Jean-Claude Bocquet

Using a Delphi process and the Analytic Hierarchy Process (AHP) to evaluate the complexity of projects

Research highlights? Describing, defining, and understanding better project complexity and its measures. ? Building up a generic project complexity framework. ? Performing a Delphi study about project complexity factors. ? Building up an AHP-based multi-criteria evaluation of project complexity. ? Exploring the sensitivity of such a measure. Project complexity is ever growing and needs to be understood, analysed and measured better to assist modern project management. The overall ambition of this paper is therefore to define a measure of project complexity in order to assist decision-making, notably when analysing several projects in a portfolio, or when studying different areas of a project. A synthesised literature review on existing complexity measures is firstly proposed in order to highlight their limitations. Then, we identify the multiple aspects of project complexity thanks to the construction and refinement of a project complexity framework thanks to an international Delphi study. We then propose a multi-criteria approach to project complexity evaluation, underlining the benefits of such an approach. In order to solve properly this multi-criteria problem, we first conduct a critical state of the art on multi-criteria methodologies. We then argue for the use of the Analytic Hierarchy Process. In the end, this tool permits to define a relative project complexity measure, which can notably assist decision-making. Complexity scales and subscales are defined in order to highlight the most complex alternatives and their principal sources of complexity within the set of criteria and sub-criteria which exist in the hierarchical structure. Finally, a case study within a start-up firm in the entertainment industry (musicals production) is performed. Conclusions, limitations and perspectives of research are given in the end.

Network theory-based analysis of risk interactions in large engineering projects

This paper presents an approach based on network theory to deal with risk interactions in large engineering projects. Indeed, such projects are exposed to numerous and interdependent risks of various nature, which makes their management more difficult. In this paper, a topological analysis based on network theory is presented, which aims at identifying key elements in the structure of interrelated risks potentially affecting a large engineering project. This analysis serves as a powerful complement to classical project risk analysis. Its originality lies in the application of some network theory indicators to the project risk management field. The construction of the risk network requires the involvement of the project manager and other team members assigned to the risk management process. Its interpretation improves their understanding of risks and their potential interactions. The outcomes of the analysis provide a support for decision-making regarding project risk management. An example of application to a real large engineering project is presented. The conclusion is that some new insights can be found about risks, about their interactions and about the global potential behavior of the project.

Toward System Architecture Generation and Performances Assessment Under Uncertainty Using Bayesian Networks

Background: Architecture generation and evaluation are critical points in complex system design. Uncertainties concerning component characteristics and their impact onto overall system performance are often not taken into account in early design stages. In this paper, we propose a Bayesian Network approach for system architecture generation and evaluation. Approach: A method relying on Bayesian network templates is proposed in order to represent an architecture design problem integrating uncertainties concerning component characteristics and component compatibility. These templates aim at modeling designers’ knowledge concerning system architecture. We also propose an algorithm for architecture generation and evaluation related to the Bayesian network model with the objective of generating all possible architectures and filtering them in view to a defined confidence threshold. Within this algorithm, expert estimations on component compatibilities are used to estimate overall architecture uncertainty as a confidence level. Results: The proposed approach is tested and illustrated on a case study of bicycle design. This first case shows how uncertainties concerning component compatibilities and components characteristics impact bicycle architecture generation. The method is, additionally, tested and implemented in the case of a radar antenna cooling system design in industry. Results highlight the relevance of the proposed approach in view to the generated solutions as well as other benefits such as reduced time for architecture generation, and a better overall understanding of the design problem. However, some limitations have been observed and call for enhancements like integration of designer’s preferences and identification of possible trade-offs within the architecture. Conclusions: This method enables generation and evaluation of complex system architecture taking into account initial system requirements and designer’s knowledge. Its usability and added-value have been verified on a large-scale system implemented in industry.

Anticipating aging failure using feedback data and expert judgment

This paper presents a methodology for anticipating failures in a component up to the end of its life cycle. Often, feedback data is not sufficient and must be complemented by the analysis of expert judgment. The methodology developed aims at anticipating the degradation mechanisms responsible for aging, and evaluating their relevance and related uncertainties. This is necessary information for risk analysis related to the operating of a component up to the end of its life cycle. Lastly, the methodology is applied to a nuclear component.

Collaborative Decision Making: Complementary Developments of a Model and an Architecture as a Tool Support

Recent years we can hear a lot about cooperative decision-making, group or collaborative decision-making. These types of decisions are the consequences of developed working conditions: geographical dispersion, team working, and concurrent working. In the article we present two research works concerning two different collective decision situations: face-to-face decision-making and synchronous distributed decision-making. These two research studies adopt different approaches in order to support decision-making process, in view to different research objectives. Nevertheless, the conclusions show complementary aspect of these two studies.

Towards a Methodology for Managing Competencies in Virtual Teams - A Systemic Approach

Virtual instruments and tools are future trends in Engineering which are a response to the growing complexity of engineering tasks, the facility of communication and strong collaborations on the international market. Outsourcing, off-shoring, and the globalization of organisations’ activities have resulted in the formation of virtual product development teams. Individuals who are working in virtual teams must be equipped with diversified competencies that provide a basis for virtual team building. Thanks to the systemic approach of the functional analysis our paper responds to the need of a methodology of competence management to build virtual teams that are active in virtual design projects in the area of New Product Development (NPD).

Collaborative Decision-making in Design Project Management. A Particular Focus on Automotive Industry

The Project Definition phase in New Product and Process Development (NPPD) is decisive because the project outcomes depend upon the success of this phase, i.e. capability to define ambitious and feasible objectives corresponding to the market. The two main research questions are: how to support collaborative decision-making in the early stages of NPPD, and what an adapted project management approach for these phases is. Research methodology used in this study is case-based using semi-structured interviews and participation/observation of working groups. The case study concerns 7 managers or engineers working in this phase and 9 vehicle development projects. The findings of this research point out the need for a new management paradigm and a proposition of a project management framework for the early stages of NPPD.

Managing uncertainty in potential supplier identification

Abstract As a benefit of modularization of complex systems, original equipment manufacturers (OEMs) can choose suppliers in a less constricted way when faced with new or evolving requirements. However, new suppliers usually add uncertainties to the system development. Because suppliers are tightly integrated into the design process in modular design and therefore greatly influence the outcome of the OEMs products, the uncertainty along with requirements satisfaction of the suppliers and their modules should be controlled starting from potential supplier identification. In addition, to better satisfy new requirements, the potential supplier identification should be combined with architecture generation to enable the new technology integration. In this paper, we propose the Architecture & Supplier Identification Tool, which generates all possible architectures and corresponding suppliers based on new requirements through matrix mapping and propagation. Using the Architecture & Supplier Identification Tool, the overall uncertainty and requirements satisfaction of generated architectures can be estimated and controlled. The proposed method aims at providing decision support for early design of complex systems, thereby helping OEMs have an integrated view of suppliers and system architectures in requirements satisfaction and overall uncertainty.

Towards Model-Based System Engineering for Simulation-Based Design in Product Data Management Systems

System integration and simulation are essential phases in design verification and optimization of system capabilities. Currently, different approaches in Systems Engineering (SE) are not entirely taking into account integration and simulation constraints thus complicating the process and enhancing its running time. The target of this paper is to propose a framework bridging the gap between Model Based System Engineering (MBSE) and current Product Lifecycle Management (PLM) and Simulation Lifecycle Management (SLM) functionalities. In this paper we address these gaps between MBSE design concepts and the current PLM capabilities and propose an innovative approach to manage architecture design for simulation. The proposed framework supports the definition of product architectures so as to organize and facilitate simulation and the specification of different types of interfaces enabling the organisation of several product behaviour simulations. An example of this framework based on an industrial case study for the structural analysis of an aero-engine is described and discussed addressing the limits and future developments.

Product Architectures Generation Under Uncertainty: Comparison Between Two Methods

Complex product architecture definition involves technological and architectural choices in order to reach defined system performances. These choices form a wide combinatorial design space whose complete exploration requires a computational method. The latter is made difficult because of the lack and the fuzziness of data and knowledge in preliminary design. To introduce this type of uncertainty, we have proposed an approach based on Bayesian nets: a Bayesian net architecture generation and clustering method is proposed. However, in recent research, lots of conceptual design problems were addressed with Constraint Satisfaction Problem (CSP). The purpose of this paper is to compare these two methods and advantages and challenges in view to design situations under uncertainty. The comparison consists in modeling a sample problem with both methods. The modeling process of each method is described, providing preliminary highlights on advantages and disadvantages of both methods. Then, the methods are evaluated in terms of modeling capabilities and easiness. The number of generated architectures and the execution time of each simulation are also analyzed with regard to the influence of uncertainty introduction in the models. The final objective is to determine which method seems to be the more appropriate to help designers in finding new innovative designs.Copyright