Identifying component modules

A computer-based system for modelling component dependencies and identifying component modules is presented. A variation of the Dependency Structure Matrix (DSM) representation was used to model component dependencies. The system utilises a two-stage approach towards facilitating the identification of a hierarchical modular structure. The first stage calculates a value for a clustering criterion that may be used to group component dependencies together. A Genetic Algorithm is described to optimise the order of the components within the DSM with the focus of minimising the value of the clustering criterion to identify the most significant component groupings (modules) within the product structure. The second stage utilises a 'Module Strength Indicator' (MSI) function to determine a value representative of the degree of modularity of the component groupings. The application of this function to the DSM produces a 'Module Structure Matrix' (MSM) depicting the relative modularity of available component groupings within it. The approach enabled the identification of hierarchical modularity in the product structure without the requirement for any additional domain specific knowledge within the system. The system supports design by providing mechanisms to explicitly represent and utilise component and dependency knowledge to facilitate the nontrivial task of determining near-optimal component modules and representing product modularity

A DSM Clustering Method for Product and Service Modularization

Part 1: Lean ProductionInternational audienceMany manufacturers are in the process of evolving from mass production to mass customization to cope with the increasing diversity of customer requirements. This induces an increasing complexity resulting from the high variety offered to customers. This problem is heightened by integrating product and service within the same offering. Modularity is considered as a driver for reducing complexity while increasing offered variety of products and services. This paper addresses the question of how to modularize products and/or services considering different criteria from the designers and domain experts. To this end, a Design Structure Matrix (DSM) based method is introduced. The method relies on a set of modularization criteria and on clustering to form product and/or service modules. The applicability of the method is illustrated through a test case in the manufacturing sector

Prognostic impact of lymphadenectomy in clinically early stage malignant germ cell tumour of the ovary

BACKGROUND: The aim of this study was to determine the impact of lymphadenectomy and nodal metastasis on survival in clinical stage I malignant ovarian germ cell tumour (OGCT). METHODS: Data were obtained from the National Cancer Institute registry from 1988 to 2006. Analyses were performed using Student's t-test, Kaplan–Meier and Cox proportional hazard methods. RESULTS: In all, 1083 patients with OGCT who have undergone surgical treatment and deemed at time of the surgery to have disease clinically confined to the ovary were included 590 (54.48%) had no lymphadenectomy (LND−1) and 493 (45.52%) had lymphadenectomy. Of the 493 patients who had lymphadenectomy, 441 (89.5%) were FIGO surgical stage I (LND+1) and 52 (10.5%) were upstaged to FIGO stage IIIC due to nodal metastasis (LND+3C). The 5-year survival was 96.9% for LND−1, 97.7% for LND+1 and 93.4% for LND+3C (P=0.5). On multivariate analysis, lymphadenectomy was not an independent predictor of survival when controlling for age, histology and race (HR: 1.26, 95% CI: 0.62–2.58, P=0.5). Moreover, the presence of lymph node metastasis had no significant effect on survival (HR: 2.7, 95% CI: 0.67–10.96, P=0.16). CONCLUSION: Neither lymphadenectomy nor lymph node metastasis was an independent predictor of survival in patients with OGCT confined to the ovary. This probably reflects the highly chemosensitive nature of these tumours