This paper investigates some of the ideas and algorithms developed for exploiting the structure of quasiseparable matrices. The case of purely scalar generators is considered initially. The process by which a quasiseparable matrix is represented as the product of matrices comprised of its generators is explained. This is done clearly in the scalar case, but may be extended to block generators. The complete factoring approach is then considered. This consists of two stages: inner-outer factorization followed by inner-coprime factorization. Finally, the stability of the algorithm is investigated. The algorithm is used to factor various quasiseparable matrices R created first using minimal generators, and subsequently using non-minimal generators. The result is that stability of the algorithm is compromised when non-minimal generators are present.

A case study at a global pharmaceutical company has been conducted toanalyse how the Hidden Structure method using the Enterprise Architecture Analyses(EAAT) tool, developed at KTH, can be used to visualize the IT architecture and tocreate a better understanding on which applications could increase a risk of therobustness of the architecture if changes was done to them. Also the measure of ITsupport metrics as incidents and changes per system has been analysed to understandif they can be used to understand the robustness of the incident. The tool was used tocreate a model of the enterprise architecture of the company and the analysis showedthat the robustness of the IT architecture was good; the core applications that couldcreate most damage were identified. The analysis of the IT support metrics showedthat it was difficult to use the change records as indicators since the number ofchanges per systems were very few. The incident analysis showed that the systemswith the highest number of incidents were classified as belonging core or peripheralcategory.

Product development process (PDP) architecture holds the key to the management of New Product Development (NPD). A lot of care is exercised in managing the NPD to reduce risk and uncertainties. There exists potential scope for improvement both in initial planning as well as execution of the NPD program by studying the PDP architecture. This research work seeks to taps this potential and presents an analytical tool to aid the NPD Managers. In this research work Design Structure Matrices (DSM) are used to represent the PDP architecture. The Work Transformation Matrix (WTM) is a kind of DSM and it was introduced for the analysis of concurrent task structures. However a generalized task structure has not been studied analytically in the literature. In order to study a generalized task structure we add two new matrix types to the WTM set to represent the task network interconnections and the task interdependence. First we study the pure sequential task iteration structure for NPD and show that it has lower engineering effort (cost and time) than the concurrent task iteration structure previously discussed in literature. Next we study the generalized task iteration structure and derive the expressions for total work and cost vectors. This is a major research contribution since only simulation based methods are currently available for studying generalized task iteration structures. The optimization of sequencing interdependent tasks is a well known NP hard problem in NPD literature. For small sized task sets, exhaustive enumeration of all possible sequencing and their corresponding time or cost vectors can be computed to determine the optimal sequence. However for large sized task networks, only heuristic methods are deployed. Using the closed form expression for cost and time vectors for a pure sequential task iteration structure derived earlier in this research work, we attempt to devise a method to optimally sequence the design tasks. We develop new matrix combining both the node and link weightages of task network. Using the time vector relationship between sequential and concurrent task structures, it is shown that the optimal task sequence corresponds to the reordering of this combined matrix whose “Dominance index” (sum above the diagonal) is minimum. Finally, we use some of the standard test cases from the PDP literature to demonstrate our research findings.