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Open Access
Article
Publication date: 6 September 2022

Rose Clancy, Ken Bruton, Dominic T.J. O’Sullivan and Aidan J. Cloonan

Quality management practitioners have yet to cease the potential of digitalisation. Furthermore, there is a lack of tools such as frameworks guiding practitioners in the…

Abstract

Purpose

Quality management practitioners have yet to cease the potential of digitalisation. Furthermore, there is a lack of tools such as frameworks guiding practitioners in the digital transformation of their organisations. The purpose of this study is to provide a framework to guide quality practitioners with the implementation of digitalisation in their existing practices.

Design/methodology/approach

A review of literature assessed how quality management and digitalisation have been integrated. Findings from the literature review highlighted the success of the integration of Lean manufacturing with digitalisation. A comprehensive list of Lean Six Sigma tools were then reviewed in terms of their effectiveness and relevance for the hybrid digitisation approach to process improvement (HyDAPI) framework.

Findings

The implementation of the proposed HyDAPI framework in an industrial case study led to increased efficiency, reduction of waste, standardised work, mistake proofing and the ability to root cause non-conformance products.

Research limitations/implications

The activities and tools in the HyDAPI framework are not inclusive of all techniques from Lean Six Sigma.

Practical implications

The HyDAPI framework is a flexible guide for quality practitioners to digitalise key information from manufacturing processes. The framework allows organisations to select the appropriate tools as needed. This is required because of the varying and complex nature of organisation processes and the challenge of adapting to the continually evolving Industry 4.0.

Originality/value

This research proposes the HyDAPI framework as a flexible and adaptable approach for quality management practitioners to implement digitalisation. This was developed because of the gap in research regarding the lack of procedures guiding organisations in their digital transition to Industry 4.0.

Details

International Journal of Lean Six Sigma, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-4166

Keywords

Article
Publication date: 3 August 2021

Pratima Verma, Vimal Kumar, Ankesh Mittal, Bhawana Rathore, Ajay Jha and Muhammad Sabbir Rahman

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely…

Abstract

Purpose

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.

Design/methodology/approach

A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.

Findings

The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.

Research limitations/implications

The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.

Originality/value

Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.

Details

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

Keywords

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

Open Access
Article
Publication date: 3 August 2021

Rose Clancy, Dominic O'Sullivan and Ken Bruton

Data-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain…

2136

Abstract

Purpose

Data-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management performance. The purpose of this study is to determine a methodology to aid the implementation of digital technologies and digitisation of the supply chain to enable data-driven quality management and the reduction of waste from manufacturing processes.

Design/methodology/approach

Methodologies from both the quality management and data science disciplines were implemented together to test their effectiveness in digitalising a manufacturing process to improve supply chain management performance. The hybrid digitisation approach to process improvement (HyDAPI) methodology was developed using findings from the industrial use case.

Findings

Upon assessment of the existing methodologies, Six Sigma and CRISP-DM were found to be the most suitable process improvement and data mining methodologies, respectively. The case study revealed gaps in the implementation of both the Six Sigma and CRISP-DM methodologies in relation to digitisation of the manufacturing process.

Practical implications

Valuable practical learnings borne out of the implementation of these methodologies were used to develop the HyDAPI methodology. This methodology offers a pragmatic step by step approach for industrial practitioners to digitally transform their traditional manufacturing processes to enable data-driven quality management and improved supply chain management performance.

Originality/value

This study proposes the HyDAPI methodology that utilises key elements of the Six Sigma DMAIC and the CRISP-DM methodologies along with additions proposed by the author, to aid with the digitisation of manufacturing processes leading to data-driven quality management of operations within the supply chain.

Details

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

Keywords

Article
Publication date: 1 September 2002

Daniel P. Lorence and Robert Jameson

The growing acceptance of evidence‐based decision support systems in healthcare organizations has resulted in recognition of data quality improvement as a key area of both…

1791

Abstract

The growing acceptance of evidence‐based decision support systems in healthcare organizations has resulted in recognition of data quality improvement as a key area of both strategic and operational management. Information managers are faced with their emerging role in establishing quality management standards for information collection and application in the day‐to‐day delivery of health care. In the USA, rigid data‐based practice and performance standards and regulations related to information management have met with some resistance from providers. In the emerging information‐intensive healthcare environment, managers are beginning to understand the importance of formal, continuous data quality assessment in health services delivery and quality management. Variation in data quality management practice poses quality problems in such an environment, since it precludes comparative assessments across larger markets or areas, a critical component of evidence‐based quality assessments. In this study a national survey of health information managers was employed to provide a benchmark of the degree of such variation, examining how quality management practices vary across area indicators. Findings here suggest that managers continue to employ paper‐based quality assessment audits, despite nationwide mandates to adopt system‐based measures using aggregate data analysis and automated quality intervention. The level of adoption of automated quality management methods in this study varied significantly across practice characteristics and areas, suggesting the existence of data quality barriers to cross‐market comparative assessment. Implications for healthcare service delivery in an evidence‐based environment are further examined and discussed.

Details

International Journal of Quality & Reliability Management, vol. 19 no. 6
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 6 August 2020

Qasim Ali Nisar, Nadia Nasir, Samia Jamshed, Shumaila Naz, Mubashar Ali and Shahzad Ali

This study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among…

1864

Abstract

Purpose

This study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the Chinese public and private hospitals. It also examined the moderating effect of big data governance that was almost ignored in previous studies.

Design/methodology/approach

The target population consisted of managerial employees (IT experts and executives) in hospitals. Data collected using a survey questionnaire from 752 respondents (374 respondents from public hospitals and 378 respondents from private hospitals) was subjected to PLS-SEM for analysis.

Findings

Findings revealed that data management challenges (leadership focus, talent management, technology and organizational culture for big data) are significant antecedents for big data decision-making capabilities in both public and private hospitals. Moreover, it was also found that big data decision-making capabilities played a key role to improve the decision-making quality (effectiveness and efficiency), which positively contribute toward environmental performance in public and private hospitals of China. Public hospitals are playing greater attention to big data management for the sake of quality decision-making and environmental performance than private hospitals.

Practical implications

This study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.

Originality/value

The proposed model provides an insight look at the dynamic capabilities theory in the domain of big data management to tackle the environmental issues in hospitals. The current study is the novel addition in the literature, and it identifies that big data capabilities are envisioned to be a game-changer player in effective decision-making and to improve the environmental performance in health sector.

Details

Journal of Enterprise Information Management, vol. 34 no. 4
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 25 January 2008

Helinä Melkas and Vesa Harmaakorpi

The purpose of this article is to investigate data, information and knowledge in regional innovation networks. Emphasis has been put recently on regional innovation…

3206

Abstract

Purpose

The purpose of this article is to investigate data, information and knowledge in regional innovation networks. Emphasis has been put recently on regional innovation systems, where various actors are involved in innovative processes. The article responds to the need to study matters related to knowledge management and information quality in such environments.

Design/methodology/approach

Regional innovation networks and data, information and knowledge as well as research on them are discussed at a theoretical level. An existing innovation network of the Lahti region, Finland, was utilised as a pilot environment when building the knowledge management framework that is introduced. The framework is based on established knowledge management literature and practice.

Findings

The results confirm that the aspects of data, information and knowledge need to be addressed systematically in regional innovation networks. They are intertwined with knowledge management and network management. The knowledge management framework introduced incorporates, apart from information quality considerations, future‐oriented self‐transcending knowledge as well as knowledge vision and knowledge assets. Considerations of absorptive capacity and information brokerage in the regional knowledge environment are emphasised.

Research limitations/implications

The limitations of the framework will be assessed in future studies. This will also improve understanding of practical implications. Research implications are related to data, information and knowledge quality – as well as absorptive capacity between the two subsystems of the regional innovation system.

Originality/value

The article combines in a novel way research fields that have previously barely been combined – information quality, knowledge management and regional innovation networks. It provides new insights into a societally important theme and shows possible avenues of further research.

Details

European Journal of Innovation Management, vol. 11 no. 1
Type: Research Article
ISSN: 1460-1060

Keywords

Article
Publication date: 20 November 2017

Jacqueline Edana Tyler

The purpose of this paper is to share the experience of the document discovery process, during the implementation of an asset management system for a rail company. This…

1038

Abstract

Purpose

The purpose of this paper is to share the experience of the document discovery process, during the implementation of an asset management system for a rail company. This system will deliver comprehensive enterprise asset management information from a single source, with information provided to mobile devices, for use by field workers. This case study presents the challenges encountered in the search, retrieval and management of documentation for use on a daily basis for civil standard maintenance tasks.

Design/methodology/approach

Evidence gathered for this paper was a result of direct and participant observation over a period of 18 months from 2014 to 2016. As a member of the project team, certain privileges were accorded to the researcher who was placed in a unique position to act as the main research instrument, able to collect data on the systems used as well as the everyday practices on information capture and document production.

Findings

Document quality and standards can be overlooked or deemed as not crucial; the value, significance and importance of documentation are lost when no one takes ownership; the understanding and application of standards, quality management and governance can have a direct bearing on the effective management and control of documents and subsequent records produced.

Research limitations/implications

Research is limited, as this is a single case study.

Practical implications

By highlighting the challenges faced and the resolutions used, this paper hopes to offer a level of practical guidance with the detection process for maintenance tasks for the civil assets discipline for a rail network.

Originality/value

This case study contributes to the understanding of quality management and the role it plays in document management and in turn the search and retrieval process. It provides evidence that documents must be systematically managed and controlled to limit risk both internally and externally.

Details

Records Management Journal, vol. 27 no. 3
Type: Research Article
ISSN: 0956-5698

Keywords

Book part
Publication date: 7 October 2015

Azizah Ahmad

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive…

Abstract

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

Details

Sustaining Competitive Advantage Via Business Intelligence, Knowledge Management, and System Dynamics
Type: Book
ISBN: 978-1-78441-764-2

Keywords

Book part
Publication date: 9 August 2017

Kathleen McDonald, Sandra Fisher and Catherine E. Connelly

As e-HRM systems move into the ‘smart’ technology realm, expectations and capabilities for both the automational and informational features of e-HRM systems are…

Abstract

Purpose

As e-HRM systems move into the ‘smart’ technology realm, expectations and capabilities for both the automational and informational features of e-HRM systems are increasing. This chapter uses the well-established DeLone and McLean (D&M) model from the information systems literature to analyze how a smart workforce management system can create value for an organization.

Methodology/approach

The chapter is based on an exploratory case study conducted with a North American industrial products firm. We review three systems-level predictors of success from the D&M model (system quality, information quality, and service quality) and evaluate the company’s systems on these attributes.

Findings

The company’s e-HRM systems fall short on the information quality dimension, which limits potential for overall system success related to smart workforce management.

Research limitations/implications

The e-HRM literature focuses on individual-level factors of system success, while the D&M model uses more macro factors. Blending these may help researchers and practitioners develop a more complete view of e-HRM systems. Conclusions from this chapter are limited due to the use of a single, exploratory case study.

Practical implications

Companies must pay attention to all three predictors of system quality when developing smart workforce management systems. In particular, implementation of a data governance program could help companies improve information quality of their systems.

Originality/value

This chapter adds to the literature on smart workforce management by using a model from the information systems literature and a practical example to explore how such a system could add value.

Details

Electronic HRM in the Smart Era
Type: Book
ISBN: 978-1-78714-315-9

Keywords

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