A Rough Set–Based Process Mining Approach for Knowledge Intensive Processes: Insights from a New Product Development Case Study

Knowledge-intensive processes (KiPs) have gained significant attention in modern organizations, where decision-making plays a fundamental role in the value chain. Therefore, the analysis of decision-making logs and the identification of decision rules and models are crucial. This paper presents a decision model for optimizing the New Product Development (NPD) process based on a case study at a prominent information technology company in Iran. The study leverages rough set theory and a fast reduction algorithm to analyze decision points within the NPD process. The model introduces a step-by-step methodology for identifying decision models at critical decision points, aiming to streamline decision-making and enhance process efficiency. The paper discusses the identification of critical decision features and the creation of decision trees for each decision point, thereby illuminating the impact of the decision model. Moreover, the study reports a reduction in the number of decisions and improved process efficiency, with a substantial ۹۰% reduction recorded after implementing the model in a real-world scenario within the organization. The paper also highlights certain limitations and proposes future research directions, emphasizing the potential for applying the model to other knowledge-intensive processes and within larger organizational contexts. The comprehensive review of the case study and the proposed decision model offer valuable insights into enhancing decision-making processes within the new product development domain.

کلیدواژه ها:

process mining ، Decision Mining ، Rough Set Theory ، knowledge-intensive process ، New product development ، Information Technology