What does operational excellence mean in the Fourth Industrial Revolution era
TL;DR: The term Operational Excellence (OE) has been widely applied over the past few decades, but its meaning is still ill-defined and may be especially aggravated after the advent of the In...
Abstract: Although the term Operational Excellence (OE) has been widely applied over the past few decades, its meaning is still ill-defined. This issue may be especially aggravated after the advent of the In...
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01 Jan 2017
TL;DR: In this paper, the authors propose a tool to help users to decide what would be a useful sample size for their particular context when investigating patterns across participants, based on the expected population theme prevalence of the least prevalent themes.
Abstract: Thematic analysis is frequently used to analyse qualitative data in psychology, healthcare, social research and beyond. An important stage in planning a study is determining how large a sample size may be required, however current guidelines for thematic analysis are varied, ranging from around 2 to over 400 and it is unclear how to choose a value from the space in between. Some guidance can also not be applied prospectively. This paper introduces a tool to help users think about what would be a useful sample size for their particular context when investigating patterns across participants. The calculation depends on (a) the expected population theme prevalence of the least prevalent theme, derived either from prior knowledge or based on the prevalence of the rarest themes considered worth uncovering, e.g. 1 in 10, 1 in 100; (b) the number of desired instances of the theme; and (c) the power of the study. An adequately powered study will have a high likelihood of finding sufficient themes of the desired prevalence. This calculation can then be used alongside other considerations. We illustrate how to use the method to calculate sample size before starting a study and achieved power given a sample size, providing tables of answers and code for use in the free software, R. Sample sizes are comparable to those found in the literature, for example to have 80% power to detect two instances of a theme with 10% prevalence, 29 participants are required. Increasing power, increasing the number of instances or decreasing prevalence increases the sample size needed. We do not propose this as a ritualistic requirement for study design, but rather as a pragmatic supporting tool to help plan studies using thematic analysis.
263 citations
TL;DR: In this article, the authors conduct a systematic literature review and bibliometric analysis of the application and contribution of I4.0 in disaster risk management (DRM) research and associated industry practices, although its origins, impacts and potential are not well understood.
Abstract: The fourth industrial era, known as ‘Industry 4.0’ (I4.0), aided and abetted by the digital revolution, has attracted increasing attention among scholars and practitioners in the last decade. The adoption of I4.0 principles in Disaster Risk Management (DRM) research and associated industry practices is particularly notable, although its origins, impacts and potential are not well understood. In response to this knowledge gap, this paper conducts a systematic literature review and bibliometric analysis of the application and contribution of I4.0 in DRM. The systematic literature review identified 144 relevant articles and then employed descriptive and content analysis of a focused set of 70 articles published between 2011 and 2021. The results of this review trace the growing trend for adoption of I4.0 tools and techniques in disaster management, and in parallel their influence in resilient infrastructure and digital construction fields. The results are used to identify six dominant clusters of research activity: big data analytics, Internet of Things, prefabrication and modularization, robotics and cyber-physical systems. The research in each cluster is then mapped to the priorities of the Sendai framework for DRR, highlighting the ways it can support this international agenda. Finally, this paper identifies gaps within the literature and discusses possible future research directions for the combination of I4.0 and DRM.
25 citations
TL;DR: In this article , the authors present the benefits and motivations of integrating Lean Six Sigma (LSS) and Industry 4.0 as well as the critical success factors and challenges within this emerging area of research.
Abstract: PurposeThis purpose of this study is to provide an overview of the current state of research on Lean Six Sigma (LSS) and Industry 4.0 and the key aspects of the relationships between them. The research analyses LSS's evolution and discusses the future role of LSS 4.0 in an increasingly digitalized world. We present the benefits and motivations of integrating LSS and Industry 4.0 as well as the critical success factors and challenges within this emerging area of research.Design/methodology/approachA systematic literature review methodology was established to identify, select and evaluate published research.FindingsThere is a synergistic nature between LSS and Industry 4.0. Companies having a strong LSS culture can ease the transition to Industry 4.0 while Industry 4.0 technologies can provide superior performance for companies who are using LSS methodology.Research limitations/implicationsOne limitation of this research was that as this area is a nascent area, the researchers were limited in their literature review and research. A more comprehensive longitudinal study would yield more data. There is an opportunity for further study and analysis.Practical implicationsThis study reviews the evolution of LSS and its integration with Industry 4.0. Organisations can use this study to understand the benefits and motivating factors for integrating LSS and Industry 4.0, the Critical Success Factors and challenges to such integration.Originality/valueThis is the first systematic literature review on LSS 4.0 and can provide insight for practitioners, organisations and future research directions.
23 citations
TL;DR: In this paper, the authors examined the impact of Industry 4.0 (I4.0) technologies on the relationship between total productive maintenance (TPM) practices and maintenance performance and provided empirical evidence of these relationships.
Abstract: PurposeIn this paper, the authors examine the impact of Industry 4.0 (I4.0) technologies on the relationship between total productive maintenance (TPM) practices and maintenance performance.Design/methodology/approachData collection was carried out through a multinational survey with 318 respondents from different manufacturing companies located in 15 countries. Multivariate data techniques were applied to analyze the collected data. Diffusion of innovations theory (DIT) was the adopted theoretical lens for our research.FindingsThe authors’ findings indicate that I4.0 technologies that aim to process information to support decision-making and action-taking directly affect maintenance performance. Technologies oriented to sensing and communicating data among machines, people, and products seem to moderate the relationship between TPM practices and maintenance performance. However, the extent of such moderation varies according to the practices involved, sometimes leading to negative effects.Originality/valueWith the advances of I4.0, there is an expectation that several maintenance practices and performance may be affected. Our study provides empirical evidence of these relationships, unveiling the role of I4.0 for maintenance performance improvement.
14 citations
References
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TL;DR: The authors operationalize saturation and make evidence-based recommendations regarding nonprobabilistic sample sizes for interviews and found that saturation occurred within the first twelve interviews, although basic elements for metathemes were present as early as six interviews.
Abstract: Guidelines for determining nonprobabilistic sample sizes are virtually nonexistent. Purposive samples are the most commonly used form of nonprobabilistic sampling, and their size typically relies on the concept of “saturation,” or the point at which no new information or themes are observed in the data. Although the idea of saturation is helpful at the conceptual level, it provides little practical guidance for estimating sample sizes, prior to data collection, necessary for conducting quality research. Using data from a study involving sixty in-depth interviews with women in two West African countries, the authors systematically document the degree of data saturation and variability over the course of thematic analysis. They operationalize saturation and make evidence-based recommendations regarding nonprobabilistic sample sizes for interviews. Based on the data set, they found that saturation occurred within the first twelve interviews, although basic elements for metathemes were present as early as six...
12,951 citations
"What does operational excellence me..." refers background in this paper
...Additionally, previous qualitative studies (e.g. Guest, Bunce, and Johnson 2006; Fugard and Potts 2015; Braun and Clarke 2016; Boddy 2016) have recommended a minimum sample size of at least twelve to reach data saturation among a relatively homogeneous population, which matches with our sample size....
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TL;DR: In this paper, the use of case study research in operations management for theory development and testing is reviewed and guidelines and a roadmap for operations management researchers wishing to design, develop and conduct case-based research are provided.
Abstract: This paper reviews the use of case study research in operations management for theory development and testing. It draws on the literature on case research in a number of disciplines and uses examples drawn from operations management research. It provides guidelines and a roadmap for operations management researchers wishing to design, develop and conduct case‐based research.
4,127 citations
"What does operational excellence me..." refers result in this paper
...0 on OE conceptualization are still unknown, a qualitative approach was carried out corroborating to the exploratory and descriptive nature of our study (Voss, Tsikriktsis, and Frohlich 2002; Barratt, Choi, and Li 2011)....
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2,526 citations
"What does operational excellence me..." refers background in this paper
...0) – or smart manufacturing (Kusiak 2018) – is the new paradigm for factories of the future, inducing remarkable improvements due to changing operative framework conditions (Lasi et al. 2014)....
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TL;DR: The current article will present the four types of triangulation followed by a discussion of the use of focus groups and in-depth individual interviews as an example of data source triangulations in qualitative inquiry.
Abstract: Triangulation refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena (Patton, 1999). Triangulation also has been viewed as a qualitative research strategy to test validity through the convergence of information from different sources. Denzin (1978) and Patton (1999) identified four types of triangulation: (a) method triangulation, (b) investigator triangulation, (c) theory triangulation, and (d) data source triangulation. The current article will present the four types of triangulation followed by a discussion of the use of focus groups (FGs) and in-depth individual (IDI) interviews as an example of data source triangulation in qualitative inquiry.
2,180 citations
TL;DR: The state of the art in the area of Industry 4.0 as it relates to industries is surveyed, with a focus on China's Made-in-China 2025 and formal methods and systems methods crucial for realising Industry 5.0.
Abstract: Rapid advances in industrialisation and informatisation methods have spurred tremendous progress in developing the next generation of manufacturing technology. Today, we are on the cusp of the Fourth Industrial Revolution. In 2013, amongst one of 10 ‘Future Projects’ identified by the German government as part of its High-Tech Strategy 2020 Action Plan, the Industry 4.0 project is considered to be a major endeavour for Germany to establish itself as a leader of integrated industry. In 2014, China’s State Council unveiled their ten-year national plan, Made-in-China 2025, which was designed to transform China from the world’s workshop into a world manufacturing power. Made-in-China 2025 is an initiative to comprehensively upgrade China’s industry including the manufacturing sector. In Industry 4.0 and Made-in-China 2025, many applications require a combination of recently emerging new technologies, which is giving rise to the emergence of Industry 4.0. Such technologies originate from different disciplines ...
1,780 citations
"What does operational excellence me..." refers background in this paper
...0 disruptive technologies (e.g. cloud computing, Internet of Things – IoT, machine learning, etc.) may imply changes on the concept of OE, as it helps to overcome traditional barriers in operations management (e.g. data collection and management, end-to-end integration, among others)....
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...0 has been claimed a promising technology-driven approach that integrates people, processes, products and services at both intra – and inter-organizational levels (Xu, Xu, and Li 2018; Rosin et al. 2020)....
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...…developments and technological advances have been enabling a viable array of solutions (e.g. digital workplace cyber security, and endto-end digital transformation services) to the growing needs of digitalization in manufacturing industries (Xu, Xu, and Li 2018; Buer, Strandhagen, and Chan 2018)....
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...0, interoperability is the one that assumes that people, partners, processes, products and services can communicate through the Internet of Things (Xu, Xu, and Li 2018; Ghobakhloo 2018), contributing to a closer interdependency....
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...0 has followed a pattern based on the integration of a few base-technologies (e.g. cloud computing, Internet of Things, big data and machine learning) into products, processes and services....
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