Quality monitoring service for distributed manufacturing systems

Grošelj, Bojan; Zupančič, Rok; Sluga, Alojzij

doi:10.1080/0951192x.2014.900861

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Manufacturing, referring to the production of merchandise by utilizing various resources like manpower, machines, tools, etc., plays an important role in supporting our lives and developments (Chryssolouris, 2013;Ruiz-de-Arbulo-Lopez et al, 2013). Modern manufacturing includes all intermediate processes and other value adding procedures such as assembly and shipping (Dai et al, 2012;Grošelj et al, 2015;Hilmola et al, 2015). The manufactured products should be delivered to suitable destinations so that the values of the manufacturing could be enabled.…”

mentioning

confidence: 99%

A customer satisfaction evaluation model for logistics services using fuzzy analytic hierarchy process

Lan

Zhang

Zhong

et al. 2016

Industrial Management &Amp; Data Systems

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Purpose – As the modern manufacturing twining seamlessly with logistics operations for value adding services, logistics service is becoming more and more significant. Under this research background, the purpose of this paper is to introduce an innovative evaluation model for customer satisfaction using fuzzy analytic hierarchy process (FAHP). Design/methodology/approach – This model uses triangular fuzzy concept to determine the weight of each index so that subjective or objective weighting is addressed. A case study from two large express companies in China is used to demonstrate the feasibility and practicality of the proposed model for examining customer satisfaction. Findings – One of the key findings is that Company B has higher customer satisfaction than Company A due to its quick response and flexible logistics strategy. This paper has several contributions. First, A FAHP-based customer satisfaction evaluation model is proposed for the logistics service. Second, the triangular fuzzy concept is introduced to determine the weight of each index so as to addresses the limitation of subjective or objective weighting method. Third, a case study demonstrates the implementation of the model. Research limitations/implications – First, this paper considers the fuzzy AHP for the customer satisfaction evaluation. Comparing with other multi-criteria decision-making methods like data envelopment analysis, evidential reasoning approach, and multi-attribute value theory will be carried out in the near future. Second, the manufacturing modes like make-to-order, make-to-stock, and mass-customized production may have different logistics support so that the final products may reach the final targets quickly. How to evaluate various mode-based logistics and their customer satisfactions have great significance. Finally, Big Data-enabled customer satisfaction evaluation approaches may be a possible solution. Practical implications – Based on the data from questionnaire, it is found that, in practical applications, manufacturing enterprises should amend the index system according to the specific business scope and the production characteristics. Manufacturing enterprises need to collect large amounts of data through market research and conduct the measurement on the related coefficient between the measurement indicators and customer satisfaction degree. After that, they can make sorting and filtering on the measurement index according to the measurement results. Social implications – Customer satisfaction is very important to manufacturing and logistics enterprises due to its time constraints. The physical products with services like logistics are paid close attention to by the final customers. Originality/value – The contribution of this paper is as follows: a FAHP-based customer satisfaction evaluation model is proposed for the logistics service; triangular fuzzy concept is introduced to determine the weight of each index so as to addresses the limitation of subjective or objective weighting method; a case study was used to demonstrate the implementation of the model. One of the key findings is that Company B has higher customer satisfaction than Company B due to its quick response and flexible logistics strategy.

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mentioning

confidence: 99%

A customer satisfaction evaluation model for logistics services using fuzzy analytic hierarchy process

Lan

Zhang

Zhong

et al. 2016

Industrial Management &Amp; Data Systems

View full text Add to dashboard Cite

show abstract

“…Kim et al (2006) designed a Web-based monitoring system for effectively process monitoring of mold manufacturing, and it was taken as the foundation of establishing E-manufacturing in mold factory. Groselj et al (2015) proposed a novel quality monitoring service which was provided via a Web application, and it offered a data interface unique to each manufacturing system in a distributed manufacturing environment. Wang et al (2011) presented a sensor-driven 3D model-based approach to remote real-time monitoring of manufacturing operations, and only less than 1 per cent of its network bandwidth was consumed when compared with camera-based monitoring system.…”

Section: Production Tracking and Monitoring In Laborintensive Manufacturing Enterprisesmentioning

confidence: 99%

RFID-based tracking and monitoring approach of real-time data in production workshop

et al. 2019

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Purpose In practical workshop production process, there are many production emergencies, e.g. new manufacturing tasks, facilities failure and tasks change. On one hand, it results in poor timeliness and reliability of real-time production data collection, acquisition and transmission; on the other hand, it increases the difficulty of real-time data tracking and monitoring. This paper aims to develop a novel RFID-based tracking and monitoring approach of real-time data in production workshop (TMrfid) to solve them. Design/methodology/approach At first, a three-layer model of real-time data based on RFID has been constructed, which contains RFID-based integrated acquisition center; “RFID & Cloud-service-rules”-based calculation and analysis center; and “RFID & Ontology-knowledge-base”-based monitoring and scheduling center. Then, a targeted analysis and evaluation method of TMrfid with feasibility, quality and performance has been proposed. Finally, a prototype platform of a textile machinery manufacturing enterprise has been built to verify the effective of TMrfid. Findings The effectiveness of TMrfid is verified by applying two groups of actual experimental data from the case enterprise, the results show that TMrfid can promote the efficiency, reliability and feasibility of tacking and monitoring of real-time data in production workshop. Originality/value RFID-based tracking and monitoring approach of real-time data in production workshop has been developed to solve the data information transmission and sharing problem. Three analysis and evaluation approaches have been introduced to solve the un-standardized evaluation problem of RFID application. A prototype platform of a textile machinery manufacturing enterprise has been constructed.

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“…It is meaningful to study the impact of Quality 4.0 on organizational KPIs (Antony et al , 2021). Some scholars have developed visualization platforms that can display quality data (Grošelj et al , 2015; Zhang, 2020; Wu et al , 2019; Gewohn et al , 2019). However, systematic methods for visualizing quality management KPIs are yet to be researched and developed (Aazam et al , 2019; Dai et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

An industrial heterogeneous data based quality management KPI visualization system for product quality control

Zhao

Luo²,

Li³

et al. 2022

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Purpose Quality management systems are commonly applied to meet the increasingly stringent requirements for product quality in discrete manufacturing industries. However, traditional experience-driven quality management methods are incapable of handling heterogeneous data from multiple sources, leading to information islands. This study aims to present a quality management key performance indicator visualization (QM-KPIVIS) system to enable integrated quality control and ultimately ensure product quality. Design/methodology/approach Based on multiple heterogeneous data, an integrated approach is proposed to quantify explicitly the relationship between Internet of Things data and product quality. Specifically, this study identifies the tracing path of quality problems based on multiple heterogeneous quality information tree. In addition, a hierarchical analysis approach is adopted to calculate the key performance indicators of quality influencing factors in the quality control process. Findings Proposed QM-KPIVIS system consists of data visualization, quality problem processing, quality optimization and user rights management modules, which perform in a well-coordinated manner. An empirical study was also conducted to validate the effectiveness of proposed system. Originality/value To the best of the authors’ knowledge, this study is the first attempt to use industrial Internet of Things and multisource heterogeneous data for integrated product quality management. Proposed approach is more user-friendly and intuitive compared to traditional empirically driven quality management methods and has been initially applied in the manufacturing industry.

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Quality monitoring service for distributed manufacturing systems

Cited by 3 publications

References 23 publications

A customer satisfaction evaluation model for logistics services using fuzzy analytic hierarchy process

A customer satisfaction evaluation model for logistics services using fuzzy analytic hierarchy process

RFID-based tracking and monitoring approach of real-time data in production workshop

An industrial heterogeneous data based quality management KPI visualization system for product quality control

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