Visualizing knowledge for decision-making in Lean Production Development settings. Insights from the automotive industry

PurposeThe 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valueFrom 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.

Multiobjective Decision-Making for Technical Characteristics Selection in a House of Quality

Technical characteristics are an important influencing factor in fuzzy front-end of product innovation design and also an important output of the house of quality. The analysis and decision-making of technical characteristics are conducive to the rational allocation of resources according to their own advantages and the design of products in line with market demand. In view of the multiple objective constraints and internal fuzziness of technical property selection in the house of quality, the traditional methods have great limitations on the decision-making of technical property. Therefore, this paper proposes a multiobjective decision-making method for technical property selection in the house of quality, which aims to calculate and modify the basic importance of technical property. Based on the rough set algorithm, the basic importance of technical characteristics is determined by using the correlation matrix between consumer demand and technical characteristics, and the relative weight of multiple objectives is determined by “least squares.” On this basis, the multiobjective decision-making model of technical characteristic selection is established and calculated. At last, an example of mobile phone product design was used to verify the practicability and effectiveness of the method.

Design of Winding Parameters Based on Multiobjective Decision-Making and Fuzzy Optimization Theory

The design tries to solve the problem of low pass rate of platinum wire production and the waste of platinum in company. The paper uses multiobjective decision system fuzzy optimization theory to analyze five parameters, which are tensile strength, ductility, fracture load, filling aperture, and resistance. Besides, MATLAB software is used to write programs and calculate. To sum up the above analysis, the weight vector of five parameters is obtained and that can be used to determine which parameter has the greatest influence on the pass rate of the wire winding process.

Classifying the Usage of Multiple Objective Decision Making Techniques in Data Envelopment Analysis

There exist a wide range of research studies that apply the Multiple Criteria Decision Making (MCDM) techniques in Data Envelopment Analysis (DEA) methodology and vice versa. Also, MCDM is divided into two subsets, Multiobjective Decision Making (MODM) and Multiattribute Decision Making (MADM). Early studies of DEA methodology utilized the MODM concepts and consequently, most studies in the relationships between MCDM and DEA have involved the usage of MODM techniques in DEA. There remains a large volume of papers in this field; yet, none of them classifies this relationship. Hence, in this research the authors focused on classification of this field that is divided into six groups. Then, some papers in each group are selected for consideration.