Ali A. Yassine

Complex Concurrent Engineering and the Design Structure Matrix Method

Concurrent engineering (CE) principles have considerably matured over the last decade. However, many companies still face enormous challenges when implementing and managing CE practices. This is due to the increased complexity of engineering products and processes, on one hand, and the lack of corresponding CE models and tools, on the other hand. This paper focuses on four critical problems that challenge management while implementing CE in complex product development (PD) projects. We refer to these problems as: iteration, overlapping, decomposition and integration, and convergence problems. We describe these problems proposing a unified modeling and solution approach based on the design structure matrix (DSM) method, which is an information exchange model that allows managers to represent complex task relationships to better plan and manage CE initiatives.

ENGINEERING DESIGN MANAGEMENT : AN INFORMATION STRUCTURE APPROACH

It is characteristic of engineering design that precedence relationships among the constituent design tasks contain information flow conflicts. The existence of these conflicts with the lack of formal methods to manage them render the development cycle time unpredictable. This paper discusses a qualitative approach to engineering design management from an information structure perspective. The objective is to model, analyse and manage the interactions manifested by the information exchanges within the design process. We introduce the notion of Structural Sensitivity Analysis (SSA), which is devised based on two measures of information dependency among design tasks: Sensitivity and Variability. Those two measures of dependency enhance the classical design structure matrix method and allow for more complex analysis to be performed.

Characterizing complex product architectures

Due to the large-scale nature of complex product architectures, it is necessary to develop some form of abstraction in order to be able to describe and grasp the structure of the product, facilitating product modularization. In this paper we develop three methods for describing product architectures: (a) the Dependency Structure Matrix (DSM), (b) Molecular Diagrams (MD), and (c) Visibility-Dependency (VD) signature diagrams. Each method has its own language (and abstraction), which can be used to qualitatively or quantitatively characterize any given architecture spanning the modular-integrated continuum. A consequence of abstraction is the loss of some detail. So, it is important to choose the correct method (and resolution) to characterize the architecture in order to retain the salient details. The proposed methods are suited for describing architectures of varying levels of complexity and detail. The three methods are demonstrated using a sequence of illustrative simple examples and a case-study analysis of a complex product architecture for an industrial gas turbine.

Information Leaders in Product Development Organizational Networks: Social Network Analysis of the Design Structure Matrix

Many models of Product Development (PD) are concerned with managing the decomposition and integration of tasks, teams and subsystems transforming a conceptual idea into a finished product. Specifically, a PD process is formed of cross-functional teams continuously exchanging information on specified tasks to integrate the products final structure. Recently, it has been shown that large PD networks (e.g., tasks, teams, or components) follow a Scale Free structure. That is, each PD network included hubs that control information flow. Nevertheless, there is no literature on the implications of these findings on PD management. As a consequence, the objective of this paper is two-folded. First, we examine a set of mathematical measures such as centrality and brokerage used in Social Networks Analysis (SNA) to identify critical players in PD networks. Second, we link these findings to insights and recommendations for the management of complex PD organizational networks; in particular, detection and role designation of information leaders based on the given PD network structure

Performance of Coupled Product Development Activities with a Deadline

This paper explores the performance of coupled development activities by proposing a performance generation model (PGM). The goal of the PGM is to develop insights about optimal strategies (i.e., sequential, concurrent, or overlapped) to manage coupled design activities that share a fixed amount of engineering resources subject to performance and deadline constraints. Model analysis characterizes the solution space for the coupled development problem. The solution space is used to explore the generation of product performance and the associated dynamic forces affecting concurrent development practices. We use these forces to explain conditions under which concurrency is a desirable strategy.

Design Process Sequencing With Competent Genetic Algorithms

In product design, it is critical to perform project activities in an appropriate sequence. Otherwise, essential information will not be available when it is needed, and activities that depend on it will proceed using assumptions instead. Later, when the real information is finally available, comparing it with the assumptions made often precipitates a cascade of rework, and thus cost and schedule overruns for the project. Information flow models have been used to sequence the engineering design process to minimize feedback and iteration, i.e., to maximize the availability of real information where assumptions might otherwise be made instead. In this paper, we apply Genetic Algorithms (GAs) to an information flow model to find an optimized sequence for a set of design activities. The optimality of a solution depends on the objective of rearrangement. In an activity sequencing context, objectives vary: reducing iteration/feedback, increasing concurrency, reducing development lead-time and cost, or some combination of these. We adopt a matrix-based representation scheme, the design structure matrix (DSM), for the information flow models. Our tests indicate that certain DSM characteristics (e.g., size, sparse-ness, and sequencing objective) cause serious problems for simple Genetic Algorithm (SGA) designs. To cope with the SGA deficiency, we investigate the use of a competent GA: the ordering messy GA (OmeGA). Tests confirm the superiority of the OmeGA over a SGA for hard DSM problems. Extensions enhancing the efficiency of both a SGA and the OmeGA, in particular, niching and hybridization with a local search method, are also investigated.

A decision analytic framework for evaluating concurrent engineering

This paper quantifies key issues with regard to concurrent engineering through the use of risk and decision analysis techniques that enable us to better understand, structure, and manage the design process. In concurrent engineering, the information structure of a design process does not usually imply the execution patterns of the corresponding design tasks. On the contrary, this gap between the information structure and execution patterns is the essence of concurrent engineering and its basic advantage over traditional sequential design. In this paper, we relate the structure of information flow in a design process to three different execution strategies: sequential, partial overlapping, and concurrent. The risks of excessive task iterations or redesigns associated with each execution pattern are probabilistically modeled. Risk and decision analysis methodology is used to determine the best execution strategy and the optimal overlapping policy for a set of activities given their information structure. Applying this theoretical framework to a real-world design application of an automotive cylinder block suggested a potential 18% reduction in development cycle time.

A Genetic Algorithm for Developing Modular Product Architectures

The architecture of a product is determined by both the elements that compose the product and the way in which they interact with each other. In this paper, we use the design structure matrix (DSM) as a tool to capture this architecture. Designing modular products can result in many benefits to both consumers and manufacturers. The development of modular products requires the identification of highly interactive groups of elements and arranging (i.e. clustering) them into modules. However, no rigorous DSM clustering technique can be found in product development literature. This paper presets a review of the basic DSM building blocks used in the identification of product modules. The DSM representation and building blocks are used to develop a new DSM clustering tool based on a genetic algorithm (GA) and the minimum description length (MDL) principle. The new tool is capable of partitioning the product architecture into an “optimal” set of modules or sub-systems. We demonstrate this new clustering method using an example of a complex product architecture for an industrial gas turbine.Copyright

Genetic algorithm design inspired by organizational theory: pilot study of a dependency structure matrix driven genetic algorithm

This study proposes a dependency structure matrix driven genetic algorithm (DSMDGA) which utilizes the dependency structure matrix (DSM) clustering to extract building block (BB) information and use the information to accomplish BB-wise crossover. Three cases: tight, loose, and random linkage, are tested on both a DSMDGA and a simple genetic algorithm (SGA). Experiments showed that the DSMDGA is able to correctly identify BBs and outperforms a SGA.

A Framework for Design Process Specifications Management

Major conflicts in design process management seem to stem from specification conflicts among the constituent coupled design tasks. Coupled tasks represent conflicts in the flow of information of a design process. Resolving these specification conflicts early on in the development process tends to reduce product development lead times and cost. In this paper, we attempt to explain the coupling or interdependency in design tasks by the specifications imposed on these tasks. Then, we formulate the fundamental equations necessary for engineering specification management and devise a graphical tool for design process improvement. The major hypothesis investigated is that any two coupled tasks can be de-coupled if the specification of one task can be designed to absorb a certain percentage of any possible variation in the output of the other task.