Organizational Use of Decision Analysis

Journal of Engineering Design, 2005

There are many different techniques in use today for the identification and evaluation of risks in new product design projects. Once identified, we need to ensure that we treat those risks in an effective manner. This process leads the project manager and his/her team into complex decision-making problems. Decisions to take a risk, reduce the level of risk or to eliminate those risks altogether are left to the project manager's judgement. Given the stakes involved in today's projects, such intuitive measures, based solely on experience, are no longer sufficient. In response to these problems and given the non-repetitive and complex nature of new product design projects, we modelled the decisionmaking process in design in accordance with systemic theories. According to Herbert Simon and his followers, we modelled the way in which the designers use their reasoning and data processing capacities to make decisions. These models, associated with real case studies, carried out on actionresearch principles enabled us to build a method that amplifies our cognitive capacities and allows us to act on the decision-making processes in design. The method involves the construction of a decision-making framework allowing the construction of a robust project satisfying both the project objectives and the team. It is complementary to risk identification methods because it facilitates the decision-making process, in particular decisions related to identified risks, which allows continuous project improvement.

Decision Analysis, 2006

D ecision support models help structure and inform complex choices under uncertainty. Two classic models are risk analysis and decision analysis. Risk analysis is understood here as risk characterization, and in some cases, the identification and benefit assessment of some risk management options. It is based on systems analysis and probability, and it excludes the actual decision phase, which requires the preferences, e.g., the utility function, of the decision maker(s). Risk analysis and decision analysis have some similarities and are often complementary. To model uncertainties, both rely on probability, generally a subjective Bayesian degree of belief. A decision analysis can include a risk analysis component, and the design of a risk management plan may require decision analysis support. The challenge for risk analysts is to characterize potential failure problems before decision options have been identified, and when there is no single decision maker, or group of decision makers, who can provide preference functions and degrees of belief. Yet, a correct and complete model of uncertainties in the probabilistic risk analysis phase is important if the results are to be used later for decision support, especially when the number of systems involved and the duration of their operations is unknown. In this paper, we explore some of the challenges inherent to probabilistic risk analysis that should be of interest to the decision analysts who intend to use risk analysis results.

Business Inform

Since the decision-making process is becoming more multifactorial and multidimensional, determined by the type of problem being solved, leadership style, limiting factors, external and internal environment of the company, it is quite logical that the materials of the article are focused on identifying the most effective decision-making tools. In the course of the study, the basic theories of decision-making were classified into 3 groups: problem-oriented theories; solution-oriented theories; role-oriented theories. It is emphasized that the dominance of theoretical approaches to decision-making determines the choice of appropriate decision-making methods, which are divided into qualitative (evaluative, expert-analytical, mixed) and quantitative (methods in conditions of certainty and methods in conditions of uncertainty). The complex of approaches and methods used forms the basis for a key decision–making strategy - analytical, heuristic or expert. The carried out content analysis o...

Le Centre pour la Communication Scientifique Directe - HAL - Université de Nantes, 2021

-La crise de la COVID-19 a mis en exergue de nombreuses faiblesses dans les entreprises, notamment les petites et moyennes. Parmi ces faiblesses, il en est une symptomatique : l'incapacité des décideurs à prendre des décisions stratégiques robustes dans un contexte incertain et fortement perturbé. Pourtant, un tel contexte, peuplé de risques et d'opportunités, est à considérer comme une nouvelle normalité. Une analyse plus fine de la situation montre que les responsables d'entreprises utilisent principalement des outils issus de la Stratégie et du Contrôle de Gestion pour soutenir leurs décisions. Or, ces outils présentent de nombreuses limites telles que leur ancrage déterministe, leurs analyses ex post, ou encore leur dimension souvent qualitative de la gestion du risque. Le présent travail de recherche propose les bases d'un premier modèle original centré sur la gestion des actifs des entreprises et inspiré du Génie Industriel et de l'Intelligence Artificielle. Ce modèle vise à mieux appréhender les risques et les opportunités dans les mécanismes de prise de décision stratégique. La proposition est illustrée sur un cas d'école permettant de mettre en perspective son potentiel. Des axes de développement futurs sont finalement proposés pour alimenter la suite de ce travail de recherche débuté récemment. Abstract-The COVID-19 crisis has demonstrated numerous weaknesses in Small-and Medium-sized Enterprises (SMEs). Among them, the incapability of top management to make robust strategic decisions in an uncertain environment composed of risks and opportunities, is probably one of the most critical. Unfortunately, such a disrupted context is now the norm. By analyzing the situation, we notice that most of these top management decision-makers use traditional Strategy and Management Accounting methods to make decisions for their business. However, these methods have numerous limits regarding the management of uncertainties. Notably, most of them are deterministic and reactive (i.e., a posteriori analysis) and they often manage risks through pure qualitative approaches. Based on these limitations, this article develops the fundamentals of a first innovative model focused on the management of assets and inspired by Industrial Engineering and Artificial Intelligence risk-and opportunity-oriented tools. The proposed approach intends to support more robust strategic decisions in disrupted contexts. An illustrative case is then developed in order to highlight the potentiality of our approach. Finally, some avenues for future research are exposed regarding the current work which is currently in its infancy.

Revista Produção e Desenvolvimento, 2022

Purpose: This paper aims to discuss the basis on which the program risk analysis currently sustains, identifies the fragility of some of its main pillars, and proposes an alternative to the tactic level of analysis. Methodology/Approach: This work is an exploratory a case study based on decision analysis methods, a framework is proposed to make easier the selection of the main risks that should be managed. A mechanism that is relatively more complex, but more efficient for this analysis task. Findings: The proposed mechanism has several advantages over the approach traditionally adopted and does not inherit the biases arising from this widespread methodology. Research Limitation/implication: Applying this methodology to real complete programs would bring a more realistic notion of the costs and levels of complexity required for its implementation. Originality/Value of paper: Program risk management seeks to improve the likelihood of achieving the strategic goals of organizations thr...