PLM maturity evaluation and prediction based on a maturity assessment and fuzzy sets theory

Abstract

Companies adopt PLM maturity models to evaluate PLM implementation and recognize relative positions in PLM selection to better harness PLM benefits. However, the majority traditional PLM maturity models are relative time-consuming and energy-consuming. This work focuses on proposing a fuzzy extended PCMA (PLM Components Maturity Assessment) maturity model to brightly evaluate the gradual process of PLM maturity accompaniment with time changes, which aims to reduce the efforts spent on maturity evaluation. The proposed PCMA uses triangular fuzzy elements to express maturity levels that can solve vague and complexity issues in PLM evaluation. The proposed fuzzy PCMA is tested by two Chinese firms. The first evaluation uses PCMA maturity model to obtain the maturity levels for a Chengdu company in 2010. The PLM maturity for this company from 2011 to 2013 is conducted by the fuzzy extended PCMA maturity model through inputting the KPIs' value. Fuzzy extended PCMA is also used to predict the maturity level for a Shanghai company. A comparison of the results obtained by fuzzy extended PCMA model and the real-life situation verify the effectiveness of the proposed model. IFIP International Federation for Information Processing 2014.