scholarly journals Visual Analytics Tools for Sustainable Lifecycle Design: Current Status, Challenges, and Future Opportunities

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Devarajan Ramanujan ◽  
William Z. Bernstein ◽  
Senthil K. Chandrasegaran ◽  
Karthik Ramani
Keyword(s):  
Visual Analytics ◽  
Domain Knowledge ◽  
Decision Makers ◽  
Data Driven ◽  
Current Status ◽  
Use Of Data ◽  
Design Changes ◽  
Human Decision ◽  
Computer Based ◽  
Lifecycle Design

The rapid rise in technologies for data collection has created an unmatched opportunity to advance the use of data-rich tools for lifecycle decision-making. However, the usefulness of these technologies is limited by the ability to translate lifecycle data into actionable insights for human decision-makers. This is especially true in the case of sustainable lifecycle design (SLD), as the assessment of environmental impacts, and the feasibility of making corresponding design changes, often relies on human expertise and intuition. Supporting human sensemaking in SLD requires the use of both data-driven and user-driven methods while exploring lifecycle data. A promising approach for combining the two is through the use of visual analytics (VA) tools. Such tools can leverage the ability of computer-based tools to gather, process, and summarize data along with the ability of human experts to guide analyses through domain knowledge or data-driven insight. In this paper, we review previous research that has created VA tools in SLD. We also highlight existing challenges and future opportunities for such tools in different lifecycle stages—design, manufacturing, distribution and supply chain, use-phase, end-of-life (EoL), as well as life cycle assessment (LCA). Our review shows that while the number of VA tools in SLD is relatively small, researchers are increasingly focusing on the subject matter. Our review also suggests that VA tools can address existing challenges in SLD and that significant future opportunities exist.

scholarly journals MOBILITY ATLAS BOOKLET: AN URBAN DASHBOARD DESIGN AND IMPLEMENTATION

2018 ◽  
Vol IV-4/W7 ◽  
pp. 51-58
Author(s):  
L. Gabrielli ◽  
M. Rossi ◽  
F. Giannotti ◽  
D. Fadda ◽  
S. Rinzivillo
Keyword(s):  
Data Mining ◽  
Mobile Phone ◽  
Public Transport ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Decision Makers ◽  
Data Sources ◽  
Electrical Mobility ◽  
Sustainability Index ◽  
Design And Implementation

<p><strong>Abstract.</strong> The new data sources give the possibility to answer analytically the questions that arise from mobility manager. The process of transforming raw data into knowledge is very complex, and it is necessary to provide metaphors of visualizations that are understandable to decision makers. Here, we propose an analytical platform that extracts information on the mobility of individuals from mobile phone by applying Data Mining methodologies. The main results highlighted here are both technical and methodological. First, communicating information through visual analytics techniques facilitates understanding of information to those who have no specific technical or domain knowledge. Secondly, the API system guarantees the ability to export aggregates according to the granularity required, enabling other actors to produce new services based on the extracted models. For the future, we expect to extend the platform by inserting other layers. For example, a layer for measuring the sustainability index of a territory, such as the ability of public transport to attract private mobility or the index that measures how many private vehicle trips can be converted into electrical mobility.</p>

scholarly journals Human-Centric Justification of Machine Learning Predictions

2017 ◽  
Author(s):  
Or Biran ◽  
Kathleen McKeown
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Decision Makers ◽  
Decision Making Process ◽  
Human Reasoning ◽  
Learning Models ◽  
Language Generation ◽  
Novel Approach ◽  
Human Decision ◽  
Machine Learning Models

Human decision makers in many domains can make use of predictions made by machine learning models in their decision making process, but the usability of these predictions is limited if the human is unable to justify his or her trust in the prediction. We propose a novel approach to producing justifications that is geared towards users without machine learning expertise, focusing on domain knowledge and on human reasoning, and utilizing natural language generation. Through a task-based experiment, we show that our approach significantly helps humans to correctly decide whether or not predictions are accurate, and significantly increases their satisfaction with the justification.

Harnessing Visible Representation to Mitigate Bias

2021 ◽  
Vol 8 (1) ◽  
pp. 27-33
Author(s):  
Jessica L. Shropshire ◽  
Kerri L. Johnson
Keyword(s):  
Mental Representations ◽  
Mental Image ◽  
Status Quo ◽  
Decision Makers ◽  
Current Status ◽  
Or Groups ◽  
Evaluative Reactions

Numerous attempts to improve diversity by way of changing the hearts of decision makers have fallen short of the desired outcome. One underappreciated factor that contributes to bias resides not in decision makers’ hearts, but instead in their minds. People possess images, or mental representations, for specific roles and professions. Which mental image or representation springs spontaneously to mind depends on the current status quo within a field. Whether or not an individual or groups’ appearance matches visual stereotypes results in perceptually mediated preferences and prejudices, both of which harbor pernicious assumptions about who belongs in a professional setting and why. Leveraging these scientific insights can enact change. Shifting visible exemplars can change people’s mental representations and their heart’s evaluative reactions to others.

scholarly journals On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1310
Author(s):  
Pablo Torres ◽  
Soledad Le Clainche ◽  
Ricardo Vinuesa
Keyword(s):  
Physical Phenomenon ◽  
Urban Pollution ◽  
Pollutant Dispersion ◽  
Urban Environments ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Thermal Fields ◽  
Use Of Data ◽  
Sustainability Challenges ◽  
New Research

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

scholarly journals On the Juridical Relevance of the Phenomenological Notion of Person in Max Scheler and Edith Stein

2021 ◽  
Author(s):  
Francesco Galofaro
Keyword(s):  
Artificial Intelligence ◽  
Edmund Husserl ◽  
Phenomenological Analysis ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Edith Stein ◽  
Max Scheler ◽  
Human Decision ◽  
The Difference ◽  
Social Units

AbstractThe paper presents a semiotic interpretation of the phenomenological debate on the notion of person, focusing in particular on Edmund Husserl, Max Scheler, and Edith Stein. The semiotic interpretation lets us identify the categories that orient the debate: collective/individual and subject/object. As we will see, the phenomenological analysis of the relation between person and social units such as the community, the association, and the mass shows similarities to contemporary socio-semiotic models. The difference between community, association, and mass provides an explanation for the establishment of legal systems. The notion of person we inherit from phenomenology can also be useful in facing juridical problems raised by the use of non-human decision-makers such as machine learning algorithms and artificial intelligence applications.

Supply chain practices and organizational performance

2017 ◽  
Vol 28 (1) ◽  
pp. 75-101 ◽  
Author(s):  
Shrikant Gorane ◽  
Ravi Kant
Keyword(s):  
Supply Chain ◽  
Customer Satisfaction ◽  
Financial Performance ◽  
Organizational Performance ◽  
Customer Value ◽  
Structural Equation ◽  
Decision Makers ◽  
Current Status ◽  
Content Type ◽  
Manufacturing Organizations

Purpose The purpose of this paper is to empirically test a framework which identifies the relationships between various supply chain practices (SCPs) and organizational performance (operational performance (OP), customer satisfaction, and financial performance) in the context of Indian manufacturing organizations. Design/methodology/approach From the literature, ten SCPs are selected which finally influences the organizational performance. In order to understand the interactions between SCPs and organizational performance, this paper grouped the ten SCPs into four constructs namely: information and communication technology, supply chain (SC) integration, operational responsiveness, and closed loop green practices. Three levels of firm performance are also examined, including OP, customer satisfaction, and financial performance. The paper-based and web-based survey yielded 292 responses from the Indian manufacturing organizations. The data collected were put through rigorous statistical analysis to test for the content, construct, and criterion-related validity, as well as reliability analyses. Further a structural equation model was developed to test the relationships between SCPs and organizational performance. Findings The finding suggests that a successful SCPs implementation not only improves the OP, but also enhances customer satisfaction and financial performance. In addition, higher financial performance is also attributable to better customer value resulting from the achievement of better customer satisfaction. Research limitations/implications SCPs are complex constructs. While this study shows the effect of broadly accepted SCPs on organizational performance, not all possible practices are covered in this study. Again the study can be further extended to sector specific so that the results can be further refined. Practical implications This is one of the few studies which attempts to investigate whether there is any relationship exits between SCPs and organizational performance. The finding will help decision makers in the organization to know the importance of SCPs and how SCPs influence the organizational performance. Second, this study has developed and validated a multi-dimensional construct of SCPs, which can assist decision makers of Indian organizations to evaluate the competence of their current status of SCPs in the organization. Originality/value As per the knowledge of the authors, this is the first kind of study which empirically investigated the relationships between SCPs and organizational performance in the context of Indian manufacturing organizations.

DATA-DRIVEN DISCOVERY OF MATERIAL STATES IN COMPOSITES UNDER FATIGUE LOADS

2021 ◽  
Author(s):  
MUTHU RAM ELENCHEZHIAN ◽  
VAMSEE VADLAMUDI ◽  
RASSEL RAIHAN ◽  
KENNETH REIFSNIDER
Keyword(s):  
Domain Knowledge ◽  
Data Science ◽  
Damage Tolerance ◽  
Electrochemical Impedance ◽  
Research Work ◽  
Rate Of Change ◽  
Data Driven ◽  
Develop Model ◽  
Damage Development ◽  
Fatigue Loads

Our community has a widespread knowledge on the damage tolerance and durability of the composites, developed over the past few decades by various experimental and computational efforts. Several methods have been used to understand the damage behavior and henceforth predict the material states such as residual strength (damage tolerance) and life (durability) of these material systems. Electrochemical Impedance Spectroscopy (EIS) and Broadband Dielectric Spectroscopy (BbDS) are such methods, which have been proven to identify the damage states in composites. Our previous work using BbDS method has proven to serve as precursor to identify the damage levels, indicating the beginning of end of life of the material. As a change in the material state variable is triggered by damage development, the rate of change of these states indicates the rate of damage interaction and can effectively predict impending failure. The Data-Driven Discovery of Models (D3M) [1] aims to develop model discovery systems, enabling users with domain knowledge but no data science background to create empirical models of real, complex processes. These D3M methods have been developed severely over the years in various applications and their implementation on real-time prediction for complex parameters such as material states in composites need to be trusted based on physics and domain knowledge. In this research work, we propose the use of data-driven methods combined with BbDS and progressive damage analysis to identify and hence predict material states in composites, subjected to fatigue loads.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

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