Scenario-based portfolio model for building robust and proactive strategies

In order to address major changes in the operational environment, companies can (i) define scenarios that characterize different alternatives for this environment, (ii) assign probabilities to these scenarios, (iii) evaluate the performance of strategic actions across the scenarios, and (iv) choose those actions that are expected to perform best. In this paper, we develop a portfolio model to support the selection of such strategic actions when the information about scenario probabilities is possibly incomplete and may depend on the selected actions. This model helps build a strategy that is robust in that it performs relatively well in view of all available probability information, and proactive in that it can help steer the future as reflected by the scenarios toward the desired direction. We also report a case study in which the model helped a group of Nordic, globally operating steel and engineering companies build a platform ecosystem strategy that accounts for uncertainties related to markets, politics, and technological development

Fostering breakthrough technologies -- How do optimal funding decisions depend on evaluation accuracy?

There is a growing interest in fostering breakthrough technologies that offer exceptionally high value to society. However, when starting technology projects, it is impossible to know which of them have the potential to lead to breakthroughs. Therefore, organizations have adopted funding policies in which on-going projects are subjected to interim evaluations based on which some projects may be abandoned to release resources for seizing new opportunities. In this paper, we study which funding policies are optimal when the objective is either (i) to maximize the expected value of the project portfolio, or (ii) to maximize the expected number of exceptionally excellent projects that may lead to breakthrough technologies. We show that the optimal policy for funding exceptionally excellent projects is to start a large number of projects and abandon a high proportion of them later, whereas the optimal policy for maximizing the expected value of the project portfolio is to grant long-term funding to a smaller set of projects based on initial evaluation. Furthermore, we show how the trade-off between these two objectives depends on the initial project evaluation accuracy and the rate at which this accuracy improves. Our results suggest that this trade-off is particularly significant when the initial project evaluations are very uncertain but become more accurate soon after the projects have been launched. In such a setting, policies that seek to maximize the expected portfolio value may fail to promote breakthrough technologies

Mesopic models : from brightness matching to visual performance in night-time driving: a review

At present, suitable methods to evaluate the visual effectiveness of lighting products in the mesopic region are not available. The majority of spectral luminous efficiency functions obtained to date in the mesopic range have been acquired by heterochromatic brightness matching. However, the most recent studies in the mesopic field have adopted a task performance-based approach. This paper summarizes the major mesopic models proposed so far, presenting in detail the experimental conditions of these studies. The authors represent a research consortium which has adopted the task performance-based approach for night-time driving in which mesopic visual performance has been divided into three subtasks. Data for each sub-task will be generated by using a set of common parameter values and 120 observers. The approach and methods used by the consortium are presented. © The Chartered Institution of Building Services Engineers 2005

Constrained Multicriteria Sorting Method Applied to Portfolio Selection

The paper focuses on portfolio selection problems which aim at selecting a subset of alternatives considering not only the performance of the alternatives evaluated on multiple criteria, but also the performance of portfolio as a whole, on which balance over alternatives on specific attributes is required by the Decision Makers (DMs). We propose a two-level method to handle such decision situation. First, at the individual level, the alternatives are evaluated by the sorting model Electre Tri which assigns alternatives to predefined ordered categories by comparing alternatives to profiles separating the categories. The DMs' preferences on alternatives are expressed by some assignment examples they can provide, which reduces the DMs' cognitive efforts. Second, at the portfolio level, the DMs' preferences express requirements on the composition of portfolio and are modeled as constraints on category size. The method proceeds through the resolution of a Mixed Integer Program (MIP) and selects a satisfactory portfolio as close as possible to the DMs' preference. The usefulness of the proposed method is illustrated by an example which integrates a sorting model with assignment examples and constraints on the portfolio definition. The method can be used widely in portfolio selection situation where the decision should be made taking into account the performances of individual alternatives and portfolio simultaneousl

Rural Road Maintenance in Madagascar: the GENIS project

International audienceThe paper reports a real world decision aiding process concerning rural road maintenance in Madagascar. The issue arises within AGETIPA, the National Agency in charge of conducting Public Works in Madagascar, and can be summarised as a problem of resource allocation to a number of competitive projects. The problem has been modeled using multiple criteria and a classification procedure under two objectives: make the most rational use of the limited available resources and promote participation and commitment of the local actors in the maintenance process. The project is part of an on-going partnership between the LAMSADE and AGETIPA aiming to enhance Decision Support Capacity within AGETIPA