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Article
Publication date: 21 May 2021

Rohit Agrawal, Vishal Ashok Wankhede, Anil Kumar and Sunil Luthra

This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify…

Abstract

Purpose

This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.

Design/methodology/approach

A systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.

Findings

The findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.

Originality/value

The paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

Keywords

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Article
Publication date: 25 July 2019

Yinhua Liu, Rui Sun and Sun Jin

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality…

Abstract

Purpose

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

Design/methodology/approach

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

Findings

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

Originality/value

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

Details

Assembly Automation, vol. 39 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 4 September 2020

Jing Lu, Lisa Cairns and Lucy Smith

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight…

Abstract

Purpose

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising.

Design/methodology/approach

Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions.

Findings

Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams.

Research limitations/implications

The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper.

Originality/value

Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

Details

Journal of Modelling in Management, vol. 16 no. 2
Type: Research Article
ISSN: 1746-5664

Keywords

Content available
Article
Publication date: 16 October 2017

Ray Zhong, Xun Xu and Lihui Wang

The purpose of this paper is to review the food supply chain management (FSCM) in terms of systems and implementations so that observations and lessons from this research…

Abstract

Purpose

The purpose of this paper is to review the food supply chain management (FSCM) in terms of systems and implementations so that observations and lessons from this research could be useful for academia and industrial practitioners in the future.

Design/methodology/approach

A systematical and hierarchical framework is proposed in this paper to review the literature. Categorizations and classifications are identified to organize this paper.

Findings

This paper reviews total 192 articles related to the data-driven systems for FSCM. Currently, there is a dramatic increase of research papers related to this topic. Looking at the general interests on FSCM, research on this topic can be expected to increase in the future.

Research limitations/implications

This paper only selected limited number of papers which are published in leading journals or with high citations. For simplicity without generality, key findings and observations are significant from this research.

Practical implications

Some ideas from this paper could be expanded into other possible domains so that involved parties are able to be inspired for enriching the FSCM. Future implementations are useful for practitioners to conduct IT-based solutions for FSCM.

Social implications

As the increasing of digital devices in FSCM, large number of data will be used for decision-makings. Data-driven systems for FSCM will be the future for a more sustainable food supply chain.

Originality/value

This is the first attempt to provide a comprehensive review on FSCM from the view of data-driven IT systems.

Details

Industrial Management & Data Systems, vol. 117 no. 9
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 27 August 2019

Qiao Li, Ping Wang, Yifan Sun, Yinglong Zhang and Chuanfu Chen

With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of…

Abstract

Purpose

With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and instructional strategies to facilitate their research tasks.

Design/methodology/approach

This study developes a theoretical model that considers data-driven RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively. The proposed model was examined by employing structural equation modeling based on a sample of 931 graduate students.

Findings

The results indicate that data-driven RTS is a second-order factor that positively affects the level of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS. Furthermore, data literacy indirectly affects the level of support of visual data presentation.

Practical implications

These findings provide support for developers of knowledge discovery systems, data scientists, universities and libraries on the optimization of data visualization and data literacy instruction that conform to students’ cognitive styles to inform RTS.

Originality/value

This paper reveals the cognitive mechanisms underlying the effects of data literacy and data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical presentations. It provides insights into the match between the visualization formats and cognitive modes.

Details

Aslib Journal of Information Management, vol. 71 no. 5
Type: Research Article
ISSN: 2050-3806

Keywords

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Article
Publication date: 27 June 2019

Yinhua Liu, Shiming Zhang and Guoping Chu

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship…

Abstract

Purpose

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.

Design/methodology/approach

Based on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.

Findings

The combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.

Originality/value

A combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

Details

Assembly Automation, vol. 39 no. 4
Type: Research Article
ISSN: 0144-5154

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Article
Publication date: 5 May 2020

Christoph F. Breidbach and Paul Maglio

The purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.

Abstract

Purpose

The purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service.

Design/methodology/approach

This study uses a midrange theorizing approach to integrate currently disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven business models as the general metatheoretical unit of analysis. The authors then contextualize the framework using data-intensive insurance services.

Findings

The resulting midrange theory offers new insights into how using machine learning, AI and big data sets can lead to unethical implications. Centered around 13 ethical challenges, this work outlines how data-driven business models redefine the value network, alter the roles of individual actors as cocreators of value, lead to the emergence of new data-driven value propositions, as well as novel revenue and cost models.

Practical implications

Future research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically.

Originality/value

At a time when future technological developments related to AI, machine learning or other forms of advanced data analytics are unpredictable, this study instigates a critical and timely discourse within the service research community about the ethical implications that can arise from the datafication of service by introducing much-needed theory and terminology.

Details

Journal of Service Management, vol. 31 no. 2
Type: Research Article
ISSN: 1757-5818

Keywords

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Article
Publication date: 1 February 2007

Silvia Rita Viola, Sabine Graf, Kinshuk and Tommaso Leo

Learning styles are incorporated more and more in e‐education, mostly in order to provide adaptivity with respect to the learning styles of students. For identifying…

Abstract

Learning styles are incorporated more and more in e‐education, mostly in order to provide adaptivity with respect to the learning styles of students. For identifying learning styles, at the present time questionnaires are widely used. While such questionnaires exist for most learning style models, their validity and reliability is an important issue and has to be investigated to guarantee that the questionnaire really assesses what the learning style theory aims at. In this paper, we focus on the Index of Learning Styles (ILS), a 44‐item questionnaire to identify learning styles based on Felder‐ Silverman learning style model. The aim of this paper is to analyse data gathered from ILS by a data‐driven approach in order to investigate relationships within the learning styles. Results, obtained by Multiple Correspondence Analysis and cross‐validated by correlation analysis, show the consistent dependencies between some learning styles and lead then to conclude for scarce validity of the ILS questionnaire. Some latent dimensions present in data, that are unexpected, are discussed. Results are then compared with the ones given by literature concerning validity and reliability of the ILS questionnaire. Both the results and the comparisons show the effectiveness of data‐driven methods for patterns extraction even when unexpected dependencies are found and the importance of coherence and consistency of mathematical representation of data with respect to the methods selected for effective, precise and accurate modelling.

Details

Interactive Technology and Smart Education, vol. 4 no. 1
Type: Research Article
ISSN: 1741-5659

Keywords

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Article
Publication date: 5 August 2021

Najah Almazmomi, Aboobucker Ilmudeen and Alaa A. Qaffas

In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's…

Abstract

Purpose

In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's competitive advantage. Though widespread attention has been given to the above concepts, there hasn't been much research done on how it could support achieving competitive advantage.

Design/methodology/approach

This research strongly lies on the theoretical background and empirically tests the hypothesized relationships. The primary survey of 272 responses was analysed by using the partial least squares structural equation modelling (PLS-SEM).

Findings

The findings of this study show a significant relationship for the constructs in the research model except for the third hypothesis. Accordingly, the firm's data-driven culture does not have a significant impact on new product newness.

Originality/value

This study empirically tests the business analytics capability, data-driven culture, and new product development features in the context of a firm's competitive advantage. The findings of this study contribute to the theoretical, practical and managerial aspects of this field.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

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Article
Publication date: 18 June 2019

Mohamed Zaki

The purpose of this paper is to discuss digital transformation and its four trajectories – digital technology, digital strategy, customer experience and data-driven

Abstract

Purpose

The purpose of this paper is to discuss digital transformation and its four trajectories – digital technology, digital strategy, customer experience and data-driven business models – that could shape the next generation of services. This includes a discussion on whether both the market and organizations are all ready for the digital change and what are the opportunities that will enable firms to create and capture value though new business models.

Design/methodology/approach

Providing services is a proven and effective way to secure a competitive position, deliver long-term stable revenues and open up new market opportunities. However, it is also clear that some organisations are struggling to digitally transform. Therefore, the commentary provides a brief insight into how firms explore the possibilities of digital transformation and navigate these uncharted waters.

Findings

Today’s digital technologies affect the organisation outside and in, enabling the creation of new business models and transforming the customer experience. The incumbents are acutely aware that they need to transform strategically – to build new networks and value chains.

Originality/value

This commentary extends earlier work exploring the digital disruption within services to highlight a number of connected areas: the challenges and opportunities of digital transformation at a strategic level, as well as understanding and enhancing the customer experience and seeing how new data-driven business models can underpin service transformation.

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