The HOSHIN KANRI TREE. Cross-plant Lean Shopfloor Management

Villalba-Díez, Javier; Ordieres‐Meré, Joaquín; Nuber, Gottfried

doi:10.1016/j.procir.2015.02.120

Cited by 25 publications

(14 citation statements)

References 15 publications

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“…As represented schematically in Figure 1a, in the mentioned work we represented the individual process owner, the node of a complex network of Industry 4.0 represented in the form of a decision graph [2], as a quantum computing unit or qubit [3,4]. This qubit is allowed to have two fundamental states, one of alignment or asymptotic stability of the key performance indicators (KPIs) defining its performance [5][6][7][8][9][10][11][12][13], represented by the state |0 and another of non-alignment, absence of such stability, represented by the state |1 . In this paper we intend to expand this study, taking the simplest case of two organizational agents, a subordinate A reporting to another agent B represented in Figure 1b, that will be simulated by means of a two-qubit quantum circuit.…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of Two Qubits: One Reports to One

Villalba-Díez

Benito

Losada

2020

Sensors

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In this paper we investigate how the relationship with a subordinate who reports to him influences the alignment of an Industry 4.0 leader. We do this through the implementation of quantum circuits that represent decision networks. In fact, through the quantum simulation of strategic organizational design configurations (QSOD) through five hundred simulations of quantum circuits, we conclude that there is an influence of the subordinate on the leader that resembles that of a harmonic under-damped oscillator around the value of 50% probability of alignment for the leader. Likewise, we have observed a fractal behavior in this type of relationship, which seems to conjecture that there is an exchange of energy between the two agents that oscillates with greater or lesser amplitude depending on certain parameters of interdependence. Fractality in this QSOD context allows for a quantification of these complex dynamics and its pervasive effect offers robustness and resilience to the two-qubit interaction.

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Section: Introductionmentioning

confidence: 99%

Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of Two Qubits: One Reports to One

Villalba-Díez

Benito

Losada

2020

Sensors

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“…In fact, the opposite is often true. For example, in the study of value-creating cyber-physical systems, the study of shop-floor management has been done for decades with almost solely qualitative methods and common sense [21][22][23]. Deep learning has been recently used to extract statistical patterns from cyber-physical systems at certain microscopic local levels [24,25]; however, there is an urgent need for algorithms to be developed that ensure a holistic understanding of cyber-physical systems at the meso-and macroscopic level of complex-networked aggregation.…”

Section: Related Workmentioning

confidence: 99%

Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks

Villalba-Díez

Molina

Ordieres-Meré

et al. 2020

Sensors

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In the near future, value streams associated with Industry 4.0 will be formed by interconnected cyber–physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean management systems in this context is determined by their ability to recognize behavioral patterns in these big data structured within non-Euclidean domains, such as these dynamic sociotechnical complex networks. We assume that artificial intelligence in general and deep learning in particular may be able to help find useful patterns of behavior in 4.0 industrial environments in the lean management of cyber–physical systems. However, although these technologies have meant a paradigm shift in the resolution of complex problems in the past, the traditional methods of deep learning, focused on image or video analysis, both with regular structures, are not able to help in this specific field. This is why this work focuses on proposing geometric deep lean learning, a mathematical methodology that describes deep-lean-learning operations such as convolution and pooling on cyber–physical Industry 4.0 graphs. Geometric deep lean learning is expected to positively support sustainable organizational growth because customers and suppliers ought to be able to reach new levels of transparency and traceability on the quality and efficiency of processes that generate new business for both, hence generating new products, services, and cooperation opportunities in a cyber–physical environment.

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“…Managers' commitment and support seem to be the main obstacles of kaizen implementation and success [21]. Researchers recently argue that not only is the support of an empowerment management system necessary, but it must also be in compliance with strategic plans and targets [22]. Literature noted that employees might rely on aspects of culture such as openness to new ideas and tolerance for risk for innovation creation and adoption.…”

Section: The Moderating Effect Of Management Support On the Relationsmentioning

confidence: 99%

The Moderating Effect of Kaizen Culture on the Relationship Between Innovation and Operational Performance

Mui¹,

Muthuveloo²

2020

Proceedings of the 8th International Conference on Entrepreneurship and Business Management (ICEBM 2019) UNTAR

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Kaizen culture and Innovation are key competitive factors deeply imbued into the organizational products and processes to achieve optimal operational performance sustainably. Literature suggested that manufacturing companies needing to differentiate themselves adopt a culture of continuous improvement (Kaizen). Kaizen culture in this paper enables a change process in a seamless manner supporting innovation to optimize operational performance. This study aims to support theory and practice. Firstly, this study findings can be applied to improve operational performance which is pivotal to business sustainability among Malaysian Manufacturing Companies (MMC). Secondly, it also contributes to extend the Socio-Technical Systems (STS) theory. Thirdly, this study has the potential contribution towards the society as resources are scarce and waste elimination is imperative. Lastly, this study may provide some understanding on why these companies are more successful in securing operational performance from innovation implementation than other seemingly similar companies.

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The HOSHIN KANRI TREE. Cross-plant Lean Shopfloor Management

Cited by 25 publications

References 15 publications

Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of Two Qubits: One Reports to One

Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of Two Qubits: One Reports to One

Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks

The Moderating Effect of Kaizen Culture on the Relationship Between Innovation and Operational Performance

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