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Smart Service Technology for Industrial Applications II

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 14001

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Prof. Dr. Kuen-Suan Chen

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Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Interests: statistical process control; fuzzy decision making; quality management; process capability analysis; six sigma; service management
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Prof. Dr. Hoang Pham

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Department of Industrial and Systems Engineering, Rutgers University, New Jersey, NJ 08854, USA
Interests: reliability engineering; software reliability; statistical inferences; fault-tolerant computing
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Prof. Dr. Kimhua Tan

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Department of Operations Management and Information Systems, Nottingham University, Nottingham NG7 2RD, UK
Interests: lean management; operations strategy; decision making; supply chain risk management
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Dr. Leanne Chung

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International Business and Management, Cardiff University, Cardiff CF10 3EU, UK
Interests: International business and business organization

Prof. Dr. Shey-Huei Sheu

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Department of Business Administration, Asia University, Taichung 413305, Taiwan
Interests: reliability; maintenance; multi-state systems; optimization; quality control
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Dear Colleagues,

As technologies associated with the Internet of Things (IoT) have gradually matured, the measurement and analysis of production data have continued to advance, enabling the collection of big production data. Effective data analysis and application can enhance manufacturing and management technologies, which not only accelerate the development of intelligent manufacturing for Industry 4.0 but are also conducive to the improvement of process quality. In addition, the rapid development and advances of emerging technologies, such as the Internet of Things, big data, and artificial intelligence, have fostered innovation and high competition in various industries around the world. Many manufacturing companies are becoming more service oriented to offer new innovative value offerings such as smart services. Smart services are a new type of digital service that use and combine the ever-growing amount of internal and external data of industrial companies to create individual solutions for customers. Smart services offer various new possibilities for manufacturing industries. In view of this, this Special Issue focuses on the latest developments and applications of smart service management for industrial application. We invite researchers to contribute original research articles, as well as review articles, to this Special Issue. The topics of this Special Issue include, but are not limited to, the following:

Smart service technology;
Manufacturing service technology;
Digital service;
Internet of Things;
Big data;
Artificial intelligence applications;
Machine learning and deep learning;
Fuzzy applications in smart service technology;
Smart service quality evaluation;
Smart service performance evaluation;
Statistical production data analysis;
Statistical decision-making.

Prof. Dr. Kuen-Suan Chen
Prof. Dr. Hoang Pham
Prof. Dr. Kimhua Tan
Dr. Leanne Chung
Prof. Dr. Shey-Huei Sheu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

smart service technology
manufacturing service technology
digital service
internet of things
big data
artificial intelligence applications
machine learning and deep learning
fuzzy applications in smart service technology
smart service performance evaluation
statistical production data analysis
statistical decision-making

Published Papers (9 papers)

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Research

15 pages, 2462 KiB

Open AccessArticle

Estimation of the Network Reliability for a Stochastic Cold Chain Network with Multi-State Travel Time

by Thi-Phuong Nguyen, Chin-Lung Huang and Yi-Kuei Lin

Appl. Sci. 2023, 13(13), 7897; https://doi.org/10.3390/app13137897 - 5 Jul 2023

Viewed by 818

Abstract

A stochastic cold chain (SCC) is a common supply chain in real life that emphasizes the need for commodities to arrive fresh within time constraints. In previous research on supply chains, the time factor was regarded as a fixed number. However, the travel time is a stochastic factor due to traffic and weather conditions during the delivery. Therefore, this paper concentrates on the two multi-state factors simultaneously. Network reliability is one of the performance indexes used to assess the cold chain efficacy, defined as the probability that the flow of SCC can satisfy the demand within the delivery time threshold. The SCC with two multi-state factors is modeled as a stochastic cold chain network with multi-state travel time (SCCNMT). To calculate the network reliability of an SCCNMT, we will calculate the demand reliability and time reliability separately, treating them as independent events, and multiply the demand and time reliability to estimate the network reliability of the two multi-state factors. Full article

(This article belongs to the Special Issue Smart Service Technology for Industrial Applications II)

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27 pages, 3040 KiB

Open AccessArticle

A Three-Phased Fuzzy Logic Multi-Criteria Decision-Making Model for Evaluating Operation Systems for Smart TVs

by Amy H. I. Lee and He-Yau Kang

Appl. Sci. 2023, 13(13), 7869; https://doi.org/10.3390/app13137869 - 4 Jul 2023

Viewed by 791

Abstract

Within the competitive global market and fast-advancing technology environment, in order to survive and to succeed, firms need to spontaneously respond to market changes and the uncertainty of customer needs. Therefore, New Product Development (NPD) is extremely important for the success of firms. Artificial Intelligence (AI) has gradually entered people’s lives, and consumer demand for AI products is increasing. Firms need to understand the AI development trend and consider the preferences of consumers for AI-related products under social changes so that suitable consumer AI products can be properly developed. In this study, the evaluation and selection of operation systems for a commercially available AI product (smart TV) is studied, and a Multi-Criteria Decision-Making (MCDM) model for facilitating the selection of the most suitable operation system for product development is constructed. The proposed model consists of three phases: Interpretative Structural Modelling (ISM) to construct a decision-making network, Fuzzy Analytic Network Process (FANP) to obtain the weights of factors, and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) to rank the operation systems. The proposed model is applied to select an operation system that companies can use to develop a smart TV. The results show that the proposed model can provide a systematic method that helps companies make appropriate operation system selection decisions. Full article

(This article belongs to the Special Issue Smart Service Technology for Industrial Applications II)

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22 pages, 1776 KiB

Open AccessArticle

Prediction and Comparative Analysis of Software Reliability Model Based on NHPP and Deep Learning

by Youn Su Kim, Kwang Yoon Song and In Hong Chang

Appl. Sci. 2023, 13(11), 6730; https://doi.org/10.3390/app13116730 - 31 May 2023

Cited by 1 | Viewed by 1088

Abstract

Over time, software has become increasingly important in various fields. If the current software is more dependent than in the past and is broken owing to large and small issues, such as coding and system errors, it is expected to cause significant damage to the entire industry. To address this problem, the field of software reliability is crucial. In the past, efforts in software reliability were made to develop models by assuming a nonhomogeneous Poisson-process model (NHPP); however, as models became more complex, there were many special cases in which models fit well. Hence, this study proposes a software reliability model using deep learning that relies on data rather than mathematical and statistical assumptions. A software reliability model based on recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), which are the most basic deep and recurrent neural networks, was constructed. The dataset was divided into two, Datasets 1 and 2, which both used 80% and 90% of the entire data, respectively. Using 11 criteria, the estimated and learned results based on these datasets proved that the software reliability model using deep learning has excellent capabilities. The software reliability model using GRU showed the most satisfactory results. Full article

(This article belongs to the Special Issue Smart Service Technology for Industrial Applications II)

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16 pages, 4869 KiB

Open AccessArticle

A Fuzzy-Based Emotion Detection Method to Classify the Relevance of Pleasant/Unpleasant Emotions Posted by Users in Reviews of Service Facilities

by Barbara Cardone, Ferdinando Di Martino and Vittorio Miraglia

Appl. Sci. 2023, 13(10), 5893; https://doi.org/10.3390/app13105893 - 10 May 2023

Cited by 2 | Viewed by 1231

Abstract

Many sentiment analysis methods have been proposed recently to evaluate, through the Web, the perceptions of users and their satisfaction with the use of products and services; these approaches have been applied in various fields in which it is necessary to evaluate, for example, the degree of appreciation of a product or a service or political orientations or emotional states following an event or the occurrence of a phenomenon. On the other hand, these methods are based on natural language processing models needed to capture information hidden in comments, which generally require a high computational cost which can affect their performance; for this reason, review-collecting providers prefer to synthetically evaluate user satisfaction by considering a score on a numerical scale entered by users. To overcome this criticality, we propose an emotion detection method based on a light fuzzy-based document classification model to capture the relevance of pleasant and unpleasant emotions expressed by users in their reviews of service facilities. This method is implemented in a geo-computational framework and tested to evaluate the satisfaction of customers of theater venues located in the municipality of Naples (Italy). A fuzzy-based approach is used to classify user satisfaction according to the relevance of the emotional categories of pleasant and unpleasant. We show that our emotion detection method refines service feature pleasure assessments expressed on scales by users in their reviews. Full article

(This article belongs to the Special Issue Smart Service Technology for Industrial Applications II)

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20 pages, 7151 KiB

Open AccessArticle

Designing a Comprehensive and Flexible Architecture to Improve Energy Efficiency and Decision-Making in Managing Energy Consumption and Production in Panama

by Ivonne Nuñez, Elia Esther Cano, Edmanuel Cruz and Carlos Rovetto

Appl. Sci. 2023, 13(9), 5707; https://doi.org/10.3390/app13095707 - 5 May 2023

Cited by 1 | Viewed by 1885

Abstract

In recent years, the integration of new elements to the electric grid, such as electric vehicles and renewable energies, requires the evolution of the electric grid as we know it, making it necessary to optimize the processes of production, distribution, and storage of energy. This situation gives rise to introducing the so-called Smart Grids (SG), which would allow a balance between energy supply and demand, thus enabling a system in which the consumer will also become a producer of its surplus energy. Under this scenario, this work proposes an architecture whose technological components, such as the internet of things (IoT), artificial intelligence (AI), cloud computing, and mobile applications, allow users to address the problem of consumption and production of electricity. In the experiments conducted, results were obtained from the components that support the functionality of the proposed platform. Full article

(This article belongs to the Special Issue Smart Service Technology for Industrial Applications II)

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14 pages, 797 KiB

Open AccessArticle

Using Statistical Test Method to Establish a Decision Model of Performance Evaluation Matrix

by Chin-Chia Liu, Chun-Hung Yu and Kuen-Suan Chen

Appl. Sci. 2023, 13(8), 5139; https://doi.org/10.3390/app13085139 - 20 Apr 2023

Cited by 1 | Viewed by 945

Abstract

Many studies have pointed out that the Performance Evaluation Matrix (PEM) is a convenient and useful tool for the evaluation, analysis, and improvement of service operating systems. All service items of the operating system can collect customer satisfaction and importance through questionnaires and then convert them into satisfaction indices and importance indices to establish PEM and its evaluation rules. Since the indices have unknown parameters, if the evaluation is performed directly by the point estimates of the indices, there will be a risk of misjudgment due to sampling error. In addition, most of the studies only determine the critical-to-quality (CTQ) that needs to be improved, and do not discuss the treatment rules in the case of limited resources nor perform the confirmation after improvement. Therefore, to address similar research gaps, this paper proposed the unbiased estimators of these two indices and determined the critical-to-quality (CTQ) service items which need to be improved through the one-tailed statistical hypothesis test by building a PEM method of the satisfaction index. In addition, through the one-tailed statistical hypothesis test method of the importance index, the improvement priority of service items was determined under the condition of limited resources. Confirmation of the effect on improvement is an important step in management. Thus, this paper adopted a statistical two-tailed hypothesis test to verify whether the satisfaction of all the CTQ service items that need to be improved was enhanced. Since the method proposed in this paper was established through statistical hypothesis tests, the risk of misjudgment due to sampling error could be reduced. Obviously, reducing the misjudgment risk is the advantage of the method in this paper. Based on the precondition, utilizing the model in this study may assist the industries to determine CTQ rapidly, implement the most efficient improvement under the condition of limited resources and also confirm the improvement effect at the same time. Finally, a case study of computer-assisted language learning system (CALL System) was used to illustrate a way to apply the model proposed in this paper. Full article

(This article belongs to the Special Issue Smart Service Technology for Industrial Applications II)

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29 pages, 9018 KiB

Open AccessArticle

An Intelligent Waste Management Application Using IoT and a Genetic Algorithm–Fuzzy Inference System

by Sumaiya Thaseen Ikram, Vanitha Mohanraj, Sakthivel Ramachandran and Anbarasu Balakrishnan

Appl. Sci. 2023, 13(6), 3943; https://doi.org/10.3390/app13063943 - 20 Mar 2023

Cited by 11 | Viewed by 4448

Abstract

The Internet of Things (IoT) is being used to create new applications for smart cities. Waste management is one issue that requires various IoT components for assistance, such as RFIDs and sensors. An efficient and innovative waste collection system is required to minimize investment, operational, and expenditure costs. In this paper, the novel idea is to develop an intelligent waste management model for smart cities using a hybrid genetic algorithm (GA)–fuzzy inference engine. The system can read, collect, and process information intelligently using a fuzzy inference engine that decides dynamically how to manage a waste collection. The aim of this model is to enhance its correctness and robustness, primarily, in addition to reducing errors that arise due to working conditions. GA is used for optimization to determine the best combination of rules for the fuzzy inference system (FIS). A Mamdani model is used to estimate waste management. The proposed model uses sensors to collect vital information, and FIS is trained using fuzzy logic to determine the probability that the smart bin is nearly full. The primary issue with the traditional genetic algorithm is that during the execution of the algorithm, there is a possibility of essential gene loss. The essential gene loss refers to information relevant to location, details regarding waste filling parameters, etc., which may lead to efficiency or accuracy loss. This problem is overcome by integrating fuzzy logic with a genetic algorithm to identify crucial genes by preserving the FIS interpretability. Our system uses cost-effective, small-size sensors and ensures this solution is reproducible. The Proteus simulator is used for experiments, and satisfactory results are obtained. Overall accuracy, precision, and recall of 95.44%, 96.68%, and 93.96% are obtained in the proposed model. Classification of recyclable items is also performed, and accuracy is determined for every item, resulting in the minimization of resource waste. The cost of manual interpretation is minimized in the intelligent smart waste management system in comparison to the traditional approach, as shown in the experiments. Full article

(This article belongs to the Special Issue Smart Service Technology for Industrial Applications II)

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11 pages, 2837 KiB

Open AccessCommunication

Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network

by Cheng-Jian Lin, Chun-Hui Lin and Frank Lin

Appl. Sci. 2023, 13(5), 3337; https://doi.org/10.3390/app13053337 - 6 Mar 2023

Cited by 2 | Viewed by 1077

Abstract

The spindle of a machine tool plays a key role in machining because the wear of a spindle might result in inaccurate production and decreased productivity. To understand the condition of a machine tool, a vector-based convolutional fuzzy neural network (vector-CFNN) was developed in this study to diagnose faults from signals. The developed vector-CFNN mainly comprises a feature extraction part and a classification part. The feature extraction phase encompasses the use of convolutional layers and pooling layers, while the classification phase is facilitated through the deployment of a fuzzy neural network. The fusion layer plays an important role by being placed between the feature extraction and classification parts. It combines the characteristics and then passes the feature information to the classification part to improve the model’s performance. The developed vector-CFNN was experimentally evaluated against existing fusion methods; vector-CFNN required fewer parameters and achieved the highest average accuracy (99.84%) in fault diagnosis relative to conventional neural networks, fuzzy neural networks, and convolutional neural networks. Moreover, vector-CFNN achieved superior fault diagnosis using spindle vibration signals and required fewer parameters relative to its counterparts, indicating its feasibility for online spindle vibration monitoring. Full article

(This article belongs to the Special Issue Smart Service Technology for Industrial Applications II)

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13 pages, 1258 KiB

Open AccessArticle

Fuzzy Evaluation and Improvement Decision-Making Model for Machining Operation Performance

by Kuen-Suan Chen, Chih-Feng Wu, Ruey-Chyn Tsaur and Tsun-Hung Huang

Appl. Sci. 2023, 13(3), 1430; https://doi.org/10.3390/app13031430 - 21 Jan 2023

Cited by 8 | Viewed by 982

Abstract

Taiwan is a major exporter and producer of machinery and machine tools in the world. There are at least hundreds of components for various machining machines. According to the concept of Taguchi loss function, when the process quality of the spare parts of machining machines is not good, the failure rate will increase after the product is sold, resulting in an increase in maintenance costs and carbon emissions. As the environment of the Internet of Things (IoT) becomes more common and mature, it is beneficial for manufacturers of machining machines to collect relevant information about process data from outsourcers, suppliers, and machining machine factories. Effective data analysis and application can help the machining machine industry move towards smart manufacturing and management, which can greatly reduce the average number of failures per unit time for all sold machines. Therefore, this paper developed a practical evaluation and improvement decision-making model for the machining operation performance to help machining machine manufacturers find out the components that often fail and improve them, so as to reduce the total loss caused by machine failures. This paper first defined the machining operation performance index for the machining machines and discussed the characteristics of this operation performance index. Subsequently, the confidence interval of the index was deduced, a fuzzy evaluation model based on this confidence interval was proposed, and decision-making rules regarding whether to make any improvement was established. The fuzzy evaluation and improvement decision-making model for the operation performance of machining machines proposed in this paper will contribute to various tool industries to boost their process quality, reduce costs, and lower carbon emissions, in order to achieve sustainable management of enterprises and the environment. Full article

(This article belongs to the Special Issue Smart Service Technology for Industrial Applications II)

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