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

Evaluating worker-centered smart interventions on the shop floor

This paper presents the evaluation strategy and the first results we obtained when we used the FACTS4WORKERS evaluation framework. The purpose of the framework is to prove whether the project interventions achieve the expected results, which are: improving workers’ job satisfaction, increasing innovation and problem solving skills as well as enhancing productivity. Because of the diversity of the industrial partners and of the workplaces where the interventions are going to be implemented, the different languages, legal and cultural environments the framework was conceived as general as possible to be adapted to any particular case. We present here one example for using the framework, the first results of these measurements and the feedback the evaluation provides both for supporting the decisions about the interventions and about the framework itself

A Prescriptive Maintenance Aligned Production Planning and Control Reference Process

Digital innovations can improve various business processes, such as production planning and control (PPC). In the last years, prescriptive maintenance (PxM) emerged as a strategy to increase overall production performance, but an alignment of the PPC process with PxM has not been examined yet. To tackle this problem, a PxM-aligned PPC process is designed and evaluated in this study using a reference model development methodology, including a narrative literature review, a multivocal literature review, and eight expert interviews. The reference model shows where process elements benefit from PxM alignment, how alignment can be achieved from a process and output, data, function, and organization view, and where fits and gaps between theory and practice are

E-liquid-related posts to Twitter in 2018: Thematic analysis.

IntroductionE-liquid is the solution aerosolized by e-cigarette devices to produce vapor. Continuously evolving e-liquids, and corresponding devices, can affect user experiences associated with these products. Twitter conversations about e-liquids can capture salient behavioral, social, and communicative cues associated with e-liquids. We analyzed Twitter data to characterize key topics of conversation about e-liquids to inform surveillance, and regulatory efforts.MethodsTwitter posts containing e-liquid-related terms ("e-liquid(s)," "e-juice(s)") were obtained from 1 January 2018 to 31 December 2018. Text classifiers were used to identify topics of the posts (n = 15,927).ResultsThe most prevalent topic was Promotional at 29.35% followed by Flavors at 24.22%, and Person Tagging at 21.47%. Juice Composition was next most prevalent at 17.61% followed by Cannabis at 16.83%, and Nicotine Health Risks at 6.39%. Quit Smoking was rare at 0.57%.ConclusionThese results suggest that flavors, cannabis, health risks of nicotine, and composition warrant consideration as targets in future surveillance, public policy, and interventions addressing the use of e-liquids. Twitter provides ample opportunity to influence the normalization, and uptake, of e-cigarette-related products among non-smokers and youth, unless regulatory restrictions, and counter messaging campaigns are developed to reduce this risk

MensSana: Design of a mental well-being self-report interface for shop floor workers

A ascensão da Indústria 4.0 trouxe consigo novas tecnologias e oportunidades que estão a mudar a natureza do trabalho, especialmente em ambientes de chão de fábrica. No entanto, essas mudanças também trouxeram novos desafios para os trabalhadores, incluindo desafios na sua saúde mental. Estes trabalhadores, em particular, enfrentam no seu trabalho estressores físicos e mentais que podem afetar seu bem-estar geral, apesar dos esforços da Indústria 4.0. O conceito de Operador 4.0 na Indústria 4.0 introduz muitos operadores, como o Operador Saudável, que enfatiza a centralidade no ser humano e visa melhorar a eficiência e o bem-estar do trabalhador por meio de tecnologias avançadas e análise de dados. Esta tese propõe o desenvolvimento de uma ferramenta protótipo, co-criada e validada no contexto da Indústria 4.0 para medir métricas do trabalhador e do local de trabalho, criando uma imagem holística do trabalhador, sua competência e bem-estar, alinhado ao conceito de um trabalhador "mais saudável" de Romero et al. Essas informações são devolvidas ao trabalhador e apresentadas de maneira legível e compreensível para identificar tendências e informar decisões futuras relacionadas ao trabalho e bem-estar.The rise of Industry 4.0 has brought about new technologies and opportunities that are changing the nature of work, particularly in factory floor settings. However, these changes have also brought about new challenges for workers, including mental health issues. Shop floor workers, in particular, face physical and mental stressors in their work that can impact their overall well-being, despite Industry 4.0 efforts. The Operator 4.0 concept in Industry 4.0 introduces a lot of operators like the Healthy Operator that emphasises human-centricity and aims to improve worker efficiency and well-being through advanced technologies and data analytics. This thesis proposes the development of a prototype tool co-created and validated in the context of Industry 4.0 to measure metrics from the worker and the workplace, creating a holistic picture of the worker, their competence and well-being in line with Romero's et al. concept of a "healthier" worker. This information is returned to the worker and presented in a readable and understandable manner to identify trends and inform future decisions concerning their work and well-being

The redesign of blue- and white-collar work triggered by digitalization:collar matters

The implementation of digital technologies in the context of Industry 4.0 radically changes methods of production and thereby the jobs of blue-collar workers. Although the work design effects of digitalization on the operator 4.0 have been explored in the existing literature, less is known about the simultaneous effects on white-collar work and the underlying (re)design process of human work including the factors that shape this process. To address this gap, we performed an in-depth industrial case study of an organization in the process of digitalization. Our findings confirm the concurrent impact of digitalization on blue- and white-collar work and suggest that its human implications highly depend on the extent to which, and at what moment, human factors are considered during the design and implementation process. Where work design knowledge lacked, the motivation of system designers turned out to be an important individual factor to realize favorable work design outcomes. At the organizational level, results show the importance of early involvement of system users and incorporating social performance indicators in addition to operational performance indicators in the statement of project goals. Our findings provide important empirical input for the further development of human-centric models and theories that integrate the challenges and opportunities for blue- and white-collar workers that are emerging when adopting digital technologies

An artificial intelligence-based collaboration approach in industrial IoT manufacturing : key concepts, architectural extensions and potential applications

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented

Personal performance: the resistant confessions of Bobby Baker

An analysis of the confessional performances of performance artist, Bobby Baker, in particular 'Box Story'

Managing changes initiated by industrial big data technologies : a technochange management model

With the adoption of Internet of Things and advanced data analytical technologies in manufacturing firms, the industrial sector has launched an evolutionary journey toward the 4th industrial revolution, or so called Industry 4.0. Industrial big data is a core component to realize the vision of Industry 4.0. However, the implementation and usage of industrial big data tools in manufacturing firms will not merely be a technical endeavor, but can also lead to a thorough management reform. By means of a comprehensive review of literature related to Industry 4.0, smart manufacturing, industrial big data, information systems (IS) and technochange management, this paper aims to analyze potential changes triggered by the application of industrial big data in manufacturing firms, from technological, individual and organizational perspectives. Furthermore, in order to drive these changes more effectively and eliminate potential resistance, a conceptual technochange management model was developed and proposed. Drawn upon theories reported in literature of IS technochange management, this model proposed four types of interventions that can be used to copy with changes initiated by industrial big data technologies, including human process intervention, techno-structural intervention, human resources management intervention and strategic intervention. This model will be of interests and value to practitioners and researchers concerned with business reforms triggered by Industry 4.0 in general and by industrial big data technologies in particular