A B S T R A C TProduct design is an important phase of the new product development process and one of the most crucial decisions in marketing. In the latest two decades, a significant number of marketing decision support systems (MDSSs) for automating new product design activities have been reported in the literature and have contributed to the evolution of knowledge in this area. Since the insights on what constitutes the design of new products are constantly evolving, it is unclear whether research on MDSSs for new product design already covers all major aspects of product design. Therefore, the aim of this research is to determine the state of the art on MDSSs for new product design: What aspects of MDSSs for new product design have been addressed until now and which gaps remain to be covered? We performed a systematic literature review of peer-reviewed articles as published between 1998 and 2018 on MDSSs for new product design. To analyze the contributions of the papers we use the Formal Concept Analysis technique. Out of a total of 375 publications, 61 met the inclusion criteria. These publications were classified into different dimensions: MDSS types, decision-making support, distributed decision-making support, and the consideration of both consumer satisfaction and distributed environment. Our findings suggest that desktop and model-driven-based systems are the type of MDSS mostly accepted for new product design. We found that important elements of this decision-making process are seldom considered in MDSSs developed so far. These include distributed decision-making support and consideration of consumer satisfaction. In this way, future developments should consider them so that they be more consistent with the current nature of this process and support it more effectively.
The Bayesian network (BN) is an important technique to represent and infer knowledge in an Intelligent Tutoring System (ITS); however, ITSs are complex to build. Diverse authors have built BNs based on ontologies to accelerate the building process; nonetheless, they did not fully automate the process, and did not follow the ontologies standard Web Ontology Language, or simplified the final domain. This work proposes a method to build BNs based on ontologies and Wikipedia information to be employed on ITSs. The proposed method constructs the qualitative part of the BN through classes and relations of ontologies; the quantitative part is created based on frequencies, hops, and a measure of similarity between concepts of the ontology represented by Wikipedia articles. This study carried out an experiment to determine the correlation of our method against domain experts opinions; the method obtained a positive correlation of $0.647$ according to the Spearman test. The method constructs a BN to represent the knowledge in ITSs, in a similar way as experts would, supporting the automatic build of these systems.
Personnel selection represents a valuable decision-making process that determines, in some way, the competitiveness and performance of an organization. The essential elements of the personnel selection task are the position requirements and the accessible information related to candidates. In this paper, the personnel selection task is modeled as a multicriteria decision-making problem, considering varied competencies and skills to assess the applicants for a specific position. We used this multicriteria model to solve personnel selection cases using sadgage, a decision support system that solves examples of the multicriteria ranking problem in decreasing order based on the preferences of the decision-maker. The electre-iii method is embedded in the software in order to construct a fuzzy outranking relation and a multi-objective evolutionary algorithm to exploit such relation and generate a ranking as a recommendation. This work presents a practical application to a personnel selection problem that evaluates a group of applicants to a software developer job in an enterprise based in northwestern Mexico. We show how the proposed multicriteria procedure offers a recommendation to the decision-maker about the applicants in decreasing order of preferences.
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