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Multi-criteria methods represent an adequate tool for solving complex decision problems that provide real support to the decision maker in the choice process. This paper analyzes a decision problem that recurs over time using one of the newer methods as the Parsimonious AHP.

In this paper we integrated the P-AHP with: (1) the weighted average which takes into account the objectivity of the data; (2) ordered weighted average (OWA) aggregation operators that address the subjective nature of the data; (3) the Choquet integral and (4) the Sugeno integral which also considers the uncertain nature of the final ranking as it is defined on a fuzzy measure.

The present paper proves that variations in the final ranking, due to the different mathematical properties of the selected aggregators, are fundamental to select the best alternative without neglecting any characteristic of the input data. In fact, it is discussed and underlined how and why the best alternative is one that never excels but has very good positions with respect to all aggregation operator rankings.

The aim and innovation presented in this work is the use of the Parsimonious AHP (P-AHP) method in a dynamic way with the use of different aggregation techniques.

The paper focuses on how knowledge visualization supports the development of a particular multiobjective decision-making problem as a portfolio optimization problem in the context of interorganizational collaboration between universities and a large automotive company. This paper fits with the emergent knowledge visualization literature because it helps to explain decision-making related to the development of a multiobjective optimization model in Lean Product Development settings. We investigate how using ad hoc visual tools supports knowledge translation and knowledge sharing, enhancing managerial judgment and decision-making.

The empirical case in this study concerns the setting up of a multiobjective decision-making model as a portfolio optimization problem to analyze and select alternatives for upgrading the lean production process quality at an FCA plant.

The study shows how knowledge visualization and the associated tools work to enable knowledge translation and knowledge sharing, supporting decision-making. The empirical findings show why and how knowledge visualization can be used to foster knowledge translation and sharing among individuals and from individuals to groups. Knowledge visualization is understood as both a collective and interactional process and a systematic approach where different players translate their expertise, share a framework and develop common ground to support decision-making.

From a theoretical perspective, the paper expands the understanding of knowledge visualization as a system of practices that support the development of a multiobjective decision-making method. From an empirical point of view, our results may be useful to other firms in the automotive industry and for academics wishing to develop applied research on portfolio optimization.