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

Facing the future : scanning, synthesizing and sense-making in horizon scanning

Erworben im Rahmen der Schweizer Nationallizenen (http://www.nationallizenz.ch)In this paper, we discuss key issues in harnessing horizon scanning to shape systemic policies, particularly in the light of the foresight exercise ‘Facing the future: Time for the EU to meet global challenges’ which was carried out for the Bureau of European Policy Advisors. This exercise illustrates how horizon scanning can enable collective sense-making processes which assist in the identification of emerging signals and policy issues; the synthesis of such issues into encompassing clusters; and the interpretation of resulting clusters as an important step towards the coordinated development of joint policy measures. In order to achieve such objectives, horizon scanning can benefit from methods of multi-criteria decision-making and network analysis for prioritizing, clustering and combining issues. Furthermore, these methods provide support for traceability, which in turn contributes to the enhanced transparency and legitimacy of foresight

Supporting strategy selection in multiobjective decision problems under uncertainty and hidden requirements

Decision-makers are often faced with multi-faceted problems that require making trade-offs between multiple, conflicting objectives under various uncertainties. The task is even more difficult when considering dynamic, non-linear processes and when the decisions themselves are complex, for instance in the case of selecting trajectories for multiple decision variables. These types of problems are often solved using multiobjective optimization (MOO). A typical problem in MOO is that the number of Pareto optimal solutions can be very large, whereby the selection process of a single preferred solution is cumbersome. Moreover, preference between model-based solutions may not be determined only by their objective function values, but also in terms of how robust and implementable these solutions are. In this paper, we develop a methodological framework to support the identification of a small but diverse set of robust Pareto optimal solutions. In particular, we eliminate non-robust solutions from the Pareto front and cluster the remaining solutions based on their similarity in the decision variable space. This enables a manageable visual inspection of the remaining solutions to compare them in terms of practical implementability. We illustrate the framework and its benefits by means of an epidemic control problem that minimizes deaths and economic impacts, and a screening program for colorectal cancer that minimizes cancer prevalence and costs. These examples highlight the general applicability of the framework for disparate types of decision problems and process models

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

Favourable social functioning and health related quality of life of patients with JIA in early adulthood

Objective: To evaluate the social functioning and health related quality of life (HRQoL) in patients with juvenile idiopathic arthritis (JIA) in early adulthood. Methods: The patient files of the Rheumatism Foundation Hospital were screened to identify patients born in 1976–1980 diagnosed as having JIA. HRQoL was measured by the RAND 36-item health survey 1.0; spousal relationships and educational and employment status were assessed by questionnaire. The patients were invited to a follow up study. Age and sex matched controls from the community were identified in the Finnish population registry. Results: Of 187 patients identified, 123 participated. Spousal relationships, educational level, and employment status were similar to controls. HRQoL in JIA patients was similar to controls except on the physical functioning scale. At follow up 35% of patients were in remission. Patients with active disease had poorer HRQoL in the physical component than those in remission or controls. The extended oligoarthritis group had the lowest physical and mental score in HRQoL compared with the other JIA subgroups. The patient's own evaluation was the explanatory factor in both the physical and mental component of HRQoL. Conclusion: Social functioning and HRQoL were similar in JIA patients and age, sex, and municipality matched controls. However, patients with extended oligoarthritis attained significantly lower scores in the physical and mental component of HRQoL than oligo- or polyarthritis patients. Special attention in everyday care should be paid to those patients who have active disease or the extended oligoarthritis type of disease