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This paper presents a joint approach to improve production and logistic processes with Lean Management and Industry 4.0. For this purpose, the concept of human-technology-organization (HTO) is used to demonstrate and structure resulting potentials.
Lean foremost enables an optimal interaction between humans and their organization which leads to a...
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Lean Management (LM) represents a complex socio-technical system where both technical and social practices should be consistently implemented and integrated in order to foster a Continuous Improvement (CI) culture. Despite initial gains in operational performances due to the implementation of the most common and well-established Lean techniques, th...
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... Industry 4.0 already focuses on the interaction between technology and human. Thus, especially the soft part of lean may contribute well to this transition since it focuses mainly on the other bridging element, namely interaction between human and organization [31]. ...
... To illustrate the transition challenges from Industry 4.0 to Industry 5.0, the Human-Technology-Organization (HTO) concept, suggested to analyze interactions between these three aspects in regards to Industry 4.0 and lean [31,32], may be used as a basis (see Fig. 2). ...
Industry has a key role in leading the digital and green transitions for the economic and societal transformations that we are experiencing. The approach towards a sustainable, human-centric, and resilient European industry, so-called Industry 5.0, complements the existing “Industry 4.0” approach by focusing on a circular, human-oriented, and durable industry. Thus, industrial systems need to have a focus shift from technology to human so that technology will serve human and not vice versa. In this way, human play a central role between technology and organizations. This paper seeks the challenge of this focus shift and how lean thinking and practicing soft lean tools such as Toyota Kata may contribute to the development of a human-centric approach in the industry. A literature review was conducted to answer these questions, and a conceptual modified Toyota Kata methodology, the so-called Human-centric Kata, was suggested to assist industry in reaching its goals towards a human-centric green-digital era.KeywordsLeanToyota KataHuman-CentricIndustry 4.0Industry 5.0
... The specificity of DD for CI and the fact that is only required when a performance problem is detected are the logical reasons to keep this data acquisition off of the digitalization platform of a production system [35,45]. Nevertheless, the time consumed by the CI professionals might be several days for a unique problem-solving project; to avoid this time (and cost), the user may employ a DD with very limited information (causing a poorer root cause analysis) [27,28]. ...
... Therefore, based on previously published literature surveys on LM and L&G together with I4.0 technologies, and on the survey done in this study, no specific research was found exploring the application of those technologies in the DD to CI projects. This enhances the purpose of the current study, enabling it to cover the gap that was also identified by other authors [24,27,81]. ...
This work responds to the gap in integrating the Internet-of-Things in Continuous Improvement processes, especially to facilitate diagnosis and problem-solving activities regarding manufacturing workstations. An innovative approach, named Automatic Detailed Diagnosis (ADD), is proposed: a non-intrusive, easy-to-install and use, low-cost and flexible system based on industrial Internet-of-Things platforms and devices. The ADD requirements and architecture were systematized from the Continuous Improvement knowledge field, and with the help of Lean Manufacturing professionals. The developed ADD concept is composed of a network of low-power devices with a variety of sensors. Colored light and vibration sensors are used to monitor equipment status, and Bluetooth low-energy and time-of-flight sensors monitor operators' movements and tasks. A cloud-based platform receives and stores the collected data. That information is retrieved by an application that builds a detailed report on operator-machine interaction. The ADD prototype was tested in a case study carried out in a mold-making company. The ADD was able to detect time performance with an accuracy between 89% and 96%, involving uptime, micro-stops, and setups. In addition, these states were correlated with the operators' movements and actions.
... LM is rooted in the Toyota production system (TPS) [11,13]. TPS integrates a set of methods and tools with a management philosophy, aiming at the constant identification and elimination of waste [14]. ...
... With an efficient identification process, several problems will be solved in the problemsolving phase. CI has its origins in the PDCA cycle: a problem-solving method consisting of a four-step iterative cycle: Plan, Do, Check, and Act [11,19]. The PDCA cycle's logic is patent in several problem-solving methodologies, such as the eight disciplines (8D) [20] and the A3 problem solving [20][21][22]. ...
... However, both LM and I4.0 paradigms aim to solve present and future challenges in manufacturing [8]. Among the publications that study the applicability of I4.0, several mention CI as a part of LM (e.g., [4,8,9]), whereas others focus exclusively on CI (e.g., [10][11][12]). The existing contributions in the literature show, directly or indirectly, the potential of CI enhancement under a I4.0 context, referring to various I4.0 technological concepts to support this transformation. ...
Continuous improvement (CI) is a key component of lean manufacturing (LM), which is fundamental for organizations to remain competitive in an ever more challenging market. At present, the new industrial revolution, Industry 4.0 (I4.0), is taking place in the manufacturing and service markets, allowing more intelligent and automated processes to become a reality through innovative technologies. Not much research was found regarding a holistic application of I4.0′s technological concepts towards CI, which clarifies the potential for improving its effectiveness. This clearly indicates that research is needed regarding this subject. The present publication intends to close this research gap by studying the main I4.0 technological concepts and their possible application towards a typical CI process, establishing the requirements for such an approach. Based on that study, a conceptual approach is proposed (PDCA 4.0), depicting how I4.0 technological concepts should be used for CI enhancement, while aiming to satisfy the identified requirements. By outlining the PDCA 4.0 approach, this paper contributes to increasing the knowledge available regarding the CI realm on how to support the CI shift towards a I4.0 industrial paradigm.
The COVID-19 pandemic resulted in major changes in education at all levels as educational institutions were forced to resort to remote teaching. While there are many success stories shared about effective shifts to remote teaching, there are also clear indications that the digital divide prevented access to education for some. There is a realization, however, that there must be a transition from traditional ways of teaching and learning to embrace technological advances while ensuring quality and access. Although the International Commission on the Futures of Education (ICFE) provides a vision of what teaching and learning might yet become (ICFE, Reimagining our futures together: a new social contract for education. UNESCO, 2021), the “new normal” for higher education is still emerging. A key consideration in the transition is quality assurance. Higher education institutions must ensure accessibility, relevance, value for money, and a positive transformative experience for all staff and students. This conceptual chapter explores perspectives on what is considered the new normal. The aim is to promote reflection on the future of teaching and learning in higher education. This chapter will answer the question “What are some key teaching and learning considerations for higher education institutions as they prepare for the new normal in higher education?”
The purpose of this paper is to conduct a comparison for Industry 4.0 maturity models in the context of Total Quality Management (TQM) principles. TQM principles have been used as criteria for evaluating alternative Industry 4.0 maturity models. Because of the critics about TQM concept for having an inadequate theoretical foundation and highly expert insight dependent nature, fuzzy logic principles have been used. In this study, linguistic fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method has been used for ranking and Decision Making Trial and Evaluation Laboratory (DEMATEL) method has been used for criteria weighting to determine which maturity model mostly fits TQM principles. Four Industry 4.0 maturity models have been evaluated according to seven criteria and 33 sub-criteria. According to the results, the model of Schumacher et al. (2016) has the highest score among the compared models. Another result is among the seven main criteria customer focus has the most influence and relationship management has the less, and among 33 sub-criteria risk identification has the most influence and relationship management has the less. Findings represented in this study can serve academicians and practitioners to compare, develop and/or improve Industry 4.0 maturity models.
