 658.406
• DOI: 10.1002/kpm.1595
• Languages: English
• ISSNs: 1092-4604

Abstract

Abstract : Processes as one of the valuable knowledge resources can create sustainable competitive advantages in organizations. There is a large number of processes in organizations. They generate a high volume of process data that leads to the high‐dimensionality problems, complex relationships, dynamic changes, and difficulties in the understanding of the process by human resources. Traditional process improvement methodologies have weaknesses in environment with the large number of processes. Data mining techniques can support process improvement in this environment. They can recommend the improvement suggestions through extracting valuable patterns from a high volume of the process dataset. Recently, knowledge‐intensive processes have been increasingly concentrated in the field of process improvement. These types of processes can induce a competitive behavior over the other processes. The main problem is the improvement of competitive and knowledge‐intensive processes in a high volume of process dataset. The main purpose of this paper is to present a model to identify the behavior of competitive and knowledge‐intensive processes and recommend improvement suggestions. For this purpose, data mining techniques are applied to extract valuable patterns hidden in a high volume of process dataset. In this regard, K‐means clustering and C5 classification algorithms are applied to extract valuable patterns. A real process dataset was used to evaluate the effectiveness and applicability of the model. The results confirmed that the proposed model can apply data mining techniques to support competitive and knowledge‐intensive process improvement in a high volume of process dataset.