scholarly journals A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry

2020 ◽  
Vol 10 (12) ◽  
pp. 4355 ◽  
Author(s):  
Raquel Redondo ◽  
Álvaro Herrero ◽  
Emilio Corchado ◽  
Javier Sedano
Keyword(s):  
Decision Making ◽  
Automotive Industry ◽  
Real Life ◽  
Projection Pursuit ◽  
Failure Detection ◽  
Competitive Advantages ◽  
Clustering Methods ◽  
Industrial Companies ◽  
Key Enabling Technologies ◽  
Exploratory Projection Pursuit

In recent years, the digital transformation has been advancing in industrial companies, supported by the Key Enabling Technologies (Big Data, IoT, etc.) of Industry 4.0. As a consequence, companies have large volumes of data and information that must be analyzed to give them competitive advantages. This is of the utmost importance in fields such as Failure Detection (FD) and Predictive Maintenance (PdM). Finding patterns in such data is not easy, but cutting-edge technologies, such as Machine Learning (ML), can make great contributions. As a solution, this study extends Hybrid Unsupervised Exploratory Plots (HUEPs), as a visualization technique that combines Exploratory Projection Pursuit (EPP) and Clustering methods. An extended formulation of HUEPs is proposed, adding for the first time the following EPP methods: Classical Multidimensional Scaling, Sammon Mapping and Factor Analysis. Extended HUEPs are validated in a case study associated with a multinational company in the automotive industry sector. Two real-life datasets containing data gathered from a Waterjet Cutting tool are visualized in an intuitive and informative way. The obtained results show that HUEPs is a technique that supports the continuous monitoring of machines in order to anticipate failures. This contribution to visual data analytics can help companies in decision-making, regarding FD and PdM projects.

scholarly journals Technologies of the distributed generation: empirical evaluations of the innovations acceptance

2018 ◽  
pp. 32-48 ◽  
Author(s):  
A. V. Trachuk ◽  
N. V. Linder
Keyword(s):  
Decision Making ◽  
Distributed Generation ◽  
Capital Investments ◽  
Competitive Advantages ◽  
Critical Factors ◽  
Industrial Companies ◽  
Gradual Accumulation ◽  
Connection Cost ◽  
Significant Factors ◽  
Prospects Of Application

The assessment of problems and prospects of application of technologies of the distributed generation is presented by the industrial companies. The concept of the distributed generation and structure of technologies included in it is considered, sources of key competitive advantages of use of technologies of the distributed generation are revealed. For the analysis of the most significant factors of perception of technologies of the distributed generationtheindustrialcompanieshaveconductedthedeepsemi-structuredinterviewstorepresentativesof 8 large industrial companies, questioning of representatives of 69 industrial companies. For the analysis the regression model, allowing to determine force and the importance of influence of the selected factors on acceptance by the companies of the decision on own generation is used.For the analysed companies possibility of technical connection, cost of the electric power and the apprehended advantages are critical factors of decision-making on use of technologies of the distributed generation. Risk factor has appeared we don’t mean. In deep interviews respondents explained it to that systems of the distributed generation minimize emergence of the listed adverse effects. Receiving cheap electric and thermal energy, gradual accumulation of power capacities, uniformity of capital investments with fast obtaining energy for production and economic needs is possible to day in connection with use of power effective decisions on the basis of technologies of the distributed generation.

scholarly journals Hybrid Unsupervised Exploratory Plots: A Case Study of Analysing Foreign Direct Investment

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Álvaro Herrero ◽  
Alfredo Jiménez ◽  
Secil Bayraktar
Keyword(s):  
Projection Pursuit ◽  
Clustering Methods ◽  
Visualization Technique ◽  
Host Countries ◽  
Descriptive Data ◽  
Multiple Dimensions ◽  
Open Issue ◽  
Internationalization Strategy ◽  
Exploratory Projection Pursuit

The curse of dimensionality has been an open issue for many years and still is, as finding nonobvious and previously unknown patterns in ever-increasing amounts of high-dimensional data is not an easy task. Advancing in descriptive data analysis, the present paper proposes Hybrid Unsupervised Exploratory Plots (HUEPs) as a new visualization technique to combine the outputs of Exploratory Projection Pursuit and Clustering methods in a novel and informative way. As a case study, HUEPs are validated in a real-world context for analysing the internationalization strategy of companies, by taking into account bilateral distance between home and host countries. As a multifaceted concept, distance encompasses multiple dimensions. Together with data from both the countries and the companies, various psychic distances are analysed by means of HUEPs, to gain deep knowledge of the internationalization strategy of large Spanish companies. Informative visualizations are obtained from the analysed dataset, leading to useful business implications and decision making.

scholarly journals Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregoire Preud’homme ◽  
Kevin Duarte ◽  
Kevin Dalleau ◽  
Claire Lacomblez ◽  
Emmanuel Bresso ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Latent Class ◽  
Latent Class Model ◽  
Real Life ◽  
Heterogeneous Data ◽  
Mixed Data ◽  
Categorical Variables ◽  
Clustering Methods ◽  
Model Based ◽  
Partitioning Around Medoids

AbstractThe choice of the most appropriate unsupervised machine-learning method for “heterogeneous” or “mixed” data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of “ready-to-use” tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

scholarly journals ACO with Intuitionistic Fuzzy Pheromone Updating Applied on Multiple-Constraint Knapsack Problem

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1456
Author(s):  
Stefka Fidanova ◽  
Krassimir Todorov Atanassov
Keyword(s):  
Decision Making ◽  
Knapsack Problem ◽  
Propositional Logic ◽  
Optimization Problems ◽  
Real Life ◽  
Combinatorial Optimization Problems ◽  
Intuitionistic Fuzzy ◽  
Life Problems ◽  
Multiple Constraint

Some of industrial and real life problems are difficult to be solved by traditional methods, because they need exponential number of calculations. As an example, we can mention decision-making problems. They can be defined as optimization problems. Ant Colony Optimization (ACO) is between the best methods, that solves combinatorial optimization problems. The method mimics behavior of the ants in the nature, when they look for a food. One of the algorithm parameters is called pheromone, and it is updated every iteration according quality of the achieved solutions. The intuitionistic fuzzy (propositional) logic was introduced as an extension of Zadeh’s fuzzy logic. In it, each proposition is estimated by two values: degree of validity and degree of non-validity. In this paper, we propose two variants of intuitionistic fuzzy pheromone updating. We apply our ideas on Multiple-Constraint Knapsack Problem (MKP) and compare achieved results with traditional ACO.

Decision-making styles in a real-life decision: Choosing a college major

2006 ◽  
Vol 41 (4) ◽  
pp. 629-639 ◽  
Author(s):  
Kathleen M. Galotti ◽  
Elizabeth Ciner ◽  
Hope E. Altenbaumer ◽  
Heather J. Geerts ◽  
Allison Rupp ◽  
...  
Keyword(s):  
Decision Making ◽  
College Major ◽  
Real Life ◽  
Decision Making Styles ◽  
Life Decision

Moral Decision Making in Business: A Phase-Model

2000 ◽  
Vol 10 (4) ◽  
pp. 773-803 ◽  
Author(s):  
Aviva Geva
Keyword(s):  
Decision Making ◽  
Ethical Decision ◽  
Ethical Decision Making ◽  
Ethical Problem ◽  
Real Life ◽  
Phase Model ◽  
Second Phase ◽  
Illustrative Case ◽  
Moral Decision ◽  
Ethical Problems

Abstract:The traditional model of ethical decision making in business suggests applying an initial set of principles to a concrete problem and if they conflict the decision maker may attempt to balance them intuitively. The centrality of the ethical conflict in the accepted notion of “ethical problem” has diverted the attention of moral decision modelers from other ethical problems that real-world managers must face—e.g., compliance problems, moral laxity, and systemic problems resulting from the structures and practices of the business organization. The present article proposes a new model for ethical decision making in business—the Phase-model—designed to meet the full spectrum of business-related ethical problems. Drawing on the dominant moral theories in business literature, the model offers additional strategies for tackling ethical issues beyond the traditional cognitive operations of deductive application of principles to specific cases and the balancing of ethical considerations. Its response to the problems of moral pluralism in the context of decision making lies in its structural features. The model distinguishes between three phases of the decision-making process, each having a different task and a different theoretical basis. After an introductory stage in which the ethical problem is defined, the first phase focuses on a principle-based evaluation of a course of action; the second phase provides a virtue-based perspective of the situation and strategies for handling unsettled conflicts and compliance problems; and the third phase adapts the decision to empirical accepted norms. An illustrative case demonstrates the applicability of the model to business real life.

Sign in / Sign up

Export Citation Format

Share Document