Informatica

Lean Six Sigma (LSS) is defined as an innovative business strategy for achieving operational excellence through continuous improvement in the manufacturing sector. By embracing LSS principles, manufacturers can create an adaptable and capable system to preserve a competitive positioning, while reducing waste and defects in the business processes. The integration of sustainability with LSS has contributed to the upward attention among scholars and practitioners worldwide by advancing knowledge of how manufacturers can improve their sustainable performance through LSS practices. For any manufacturing firm, the challenge lies in exploring enablers that support successful adoption of sustainable LSS. Consequently, this study aims to develop an intuitionistic fuzzy decision-making framework for identifying and assessing the enablers influencing an integrated sustainable LSS in electric manufacturing companies. The proposed framework integrates the Weight by Envelope and Slope (WENSLO) and Modified Preference Selection Index (MPSI) models taking into account the developed score and distance formulae under the setting of intuitionistic fuzzy sets. Using an integrated intuitionistic fuzzy WENSLO-MPSI model, this study further evaluated thirteen sustainable LSS enablers of five electric manufacturing companies, followed by sensitivity and comparative analyses. The findings indicated that “Linking SLSS to business strategies”, “Green design principles” and “Effective scheduling” are the most significant enablers to implement sustainable LSS in an electrical manufacturing company.

The Autonomous Vehicle (AV) industry is constantly growing, thus analysing its perspectives is essential. However, for this analysis a sophisticated approach is necessary which considers the ambiguity of decision-makers, and different objectives and criteria related to stakeholders. In this paper a new model is proposed based on Decomposed Fuzzy Sets and the Best-Worst Method to deal with possible non-reciprocity of pairwise comparisons and different preferences of stakeholders in the AV industry. The main advantage of the model is that it is capable of considering optimistic and pessimistic attitudes along with the different objectives and criteria of the involved groups. The results show that users require short travel time, while operators, manufacturers and legislators expect mainly the increase of revenues from the AV implementation. Among the most important criteria, our analysis indicates the need of regulatory and safety issues are the most essential obstacles of expanding the AV industry. The new model can also be applied for evaluating the perspectives of other emerging technologies and industrial sectors.

Grey numbers facilitate the representation of uncertainty not only for elements of a set, but also the set itself as a whole. This paper utilizes the notion of possibility degree from grey system theory coupled with the idea of dominance relation and partial order set (poset) from rough theory to represent uncertain information in a manner that maintains the degree of uncertainty of information for each tuple of the original data. Concept lattices of grey information system are constructed and a decision-making algorithm that combines with grey relational grade is described. A case study is used to demonstrate the supplier selection problem applying the proposed method. The research has concluded that the method is appropriate to use.

This paper proposes a new multi-attribute ranking procedure based on distance from decision-maker preferences. This method has two phases. In the first phase, the decision maker is asked to define the preferred performance for each attribute. In the second phase, Weighted Sum method and new distance-based normalization procedure are used to determine the overall performance rating of alternatives.

Two-dimensional signals of physical phenomena may be inadvertently altered before recording through the system whose bandwidth is smaller than that of the signal. It is often desired to restore later such data by removing the effects of the linear system. This restoration may be accomplished by synthesizing two-dimensional (2-D) inverse filters on computers. Approximations are necessary to insure the stability of the inverse filter.