scholarly journals The Use of Intelligent Systems to Support the Decision-Making Process in Lean Maintenance Management

2019 ◽  
Vol 52 (10) ◽  
pp. 148-153 ◽  
Author(s):  
Katarzyna Antosz ◽  
Lukasz Pasko ◽  
Arkadiusz Gola
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Maintenance Management ◽  
Decision Making Process

An Architecture for the Integration of Decision Making Support Functionalities

2011 ◽  
pp. 1-19 ◽  
Author(s):  
Guisseppi A. Forgionne
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Intelligent Systems ◽  
Support Systems ◽  
Decision Making Process ◽  
Executive Information Systems ◽  
Robust Decision Making ◽  
Advantages And Disadvantages ◽  
The Individual ◽  
Decision Making Support

Various information systems have evolved to support the decision making process. There are decision support systems (DSS), executive information systems (EIS), artificially intelligent systems (AIS), and integrated combinations of these systems. Each of the individual systems supports particular phases and steps of the decision making process, but none of the individual systems supports the entire process in an integrated and complete manner. The integrated systems alleviate the support deficiencies, and each of the integration approaches has specific advantages and disadvantages. By studying these advantages and disadvantages, researchers and practitioners can better design, develop, and implement robust decision making support systems. This chapter facilitates such study by presenting and illustrating the underlying information system architectures for robust decision making support.

Sustainable built heritage: maintenance management appraisal approach

2019 ◽  
Vol 9 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Brit Anak Kayan
Keyword(s):  
Decision Making ◽  
Design Methodology ◽  
Maintenance Phase ◽  
Maintenance Management ◽  
Decision Making Process ◽  
Maintenance Strategies ◽  
Content Type ◽  
Built Heritage ◽  
Practical Implications ◽  
Survey Questionnaires

PurposeSustainability encapsulated economic, environmental and societal parameters. Without exception, these parameters also conforms the efficiency and increasingly importance of sustainable maintenance management for built heritage. However, there is less attention to the appraisal approach for maintenance management of built heritage, twinned with inconsistent and impractical assessment upon their maintenance strategies. With the aim to support sustainability, the purpose of this paper is to give an insight to the question on how the maintenance management appraisal approach practically determines and ultimately substantiates the decision-making process that promotes sustainable built heritage, based on current scenarios and practices in Malaysia.Design/methodology/approachMaintenance management appraisal for sampling of built heritage enables assessment of efficiency of maintenance and repair during maintenance phase based on survey (questionnaires) and statistical analysis.FindingsIt recognises the importance of maintenance management appraisal in achieving efficiency and underpinning rationale decision making for maintenance strategies and service quality (SERVQUAL).Practical implicationsIt must be emphasised that maintenance management appraisal is not confined to built heritage, and can be applied to any types and forms of property. The decision made as a result of its utilisation is practically support sustainable repair.Social implicationsThe implementation of this appraisal highlights the efficacy of maintenance strategies and SERVQUAL that may be adopted.Originality/valueThe paper is a rigorous appraisal of maintenance management of built heritage. This appraisal relays the “true” sustainable built heritage, contextualised within maintenance strategies and SERVQUAL that consequently allows rationale in achieving sustainable development.

scholarly journals Social impact assessment process for industry 4.0 to achieve sustainable artificial intelligence systems

2020 ◽  
Vol 10 (2) ◽  
pp. 27-47
Author(s):  
Vijayan Gurumurthy Iyer
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Impact Assessment ◽  
Intelligent Systems ◽  
Social Impact ◽  
Project Planning ◽  
Assessment Process ◽  
Decision Making Process ◽  
Business Economics ◽  
Artificial Intelligence Systems

The strategic environmental assessment (SEA) process can be broadly defined as a study of the social impacts of a proposed project, plan, policy or legislative action of intelligence systems on the society, environment and sustainability. The SEA process for sustainable intelligent systems has been aimed to incorporate society, environment and sustainability factors into the project planning and decision-making process for sustainable intelligent systems. Artificial intelligence systems (AIS) should consider the titled ‘environmental impact assessment (EIA)’ process that can be defined as the systematic identification and evaluation of the potential impacts (effects) of proposed projects, plans, programmes, policies or legislative actions relative to the biological physical, physico-chemical, biological, cultural, socio-economic and anthropological components of the total environment. The SEA process protocol is important as it has been proposed for studying and checking the productivity and quality of AIS. This treaty and official government procedures of SEA were helpful in the decision-making process much earlier than the EIA process.   Keywords: Artificial intelligence, business, economics, environment, industry.

scholarly journals HIERARCHICAL REPRESENTATION OF CAUSAL RELATIONSHIPS TO DETAIL EXPLANATIONS IN INTELLIGENT SYSTEMS

2021 ◽  
Vol 5 (4) ◽  
pp. 103-108
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi
Keyword(s):  
Decision Making ◽  
Information System ◽  
Intelligent Systems ◽  
Input Data ◽  
Intelligent System ◽  
Decision Making Process ◽  
Causal Relationships ◽  
Hierarchical Representation ◽  
Cause And Effect ◽  
Intelligent Information

The subject of research in the article is the processes of constructing explanations in intelligent systems based on the use of causal dependencies. The aim is to develop a hierarchical representation of causal relationships between the actions of an intelligent system to form an explanation of the process of the system's operation with a given degree of generalization or detailing. Representation of the hierarchy of cause-and-effect relationships allows you to form an explanation at a given level of detail using the input data in the form of a temporally ordered sequence of events reflecting the known actions of an intelligent system. Tasks: structuring the hierarchy of cause-and-effect relationships for known variants of the decision-making process in an intelligent information system, considering the temporal ordering of the corresponding actions; development of a model of a multi-level representation of causal dependencies for description for explanations in an intelligent system. The approaches used are: counterfactual analysis of causality, used to describe alternative dependencies for possible decision-making options; linear temporal logic to reflect the temporal aspect of causation. The following results were obtained. A generalized hierarchy of cause-and-effect relationships is highlighted for the known variants of the process of obtaining recommendations in an intelligent information system based on the temporal ordering of the corresponding decision-making actions. A model of hierarchical representation of causal dependencies has been developed to describe explanations in an intellectual system with a given degree of detail. Conclusions. The scientific novelty of the results obtained is as follows. A model of hierarchical representation of time-ordered causal relationships is proposed to describe the explanations of the operation of an intelligent system with a given degree of detail. At the top level of the hierarchy, the model defines a generalized causal relationship between the event of using the input data and the event of the result of the system's operation. This connection describes the current task that the intelligent information system solves. At the lower level, cause-and-effect relationships are set between events sequential in time, between which there are no other events. At intermediate levels of the hierarchical representation, the causal dependencies of pairs of events are determined, between which there are other events. The developed model creates conditions for constructing explanations with a given degree of detailing of the actions of the decision-making process in an intelligent system. The model also provides the ability to describe early and late anticipation of alternative sequences of the decision-making process by describing causal dependencies for events between which there are other events.

AAC Decision-Making and Mobile Technology: Points to Ponder

2014 ◽  
Vol 23 (2) ◽  
pp. 104-111 ◽  
Author(s):  
Mary Ann Abbott ◽  
Debby McBride
Keyword(s):  
Decision Making ◽  
Mobile Technology ◽  
Communication System ◽  
Augmentative And Alternative Communication ◽  
Evaluation Process ◽  
Decision Making Process ◽  
Alternative Communication ◽  
Ipod Touch ◽  
Feature Match

The purpose of this article is to outline a decision-making process and highlight which portions of the augmentative and alternative communication (AAC) evaluation process deserve special attention when deciding which features are required for a communication system in order to provide optimal benefit for the user. The clinician then will be able to use a feature-match approach as part of the decision-making process to determine whether mobile technology or a dedicated device is the best choice for communication. The term mobile technology will be used to describe off-the-shelf, commercially available, tablet-style devices like an iPhone®, iPod Touch®, iPad®, and Android® or Windows® tablet.

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