As the content and variety of technology increases in automobiles, the complexity of the system increases as well. Decomposing systems into modules is one of the ways to manage and reduce system complexity. This paper surveys and compares a number of state-of-art components modularity metrics, using 8 sample test systems. The metrics include Whitney Index (WI), Change Cost (CC), Singular value Modularity Index (SMI), Visibility-Dependency (VD) plot, and social network centrality measures (degree, distance, bridging). The investigation reveals that WI and CC form a good pair of metrics that can be used to assess component modularity of a system. The social network centrality metrics are useful in identifying areas of architecture improvements for a system. These metrics were further applied to two actual vehicle embedded software systems. The first system is going through an architecture transformation. The metrics from the old system revealed the need for the improvements. The second system was recently architected, and the metrics values showed the quality of the architecture as well as areas for further improvements.
This paper presents a study at Ford Motor Company using the Design Structure Matrix (DSM) method to reveal the true workload associated with reconciling the requirements concerning major system interfaces. This study found that the common management practice underestimated the workload because the focus was on locally owned responsibilities. The workload associated with system interfaces was not accounted for. Three levels of responsibilities were uncovered—local responsibility, first order system interfaces, and second order system interfaces. The results of this study provide engineers and managers a framework and a vocabulary to better understand and manage system interface requirements reconciliation.
This paper presents the research work to investigate how well we can predict system interactions at early phase of the product development process using the matrix transformation technique presented by Dong and Whitney [1] at DETC 2001. The technique to predict design information flow patterns using requirements was applied to a case study at Johnson and Johnson Ortho-clinical Diagnosis. Several Design Structure Matrices (DSM) were created. The DSM’s were compared to the system interactions that engineers actually experienced during the design process, recorded in their own DSM. The observations from this case study provided insights to the predictability of various types of product development process, and demonstrated the value of the matrix transformation process.
Identifying the factors that could lead to the loss of quality is difficult for large complex product systems. Traditional design methods such as Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and Robust Design have been proven effective at identifying component failures, but are less effective for causes of quality loss that involve interactions between components, software flaws, or external noises. This research applied System Theoretic Process Analysis (STPA) to a case study at Cummins, Inc. The case study was a technology change to a subsystem in a new product development project. The intent of this case was to determine if STPA, developed for safety engineering and hazard analysis, would be effective in identifying causes of quality losses. The results of the case study were compared to the traditional design methods. STPA allowed design teams to identify more causal factors for quality losses than FMEA or FTA, especially those involving component interactions, software flaws, and external noises. STPA was also found to be complementary to Robust Design methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.