Recent Developments in Quality Management in the Era of Digital Transformation – A Review

The purpose of the current exploratory research is to trace the growth and evolution of the Quality Management as a critical function in organizations and as a discipline of study in academia and research. The methodology adapted is to review some of the classical works and research in the area of Quality Management, which indicates direction of growth and evolution. There are several pioneers who have contributed richly for building and shaping the Quality Management principles, practices and methodologies over several decades. The current study involved the task of summarizing significant trends of Quality Management starting from the crafts man era and going up to the current trend of managing Quality as part of digital transformation. In the digital era there is an increased emphasis on automation of all the activities related to product and process quality management. The use of IoT based automation starting from data capturing, archiving and the point of self-diagnostic and autonomous way of managing quality issues is common place in today’s industries Quality 4.0 era. There are several challenges along the way for which quality professionals must be equipped in terms of knowledge, skills and attitude necessary for quality problem solving using modern techniques. This aspect is also researched in this study. Familiarity with technology platforms such as artificial intelligence, machine learning, image processing, sensors and actuators and such other emerging technologies must form the arsenal for analyzing data and data patterns in the face of data deluge. This requires several inter and multi-disciplinary knowledge exchange forums for grooming future quality professional. This article aims at tracing the metamorphosis of quality management with focus on people development and continuous process improvements in the manufacturing and allied sectors

Implications of industry 4.0 on financial performance: an empirical study

With this thesis, we explore the relationship between industry 4.0 technologies and financial performance. After presenting the fourth industrial revolution and the analysis of management articles and reviews, we describe the database used. Finally research questions are investigated though t-tests and multiple linear regression model

Cloud platforms for remote monitoring system : a comparative case study

Currently, industrial companies are increasingly introducing services to extend their tangible products. Remote monitoring solutions are one of the most implemented services by machine builders to manage their relationship with customers and also improve their business performance in the digital manufacturing era. However, the conventional method of remote monitoring cannot fulfil distributed business environments. Therefore, new solutions are needed to enable remote connection in manufacturing. By reviewing recent literature and proposing new features for software which can be used for remote service and operations, this research paper introduces a remote monitoring system connecting into a central cloud-based system with edge computing network architecture, namely Cloud-based Remote Monitoring (CloudRM). This proposed CloudRM also has been implemented in two different case companies for analysis and evaluation from a value proposition and technical implementation point of view. It shows significant improvement of production management and measurement by using CloudRM.fi=vertaisarvioitu|en=peerReviewed

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

Regenerating the Logistics Industry through the Physical Internet Paradigm: A Systematic Literature Review and Future Research Orchestration

The operations of other businesses rely heavily on the logistics sector, making logistics the most crucial industry. There are multiple issues available in the existing logistics sector including the lack of logistics optimization and unsustainability operations. To deal with these issues, the concept of the Physical Internet (PI) has arisen. For this reason, the current study conducts a comprehensive literature review to determine how PI has emerged within the logistics sector to improve its condition. In this research, we present a comprehensive and in-depth analysis of the present situation of the PI in the logistics literature by conducting a systematic review of 114 publications published in 39 top journals on the topic between 2007 and 2022. This paper makes three significant contributions to the existing literature using such an analysis. To begin with, it provides an overarching context for the part played by the PI in the overall logistics industry. Second, it provides a road layout of the breadth and depth of the research on PI and the overall logistics, including the approaches taken by researchers, regions covered, sectors examined, and theoretical stances taken by those who have explored the topic thus far. Finally, it addresses the conclusions based on the different clusters discovered and the issues of existing logistics systems along with the moderators of influencing the PI and its outcomes. Given the rising significance of PI and channels for sustainability in the logistics sector, this is the first time that an effort has been made to investigate the function of the PI within the context of overall logistics performance. The paper identifies crucial gaps in research and brings to light different factors that have the potential to shed light on this essential subject. Furthermore, we argue that there is an immediate need to build new business models for enhanced adaptation and execution of the PI strategy, and we urge business managers, academics, and regulators to consider it. In addition, we advocate that professionals scrutinize ways in which the PI approach may be applied to the existing business structures

How much does Lean Manufacturing need environmental and information technologies?

This paper analyses the role played by Environmental and Information Technologies (ET&IT) in the capability of Lean Manufacturing (LM) to achieve improved industrial performance. In contrast to seminal literature about lean practices, and in view of increasing consumer requirements regarding response times and environmental concerns, we suggest that shop-floor technologies are crucial for transforming lean routines into enhanced performance. Hypotheses were tested in a multisectoral sample of 763 manufacturing plants (NACE codes 15–37) from five different European countries. Results confirm total mediation by both technologies between lean routines and industrial performance, which entails that LM establishes efficient conditions on the shop floor for developing technology-enabled capabilities that can be leveraged to improve industrial performance. From a managerial perspective our findings highlight the need for avoiding short-sighted attitudes and for internalising plant technologies within lean transformation projects. This is important not only because such technologies are determinant for maximising the potential of organisational routines in current manufacturing systems but also because of their intrinsic benefits.Ministerio de Economía y Competitividad | Ref. ECO2016-76625-

Towards sustainable textile and apparel industry: Exploring the role of business intelligence systems in the era of industry 4.0

Industry 4.0 is a new era of industrial revolution in which textile and apparel (T&A) companies are adopting and integrating advanced technologies to achieve sustainability and a competitive edge. Previous studies have just focused on the perspective of big data utilization in Industry 4.0 and neglected the role of business intelligence systems (BIS), especially in the T&A industry. The current study is one of the first to investigate the determinants of BIS adoption with an eye towards understanding how BIS can resolve sustainability issues in T&A companies with Industry 4.0 technologies. Methodology: A qualitative research approach is applied with 14 semi-structured in-depth interviews from 12 of the world's high-end T&A companies. The snowball and purposeful sampling strategy is used to select the participants. The qualitative content analysis technique is used to analyze the interview data. Results: The findings revealed various themes, such as sustainability issues in T&A companies, improved value creation processes with leading BI solutions, and perceived difficulties in the adoption of BIS. Major improvements are perceived in the apparel retail business because apparel companies are more prone to adopt the Industry 4.0 technologies with advanced business intelligence (BI) solutions. The results prove the pivotal role of economic sustainability in the adoption of BIS and Industry 4.0 technologies in T&A companies

Unfolding the link between big data analytics and supply chain planning

Big data analytics (BDA) has captured growing research interests in operations and supply chain management literature, yet, despite the significant implication, extant knowledge falls short in drawing the link between BDA and supply chain planning (SCP) with in a structured manner. This paper employs the Delphi technique to uncover the synergies between BDA technology, conceptualized as big data sources and BDA methods, and the SCP activities framed with the SCP matrix. The panel runs for three rounds with 35 experts including scholars, supply chain practitioners, and BDA specialists. The results of this paper suggest that the relevance of BDA depends on the focal SCP activity. Thirty-five projections are presented on the expected impact of BDA on SCP that are classified into three groups based on the significance of impact and probability of occurrence. This work advances the understanding of BDA in supply chain management drawing implications to prioritize BDA investment for SCP

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions