Quality 4.0 in action: Smart hybrid fault diagnosis system in plaster production

UIDB/00066/2020Industry 4.0 (I4.0) represents the Fourth Industrial Revolution in manufacturing, expressing the digital transformation of industrial companies employing emerging technologies. Factories of the future will enjoy hybrid solutions, while quality is the heart of all manufacturing systems regardless of the type of production and products. Quality 4.0 is a branch of I4.0 with the aim of boosting quality by employing smart solutions and intelligent algorithms. There are many conceptual frameworks and models, while the main challenge is to have the experience of Quality 4.0 in action at the workshop level. In this paper, a hybrid model based on a neural network (NN) and expert system (ES) is proposed for dealing with control chart patterns (CCPs). The idea is to have, instead of a passive descriptive model, a smart predictive model to recommend corrective actions. A construction plaster-producing company was used to present and evaluate the advantages of this novel approach, while the result shows the competency and eligibility of Quality 4.0 in action.publishersversionpublishe

A Model to Evaluate the Organizational Readiness for Big Data Adoption

Evaluating organizational readiness for adopting new technologies always was an important issue for managers. This issue for complicated subjects such as Big Data is undeniable. Managers tend to adopt Big Data, with the best readiness. But this is not possible unless they can assess their readiness. In the present paper, we propose a model to evaluate the organizational readiness for Big Data adoption. To accomplish this objective, firstly, we identified the criteria that impact organizational readiness based on a comprehensive literature review. In the next step using Principal Component Analysis (PCA) for criterion reduction and integration, twelve main criteria were identified. Then the hierarchical structure of criteria was developed. Further, Fuzzy Best- Worst Method (FBWM) has been used to identify the weight of the criteria. The finding enables decision-makers to appropriately choose the more important criteria and drop unimportant criteria in strengthening organizational readiness for Big Data adoption. Statistics-based hierarchical model and MCDM based criteria weighting have been proposed, which is a new effort in evaluating organizational readiness for Big Data adoption

A FBWM-PROMETHEE approach for industrial robot selection

Industrial engineering; Multidisciplinary design optimization; Manufacturing engineering; Technology management; Operations management; Industry management; Business management; Industrialization; Industrial robots; Fuzzy best-worst method; PROMETHEE; MCDM; Robot selection; Criteria.publishersversionpublishe

Collaborative networks: A pillar of digital transformation

UID/EEA/00066/2019 POCI-01-0247-FEDER-033926The notion of digital transformation encompasses the adoption and integration of a variety of new information and communication technologies for the development of more efficient, flexible, agile, and sustainable solutions for industrial systems. Besides technology, this process also involves new organizational forms and leads to new business models. As such, this work addresses the contribution of collaborative networks to such a transformation. An analysis of the collaborative aspects required in the various dimensions of the 4th industrial revolution is conducted based on a literature survey and experiences gained from several research projects. A mapping between the identified collaboration needs and research results that can be adopted from the collaborative networks area is presented. Furthermore, several new research challenges are identified and briefly characterized.publishe

Novel Approaches to Handle Disruptions in Business Ecosystems

Part 2: Collaboration and Resilient SystemsInternational audienceToday’s business world is continuously challenged by unexpected disruptive events, which are increasing in their frequency and effects. As a consequence, it is plausible to foresee future scenarios in which turbulence and instability are no longer considered as episodic crises, but rather somewhat the “norm” or the default status. This trend naturally raises the question of how organizations can strive and even gain in such disruptive environments, and which characteristics are required for combating disruptions. Resilience and antifragility are two emerging approaches to handle disruptions. Through a literature review, this paper identifies several strategies that contribute to business ecosystem’s resilience or antifragility. Furthermore, it is also shown that contributions from a number of disciplinary areas, including Collaborative Networks, Systems Thinking, Thermodynamics, Management science, and ICT, can provide complementary views and support. A set of promising examples of applications of the discussed approaches are presented and briefly analyzed. Finally, a number of open questions and directions for further research are presented

The impact of big data adoption on smes’ performance

Funding Information: Acknowledgments: This work was supported by the Portuguese Foundation for Science and Technology (FCT) and the Center of Technology and Systems (CTS). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The notion of Industry 4.0 encompasses the adoption of new information technologies that enable an enormous amount of information to be digitally collected, analyzed, and exploited in organizations to make better decisions. Therefore, finding how organizations can adopt big data (BD) components to improve their performance becomes a relevant research area. This issue is becoming more pertinent for small and medium enterprises (SMEs), especially in developing countries that encounter limited resources and infrastructures. Due to the lack of empirical studies related to big data adoption (BDA) and BD’s business value, especially in SMEs, this study investigates the impact of BDA on SMEs’ performance by obtaining the required data from experts. The quantitative investigation followed a mixed approach, including survey data from 224 managers from Iranian SMEs, and a structural equation modeling (SEM) methodology for the data analysis. Results showed that 12 factors affected the BDA in SMEs. BDA can affect both operational performance and economic performance. There has been no support for the influence of BDA and economic performance on social performance. Finally, the study implications and findings are discussed alongside future research suggestions, as well as some limitations and unanswered questions.publishersversionpublishe

Simultaneous interpretive structural modelling and weighting (SISMW)

Multi-criteria decision-making (MCDM) methods have been implemented in many fields. In the meantime, several methods have been proposed to obtain the weight of the criteria determined by various methods in different ways. In this paper, a new approach, called simultaneous interpretive structural modelling and weighting (SISMW), is proposed to solve a multi-criterion decision-making (MCDM) problem. Using SISMW, the weight of the criteria and the relationship between them could be determined simultaneously. In this approach, like the ISM method, pair comparison between criteria was made by the decision-maker to determine the relationships among the different criteria. With the help of this data, the weight of the criteria, as well as the causal (cause and effect) relationships between them, were determined in 12 steps. The main advantage of this method is that only one stage of data collection is required for obtaining weights and modelling, and so the research process may be faster. This may increase the reliability of the collected data because, in a one-step survey, the impact of time is minimized. This process can be useful for conceptualizing and developing theories to help decisionmakers understand the problem better